INTEGRATING RETIRED ELECTRIC VEHICLE BATTERIES
WITH PHOTOVOLTAICS IN MICROGRIDS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Feng Guo
Graduate Program in Electrical and Computer Science
The Ohio State University
2014
Dissertation Committee:
Dr. Jin Wang, Advisor
Dr. Longya Xu
Dr. Mahesh S. Illindala
Copyright by
Feng Guo
2014
Abstract
It is expected that by 2020, there will be a large number of battery packs retired from
Electric Vehicles (EVs). These retired EV batteries may be utilized as energy storage
systems in a Microgrid to help with the integration of renewable energy resources and
provide different functions, such as Time-of-Use energy management, frequency and
voltage regulation, emergency power supply, etc. In this dissertation, both system and
circuit level studies are performed on the implementation of retired EV batteries with
Photovoltaic (PV) systems in Microgrid applications.
First, an optimization algorithm is presented to determine the usage profile of the
retired EV battery and the size of the PV system for a residential application. The electrical
and economic models of the Battery Energy Storage System (BESS) and the PV system
are derived. In addition, irradiance, load profile, and electricity price information are
collected as calculation input. An Energy Management Strategy (EMS) is implemented to
minimize the yearly system operation cost under different PV sizes. The proposed
optimization algorithm is realized in Matlab, and simulation results are provided to validate
the effectiveness of the algorithm.
Then, a hybrid Microgrid testbed is developed for the study on experimental
implementation of the retired EV battery in a Microgrid. The testbed combines Power
Hardware-in-the-Loop (PHIL) simulation of an electric power network and System-in-theii
Loop (SITL) simulation of a communication network with real hardware. The PHIL
system can interface with other power hardware and emulate different systems connected
to the testbed, such as renewable energy conversion systems, different topologies of utility
grid, and other Microgrids, etc. The SITL system is a component of the real-time
Supervisory Control and Data Acquisition (SCADA) system. It is able to accept real-time
traffic from a physical network and simulate different communication networks and
phenomena. In addition, multiple power electronics converters and programmable power
sources/loads are integrated in the testbed to emulate different renewable energy
conversion systems. System-level experimental results are presented to demonstrate the
functionality of the testbed.
Next, a full-bridge current-source isolated Quasi-Switched-Capacitor (QSC) dc/dc
converter is proposed for PV applications. The proposed converter features reduced input
current ripple and improved performance under partial shading, reduced number of
switches and voltage stress on the high voltage side QSC circuit, and soft switching for
both primary side and secondary side switches. The operation principle is presented and
the design guidelines are studied. A 1.2 kW, 1 MHz, 40 V/ 400 V prototype utilizing
Gallium Nitride (GaN) switching devices is built in the lab, and a peak efficiency of 92.7%
at 500 kHz and 89.0% at 1 MHz is achieved. Compared to other current-source isolated
dc/dc converters in the literature, a comparable efficiency is achieved with a much higher
switching frequency.
In the end, a family of dual-input dc/dc converters derived from the current-source
isolated QSC dc/dc converter is proposed for integration of the PV and battery. This family
iii
of converters utilizes a current-source full-bridge or half-bridge topology as the primary
side, and the QSC topology as the secondary side. The proposed converters have a similar
operation principle as the full-bridge current-source isolated QSC PV converter, and inherit
all the merits of it. In addition, a secondary battery is integrated in the QSC circuit to
realize a dual-input operation. A 1.3 kW half-bridge circuit prototype based on Silicon
Carbide (SiC) switching devices and a 2 kW full-bridge circuit prototype, based on GaN
switching devices, are built in the lab. The full-bridge prototype achieves a peak efficiency
of 95.9% at 500 kHz and 93.5% at 1 MHz under the dual-input operation condition.
Compared to other isolated multiple-input dc/dc converters in the literature, the proposed
converter achieves a higher efficiency at a much higher switching frequency.
iv
Dedicated to my family.
v
Acknowledgments
First and foremost, my sincerest gratitude goes to my advisor, Dr. Jin Wang. He taught
me research skills with patience and gave me opportunities to probe different areas of
power electronics. He shared with me his experience in life selflessly and helped me build
the path to my future career. I would not have completed this dissertation without his
guidance and support. It is my great honor to be his student.
I am grateful to my Ph.D. dissertation and qualifying exam committee members, Dr.
Longya Xu, Dr. Mahesh Illindala, Dr. Donald Kasten, and Dr. Vadim Utkin, for their
insightful suggestions and beneficial discussions with me.
My teammates have made my experience at Ohio State better in various ways. I would
like to thank them for their help to my course work and research. I felt fortunate that Mr.
Cong Li, Mr. Xuan Zhang and Mr. Chengcheng Yao were both my labmates and
roommates, so we could share the happiness and sadness in both research and daily life. I
would also like to express my great appreciation to Mr. Lixing Fu and Mr. He Li, who have
worked closely with me in the last two years and shared all the failures en route to success
in our collaborated projects.
I owe my deepest thanks to my family for their unconditional love and support, which
is the greatest gift I ever received. Specially, I would like to thank my wife, Meiyu Lu.
vi
Her tremendous love and encouragement in our long-distance relationship in the past eight
years never stopped, which helped me through the hard times in finishing this work.
vii
Vita
July 2009 ...............................................B.S. Electrical Engineering, Wuhan University
Sept. 2009 to present .............................Ph.D. Student, Department of Electrical and
Computer Engineering, The Ohio State University
Publications
[1].
F. Guo, L. Fu, X. Zhang, and J. Wang, “A family of dual-input dc/dc converters
based on Quasi-Switched-Capacitor circuit,” in Proc. IEEE Energy Conversion Congress
and Exposition (ECCE), accepted in Jul. 2014.
[2].
F. Guo, L. H. Li, M. Alsolami, and J. Wang, “Residential usage profile
optimization and experimental implementation of the retired HEV battery with a hybrid
Microgrid testbed,” in Proc. IEEE Energy Conversion Congress and Exposition (ECCE),
accepted in Jul. 2014.
[3].
C. Li, C. Yao, X. Zhang, F. Guo, A. Lang, H. Liu, S. Shan, and J. Wang, “An
isolated hybrid switched L-C dc-dc circuits with high step-up ratio and reduced switch
voltage stress,” in Proc. IEEE Energy Conversion Congress and Exposition (ECCE),
accepted in Jul. 2014.
viii
[4].
H. Li, F. Guo, L. Zhu, S. Yang, and J. Wang, “Usage profile optimization of the
retired HEV battery in smart households,” IEEE Transportation Electrification Conference
Asia-Pacific, accepted in June 2014.
[5].
X. Zhang, C. Yao, F. Guo, L. Fu, and J. Wang, “A family of Quasi-Switched-
Capacitor converters,” IEEE Transportation Electrification Conference Asia-Pacific,
accepted in June 2014.
[6].
C. Li, C. Yao, L. Fu, F. Guo, and J. Wang, “A family of high gain hybrid switched
capacitor-inductor dc-dc circuits for renewable energy applications,” IEEE Transportation
Electrification Conference Asia-Pacific, accepted in June 2014.
[7].
C. Li, D. Jiao, J. Jia, F. Guo, and J. Wang, “Thermoelectric cooling for power
electronics circuits: modeling and active temperature control,” IEEE Trans. Ind. Appl.,
accepted in Mar. 2014.
[8].
X. Zhang, C. Li, C. Yao, L. Fu, F. Guo, and J. Wang, “A wide bandgap device
based isolated Quasi-Switched-Capacitor dc/dc converter,” IEEE Trans. Power
Electronics, vol. 29, no. 5, pp. 2500-2510, May 2014.
[9].
L. Herrera, E. Inoa, F. Guo, and J. Wang, “Small signal modeling and networked
control of a PHEV charging facility,” IEEE Trans. Industry Applications, vol. 50, no. 2,
pp. 1121-1130, Mar. 2014.
[10].
F. Guo, L. Fu, C. Lin, C. Li, W. Choi, and J. Wang, “Development of an 85 kW
bidirectional Quasi-Z-Source inverter with DC-Link feed forward compensation for
Electric Vehicle applications,” IEEE Trans. Power Electronics, vol. 28, no. 12, pp. 54775488, Dec. 2013.
ix
[11].
F. Guo, L. Herrera, M. Alsolami, H. Li, P. Xu, X. Lu, A. Lang, Z. Long, and J.
Wang, “Design and development of a reconfigurable hybrid Microgrid test bed,” in Proc.
IEEE Energy Conversion Congress and Exposition (ECCE), Denver, CO, Sept. 2013, pp.
1350-1356.
[12].
X. Zhang, C. Yao, F. Guo, C. Li, L. Fu, C. Deng, and J. Wang, “Soft switching,
frequency control, and bidirectional power flow of an isolated quasi-switched-capacitor
dc/dc converter for automotive applications,” in Proc. IEEE Energy Conversion Congress
and Exposition (ECCE), Denver, CO, Sept. 2013, pp. 1393-1400.
[13].
C. Li, D. Jiao, J. Jia, F. Guo, and J. Wang, “Thermoelectric cooling for power
electronics circuits: small signal modeling and controller design,” in Proc. IEEE Energy
Conversion Congress and Exposition (ECCE), Denver, CO, Sept. 2013, pp. 2201-2207.
[14].
M. Scott, E. Davison, C. Li, R. Darbali-Zamora, R. Duarte, X. Lu, T. Chen, F. Guo,
and J. Wang, “Bidirectional three port, three phase multilevel inverter based on switched
capacitor cells,” in Proc. IEEE Energy Conversion Congress and Exposition (ECCE),
Denver, CO, Sept. 2013, pp. 3057-3061.
[15].
D. Hu, F. Guo, and L. Xu, “Hardware-in-the-Loop simulation verification of a
smooth transition algorithm between maximum torque per ampere control and single
current regulator,” in Proc. IEEE Energy Conversion Congress and Exposition (ECCE),
Denver, CO, Sept. 2013, pp. 1765-1769.
[16].
X. Zhang, C. Yao, F. Guo, and J. Wang, “Reverse power flow study of an isolated
Quasi-Switched-Capacitor DC/DC converter for automotive applications,” in Proc. IEEE
x
56th Int. Midwest Symposium on Circuits and Systems (MWCAS 2013), Columbus, OH,
Aug. 2013, pp. 41-44.
[17].
F. Guo, L. Herrera, R. Murawski, E. Inoa, C. Wang, P. Beauchamp, E. Ekici, and
J. Wang, “Comprehensive real-time simulations of the smart grid,” IEEE Trans. Industry
Applications, vol. 49, no. 2, pp. 899-908, Mar.-Apr. 2013.
[18].
X. Zhang, C. Li, C. Yao, L. Fu, F. Guo, and J. Wang, “An isolated dc/dc converter
with reduced number of switches and voltage stresses for Electric and Hybrid Electric
Vehicles,” in Proc. IEEE Applied Power Electronics Conference and Exposition (APEC),
Long Beach, CA, Mar. 2013, pp. 1759-1767.
[19].
C. Li, D. Jiao, H. Mohan, F. Guo, and J. Wang, “Thermoelectric cooling for power
electronics circuits: modeling and applications,” in Proc. IEEE Applied Power Electronics
Conference and Exposition (APEC), Long Beach, CA, Mar. 2013, pp. 3275-3282.
[20].
J. Zhang, X. Wang, and F. Guo, “Radial suspension control of magnetic bearing
switched reluctance motor based on the ITAE optimization,” in Proc. 15th International
Conference on Electrical Machines and Systems (ICEMS), Sapporo, Japan, Oct. 2012, pp.
1-6.
[21].
F. Guo, L. Fu, C. Lin, C. Li, and J. Wang, “Small signal modeling and controller
design of a bidirectional Quasi-Z-Source inverter for Electric Vehicle applications,” in
Proc. IEEE Energy Conversion Congress and Exposition (ECCE), Raleigh, NC, Sept.
2012, pp. 2223-2228.
xi
[22].
L. Herrera, E. Inoa, F. Guo, and J. Wang, “Small signal modeling and networked
control of a PHEV charging facility,” in Proc. IEEE Energy Conversion Congress and
Exposition (ECCE), Raleigh, NC, Sept. 2012, pp. 3411-3416.
[23].
F. Guo, E. Inoa, W. Choi, and J. Wang, “Study on global optimization and control
strategy development for a PHEV charging facility,” IEEE Trans. Vehicular Technology,
vol. 61, no. 6, pp. 2431-2441, Jul. 2012.
[24].
X. Yao, Y. Huang, F. Guo, and J. Wang, “Advanced concepts for vertical stability
power supply in Fusion Devices,” IEEE Trans. Plasma Science, vol. 40, no. 3, pp. 761768, Mar. 2012.
[25].
F. Guo, L. Herrera, R. Murawski, E. Inoa, C.L. Wang, Y. Huang, E. Ekici, J. Wang,
and P. Beauchamp, “Real time simulation for the study on smart grid,” in Proc. IEEE
Energy Conversion Congress and Exposition (ECCE), Phoenix, AZ, Sept. 2011, pp. 10131018.
[26].
L. Herrera, R. Murawski, F. Guo, E. Ekici, and J. Wang, “PHEVs charging stations,
communications, and control simulation in real time,” in Proc. IEEE Vehicle Power and
Propulsion Conference, Chicago, IL, Sept. 2011, pp. 1-5.
[27].
F. Guo, H. Hayat, and J. Wang, “Energy harvesting devices for high voltage
transmission line monitoring,” in Proc. IEEE Power and Energy Society General Meeting,
Detroit, MI, Jul. 2011, pp. 1-8.
[28].
X. Yao, Y. Huang, F. Guo, and J. Wang, “Advanced concepts for Fusion Power
Supplies,” in Proc. IEEE/NPSS 24th Symposium on Fusion Engineering (SPFE), Chicago,
IL, Jun. 2011, pp. 1-6.
xii
[29].
E. Inoa, F. Guo, J. Wang, and C. Woongchul, “A full study of a PHEV charging
facility based on global optimization and real time simulation,” in Proc. IEEE 8th
International Conference on Power Electronics and ECCE Asia (ICPE&ECCE), Korea,
May 30-June 3, 2011, pp. 565-570.
[30].
L. Herrera, F. Guo, R. Murawski, E. Ekici, and J. Wang, “Combined studies of
power electronics and communication networks for the smart grid,” in Proc. IEEE
Energytech, Cleveland, OH, May 2011, pp. 1-5.
Fields of Study
Major Field: Electrical and Computer Engineering
xiii
Table of Contents
Abstract ............................................................................................................................... ii
Acknowledgments.............................................................................................................. vi
Vita................................................................................................................................... viii
Table of Contents ............................................................................................................. xiv
List of Tables .................................................................................................................. xvii
List of Figures ................................................................................................................ xviii
CHAPTER 1 INTRODUCTION ....................................................................................... 1
1.1 Background and Motivation ...................................................................................... 1
1.2 Integration of Retired EV Batteries with PV Systems in a Microgrid ...................... 9
1.3 Power Electronics Circuits in the PV/Battery System ............................................ 13
1.4 Chapter Review ....................................................................................................... 20
CHAPTER 2 RESIDENTIAL USAGE PROFILE OPTIMIZATION OF THE RETIRED
EV BATTERY AS AN ENERGY STORAGE UNIT ...................................................... 22
2.1 Introduction ............................................................................................................. 22
2.2 The Proposed Optimization Algorithm ................................................................... 24
2.3 The Developed Energy Management Strategy (EMS) ............................................ 27
xiv
2.4 Optimization Results ............................................................................................... 34
2.5 Summary ................................................................................................................. 38
CHAPTER 3
A HYBRID MICROGRID TESTBED FOR THE EXPERIMENTAL
STUDY OF THE RETIRED EV BATTERY ................................................................... 39
3.1 Introduction ............................................................................................................. 39
3.2 Overview of the Hybrid Microgrid Testbed ............................................................ 40
3.3 Previous Work ......................................................................................................... 44
3.4 Configuration of the Electric Power Network......................................................... 45
3.5 Configuration of the SCADA System ..................................................................... 55
3.6 System Integration Test ........................................................................................... 61
3.7 Summary ................................................................................................................. 64
CHAPTER 4
A FULL-BRIDGE CURRENT-SOURCE ISOLATED DC/DC
CONVERTER WITH REDUCED NUMBER OF SWITCHES AND VOLTAGE STRESS
FOR PV APPLICATIONS ............................................................................................... 65
4.1 Introduction ............................................................................................................. 65
4.2 Overview of the Isolated DC/DC Converter for PV Applications .......................... 65
4.3 Proposed Circuit Topology ..................................................................................... 67
4.4 The Operation Principle .......................................................................................... 70
4.5 Circuit Analysis ....................................................................................................... 85
xv
4.6 Design Guidelines ................................................................................................... 87
4.7 Simulation and Experimental Results ..................................................................... 95
4.8 Summary ............................................................................................................... 101
CHAPTER 5
A FAMILY OF QSC CIRCUIT BASED DUAL-INPUT DC/DC
CONVERTERS FOR INTEGRATION OF PV AND RETIRED EV BATTERIES ..... 103
5.1 Introduction ........................................................................................................... 103
5.2 Overview of the Multiple-Input DC/DC Converter .............................................. 104
5.3 Synthesis of the Dual-Input DC/DC Converter..................................................... 105
5.4 Operation Principle Based on the Half-Bridge Configuration .............................. 108
5.5 Circuit Analysis and Power Sharing Strategy ....................................................... 116
5.6 Simulation and Experimental Results ................................................................... 118
5.7 Summary ............................................................................................................... 130
CHAPTER 6 CONCLUSIONS AND FUTURE WORK .............................................. 132
6.1 Conclusions ........................................................................................................... 132
6.2 Recommendations for Future Work ...................................................................... 134
References ....................................................................................................................... 137
xvi
List of Tables
Table 1.1 Typical characteristics of different batteries [8]. ............................................... 5
Table 1.2 Battery parameters in different EVs ([9] - [14]). ............................................... 5
Table 2.1 Annual system cost and revenue. ..................................................................... 38
Table 3.1 Main specifications of the hybrid Microgrid testbed. ...................................... 42
Table 4.1 Circuit parameters and control variables of the simulation model. ................. 95
Table 4.2 Comparison between calculation results and simulation results. .................... 96
Table 5.1 Circuit parameters and control variables of the half-bridge simulation model.
......................................................................................................................................... 120
xvii
List of Figures
Figure 1.1 U.S. hybrid electric vehicle sales from 1999 to 2013....................................... 1
Figure 1.2 Summary of the procedure to refurbish EV batteries for ESS. ........................ 8
Figure 1.3 Emerging PV system topologies. ................................................................... 14
Figure 1.4 Cascade multilevel inverter topologies for PV systems. ................................ 15
Figure 1.5 P/V curves of a PV panel under different irradiance [66]. ............................. 17
Figure 1.6 Emerging energy storage system topologies in PV systems. ......................... 18
Figure 1.7 Dual input dc/dc converter for PV/battery systems. ....................................... 20
Figure 2.1 Overall configuration of the facility. .............................................................. 24
Figure 2.2 Calculation flow chart of the proposed optimization algorithm. ..................... 26
Figure 2.3 Examples of the irradiance profile. ................................................................ 30
Figure 2.4 Examples of the retail electricity price. .......................................................... 32
Figure 2.5 Examples of the residential load profile. ........................................................ 33
Figure 2.6 Total yearly cost with different PV sizes. ...................................................... 35
Figure 2.7 Optimal usage profile of the battery pack. ..................................................... 36
Figure 2.8 Comparison of the instantaneous energy cost without and with the battery pack.
........................................................................................................................................... 37
Figure 3.1 Overall schematic of the proposed Microgrid testbed. ................................... 42
Figure 3.2 Illustration of the PHIL system. ..................................................................... 46
xviii
Figure 3.3 Schematic and experimental setup of the PHIL system. ................................ 47
Figure 3.4 Experimental result of the PHIL system. ....................................................... 48
Figure 3.5 Test setup of the power electronics unit. ........................................................ 49
Figure 3.6 Experimental result of the power electronics circuit. ..................................... 50
Figure 3.7 Circuit schematic and control algorithm of the LES system. ......................... 51
Figure 3.8 Step response of the LES system.................................................................... 51
Figure 3.9 PHEV charging station. .................................................................................. 52
Figure 3.10 Test result of the charging station with a nonlinear load. ............................. 53
Figure 3.11 PV simulator control panel. .......................................................................... 54
Figure 3.12 Test of the programmable ac load. ............................................................... 55
Figure 3.13 Schematic of the real-time SCADA system. ................................................ 56
Figure 3.14 Screenshot of the HMI.................................................................................. 58
Figure 3.15 Experimental study of the real-time SCADA system................................... 59
Figure 3.16 Setup of the Microgrid testbed. .................................................................... 61
Figure 3.17 Experimental results in system integration test. ........................................... 63
Figure 4.1 The topology of the QSC circuit. ................................................................... 68
Figure 4.2 Circuit topology of the proposed isolated dc/dc converter. ............................ 69
Figure 4.3 Equivalent circuit of the proposed dc/dc converter. ....................................... 70
Figure 4.4 Simplified equivalent circuit of the proposed isolated dc/dc converter. ........ 71
Figure 4.5 Key waveforms of the circuit. ........................................................................ 73
Figure 4.6 Current flow in State I. ................................................................................... 74
Figure 4.7 Current flow in State II. .................................................................................. 75
xix
Figure 4.8 Current flow in State III. ................................................................................ 78
Figure 4.9 Current flow in State IV. ................................................................................ 80
Figure 4.10 Current flow in State V................................................................................. 81
Figure 4.11 Current flow in State VI. .............................................................................. 83
Figure 4.12 Simulation results of the proposed converter. .............................................. 96
Figure 4.13 Converter prototype. ..................................................................................... 98
Figure 4.14 Experimental result at switching frequency of 500 kHz. ............................. 99
Figure 4.15 Experimental result at switching frequency of 1 MHz. ................................ 99
Figure 4.16 Soft switching waveforms of the proposed converter. ............................... 100
Figure 4.17 Efficiency curves at different switching frequencies. ................................ 101
Figure 5.1 The topology of the QSC circuit with a voltage source. .............................. 106
Figure 5.2 The topologies of the dual input dc/dc converter based on QSC circuit. ..... 107
Figure 5.3 Equivalent circuit of the half-bridge configuration. ..................................... 109
Figure 5.4 Key waveforms in the circuit. ...................................................................... 110
Figure 5.5 Operation State I (t0-t1). ................................................................................ 111
Figure 5.6 Operation State II (t1-t2)................................................................................ 112
Figure 5.7 Operation State III (t2-t3). ............................................................................. 113
Figure 5.8 Operation State IV (t3-t4). ............................................................................. 113
Figure 5.9 Operation State V (t4- t5). ............................................................................. 114
Figure 5.10 Operation State VI (t5-t6). ........................................................................... 115
Figure 5.11 QSC circuit with secondary voltage source put between points a and b.... 119
xx
Figure 5.12 Simulation results of the half-bridge dual-input converter under different load
conditions. ....................................................................................................................... 121
Figure 5.13 Prototype of the half-bridge dual-input dc/dc converter. ........................... 123
Figure 5.14 Experimental results of the dual-input half-bridge converter with RLoad=320
Ω...................................................................................................................................... 124
Figure 5.15 Experimental results of the dual-input half-bridge converter with RLoad=240
Ω...................................................................................................................................... 125
Figure 5.16 Experimental results of the dual-input full-bridge converter under fs=500 kHz.
......................................................................................................................................... 127
Figure 5.17 Experimental results of the dual-input full-bridge converter with a different
battery voltage. ................................................................................................................ 128
Figure 5.18 Experimental results of the dual-input full-bridge converter under load
dynamics . ....................................................................................................................... 129
Figure 5.19 Efficiency curves of the dual-input full-bridge converter under different
switching frequencies...................................................................................................... 130
Figure 6.1 Topology of the cascade multilevel converter combined with dual-input dc/dc
converter for PV/battery systems. ................................................................................... 136
xxi
CHAPTER 1
INTRODUCTION
1.1 Background and Motivation
The increasing concern about the energy crisis and the rapid improvement of
technology have stimulated the growth of the electric vehicle (EV) market. Figure 1.1
shows the number of hybrid electric vehicles (HEVs) sold in U.S. from 1999 to 2013 [1].
Sales have increased steadily since the introduction of the HEV into the market, with a
surge in 2005. Although there is a decline between 2008 and 2011, it is consistent with the
overall vehicle sales decline during the economic recession. After the recovery of the
economy, HEV sales reach a historical peak of 0.49 million units in 2013.
Figure 1.1 U.S. hybrid electric vehicle sales from 1999 to 2013.
1
1.1.1 EVs and their batteries
EVs sold in the passenger vehicle market can be divided into three broad categories.
Their descriptions are as follows:
Hybrid Electric Vehicles (HEVs): HEVs are powered by both an internal combustion
engine (ICE) that uses conventional fuel as an energy source and an electric motor that uses
energy stored in a battery. Depending on the hybridization level, HEVs can be categorized
further into mild and full HEVs. Mild HEVs cannot power the vehicle using electric motor
alone, but generally cost less than full HEVs. Full HEVs can run the vehicle at a low speed
and in a short range with the electric motor alone, so more fuel economy benefit can be
achieved. In HEVs, the battery size is relatively small, and is charged by the regenerative
brake or the energy from an ICE.
Plug-in Hybrid Electric Vehicles (PHEVs): PHEVs also have both an ICE and electric
motor as the propulsion source. In contrast to HEVs, the battery in PHEVs can be charged
by an off-board electric power source, which further reduces the petroleum consumption.
PHEVs generally have a larger battery than HEVs do, so a PHEV can be propelled solely
by an electric motor for a moderate distance. An ICE will begin to operate when the State
of Charge (SoC) of the battery decreases to a certain level.
All Electric Vehicles (AEVs): AEVs are also known as pure electric vehicles or
sometimes directly called electric vehicles. In contrast to HEVs and PHEVs, AEVs have
only an electric motor as the propulsion source. Since there is no ICE on board, the battery
in an AEV needs to be recharged by an off-board electric power source after it is depleted.
2
This requires that AEVs have a relatively large battery to support a long driving distance
per charge.
As can be seen from the description, a battery is one of the most important components
in an EV, and the batteries in different types of EVs are different. Some critical parameters
describe the performance of a battery. The capacity of the battery is measured by the
Ampere-hour (Ah), defined as the amount of current that a battery can deliver at a constant
rate before depletion, which reflects the amount of charge stored in a battery. Another two
important parameters are the energy capacity and power capacity. The energy capacity is
the amount of energy stored in a battery, measured in watt-hour, while the power capacity
is the amount of power that can be extracted from a battery. When used over time, the
capacity, energy capacity, and power capacity of the battery can be gradually degraded.
The number of charge/discharge cycles that the battery runs before the capacity drops to a
certain level is defined as the life cycle. The battery life cycle depends upon multiple
factors, such as chemistry, the depth of discharge, charge current, temperature, etc.
Currently, there are mainly three types of battery chemistries widely used in EVs, and
their characteristics vary significantly.
Lead-acid batteries: Lead-acid batteries use lead oxide as the positive plate, spongy
lead as the negative plate, and diluted sulfuric acid as the electrolyte. They are widely used
for the starting-lighting-ignition (SLI) function in vehicles [2] [3]. Lead-acid batteries are
the most inexpensive and reliable chemistries among the three. However, lead-acid
batteries have a shorter life cycle, and the energy density (energy capacity per mass) is low.
Moreover, they are not suitable to be discharged for more than 20% of their rated capacity
3
[4]. Therefore, lead-acid batteries are not preferred as the main energy storage unit in most
EVs.
Nickel-metal hydride batteries: Nickel-metal hydride batteries use nickel hydroxide on
the positive electrode, an alloy of vanadium, titanium, nickel, and other metals on the
negative electrode, and an alkaline solution as the electrolyte. Most current HEVs in the
market use nickel-metal hydride batteries. They have much better life cycle, energy
density, and power density than lead-acid batteries. In addition, nickel-metal hydride
batteries are environmental friendly and can be recycled [5]. They can be safely operated
at a wide variation in temperature, and are resistant to over-charge and over-discharge.
However, nickel-metal hydride batteries are more expensive than lead-acid batteries. They
also suffer from memory effect, which reduces the usable SoC of the battery [6].
Lithium-ion batteries: There are various kinds of lithium-ion batteries. Some of the
prevalent chemistries are graphite as the anode, lithium-cobalt-oxide, lithium-manganeseoxide or lithium-phosphate as the cathode, and lithium salt dissolved in an organic solvent
as the liquid electrolyte. There is also a type of lithium-ion polymer batteries, which use a
solid conducting polymer as an electrolyte. Lithium-ion batteries are currently widely used
in PHEVs and AEVs. They have a higher energy density, longer life cycle than nickelmetal hydride batteries, and a low memory effect [7]. They are also environmentally
friendly and can be recycled. The primary disadvantage of lithium-ion batteries is that they
are not as safe as lead-acid batteries, and special care is needed to control the voltage and
temperature of the battery. In addition, the cost of lithium-ion batteries is relatively high.
4
Table 1.1 demonstrates the typical characteristics of these three batteries. It can be seen
that lithium-ion batteries are leading the role for future EVs. Table 1.2 shows the battery
information of some EVs available on the market.
Table 1.1 Typical characteristics of different batteries [8].
Energy Density
Power Density
(Wh/kg)
(W/kg)
Lead-Acid
30 - 40
120 - 200
200 - 300
Nickel-Metal Hydride
50 - 80
250 - 1000
300 - 500
Lithium-Ion
100 - 150
1000 - 1500
500 - 1000
Life Cycle
Table 1.2 Battery parameters in different EVs ([9] - [14]).
Model
Toyota Prius
HEV
Honda Insight
PHEV
AEV
Type of
Battery
Nickel-metal
hydride
Nickel-metal
hydride
Nominal
Energy
Maximum
Voltage
Capacity
Power
(V)
(kWh)
(kW)
201.6
1.34
60
101
0.6
10
Chevy Volt
Lithium-ion
355.2
16.5
111
Ford Fusion
Lithium-ion
310.8
7.6
88
Nissan Leaf
Lithium-ion
364.8
24
80
Tesla Model S
Lithium-ion
375
85
310
5
1.1.2 Second use of EV batteries
An EV battery is expected to be replaced in the vehicle when it cannot provide 80% of
the energy or peak power [15]. According to this criterion, it is expected that by 2020, a
large number of battery packs will be retired from EVs. From the estimation in [16], if the
service life of the battery is assumed to be five years, there will be over 1,423,000 retired
battery packs in 2020 in the United States alone. If the service life is assumed to be ten
years, the availability of retired battery packs will still be greater than 295,000 in the United
States in this time frame.
Though these batteries cannot conform to the vehicles’ specifications, they still have
substantially functional lives and can be used in other less demanding applications. The
energy storage application is one of the examples. Since energy storage systems (ESSs)
do not have severe constrains on the weight and volume - meaning that lower energy
density and power density are acceptable - retired EV batteries can satisfy the requirement
for these applications. Argonne National Laboratory has examined the secondary use of
nickel-metal hydride EV batteries for energy storage applications [17]. The study showed
that battery modules tested to end-of-life following the United States Advanced Battery
Consortium (USABC) Dynamic Stress Test (DST) Profile [18], which simulates EV
lifetime usage, could provide performances similar to new lead-acid batteries.
There are several reasons to promote the secondary use of EV batteries. From an
economic perspective, it is believed that the high cost of batteries is one of the main
constrains for the more widely acceptance of EVs. The secondary use of batteries can bring
extra profits to either EV owners or automobile Original Equipment Manufacturers
6
(OEMs), which translates into the reduced cost of EVs. In the interim, utilizing retired EV
batteries will reduce the cost of the ESS, potentially helping the growth of the energy
storage market. From an environmental perspective, the secondary use of EV batteries can
substantially decrease waste during the disposal of retired batteries, and reduce the amount
of materials and energy that otherwise would be needed to assemble new batteries.
After removal from the vehicle, retired EV batteries are not immediately available for
secondary use. The procedure to refurbish the batteries for ESS is summarized in Figure
1.2 [15]. The first step is to collect retired EV batteries or battery modules from dealerships
and transport them to the dedicated refurbishing facility. Then basic inspections will be
performed in the second stage to separate out the modules that have no value for reuse,
which include modules with physical damage, leaks, signs of abuse, and failed modules
with abnormal voltages and internal resistances. In the third stage, limited cycle testing
will be performed, based on the USABC Reference Performance Test sequence [18].
These tests will determine the characteristics of the batteries, including energy capacity,
power capacity, and an estimation of expected life. Based on the information obtained, the
fourth stage is to regroup modules into appropriate packs for their specific applications. In
the last stage, these packs will be shipped to the assembler of the complete ESS.
7
Start
Collect retired EV modules from dealerships
and transport to refurbishing facility.
Visually inspect modules for damage or
signs of abuse.
Determine manufacturer ratings and age
from label or barcode.
Measure voltage and resistance to identify
failed modules.
Determine module characteristics through
charge/discharge cycles from the USABC
Reference Performance Test.
Recycle damaged, old, or
failed modules.
Stop
Sort modules by capacity, power capacity,
and age.
Assemble into new battery packs.
Ship to assembler of complete energy
storage system.
Stop
Figure 1.2 Summary of the procedure to refurbish EV batteries for ESS.
8
1.2 Integration of Retired EV Batteries with PV Systems in a Microgrid
A Microgrid is a group of interconnected loads and distributed energy resources. It
acts as a single controllable entity and has clearly defined electrical boundaries with respect
to the grid [19]. A Microgrid can be operated in both grid-connected mode and island
mode. Retired EV batteries can be used in a Microgrid as ESSs to provide multiple
functions.
1.2.1 Functions of ESSs in a Microgrid with renewable energy resources
With more renewable energy resources penetrated in the Microgrid, such as PVs and
wind turbines, the demand for ESSs is also increased. This is because the renewable energy
resources are non-dispatchable, and their output power fluctuates frequently. For example,
the output power of PV systems is directly related to the instantaneous irradiance reaching
the arrays. A large PV system, with ratings in the order of tens of megawatts, can change
output power by 70% in five to ten minutes [20]. This rapid variation of output power
threatens the stability of electrical grids [21] [22]. To mitigate the power fluctuations, ESSs
can be used to compensate the unbalance between the output power from the renewable
energy resources and the demand power of the grid [23].
In addition, with the capability to rapidly provide bidirectional active and reactive
power, the ESS can provide ancillary functions for system operation [24]-[34]. This further
increases the implementation demand of the ESS in Microgrids. In [25] and [26], a
dynamic frequency control supported by an ESS was studied. The ESS acted as a synthetic
inertia, which can improve the transient frequency response of the utility grid. [27]-[30]
studied the voltage regulation of local distribution networks with the help of the ESS. The
9
size requirement and economic benefits were analyzed in detail. [31]-[33] studied the ESS
for power quality enhancement and power factor correction. Furthermore, the ESS also
can be used for spinning reserve and load shaving, which can reduce the transmission
capacity requirement and congestion, and defer the upgrade requirement [34]. During loss
of the grid, the ESS can be used as an emergency supply and can facilitate the black start
of the whole system.
Besides the functions mentioned above, the ESS also can directly make economic profit
for residential, commercial, and industrial customers/end-users with or without renewable
energy generations. Time-of-use (TOU) energy cost management utilizes the electricity
price differences during different times of a day. The ESS is controlled to absorb energy
when the electricity price is low, while it sends out energy when the electricity price is
high. Therefore, the overall electricity cost can be reduced.
However, to maximize the economic profit in the TOU energy cost management
scenario, the optimal usage profile of the ESS needs further study. Specifically, a proper
cost function and suitable optimization algorithm are needed to determine this usage
profile. As the calculation input, the practical real-time electricity price and load profile
also need to be collected. In addition, the energy capacity and power limit of the ESS
should be implemented as boundary conditions to ensure that they are not violated at any
time. If a PV system installation is planned, the size of the PV system also needs to be
optimized with the presence of the ESS, since it will be factored into the overall cost of the
entire system. Moreover, utility companies usually set different prices for the energy sold
to the customer and the energy bought from the customer, which makes the optimization
10
process more complex. Therefore, a comprehensive optimization procedure is needed to
solve this problem.
1.2.2 Testbed for the experimental study of retired EV batteries in Microgrids
To realize the functions that retired EV batteries can provide in a Microgrid,
sophisticated algorithms must be implemented in the control system. It is important to
verify those functions in an experimental platform before they are implemented in practice.
More importantly, with these functions and with the presence of renewable energy
resources, the overall characteristics of the power system are changed, and innovative
methodologies are implemented to enhance the system performance. These methodologies
also need further experimental study. Therefore, the development of a scaled-down testbed
that is capable of emulating a Microgrid with retired EV batteries becomes necessary [35][48].
A typical Microgrid testbed usually includes several or all of the following parts:
1). Utility grid: To provide a common ac bus and study the operation of the Microgrid
in grid-connected mode, the testbed can be connected directly to the utility grid. However,
since the power level of the testbed is far lower than the utility grid, this configuration
cannot support the study of the influence between the Microgrid and the utility grid, such
as frequency or voltage regulation. As an improvement, a passive impedance bank was
placed between the utility grid and the testbed to study the grid-inverter interactions [40];
in [41], a lab-scale power system was developed as the utility side, which includes a
generation station model, a transmission line model, a bus model, and a synchronizer
model.
11
2). Distributed Energy Resources (DERs): Most Microgrid testbeds include different
types of DERs, such as diesel generators, PVs, wind turbines, and ESSs. Since the output
power of both the PV and the wind turbine depends on weather conditions, power
emulators also are used in the testbed to mimic the characteristics of these renewable
energy resources.
3). Power electronics circuits: Most control studies of the Microgrid are related to
power electronics circuits. The testbed normally includes more than one dc/ac inverter and
dc/dc converter with digital controllers that can easily implement different control
algorithms and communicate with high-level controllers.
4). Load: Both passive and programmable loads are implemented in most of the
testbeds. The programmable load usually is operated to emulate different load profiles in
a Microgrid.
5). Supervisory Control and Data Acquisition (SCADA) System: A SCADA system
can be implemented in the testbed to collect different types of information, such as voltage,
current, and circuit breaker status. The information is directed to the control center and
displayed through a Human-Machine Interface (HMI). The control center can send out
commands to other units in the testbed.
With the widespread implementation of
communication networks in a Microgrid, some testbeds also have a more advanced
SCADA system. For example, different types of communication networks are applied,
such as wireless and Ethernet. Some testbeds particularly are designed for the study of
communication networks in the Microgrid, or so called Smart Grid [49]-[51].
12
One issue with current testbed structures is that their hardware configuration is fixed,
and the number of hardware components is limited for both the electric and communication
networks. Once setup, it is difficult and time-consuming to reconfigure the testbed for
different studies. The real-time simulation and Hardware-in-the-Loop (HIL) technology
provides a cost-effective way to solve this problem, which enables the realization of a
reconfigurable testbed for Microgrid related studies.
1.3 Power Electronics Circuits in the PV/Battery System
Power electronics circuits play an important role in integrating the retired EV batteries
into the PV system.
1.3.1 Existing grid-connected PV system topologies and related issues
Before discussing on the integration of ESS, the topologies of existing PV systems need
to be examined.
In large-scale PV systems, several strings of PV panels are connected to a centralized
dc/ac inverter to realize the connection to the grid, as shown in Figure 1.3(a). Since only
a single Maximum Power Point (MPP) can be tracked, this topology suffers from partial
shading loss, mismatch loss, and high heat dissipation on the bypass diode [52]-[55].
To solve the problem related to the centralized inverter topology, the topology in Figure
1.3(b) is implemented. Rather than connected to a centralized inverter, the PV panels are
divided into several strings and each string is connected to a separate inverter. This
topology is also widely used in medium and small-scale PV systems. To alleviate the
burden of the inverter and increase the flexibility of the system operation, the topology in
Figure 1.3(c) is used. In this architecture, each PV string is first connected to a dc/dc
13
converter and then to an inverter. In these two architectures, the issues of partial shading
losses and mismatch losses are reduced but not eliminated.
Utility Grid
DC/AC
DC/AC
DC/AC
DC/AC
DC/AC
DC/DC
(a)
Centralized
Inverter
Optional
(b)
String
Inverter
DC/DC
(c)
Multi-String
Inverter
DC/AC
DC/AC
(d)
Micro
Inverter
DC/DC
DC/DC
(e)
Micro
Converter
Figure 1.3 Emerging PV system topologies.
Recently, the concept of micro inverter and micro converter attracts more and more
attention [56]-[58]. As shown in Figure 1.3(d) and Figure 1.3(e), each PV panel connects
to its own dc/ac or dc/dc converter. Therefore, the Maximum Power Point Tracking
(MPPT) for each PV panel is achieved, and the problem of mismatching losses and partial
shading between different panels can be totally solved. However, the cost for the micro
inverter or converter topology based PV system is the highest.
In addition, a line frequency transformer commonly is installed in these topologies. It
can boost the output voltage of the dc/ac stage, so the system can connect to the medium
or high voltage utility grid. This transformer also can eliminate the ground leakage current
in the PV system. However, this low frequency transformer is bulky and costly. It
significantly increases the system cost and maintenance fee.
14
To eliminate this low
frequency transformer, a front stage dc/dc converter with high frequency transformer may
be utilized, and some leading companies in the solar industry already have released related
products [59] [60].
As an alternative to an isolated dc/dc stage, several papers have discussed using cascade
multilevel inverters to boost the output ac voltage and realize the connection to the medium
voltage grid without line frequency transformers [61]-[63], as shown in Figure 1.4(a). In
this topology, each PV panel or string is connected to an H-bridge converter, and the output
of each H-bridge are connected in series to increase the output voltage. The Distributed
MPPT (DMPPT) is accomplished by proper control of the H-bridge, and the cascaded ac
output voltage increases both the power quality and efficiency. To cope with the PV
insulation and leakage current issues, and at the same time release the control burden of
the H-bridge, [64] and [65] have proposed the topologies that insert an isolated dc/dc
converter between the PV and the H-bridge in each module, as shown in Figure 1.4(b).
PV11
PV1n
S11
S13
S12
S14
Grid
Sn1
Sn3
Sn2
Sn4
PVn1
PVnn
GND
(a) Direct connection between PV and H-bridge.
Continued
Figure 1.4 Cascade multilevel inverter topologies for PV systems.
15
Figure 1.4 Continued
PV11
PV1n
Isolated
DC/DC
Converter
1
S11
S13
S12
S14
Grid
PVn1
PVnn
Isolated
DC/DC
Converter
n
Sn1
Sn3
Sn2
Sn4
GND
(b) Indirect connection between PV and H-bridge.
In the topologies described in Figure 1.3(a)-(c) and Figure 1.4, PV panels most often
are connected in series to provide a high input voltage. As mentioned above, under partial
shading conditions, it can be observed from a typical P/V curve of a PV panel (Figure 1.5)
that the MPP currents between the shaded and unshaded panels are significantly different
[66][67]. Therefore, the total power from the string decreases dramatically because of the
conduction of the bypass diode. However, a further observation from Figure 1.5 shows
that the irradiance has a minor effect on the MPP voltage of the PV panel, so parallelconnected panels can achieve a higher output power under partial shading conditions [68].
Furthermore, the relatively low input voltage can reduce the chances of a dc arc in the
system, and therefore is safer for maintenance personnel [69]. The issue with parallelconnected PV panels is that a high boost ratio dc/dc converter is needed to realize the grid
connection. The same issue exists in the micro inverter and micro converter topologies
shown in Figure 1.3(d) and (e).
16
250
200
1000 W/m2
800 W/m2
600 W/m2
Power (W)
400 W/m2
150
200 W/m2
100
50
0
0
5
10
15
20
Voltage (V)
25
30
Figure 1.5 P/V curves of a PV panel under different irradiance [66].
In summary, a front stage isolated dc/dc converter can help eliminate the line frequency
transformer and reduce the ground leakage current for grid-connected PV systems. It
provides more control flexibility and improves the system performance. This converter
needs a high efficiency, low cost topology. To take advantage of the parallel-connected
PV panels as input, and in a micro inverter/ converter topology, a high-boost ratio is also
required for this converter. The current-source isolated dc/dc converter can easily achieve
a high boost ratio, and features a lower input current ripple, which will be a suitable
candidate for this application.
1.3.2 Existing ESS topologies in PV systems and related issues
The integration of ESSs into PV systems requires a reconsideration of the power
interface topology. At present, several topologies are under discussion. The first topology
shown in Figure 1.6(a) does not need a major reconfiguration of the existing PV-only
system topology. A high voltage battery pack is connected directly to the ac system
17
through an inverter. The drawback of this centralized connection is that the PV and battery
need separate inverters to connect to the grid, which increase the system cost. In the
meantime, for large-scale PV systems, the high voltage and high power battery pack is
usually expensive and requires high maintenance fees. Moreover, a relatively complex
battery monitoring system and balancing system are required for this high power level
battery [71], which will further increase the cost of the ESS. In [72], a dc/dc converter is
inserted between the battery and dc/ac inverter to utilize a low voltage battery and realize
the grid connection; several possible topologies are discussed. Though the voltage level of
the battery can be reduced, the extra dc/dc converter stage increases the system cost.
Utility Grid
Optional
DC/AC
DC/AC
DC/AC
DC/AC
Battery
Battery
DC/DC
DC/DC
DC/DC
Battery
Battery
Battery
(a)
Centralized
connection
(b)
Battery as a
pure energy buffer
(c)
Battery as a
separate power source
Figure 1.6 Emerging energy storage system topologies in PV systems.
18
The topology in Figure 1.6(b) is used widely in standalone or residential PV systems
[74] [75]. In this architecture, the battery functions purely as an energy buffer and is placed
between the dc/dc converter and dc/ac inverter. Compared to other topologies, it does not
need many power electronics circuits and the control strategy is relatively straightforward.
However, the dc bus voltage of the inverter is clamped by the battery voltage, which is
related to the SoC and varies widely.
To independently control the battery charging/discharging current, the topology in
Figure 1.6(c) is used [72] [76]. The battery connects to the dc bus through a dc/dc
converter. The power flow in the system is more flexible and certain control strategies can
be implemented to realize MPPT, battery charge/discharge control, and output
active/reactive power control simultaneously. The drawback of this topology is that a large
number of power electronics circuits are used and the system cost is high.
To solve the issue related to the topology shown in Figure 1.6(c), a dual-input dc/dc
converter topology that has two input ports and one output port can be utilized to combine
the inputs of PV and battery, as shown in Figure 1.7. The dual-input dc/dc converter can
adjust the power from the two input ports independently, and at the same time regulate the
output voltage [77]. It can reduce the number of switches and passive components in the
system, so a more efficient and low-cost PV/battery system can be achieved.
19
PV
Inverter
Dual-Input
Converter
Utility Grid
Energy Storage
System (ESS)
Figure 1.7 Dual input dc/dc converter for PV/battery systems.
1.4 Chapter Review
This dissertation is divided into two parts. Chapter 2 and 3 focus on the system level
investigation of retired EV batteries for the PV/battery system, which include formulation
of the battery optimal usage profile and development of a Microgrid testbed for
experimental study. Chapter 4 and 5 propose new circuit topologies to integrate retired EV
batteries in PV systems.
In Chapter 2, an algorithm is proposed to determine the optimal usage profile of the
retired EV battery and optimal size of the PV system for residential applications. The
electrical and economic models of the battery ESS (BESS) and the PV system are
developed in the algorithm. Simulation results are provided to validate the effectiveness
of the algorithm.
In Chapter 3, a hybrid Microgrid testbed that combines Power Hardware-in-the-Loop
(PHIL) simulation of an electric power network and System-in-the-Loop (SITL) simulation
of a communication network with real hardware is presented. Using this testbed, the
20
experimental implementation of the retired EV battery in a Microgrid with PV systems is
studied.
In Chapter 4, a full-bridge current-source isolated Quasi-Switched-Capacitor (QSC)
dc/dc converter is proposed for PV applications. The operation principle is presented and
the design guidelines are discussed. Theoretical analysis is verified by both simulation and
experimental results. The analysis also shows that a secondary source can be integrated
into the circuit.
In Chapter 5, a family of dual-input dc/dc converters derived from the circuit in Chapter
4 is proposed for integration of the PV and battery. This family of converters utilizes a
full-bridge or half-bridge current-source topology as the primary side, and the QSC
topology as the secondary side. The operation principle is analyzed in detail based on a
half-bridge topology. Simulation and experimental results for both half-bridge and fullbridge topologies are presented to verify the theoretical analysis.
In Chapter 6, contributions of the work are summarized and future work is
recommended.
21
CHAPTER 2
RESIDENTIAL USAGE PROFILE OPTIMIZATION OF THE
RETIRED EV BATTERY AS AN ENERGY STORAGE UNIT
2.1 Introduction
In this chapter, the retired EV battery is utilized as the energy storage unit in a
residential house equipped with a rooftop PV system. It is employed to minimize the cost
of electricity of the facility. To operate this system efficiently, the optimal usage profile
of the battery and the size of the PV system need further study.
The existing literature on the determination of the optimal battery usage profile in smart
grid applications is diverse. One line of research seeks to achieve various grid support
functions with the battery storage. For example, [78]-[81] have studied the usage profile
and optimized size of the battery to smooth the output power of renewable energy resources.
Another line of research, such as [82]-[84], seeks to optimally operate the battery storage
to bid in the electricity market and reserve market. However, these scenarios are not
suitable for the residential application discussed in this chapter.
Similar to the scenario discussed in this chapter, there are some papers aimed at
minimizing the electricity purchase cost of the system with the help of the battery. A sizing
algorithm is proposed in [85] to cost-effectively integrate battery with renewable energy.
The condition with ideal PV generation and constant load is considered. [86]-[88] consider
22
the optimal sizing of energy storage with renewable energy to minimize the electricity bill,
but the electricity price is fixed or based on a TOU scenario with only two rated periods.
In [89], a price-based energy management system is proposed in which the battery usage
profile is determined by PV output power, simulated residential power consumption, and
the time-varying electricity price.
In the aforementioned papers, the battery size can be adjusted continuously and is the
main optimization objective to minimize the system cost. However, since the battery pack
is taken from the EV in this study, the size cannot be changed freely. In fact, the vehicle
manufacturer determines it. Although certain tests and reconfiguration are needed before
these batteries can be reused, it is assumed that the basic physical parameters do not change
significantly during this process. Considering that the typical power level of a residential
house is in the range of several kilowatts, a single battery pack already has the comparable
power and energy level for this application.
In addition, earlier studies usually assume a fixed size of the PV system, and the
installation/maintenance fee is not included in the system cost. As a result, the influence
of the BESS to the size of the PV system is ignored. However, since the price of the rooftop
PV system is relatively high, the size of the PV system needs to be optimized with the
presence of the BESS.
In this chapter, an algorithm for determining the optimal usage profile of the retired EV
battery and the optimal size of the PV system for a residential house is proposed. As the
core of the algorithm, an Energy Management Strategy (EMS) is developed to minimize
the yearly operational cost of the system.
23
2.2 The Proposed Optimization Algorithm
The facility in this study is a residential house equipped with a retired EV battery as an
energy storage unit, and a rooftop PV system, as shown in Figure 2.1. It should be
emphasized that, although the algorithm is presented based on specific conditions, it could
be used in circumstances for battery usage profile optimization with different renewable
energy resources. Examples include commercial customers, industrial customers, or
utilities that have renewable energy generation facilities and would like to minimize the
cost, using BESS.
Figure 2.1 Overall configuration of the facility.
In the proposed optimization algorithm, an EMS is first developed to gain the
maximum economic profit of the BESS at a fixed size of the PV system. It is based on the
minimization of the yearly energy cost, which is reflected by the following equation:
CEnergyCost ( S PV ) 
N
{[ P
t 0
Load
(t ) - PPV (t ) - PBattery (t )]  T  EPrice (t , state)} ,
24
(2.1)
where SPV is the size of the PV system, measured in kW. N is the total number of sequential
time steps. PLoad(t), PPV(t), and PBattery(t) represent the power requirement of the load, the
power obtained from the PV system, and the power extracted from the battery pack as a
function of time, respectively. ΔT is the calculation time interval. EPrice(t, state) is the
electricity price per kilowatt-hour (kWh), which is determined by the utility company and
is assumed to be independent of the renewable energy generation.
After the energy cost is minimized by the EMS, the optimization algorithm evaluates
the tradeoff between the value that the PV system and BESS create and their capital cost.
It searches for the minimum of the following cost function with different sizes of the PV
system:
CTotalCost ( S PV )  CPV ( S PV )  CBattery  CEnergyCost (S PV ) ,
(2.2)
where CPV(SPV) and CBattery are the yearly-prorated installation/maintenance cost of the PV
system and BESS, respectively.
In sum, the proposed algorithm contains four steps:
1) After the profiles of load, irradiance, and kWh price are collected, set PV size to be
zero as a starting point.
2) Apply the EMS to minimize (2.1), which results in a minimized yearly cost for the
selected PV size. Basically, the EMS will determine an optimal charging/discharging
profile for the battery pack to achieve the minimal electricity purchase from the utility grid,
i.e., when the electricity price is high, the battery pack will send power to the grid, whereas
when the electricity price is low, the battery pack will absorb power from the grid.
25
3) Linearly increase the PV size, and repeat step 2) to form a curve that shows the
relationship between the size of the PV system and the total yearly cost. An optimal PV
size will be identified as the minimum in this curve.
4) At the optimal PV size, run 2) again to obtain the optimal usage profile of the battery.
The calculation flow chart of the proposed optimization algorithm is shown in Figure
2.2.
Start
Load Input Data
 Irradiance
 Load profile
 Electricity price
Set PV size Spv=0
Find the minimum
system cost and
corresponding PV size
Apply EMS to
calculate the
minimum energy cost
Calculate the optimal usage
profile of the battery pack
with the optimal PV size
Determine the total
cost based on (2.2)
Stop
Increase Spv
No
Maximum PV size
reached?
Yes
Figure 2.2 Calculation flow chart of the proposed optimization algorithm.
26
2.3 The Developed Energy Management Strategy (EMS)
The objective of the proposed EMS is to gain the maximum economic profit for the
facility over a period of one calendar year. It utilizes the Matlab-based pseudospectral
optimal control software (GPOPS) [91] to minimize the cost function shown in (2.1).
Models of the BESS and PV systems are built into the EMS, and irradiance, load profile,
and electricity price information are collected as calculation inputs, which will be described
in the following sections.
2.3.1 The BESS model
The BESS is one of the core components in the system. The parameters of a retired
EV battery are utilized as the energy storage unit in this study [92]. The initial energy
capacity is ECBattery=7.6 kWh. At the time of retirement from the vehicle, 80% of the
capacity remains.
During operation, the SoC of the battery pack should be within certain limits at any
time, which is expressed as:
SoCmin  SoC(t )  SoCmax ,
(2.3)
where SoCmin and SoCmax are the lower and upper SoC limits, and are set to be 20% and
80%, respectively.
Limited by the power level of the power electronics interface circuit, the output power
of the BESS is also within a certain range, which is expressed as:
PBattery_min  PBattery (t )  PBattery_max ,
(2.4)
where PBattery_min and PBattery_max are the lower and upper output power limits, and are chosen
as -5 kW and 5 kW, respectively.
27
The power loss during battery charging/discharging is considered in the model, which
can be expressed by the following relationship:
2
PBattery (t )  U OCV  I Battery (t )  I Battery
(t )  Rin ,
(2.5)
where UOCV is the open circuit voltage, IBattery(t) is the current and Rin is the internal
resistance of the battery pack, respectively. To simplify the model, it is assumed that UOCV
and Rin are fixed, and unchanging with SoC and the health condition of the battery pack.
They are chosen as 334 V and 0.1 Ω, respectively.
The estimated installation/maintenance cost of the BESS consists of the retired battery
pack and the power electronics circuit. The cost of the retired battery pack is proportional
to the energy capacity, while the cost of the power electronics circuit is proportional to the
maximum power level. The yearly-prorated installation/maintenance cost of the BESS can
be calculated as:
CBattery 
CTBattery  ECBattery  CTPE  PBattery_max
LTBattery
,
(2.6)
where CTBattery is the cost of the retired battery pack per kWh, and CTPE is the cost of the
power electronics circuit per kW. They are assumed to be $100/kWh and $260/kW,
respectively. LTBattery is the lifetime of the BESS, and is assumed to be ten years [16].
2.3.2 The PV system model
A simplified PV system model is utilized in the proposed EMS. It is assumed that the
output power of the PV system is determined only by the irradiance and is not influenced
by the temperature:
28
 I rr (t )
S PV , when I rr (t )  I rr_max

PPV (t )   I rr_max
,
S ,
when I rr (t )  I rr_max
 PV
(2.7)
where Irr(t) is the irradiance at time t. Irr_max is the saturation irradiance, and is chosen as
1000 W/m2. SPV is the size of the PV system, which is measured in kW and varies during
each calculation step. The maximum size of the PV system is set as SPV_max=10 kW.
The estimated installation/maintenance cost of the PV system consists of the PV panels
and the power inverter, which are both proportional to the size of the PV system. Therefore,
the yearly-prorated cost can be calculated as:
CPV 
(CTPV  CTInverter )  S PV
,
LTPV
(2.8)
where CTPV is the cost of the PV panel per kW, and CTInverter is the cost of the PV inverter
per kW. They are assumed to be $ 1.85/W in total (with government incentive). LTPV is
the lifetime of the PV system, and is assumed to be 25 years [93].
The irradiance profile is needed to determine the output power of the PV system at any
given time. One-year irradiance data in the Columbus, Ohio area are retrieved from the
National Solar Radiation Database, with a time interval of one hour [94]. As an example,
the irradiance profiles on a typical winter day (January 1st) and summer day (July 1st) are
plotted in Figure 2.3(a) and (b), respectively.
29
Solar Irradiation (W/m2)
1000
800
600
400
200
0
0:00
6:00
12:00
Time
18:00
24:00
18:00
24:00
(a) Winter day (January 1st).
Solar Irradiation (W/m2)
1000
800
600
400
200
0
0:00
6:00
12:00
Time
(b) Summer day (July 1st).
Figure 2.3 Examples of the irradiance profile.
2.3.3 Electricity price
Real-time electricity pricing strategy is utilized in this study. The pricing information
is collected from a utility company [95] and updated every hour. In addition, it is assumed
30
that the utility company will pay the customers for the energy feedback to the grid, when
the output power of the PV system and BESS are higher than the load power. However,
the retail price, which is the price that the utility company sells the electricity to the
customers, is twice the purchase price, which is the price that the utility company buys the
electricity from the customers, as reflected in the cost function:
 EPrice (t , 0)  ERetailPrice (t )
,

 EPrice (t , 1)  EPurchasePrice (t )
(2.9)
where
0, when PLoad (t ) - PPV (t ) - PBattery (t )  0
state  
,
1, when PLoad (t ) - PPV (t ) - PBattery (t )  0
and
ERetailPrice (t )  2EPurchasePrice (t ) .
This setting is based on the real world scenario that, currently, the utility company is
not in favor of purchasing energy from the customers.
As an example, the daily retail prices on a typical winter day (January 1st) and summer
day (July 1st) are plotted in Figure 2.4(a) and (b), respectively.
31
Electricity Price ($/kWh)
0.1
0.08
0.06
0.04
0.02
0
0:00
6:00
12:00
Time
18:00
24:00
18:00
24:00
(a) Winter day (January 1st).
Electricity Price ($/kWh)
0.1
0.08
0.06
0.04
0.02
0
0:00
6:00
12:00
Time
(b) Summer day (July 1st).
Figure 2.4 Examples of the retail electricity price.
32
2.3.4 Residential load
The last information needed concerns the load. The residential load profile of one year
is obtained from a utility company. It is collected from an anonymous customer and
updated every fifteen minutes. As an example, the load profiles on a typical winter day
(January 1st) and summer day (July 1st) are plotted in Figure 2.5(a) and (b), respectively.
10000
Power (W)
8000
6000
4000
2000
0
0:00
6:00
12:00
Time
18:00
(a) Winter day (January 1st).
Figure 2.5 Examples of the residential load profile.
33
24:00
Continued
Figure 2.5 Continued
10000
Power (W)
8000
6000
4000
2000
0
0:00
6:00
12:00
Time
18:00
24:00
(b) Summer day (July 1st).
2.4 Optimization Results
The proposed optimization algorithm is realized in Matlab. To achieve a relatively
accurate model, the time step of the calculation is set to be one hour. However, the amount
of data is extremely large for a time scope of one year and difficult to manage. Without
loss of generality, a 48-day data set is implemented to represent the whole year in this study.
The data from the 1st, the 8th, the 15th, and the 22nd day of each month are selected and
grouped together to represent the data of a calendar year. Therefore, the system cost in
different seasons can be considered, and at the same time the amount of calculation can be
greatly reduced.
The simulation result to determine the optimal size of the PV system is shown in Figure
2.6. It can be noted that when the PV size is 2 kW, the yearly cost is minimized.
34
Total Yearly Cost ($)
1230
1185
1140
1095
0
2000
4000
6000
PV Power Rating (W)
8000
10000
Figure 2.6 Total yearly cost with different PV sizes.
With this amount of PV installed, the optimal battery pack usage profile can be
calculated. The optimal usage profile of the batter pack on a typical winter day (January
1st) is shown in Figure 2.7(a), and the result on a typical summer day (July 1st) is shown in
Figure 2.7(b), respectively. Compared with the electricity price information shown in
Figure 2.4, it can be noted that when the electricity price is low, the battery is charged,
while when the electricity price is high, the battery is discharged. These cycles are repeated
multiple times in one day to gain the maximum economic profit.
35
Power (W)
5000
0
-5000
0:00
6:00
12:00
Time
18:00
24:00
18:00
24:00
(a) Winter day (January 1st).
Power (W)
5000
0
-5000
0:00
6:00
12:00
Time
(b) Summer day (July 1st).
Figure 2.7 Optimal usage profile of the battery pack.
As a comparison, Figure 2.8(a) and (b) show the difference of energy cost with and
without using the battery pack on a typical winter day (January 1st) and typical summer day
36
(July 1st), respectively. In general, the instantaneous energy cost is lower with the battery,
and the total energy cost is reduced. There are also occasions that the instantaneous energy
cost with the battery is higher. This is because that the EMS takes advantage of the cheap
energy to charge the battery.
A summary of the annual system cost and revenue is shown in Table 2.1. The
investment includes the installation/maintenance cost of the PV and battery systems. A
total of $ 515.56 can be saved on the electricity bill for the system. Subtracting the
investment from the total savings, the annual net savings of the system is $176.76, which
proves the economic benefit of the PV/battery system for residential applications.
Instantaneous Energy Cost (US$/sec)
0.2
Without Battery
With Battery
0.15
0.1
0.05
0
-0.05
0:00
6:00
12:00
Time
(a) Winter day (January 1st).
18:00
24:00
Continued
Figure 2.8 Comparison of the instantaneous energy cost without and with the battery pack.
37
Figure 2.8 Continued
Instantaneous Energy Cost (US$/sec)
0.2
Without Battery
With Battery
0.15
0.1
0.05
0
-0.05
0:00
6:00
12:00
Time
18:00
24:00
(b) Summer day (July 1st).
Table 2.1 Annual system cost and revenue.
Investment ($)
Electricity Cost Savings ($)
Net Savings ($)
338.8
515.56
176.76
2.5 Summary
In this chapter, an algorithm is proposed to determine the optimal usage profile of the
retired EV battery and optimal size of the PV system for residential applications. The
models of the BESS and PV system are built in the algorithm. The information of
irradiance, load profile, and electricity price for a time scope of one year is collected. The
proposed optimization algorithm is realized in Matlab, and simulation results have
validated the effectiveness of the algorithm.
38
CHAPTER 3
A HYBRID MICROGRID TESTBED FOR THE EXPERIMENTAL
STUDY OF THE RETIRED EV BATTERY
3.1 Introduction
Currently, most existing researches on the retired EV battery focus on theoretical study.
There are not many investigations on the experimental implementation of the retired
battery. Though an experimental test of second use battery has been performed at the
University of California, San Diego [96], more experimental studies on integration of the
retired battery pack for Microgrid related applications are still needed to validate the
theoretical study and accumulate practical implementation experience.
In this chapter, a hybrid Microgrid testbed that combines PHIL simulation of the
electric power network and SITL simulation of the communication network with real
hardware is presented. The proposed testbed provides reconfigurable test conditions for
the experimental study of retired EV batteries. Then the residential facility, described in
Chapter 2, is emulated in the testbed, and the optimized usage profile of the retired battery
from the theoretical study in Chapter 2 is tested. Experimental results are presented at the
end of the chapter.
39
3.2 Overview of the Hybrid Microgrid Testbed
Rapid development of the smart grid urges power engineers to study the operation of
power grids with high penetration of renewable energy resources, energy storage systems,
and communication networks. Around the world, a large number of Microgrid testbeds
have been built in industry and academia to provide experimental platforms for related
study [35]-[42]. These testbeds usually involve a fixed hardware configuration and a
limited number of hardware components. Once setup, it is difficult and time-consuming
to reconfigure the testbed for different studies. Likewise, with large numbers of distributed
energy resources implemented in a power grid, the influence of Microgrid to the utility grid
and the influence between different Microgrids become increasingly significant [40].
However, most of the current testbeds cannot effectively study this influence because they
are connected directly to a fixed utility grid topology.
In addition, a communication network is expected to be an integrated part of the smart
grid. The coexistence of communication and power networks, and their joint operation
necessitate an accurate analysis of communication events. However, existing Microgrid
testbeds usually contain a traditional communication network with fixed hardware
configurations. Therefore, it is difficult to reconfigure and study different communication
network topologies in smart grid. Furthermore, the latency in these communication
networks is relatively high and it is not feasible to intentionally control and adjust
communication events.
The presented Microgrid testbed in this chapter can solve aforementioned issues in a
cost-effective manner. It integrates a hybrid setup of physical hardware and real-time
40
simulation. The testbed includes a PHIL system that emulates power hardware that is not
installed in the testbed. It can also emulate a complex power grid or another Microgrid to
study the influence between the Microgrid and the utility grid, or between different
Microgrids. The PHIL system provides a near-to-real test condition for the system under
test (SUT) and make the electric power network in the testbed reconfigurable. In addition,
the testbed includes a real-time Supervisory Control and Data Acquisition (SCADA)
system with an OPNET [97] based real-time SITL communication network simulation
system. The SCADA system utilizes Field-Programmable Gate Arrays (FPGAs) in the
data acquisition (DAQ) system and a real-time simulator as the control center, so the
latency in the communication and control system is minimized. The SITL system can
exchange real-time traffic with physical network devices and simulate different
communication network topologies, which makes the communication network in the
testbed reconfigurable. Based on the setup, it is able to intentionally introduce and
accurately control different communication phenomena in the testbed, such as protocols,
latency and bandwidth requirements, loss of package, cyber attack, and data management.
This provides a powerful tool for communication related studies.
Besides, the testbed contains multiple power electronics circuits and programmable
power sources/loads to emulate different hardware components in a Microgrid, such as
renewable energy resources and nonlinear loads.
The overall schematic of the testbed is shown in Figure 3.1.
41
Fuse31 CB31 L31
PV31
PV
Inverter
Converter
Inverter
Converter
Fuse32 CB32 L32
PV32
L61
PV
CB61
Transformer11
Fuse11 CB11
Main
Grid
L21
Inverter
CB21 Fuse21
Converter
Fuse61
Fuse41
Fuse42
CB41
CB42
L41
L42
Inverter
Converter
Local Energy
Storage (LES)
Voltage, Current,
Power,
Circuit breaker Status,
DSP control command
Inverter
Converter
PHEV2
PHEV1
Power
Amplifier
Real-Time
Simulator
PHIL
Fuse51
Fuse52
CB51
CB52
Load1
(Normal)
Load2
(Nonlinear)
Switchbox
Microgrid
Control Center
and HMI
OPNET SITL
Communication
Network Simulator
Figure 3.1 Overall schematic of the proposed Microgrid testbed.
The main specifications of the testbed are shown in Table 3.1.
Table 3.1 Main specifications of the hybrid Microgrid testbed.
Overall
System
Description
Specification
Voltage Rating
208 V 3-ph/ 1-ph
Power Rating
20 kVA
Number of Branches
8
Manufacturer
Continued
42
Table 3.1 Continued
PHIL Unit
LES Unit
PV Unit
PHEV Unit
DC Power Supply
800 V, 30 A
Magnapower
TSA800-30
Power Electronics
Module
1200 V, 200 A
Powerex
PM200CLA120
Real-Time Simulator
3.33 GHz 6 Cores
CPU×2
OpalRT
Line Inductance
1 mH
Battery Emulator
400 V, 30 kVA,
Programmable AC &
DC Power Source
California
Instruments
MX30-3PI
Power Electronics
Module
1200 V, 35 A
Semikron
SKM50GB123D
Line Inductance
8 mH
PV Emulator
120 VDC, 12 kW
NH Research 4912
Power Electronics
Module
1200 V, 35 A
Semikron
SKM50GB123D
Line Inductance
8 mH
Battery Emulator
600 VDC, 12 kW
NH Research 476012
Power Electronics
Module
1200 V, 35 A
Semikron
SKM50GB123D
Line Inductance
8 mH
Commercial Electric
Vehicle Charging
Station
Level II, 240 V, 7.2 kW Bosch Power Xpress
Continued
43
Table 3.1 Continued
Programmable Load
9 kW
NH Research
9/3/4600
Passive Load
60 uH, 600 A
REX 3PR-0600C5H
Real-Time DAQ
1.33 GHz CPU,
Spartan-6 FPGA
NI CompactRIO
9082
Microgrid Control
Center
3.33 GHz 6 Cores
CPU×2
OpalRT
AC Load
SCADA
Unit
3.3 Previous Work
As mentioned in the last section, the Microgrid testbed utilizes a comprehensive realtime simulation platform of a smart grid previously set up in the lab [98]. The platform
consists of 4 real-time simulators with a total of eight CPUs, 48 cores, five FPGA chips,
and more than 500 analog and digital inputs/outputs (IOs) [99]. Dolphin PCI boards are
used to provide an extremely high speed and low latency real-time communication link
between simulators [100]. With the technology of switch event interpolation [101], State
Space Nodal (SSN) solver [102], and parallel computation, a switching frequency of up to
10 kHz can be achieved in real time, and a large number of switches can be included in one
model. Moreover, the system allows multiple users to connect to the real-time simulator
concurrently to perform collaborative simulation, for which distributed control can be
easily realized. Simulation results in [98] have verified that the developed platform is able
to simulate accurately complex smart grid models with large numbers of renewable energy
resources and power electronics circuits in real time.
44
Meanwhile, with the SITL technology, an OPNET-based communication network
simulator that can accept real-time traffic is developed in the simulation platform, so the
real-time simulations of a communication network and an electric power network can be
combined together. This provides an effective approach to examine communication and
distributed control related issues in the smart grid. A case study in [98] shows that the
communication network can bring great benefits to the operation of a smart grid in allowing
for the implementation of new control and protection schemes.
However, without
consideration of the communication network, the actual system response cannot be
predicted accurately.
To integrate the function of the real-time simulation platform into the testbed, the
simulators in the platform and power hardware in the testbed are bridged together through
PHIL technology, which provides a flexible and reconfigurable power network. The
OPNET-based SITL network simulator in the simulation platform also is integrated into
the SCADA system in the testbed to provide a flexible and reconfigurable communication
network. In addition, the previously developed electric power network and communication
network models in the platform also are adopted into the testbed.
3.4 Configuration of the Electric Power Network
The main power network components in the testbed are the PHIL system,
programmable power sources, power electronics circuits, and programmable electronic
loads. They are grouped into several branches to emulate different distributed energy
conversion systems. In the test setup, the SUT is realized by hardware components close
to a real world scenario. Peripheral electric circuits that are relatively unimportant are
45
emulated by the PHIL system. With this configuration, the accuracy of the experimental
results are guaranteed; at the same time, different test scenarios are realized easily within
the testbed.
3.4.1 The PHIL system
The PHIL system provides a flexible and reconfigurable electric power network, as
shown in Figure 3.2. It can emulate: 1) A variety of subsystems in the Microgrid testbed,
such as stationary battery units, charging stations, renewable energy resources, etc.; or 2)
A fairly complex utility grid, such as a 9-bus system or a 14-bus system, studying the
impact between the utility grid and Microgrid; or 3) Multiple types of Microgrids, studying
the interaction between different Microgrids.
Figure 3.2 Illustration of the PHIL system.
The schematic of the PHIL system is shown in Figure 3.3(a). It contains one or more
real-time simulators and a power amplifier. The power amplifier is composed of a 27 kVA
240 V Delta / 480 V Wye isolation transformer, a bidirectional dc power supply, a
bidirectional dc/ac inverter, and a DSP controller. The real-time simulator senses voltage
46
and current information from the testbed, and then calculates the system response based on
the simulated power network. The calculation results are sent directly to the DSP. Based
on the results from the real-time simulator, the DSP controller regulates the output of the
bidirectional dc/ac inverter using the Pulse Width Modulation (PWM) method. The
isolation transformer and bidirectional dc power supply together provide a bidirectional
power source for the PHIL system, which can realize a bidirectional power exchange with
the testbed. The experimental setup is shown in Figure 3.3(b).
Simulated
Power Network
Real-Time
Simulator
DAC
Vref, Iref
Isolation
Transformer
g
Bidirectional
DC Power
Supply
ADC
Vmeas, Imeas
DSP
Bidirectional
DC/AC
Inverter
Real Power
Network
(a) Schematic.
DC Power Supply
DC/AC Inverter
Real-Time Simulator
DSP
(b) Experimental setup.
Figure 3.3 Schematic and experimental setup of the PHIL system.
47
The experimental result is shown in Figure 3.4. In the test, a grid-tied PV system was
emulated in the PHIL system, which included PV panels, a boost converter, and a singlephase inverter. A MPPT function was implemented in the boost converter. The grid side
voltage was 208 V and the dc bus voltage was 400 V. A step change of the irradiance from
1000 W/m2 to 0 W/m2 was emulated, as shown on the 4th channel of the scope. Because
of the characteristics of the PV panels and the MPPT controller, the current reference sent
from the real-time simulator to the DSP was dropped to zero, as shown in the 2nd channel
of the scope. As seen from the 3rd channel of the scope, the inverter current tracked the
current reference from the real-time simulator and dropped to zero at the same time. The
experimental result verifies the functionality of the PHIL system.
Figure 3.4 Experimental result of the PHIL system.
48
3.4.2 Power electronics circuit
A three-phase IGBT module is utilized in the PHEV/EV charging system, PV system,
and LES system. It can be either configured as a three-phase dc/ac inverter, or as a dc/dc
converter connected with a single phase inverter. Peripheral control and protection circuits
also are developed for the unit. The system test setup is shown in Figure 3.5.
Contactor
Power Electronics Unit
Power Supply
DSP
Interface
Figure 3.5 Test setup of the power electronics unit.
The whole setup is tested under a rated voltage of 400 Vdc and a current of 30 Arms,
with a three-phase configuration. The experimental result is shown in Figure 3.6.
49
Figure 3.6 Experimental result of the power electronics circuit.
A Dq synchronous rotating frame control is implemented for the dc/ac inverter to
realize independent control of active and reactive power. Depending on the operation
mode of the testbed, different control algorithms will be implemented. The control
algorithms of the dc/dc converters are determined by the renewable energy resources, and
are different between branches. The detailed control strategy is introduced in [103].
3.4.3 The Local Energy Storage (LES) system
The five kW LES system is configured as a single-phase bidirectional battery charger
and is connected to a battery simulator, which is a bidirectional dc power supply. In the
future, a retired EV battery can be integrated in this branch to conduct related study. The
circuit topology and control algorithm are shown in Figure 3.7. The system is composed
of a boost converter with a single phase inverter. The battery reference current is based on
the optimized usage profile obtained in Chapter 2.
50
vdc_Fdb
vac_Fdb
θPLL
PLL
Lac1
C
Ldc
Retired Battery Pack
or
Battery Emulator
Utility Grid
Lac2
iBatt_Fdb
IBatt_Ref
iac_Fdb
Ipeak_Ref
Vα
PI
Modulator
PI
sin
vdc_Fdb
PI
Vdc_Ref
θPLL
Figure 3.7 Circuit schematic and control algorithm of the LES system.
The dynamic response of the system is tested, and the result is shown in Figure 3.8. In
the test, a step change in the battery current reference value is applied. The dc side current
stabilizes in less than 20 ms, while the ac side current stabilizes within two fundamental
cycles.
Figure 3.8 Step response of the LES system.
51
3.4.4 The PHEV/ EV charging system
Two five kVA PHEV/EV charging branches are implemented in the testbed. Branch
one utilizes a level II commercially available charging station. A control pilot is added, so
the charging station also can be configured as a general-purpose power source with remote
control capability, as shown in Figure 3.9.
Control Pilot
Figure 3.9 PHEV charging station.
The test result showing that the charging station outputs power to a resistive load
through a diode rectifier is shown in Figure 3.10.
52
Vgrid: 500 V/div
Igrid: 50 A/div
Vload: 250 V/div
Iload: 5 A/div
10 ms/div
Figure 3.10 Test result of the charging station with a nonlinear load.
In branch two, self-configured power electronics circuit is developed to fully control
the battery charging profile.
3.4.5 The PV system
Two five kVA PV branches are developed in the testbed. Each branch includes a PV
simulator, which is a programmable dc source configured to have the output I-V
characteristics similar to a real PV module. A control panel is developed to adjust the
parameters of the PV simulator, as shown in Figure 3.11.
53
Figure 3.11 PV simulator control panel.
3.4.6 The programmable ac load
A programmable ac load is included in the testbed. It is utilized to emulate different
load profiles in the Microgrid. The load profile shown in Chapter 2 can be implemented
in this branch. To test the function, a group of data is implemented in the ac load and
programmed to change every 10 s. The test result is shown in Figure 3.12, which verifies
the capability of the ac load.
54
Figure 3.12 Test of the programmable ac load.
3.5 Configuration of the SCADA System
The real-time SCADA system consists of three main parts: a DAQ, a real-time virtual
communication network, and a real-time control center with a human machine interface
(HMI). It enables fast data exchange between the local controller and control center,
flexible and reconfigurable communication network configuration, and system monitoring
and supervisory control. The schematic of the SCADA system is shown in Figure 3.13. In
general, the DAQ and real-time control center have similar functions as in a traditional
SCADA system, but are designed with much faster speeds. This helps to eliminate the
uncertain delay in the system, so the total delay can be accurately controlled by the virtual
communication network.
55
Figure 3.13 Schematic of the real-time SCADA system.
3.5.1 The DAQ
The DAQ part utilizes a dedicated CompactRIO (cRIO) system from National
Instruments (NI) [104]. It includes 16-bit analog input modules with a sampling rate of
100 kS/s/ch and 16-bit analog output modules with a sampling rate of 100 kS/s. The
Spartan-6 LX150 FPGA is utilized to interface between the analog I/O modules and a realtime controller. Most calculation is also realized in FPGA for fast data processing. The
real-time controller in DAQ has an embedded real-time operation system running on a 1.33
GHz dual-core Intel Core i7 processor with 32 GB nonvolatile storage and 2 GB RAM. It
has two Gigabit Ethernet ports to exchange information with other parts of the SCADA
system, especially the real-time virtual communication network.
56
3.5.2 The real-time virtual communication network
All concerned real network traffic is first channeled through an OPNET-based realtime SITL communication network simulator before reaching its destination. OPNET is a
commercially well-developed network simulator that is used extensively in research. With
the SITL package, OPNET can bridge the simulated network with physical hardware. It
has the ability to accept real-time network traffic and simulate different types of
communications, such as Ethernet, fiber optics, wireless, etc.
In addition, the OPNET allows significant flexibility in terms of network development.
By placing the SITL gateways, simulated routing, and switching hardware within the
OPNET, different network topologies between power branches, delays, and bit error rates
(BER) associated with transmitting packets are simulated. The OPNET SITL also allows
for the transmission of individual packets from the input ports to the output ports of the
simulated network by following the exact steps of buffering, protocol processing,
transmission, and reception. Therefore, it is able to intentionally introduce and accurately
control different communication phenomena in the testbed, such as protocols and latency.
3.5.3 The real-time control center and HMI
A real-time simulator is utilized as the control center. It collects information directly
from the DAQ part or from the virtual communication network. It also accepts supervisory
control commands from the HMI. Utilizing the fast calculation capability of the real-time
control center, distributed control that requires complex algorithms are implemented,
which include power distribution between different renewable energy resources, power
quality control, economic energy dispatch, protection, etc. After calculation, control
57
commands are distributed back to each branch using the same communication network.
Results in the control center also can be observed from the HMI.
The HMI is a Microsoft/Windows-based non-real-time PC. It provides an intuitive
way to monitor the status of the testbed and sends out supervisory control commands. For
example, the instantaneous current, voltage, and circuit breaker status in the testbed can be
displayed. The HMI is based on the Labview from NI and has a good integration with the
DAQ part. The screenshot of the HMI in a test is shown in Figure 3.14.
Figure 3.14 Screenshot of the HMI.
The schematic of the test setup to examine the real-time SCADA system is shown in
Figure 3.15(a). In the first experiment, the communication was directly between the DAQ
and control center, and did not go through the virtual communication network. The
experimental result is shown in Figure 3.15(b). The yellow curve is the original data and
the green curve is the data after passing through the SCADA system. Of note is that the
58
total latency is only 400 us. Therefore, compared to the traditional SCADA system, the
proposed real-time SCADA system can achieve a much faster communication speed
between local and central controllers.
In the second experiment, the signal passed to the SCADA system through the virtual
communication network, and a 5 ms delay was intentionally added. In a real world scenario,
this delay may be caused by the communication network between the local controller and
the control center. The experimental result is shown in Figure 3.15(c). The measured
communication delay in the system is 5.3 ms, which is close to the preset value. This
verifies the capability of the virtual communication network.
(a) Schematic of the test setup.
Figure 3.15 Experimental study of the real-time SCADA system.
59
Continued
Figure 3.15 Continued
1 ms/div
(b) Without the virtual communication network.
1 ms/div
(c) With the virtual communication network.
60
3.6 System Integration Test
A system level setup of the testbed is shown in Figure 3.16. To ensure the safety of the
personnel, the HMI and scope monitor are located in an observation room, which is
separate from the hardware setup. An emergency stop system is also configured in the
testbed.
A test setup to emulate the facility studied in Chapter 2 was configured to validate the
functionality of the testbed, which included a roof-top PV system, an ESS with retired EV
batteries, and a residential load. In the test, the roof-top PV system was emulated by the
PHIL system. The programmable ac load generated the residential load profile. The ESS
was emulated by the LES system, which was controlled to follow the optimized usage
profile from Chapter 2. The real-time SCADA system was collecting information from the
testbed and displaying it in the HMI.
(a) Obeservation room.
Figure 3.16 Setup of the Microgrid testbed.
61
Continued
Figure 3.16 Continued
(b) Hardware layout.
A 75 min period was emulated, and the test results are shown in Figure 3.17. It can be
concluded from the test results that each branch was functioning properly in the system.
With the presence of the PHIL that emulated the PV system and AC load that emulated the
residential load, the testbed provided a flexible test condition for the operation of the retired
EV batteries. In addition, the capability of the testbed to emulate a Microgrid with multiple
renewable energy resources, power electronics interface circuits, and communication
network is verified. Furthermore, it can be noted in Figure 3.17(a) that the ESS can follow
the optimized usage profile obtained from the study in Chapter 2, which also proves the
feasibility of the proposed optimization algorithm.
62
(a) ESS power.
(b) Residential load power.
(c) PV system power.
(d) Grid power.
Figure 3.17 Experimental results in system integration test.
63
3.7 Summary
In this chapter, the design and development of a reconfigurable hybrid Microgrid
testbed is presented.
The testbed features a PHIL system that enables flexible
reconfiguration of the electric power network, and a real-time SCADA system with SITL
based reconfigurable communication network. Multiple power electronics circuits and
programmable power sources/loads are setup to emulate different branches in the
Microgrid.
Based on the testbed, the experimental implementation of the retired EV battery in the
Microgrid is performed. The optimal usage profile of the retired battery obtained from
Chapter 2 is realized in the experiment. The test results showcase the capability of the
testbed for experimental study of the retired batteries and Microgrid. The experiment also
proves the feasibility of the theoretical study on optimal usage profile of the retired battery.
64
CHAPTER 4
A FULL-BRIDGE CURRENT-SOURCE ISOLATED DC/DC
CONVERTER WITH REDUCED NUMBER OF SWITCHES AND
VOLTAGE STRESS FOR PV APPLICATIONS
4.1 Introduction
Earlier analysis shows that the retired EV battery usually operates together with a PV
system. In this chapter, a full-bridge current-source isolated dc/dc converter for PV
applications is proposed. The converter utilizes a unique Quasi-Switched-Capacitor (QSC)
circuit as the secondary side, which features a reduced number of switches and voltage
stress, and an additional boost function. Soft-switching functions also can be realized in
both the primary and secondary side switches to increase the efficiency of the circuit.
Furthermore, through analysis, it is discovered that a second voltage source is able to be
integrated into the circuit. This allows for the combined inputs of the PV and the retired
EV battery with a single converter, which is analyzed in Chapter 5.
4.2 Overview of the Isolated DC/DC Converter for PV Applications
To eliminate the bulky and costly low frequency transformer, a front stage dc/dc
converter with a high frequency transformer can be used in PV systems. Voltage-source
isolated dc/dc converters have been widely used in this application, such as flyback, dual
active bridge [105], and resonant topologies [106] [107]. At the dc input side of these
65
converters, several PV panels usually are arranged in series to increase the voltage level,
which brings the partial shading issue to the system. As mentioned in Chapter 1, this issue
can be solved completely by utilizing a micro inverter or a micro converter, which only use
one PV panel at the input side. Alternatively, the partial shading effect can be reduced
greatly by putting the PV panels in parallel. However, in either method, a high boost ratio
is required for the front side dc/dc converter.
For voltage-source isolated dc/dc converters, the efficiency decreases with a high boost
ratio or large input current. In addition, the input current ripple is usually large, which
influences the tracking of MPP and shortens the lifetime of the PV panels. In contrast, the
current-source converters feature inherent boost capability, lower input current ripple, and
easy current controllability, which become satisfactory candidates in this application.
Current-source isolated dc/dc converters have been used widely in high power or high
boost ratio applications [108]-[128]. At the secondary side of these converters, a bridge
structure for rectification is implemented. The semiconductor devices in the bridge need
to sustain the full output dc voltage, which is a fairly high value. Furthermore, this bridge
structure cannot offer any boost function.
Another practical issue with the current-source isolated dc/dc converter is that the
current in the leakage inductor of the transformer can introduce a large voltage spike when
the switch turns off. Different passive and active snubber circuits have been proposed to
solve this issue [109]-[121], and, at the same time can provide soft-switching functions.
However, the snubber circuit inevitably introduces additional cost and power loss. [122][125] have proposed to eliminate the snubber circuit and realize the soft-switching function
66
by utilizing the leakage inductor and parasitic capacitor of the high-frequency transformer.
However, the resonant current is very sensitive to the parasitic parameters and is difficult
to control. [126] and [127] have proposed a modulation method to realize a naturally
commutated soft-switching in a wide operation range, but a discrete inductor is needed in
the ac link of the circuit.
Compared to the aforementioned topologies, the proposed topology in this chapter has
the following features: 1) The high voltage side Quasi-Switched-Capacitor (QSC) circuit
has less numbers of switches and voltage stress; 2) The QSC circuit has an additional boost
function, through which a high boost ratio can be achieved easily. In addition, because of
this boost function, the voltage stress on the transformer is also reduced; 3) Soft-switching
can be realized for both primary and secondary side switches in a wide operation range,
and only a small RCD snubber circuit is needed for each primary side switch; and 4) A
second voltage source can be integrated in the topology to realize the dual-input operation.
4.3 Proposed Circuit Topology
4.3.1 Secondary side Quasi-Switched-Capacitor circuit
The topology of the secondary side QSC circuit is shown in Figure 4.1. The circuit is
derived by applying the traditional voltage tripler ac/dc circuit [129] and replacing the three
diodes with three controllable switches. In the circuit, Sa and Sb are a pair of switches that
turn on and off simultaneously. The control signal for Sc is complementary to that of Sa&Sb
with a certain amount of dead time inserted in between. In contrast to [130] [131], which
fixed the duty ratio of Sa&Sb to be 0.5, a variable duty ratio D can be implemented for
67
Sa&Sb. C1 and C2 have identical capacitance. In one switching cycle, they will be charged
in parallel and discharged in series. Lm represents the transformer magnetizing inductance.
Sa
a
Lm
VC1
Sc
C2
C1
VC2
c
C3
Vout
b
Sb
Figure 4.1 The topology of the QSC circuit.
Based on the voltage-second balance of Lm, it can be derived that
VC1  VC 2 
1 D
Vout .
2D
(4.1)
From (4.1), it is noted that, by utilizing the magnetizing inductor of the transformer,
the output voltage can be boosted higher than the capacitor voltage, so an additional boost
function can be realized by the QSC circuit. Furthermore, the voltage relationship between
the capacitor voltage and output voltage can be adjusted. A voltage source can be put either
between points a and b (in parallel with C1), or between points a and c. By regulating the
duty ratio, the output voltage can be controlled. Further discussion on this input port is
presented in Chapter 5.
In addition, compared to other circuit topologies, where the voltage stress on the
switches equals Vout, the voltage stress on the switches in the QSC circuit is reduced to:
68
VSa _ max  VSb _ max  VSc _ max  Vout  VC1 
1
Vout .
2 D
(4.2)
The voltage across the transformer winding, which is VLm, is also reduced from Vout to
the following value:
VLm _ max  max{
1 D
D
Vout ,
Vout } .
2 D
2 D
(4.3)
4.3.2 Full dc/dc converter circuit
The proposed full dc/dc converter circuit topology is presented in Figure 4.2. The QSC
circuit is used on the secondary side. The primary side of the converter is a full-bridge
current-source circuit, and the reverse blocking diode is not needed. The primary side input
inductor limits the current ripple flowing into the PV module, so the lifetime can be
extended and a more accurate MPPT can be achieved. A high-frequency transformer is
utilized to connect the primary and secondary sides, and in the meantime provides galvanic
isolation in the circuit. The ground leakage current of the PV modules can be greatly
reduced by this configuration.
vLin
iLin
Lin
S1
S5
S2
VPV
vC1
S3
S4
C1
S7
C2
vC2
1:N
S6
Figure 4.2 Circuit topology of the proposed isolated dc/dc converter.
69
C3
Rout
vout
4.4 The Operation Principle
The equivalent circuit of the proposed isolated dc/dc converter is shown in Figure 4.3.
On the primary side, S1&S4 are one pair of switches that turn on and off at the same time.
S2&S3 are the other pair of switches. On the secondary side, S5&S6 are the third pair of
switches. S5&S6 and S7 are controlled as complementary pairs. There are totally six states
in one switching cycle. In contrast to traditional control methods, the control signals of
primary side switches are synchronized with the secondary side QSC circuit and in most
cases are asymmetrical. In the following sections, the issue with the leakage inductor will
be addressed first, and then the operation principle of the circuit will be analyzed step by
step.
vLin
iLin
Lin
S1
S7
vLs
a
iLs
Vin
S3
S5
c
S2
Ls
vC1/N
iLm
vLm
C1
C2
vC2/N
Lm
C3
Rout
Vout/N
b
S4
d
S6
Figure 4.3 Equivalent circuit of the proposed dc/dc converter.
4.4.1 The leakage inductor issue
Since the coupling between transformer windings is not perfect, there is always leakage
inductor existing in the circuit. This leakage inductor will bring issues with the operation
of the circuit if not addressed properly.
70
The primary side of the circuit has an inductor in front of the full bridge and the inductor
current is almost constant. Therefore, the equivalent circuit between points a and b in
Figure 4.3 is a current source in parallel with the switch Sp. The direction of the current
between points a and b can be either positive (when S1 and S4 are on) or negative (When
S2 and S3 are on).
On the secondary side, since the capacitor voltages are almost constant, the equivalent
circuit between points c and d in Figure 4.3 is a voltage source in series with the switch Ss.
The voltage direction between points c and d can be either positive (when S5&S6 are on) or
negative (when S7 is on), and the magnitudes also can be different. The simplified
equivalent circuit of the proposed isolated dc/dc converter is shown in Figure 4.4.
Ls
a
c
Ss
Current
Source
Sp
Voltage
Source
Lm
b
d
Figure 4.4 Simplified equivalent circuit of the proposed isolated dc/dc converter.
In Figure 4.4, Sp represents the combined states of all the primary side switches S1 to
S4. When Sp is on, it represents that S1 to S4 are all on. When Sp is off, it represents that
either S1&S4 are on or S2&S3 are on, depending on the direction of the current source.
Similarly, Ss represents the combined states of secondary side switches S5 to S7. When Ss
71
is on, it represents that either S5& S6 are on or S7 is on, depending on the direction of the
voltage source. When Ss is off, it represents that S5 to S7 are all off.
During operation, when Sp is off and Ss is on, the current in the leakage inductor is equal
to the current source. If the direction of the current source suddenly changes because of
the switching function (for example, S1&S4 turn from on to off, at the same time S2&S3 turn
from off to on), the direction of the current in the leakage inductor will be forced to be
reversed. During this process, a large voltage spike will appear and cause a voltage
overshoot problem.
Conceptually, one solution to this issue would be that, each time before the current
source changes direction, Sp will first be turned on. The leakage inductor current will be
changed by the voltage source on the secondary side. Sp can be turned off again when the
leakage inductor current changes direction and the magnitude is equal to, or larger than the
current source. By so doing, the voltage spike can be eliminated. Furthermore, softswitching can be achieved. The detailed realization of this concept will be discussed in the
following section.
4.4.2 Detailed operation principle
The key waveforms in the circuit are shown in Figure 4.5. There is a total of six states
in one switching cycle.
72
δ1
S1&S4
0
S2&S3
0
S5&S6
S7
δ2 δ3
δ5 δ6
δ4
ON
ON
ON
ON
t
ON
ON
0
ON
ON
0
t
t
t
ILin
0
t
0
t
Im
ILs
ILin
t
0
-ILin
ILin
IS1&IS4
t
0
ILin
IS2&IS3
t
0
0
IS5&IS6
IS7
-ILin/2N
0
t0
t1 t'1 t2 t3
t4 t'4 t5 t6
Figure 4.5 Key waveforms of the circuit.
73
-ILin/N
t
t
1) State I (t0-t1), C1&C2 charging.
In this state, S1&S4 and S5&S6 are on. S2&S3 and S7 are off.
This state starts when:
iLin _1 (t0 )  iLs _1 (t0 ) .
(4.4)
In this state, C1&C2 are connected in parallel and charged by the input inductor. The
mutual inductor current is increasing. C3 and load are disconnected from the main circuit
and the load voltage is held by C3. The current flow in State I is shown in Figure 4.6.
Lin
S1
S5
S2
Ls
S7
C2
C1
Lm
Vin
S3
C3
Rout
S4
S6
Figure 4.6 Current flow in State I.
The currents in the inductors are:
iLin _1 (t )  iLs _1 (t )  I Lin _1 (t0 ) 
iLm _1 (t )  I Lm _1 (t0 ) 
(Vin  VC1 / N)
t ,
Lin  Ls
(4.5)
VC1
t ,
NLm
(4.6)
where Lin, Ls, and Lm are the input inductance, leakage inductance, and mutual inductance,
respectively. N is the transformer during ratio.
74
The currents in the switches are:
iS1_1 (t )  iS 4_1 (t )  iLin _1 (t ) .
iS 5_1 (t )  iS 6 _1 (t ) 
iLm _1 (t )  iLin _1 (t )
2N
(4.7)
.
(4.8)
2) State II (t1-t2), leakage inductor current changing direction.
In this state, S1&S4, S2&S3, and S5&S6 are on. S7 is off.
This state starts when S2&S3 are turned on. The input inductor is charged by the input
voltage source. C1&C2 are connected in parallel to charge the leakage inductor and the
mutual inductor. The current in the leakage inductor drops to zero at t'1, and then continues
to increase at the reverse direction. The mutual inductor current keeps increasing. The
current flow in State II is shown in Figure 4.7.
Lin
S1
S5
S2
Ls
C2
C1
Lm
Vin
S3
S7
C3
Rout
S4
S6
(a) t1- t'1.
Figure 4.7 Current flow in State II.
75
Continued
Figure 4.7 Continued
Lin
S1
S5
S2
Ls
C2
C1
Lm
Vin
S3
S7
C3
Rout
S4
S6
(b) t'1-t2.
Ideally, this state should end when the current in the leakage inductor has the same
magnitude but opposite direction as the input current. In practice, the leakage inductor
current is charged a little higher than the input current to leave some margin and avoid a
voltage overshoot.
The currents in the inductors are:
Vin
 (t  t1 ) .
Lin
(4.9)
iLs _ 2 (t )  I Ls _1 (t1 ) 
VC1
 (t  t1 ) .
NLs
(4.10)
iLm _ 2 (t )  I Lm _1 (t1 ) 
VC1
 (t  t1 ) .
NLm
(4.11)
iLin _ 2 (t )  I Lin _1 (t1 ) 
The currents in the switches are:
iS1_ 2 (t )  iS 4 _ 2 (t ) 
iS 2 _ 2 (t )  iS 3_ 2 (t ) 
iLin _ 2 (t )  iLs _ 2 (t )
2
iLin _ 2 (t )  iLs _ 2 (t )
2
76
.
(4.12)
.
(4.13)
iS 5_ 2 (t )  iS 6 _ 2 (t ) 
iLm _ 2 (t )  iLs _ 2 (t )
2N
.
(4.14)
At t1, when S2&S3 are turned on, the currents in the input inductor and the leakage
inductor are the same. From (4.13), it is seen that, with the decrease of the leakage inductor
current, the current in S2&S3 will begin to increase from zero. Since Ls limits the switch
current increase rate while the voltages across the switches drop instantly, S2&S3 realize
zero-current switching (ZCS) during the turn-on transition.
3) State III (t2-t3), leakage inductor current freewheeling.
In this state, S2&S3 and S7 are on. S1&S4 and S5&S6 are off. The body diodes of S1&S4
are on.
This state starts when S1&S4 and S5&S6 are turned off, and S7 is turned on. Since the
magnitude of the current in the leakage inductor is larger than the input current, the energy
at the input side cannot be transferred to the load. Instead, the input inductor remains
charged by the input voltage source in this state.
In the meantime, C1&C2 are connected in series with the load. Since the body diodes
of S1&S4 are conducting, the current in the leakage inductor is decreasing rapidly. This
state ends when the magnitude of the current in the leakage inductor is equal to the input
current. The mutual inductor current starts to decrease in this state. The current flow in
State III is shown in Figure 4.8.
77
Lin
S1
S5
S2
Ls
C2
C1
Lm
Vin
S3
S7
C3
Rout
S4
S6
Figure 4.8 Current flow in State III.
The currents in the inductors are:
Vin
 (t  t2 ) .
Lin
(4.15)
iLs _ 3 (t )  I Ls _ 2 (t2 ) 
(Vout  VC1  VC 2 )
 (t  t2 ) .
NLs
(4.16)
iLm _ 3 (t )  I Lm _ 2 (t2 ) 
(VC1  VC 2  Vout )
 (t  t2 ) .
NLm
(4.17)
iLin _ 3 (t )  I Lin _ 2 (t2 ) 
The currents in the switches are:
iS1_ 3 (t )  iS 4 _ 3 (t ) 
iS 2 _ 3 (t )  iS 3_ 3 (t ) 
iS 7 _ 3 (t ) 
iLin _ 3 (t )  iLs _ 3 (t )
2
.
iLin _ 3 (t )  iLs _ 3 (t )
2
iLs _ 3 (t )  iLm _ 3 (t )
N
78
.
(4.18)
.
(4.19)
(4.20)
At t2, when S1&S4 are turned off, the magnitude of the current in the leakage inductor
is larger than the input current, and the body diodes of S1&S4 are conducting. Therefore,
S1&S4 realize zero-voltage switching (ZVS) during turn-off transition.
In addition, since the current in leakage inductor is a negative value and larger than the
mutual inductor current, the body diode of S7 will conduct first considering the dead time
between S5&S6 turning-off and S7 turning-on. When S7 is turned on, it will operate under
synchronous rectification mode. Therefore, S7 realizes ZVS during turn-on transition.
The turning-off of S5&S6 is hard switching. However, by adding an additional small
capacitor between the drain and source of S5-S7, the voltage across S5&S6 will increase
slowly during the turn-off transition, and the switch current will decrease to zero before
the drain-to-source voltage is built up to the steady state value. Therefore, a ZVS also can
be realized during the turning off of S5&S6.
4) State IV (t3-t4), energy transferring to the load.
In this state, S2&S3 and S7 are on. S1&S4 and S5&S6 are off.
This state starts when the body diodes of S1&S4 are turned off at t3. C1&C2 and input
source are connected in series with the load. C3 is charged and the energy is transferred
from the input side to the load. C1&C2 are also discharging energy to the load. Mutual
inductor current keeps decreasing. The current flow in State IV is shown in Figure 4.9.
79
Lin
S1
S5
S2
Ls
S7
C2
C1
Lm
Vin
S3
C3
Rout
S4
S6
Figure 4.9 Current flow in State IV.
The currents in the inductors are:
iLin _ 4 (t )  iLs _ 4 (t )  I Lin _ 3 (t3 ) 
iLm _ 4 (t )  I Lm _ 3 (t3 ) 
(Vin  (VC1  VC 2  Vout ) / N )
 (t  t3 ) .
( Lin  Ls )
(4.21)
(VC1  VC 2  Vout )
 (t  t3 ) .
NLm
(4.22)
The currents in the switches are:
iS 2 _ 4 (t )  iS 3_ 4 (t )  iLin _ 4 (t ) .
iS 7 _ 4 (t ) 
iLin _ 4 (t )  iLm _ 4 (t )
N
(4.23)
.
(4.24)
5) State V (t4-t5), leakage inductor current changing direction.
In this state, S1&S4, S2&S3, and S7 are on. S5&S6 are off.
This state starts when S1&S4 are turned on. The input inductor is charged by the input
voltage source. C1&C2 and the load are connected in series to charge the leakage inductor
and the mutual inductor. The current in the leakage inductor increases to zero at t'4, and
80
then continues to increase in the positive direction. The mutual inductor current keeps
decreasing. The current flow in State V is shown in Figure 4.10.
Lin
S1
S5
S2
Ls
S7
C2
C1
Lm
Vin
S3
C3
Rout
C3
Rout
S4
S6
(a) t4- t'4.
Lin
S1
S5
S2
Ls
C2
C1
Lm
Vin
S3
S7
S4
S6
(b) t'4-t5.
Figure 4.10 Current flow in State V.
Ideally, this state should end when the current in the leakage inductor has the same
magnitude and direction as the input current. In practice, the leakage inductor current is
charged a little higher than input current to leave some margin and avoid a voltage
overshoot.
81
The currents in the inductors are:
Vin
 (t  t4 ) .
Lin
(4.25)
iLs _ 5 (t )  I Ls _ 4 (t4 ) 
(Vout  VC1  VC 2 )
 (t  t4 ) .
NLs
(4.26)
iLm _ 5 (t )  I Lm _ 4 (t4 ) 
(VC1  VC 2  Vout )
 (t  t4 ) .
NLm
(4.27)
iLin _ 5 (t )  I Lin _ 4 (t4 ) 
The currents in the switches are:
iS1_ 5 (t )  iS 4 _ 5 (t ) 
iS 2 _ 5 (t )  iS 3_ 5 (t ) 
iS 7 _ 5 (t ) 
iLin _ 5 (t )  iLs _ 5 (t )
2
.
iLin _ 5 (t )  iLs _ 5 (t )
2
iLs _ 5 (t )  iLm _ 5 (t )
N
(4.28)
.
.
(4.29)
(4.30)
At t4, when S1&S4 are turned on, the currents in the input inductor and the leakage
inductor have the same magnitude, but different directions. From (4.28), it can be seen
that with the decrease of the leakage inductor current, the current in S1&S4 will start to
increase from zero. Since Ls limits the switch current increase rate while the voltages
across switches drop instantly, S1&S4 realize ZCS during the turn-on transition.
6) State VI (t5-t6), leakage inductor current freewheeling.
In this state, S1&S4 and S5&S6 are on. S2&S3 and S7 are off. The body diodes of S2&S3
are on.
82
This state starts when S2&S3 and S7 are turned off, and S5&S6 are turned on. Since the
magnitude of the current in the leakage inductor is larger than the input current, the energy
on the input side cannot be transferred to the load. Instead, the input inductor is still
charged by the input voltage source in this state.
In the meantime, C1&C2 are connected in parallel. Since the body diodes of S2&S3 are
conducting, the current in the leakage inductor is decreasing rapidly. This state ends when
the magnitude of the current in the leakage inductor is equal to the input current. The
mutual inductor current starts to increase in this state. The current flow in State VI is shown
in Figure 4.11.
Lin
S1
S2
S5
Ls
Vin
C1
Lm
S3
C2
S7
C3
Rout
S4
S6
Figure 4.11 Current flow in State VI.
The currents in the inductors are:
Vin
 (t  t5 ) .
Lin
(4.31)
VC1
 (t  t5 ) .
NLs
(4.32)
iLin _ 6 (t )  I Lin _ 5 (t5 ) 
iLs _ 6 (t )  I Ls _ 5 (t5 ) 
83
iLm _ 6 (t )  I Lm _ 5 (t5 ) 
VC1
 (t  t5 ) .
NLm
(4.33)
The currents in the switches are:
iS1_ 6 (t )  iS 4 _ 6 (t ) 
iS 2 _ 6 (t )  iS 3_ 6 (t ) 
iS 5_ 6 (t )  iS 6 _ 6 (t ) 
iLin _ 6 (t )  iLs _ 6 (t )
2
iLin _ 6 (t )  iLs _ 6 (t )
2
iLm _ 6 (t )  iLs _ 6 (t )
2N
.
(4.34)
.
(4.35)
.
(4.36)
At t5, when S2&S3 are turned off, the magnitude of current in the leakage inductor is
larger than the input current, and the body diodes of S2&S3 are conducting. Therefore,
S2&S3 realize ZVS during the turn-off transition.
In the meantime, since the current in the leakage inductor is a positive value and larger
than the mutual inductor current, the body diodes of S5&S6 will conduct first considering
the dead time between S5&S6 turning-on and S7 turning-off. When S5&S6 are turned on,
they will operate under synchronous rectification mode. Therefore, S5&S6 realize ZVS
during the turn-on transition.
The turning-off of S7 is hard switching. However, by adding an additional small
capacitor between the drain and source of S5-S7, the voltage across S7 will increase slowly
during the turn-off transition, and the switch current will decrease to zero before the drainto-source voltage is built up to the steady state value. Therefore, a ZVS also can be realized
during the turning off of S7.
After State VI, the circuit will operate from State I again.
84
4.5 Circuit Analysis
Based on Figure 4.5, define the duty ratio of S5&S6 as D, then the duty ratio of S7 is (1D). Therefore:
 D  1   2   6
,

1  D   3   4   5
(4.37)
where δ1 is the duty ratio of State I, δ2 is the duty ratio of State II, etc.
Combining (4.6), (4.11), (4.17), (4.22), (4.27), and (4.33), based on the voltage-second
balance of the mutual inductor, it can be derived that:
Vout 
1  3   4  5
2D
VC1 
VC1 .
3   4  5
1 D
(4.38)
Comparing (4.38) to (4.1), it is noted that the relationship between Vc1 and Vout does
not change in the full circuit.
In the meantime, combining (4.5), (4.9), (4.15), (4.21), (4.25), and (4.31), based on the
voltage-second balance of the input inductor, it can be derived that:
Vin =
VC1
D
(1   4
).
N
1 D
(4.39)
Assume that the current ripple in the input inductor is negligible, and the average
current in the input inductor is Iin. Then at the end of State III, the current in the leakage
inductor is -Iin. Based on (4.10) and (4.16):
3 
(Vout
VC1
2 I in NLs
2 
.
 VC1  VC 2 )
(Vout  VC1  VC 2 )Ts
(4.40)
Similarly, at the end of State VI, the current in the leakage inductor is Iin. Based on
(4.26) and (4.32):
85
6 
(Vout  VC1  VC 2 )
2 I NL
 5  in s .
VC1
VC1Ts
(4.41)
Notice that based on energy conservation:
2
Vout
Vin I in 
.
Rout
(4.42)
Combine (4.37) to (4.42), and it can be derived that:
4 Ls Vout 2 2(1  D)
V
D
(
) 
(D  2 
 5 ) out  1  0 .
RoutTs Vin
N (2  D)
1 D
Vin
(4.43)
Therefore, the boost ratio of the proposed circuit can be calculated based on (4.43).
To simplify the control and ensure that the peak leakage inductor current at the end of
State II and State V are the same, the following relationship should be satisfied based on
(4.10) and (4.26):
VC1
(V  V  V )
  2  out C1 C 2   5
NLs
NLs
1 D
 5 
2
D
.
(4.44)
Therefore, (4.43) can be simplified as:
4 Ls Vout 2 2(1  D)
V
(
) 
( D  2 2 ) out  1  0 .
RoutTs Vin
N (2  D)
Vin
(4.45)
It also can be observed from (4.45) that by controlling D, δ2, and Ts, the boost ratio can
be controlled.
Because of the asymmetrical control signal, there can be dc offset current in the mutual
inductor of the transformer. To calculate it, assume that the current ripple in the mutual
inductor is negligible and the average value is Im. Notice that the average current of S7 is
86
the same as the average load current, so it can be derived based on (4.20), (4.24), and (4.30)
that:
I out _ ave   I S 7 _ ave 
t3
t4
t5
1
(  (iS7 _ 3 (t ))dt   (iS7 _ 4 (t ))dt   (iS7 _ 5 (t ))dt )
t2
t3
t4
NTs
t3
t4
t5
1

(  (iLs _ 3 (t )  I m )dt   ( I in  I m )dt   (iLs _ 5 (t )  I m )dt )
t2
t3
t4
NTs
. (4.46)
Combining (4.16), (4.26), and (4.42) with (4.46), it can be derived that:
Im 
V
  (1  D)Ts Vout ( 32   52 ) DTs Vout
1
( N out  (1  D  2 3 )  I in  2 3



).
1 D
Rout
2 D
NLs
2 D
2 NLs
(4.47)
4.6 Design Guidelines
4.6.1 Simplified circuit model
Since the overshoot current in State II and State V will introduce extra power loss in
the circuit, it usually is controlled to be very small, so State III and State VI are very short
during the operation. In extreme condition, if State II and State V end when the magnitude
of the leakage inductor current equals the magnitude of the input current, State III and State
VI can be totally eliminated. In the following analysis, to simplify the calculation, it is
assumed that there is no overshoot current at the end of State II and State V, so State III
and State VI are totally eliminated. Therefore,
3   6  0 .
87
(4.48)
4.6.2 Semiconductor devices
1) Switch S1 and S4
The maximum voltage stress on S1&S4 is the same and can be decided by the following
equation:
VS 1  VS 4 
Vout  VC1  VC 2
.
N
(4.49)
The rms current of S1&S4 can be calculated based on (4.7), (4.12), and (4.28):
I S21_ rms  I S24 _ rms  1 I in2 
1
Ts

 2Ts
0
 (1   2 ) I in2   22Ts I in (
( I in 
VC1
1
 t ) 2 dt 
2 NLs
Ts

 5Ts
0
(
Vout  VC1  VC 2 2
 t ) dt
2 NLs
VC1
 3T 2 V
 3T 2 V  V  V
)  2 s ( C1 ) 2  5 s ( out C1 C 2 ) 2
2 NLs
3 2 NLs
3
2 NLs
.
(4.50)
Assume that the same type of switches are used, then the conduction loss of S1&S4 can
be calculated as
Pcon _ S 1  Pcon _ S 4  I S21_ rms RDS ,
(4.51)
where RDS is the on resistance of the switch.
Since S1&S4 realize zero-current turn-on and zero-voltage turn-off, the switching losses
are approximately zero.
Psw_ S1  Psw_ S 4  0 .
(4.52)
The energy stored in the parasitic capacitor of the switches is dissipated in the switch
during turning on, and the power loss is:
PCoss _ S 1  PCoss _ S 4 
V  VC1  VC 2 2
1
1
CossVds2 f s  Coss ( out
) fs ,
2
2
N
88
(4.53)
where Coss is the output capacitance of the switch, and fs is the switching frequency.
The total loss in S1&S4 is the sum of all power losses calculated above.
2) Switch S2 and S3
The maximum voltage stress on S2&S3 is the same and can be decided by the following
equation:
VS 2  VS 3 
VC1
.
N
(4.54)
The rms current of S2 and S3 can be calculated based on (4.13), (4.23), and (4.29),
1
Ts

 23Ts2
(
I S22 _ rms  I S23_ rms   4 I in2 
 ( 4   5 ) I in2 
3
 2Ts
0
(
VC1
1
 t ) 2 dt 
2 NLs
Ts

 5Ts
0
( I in 
Vout  VC1  VC 2 2
 t ) dt
2 NLs
VC1 2
V V V
 3T 2 V  V  V
)   52Ts I in ( out C1 C 2 )  5 s ( out C1 C 2 ) 2
2 NLs
2 NLs
3
2 NLs
.
(4.55)
Assume that the same type of switches are used, then the conduction loss of S2&S3 can
be calculated as
Pcon _ S 2  Pcon _ S 3  I S22 _ rms RDS .
(4.56)
Since S2&S3 realize zero-current turn-on and zero-voltage turn-off, the switching losses
are approximately zero.
Psw_ S 2  Psw_ S 3  0 .
(4.57)
The energy stored in the parasitic capacitor of the switches is dissipated in the switch
during turning on, and the power loss is:
PCoss _ S 2  PCoss _ S 3 
V
1
1
CossVds2 f s  Coss ( C1 )2 f s .
2
2
N
89
(4.58)
The total loss in S2&S3 is the sum of all power losses calculated above.
3) Switch S5 and S6
The maximum voltage stress on S5&S6 is the same and can be derived based on (4.2):
VS 5  VS 6  Vout  VC1 
1
Vout .
2 D
(4.59)
The rms current of S5&S6 can be calculated based on (4.8) and (4.14):
I S25 _ rms  I S26 _ rms  1 (
I in
I
1
 m )2 
2N 2N
Ts

 2Ts
0
(
I in
I
V
 m  C21  t ) 2 dt
2 N 2 N 2 N Ls
I in
Im 2
I in
Im
VC1
 23Ts2 VC1 2
2
 (1   2 )(

)   2 Ts (

)(
)
(
)
2N 2N
2 N 2 N 2 N 2 Ls
3 2 N 2 Ls
.
(4.60)
Assume that the same type of switches are used, then the conduction loss of S5&S6 can
be calculated as:
Pcon _ S 5  Pcon _ S 6  I S25 _ rms RDS .
(4.61)
Since S5&S6 realize zero-voltage turn-on and turn-off, the switching losses are
approximately zero.
Psw_ S 5  Psw_ S 6  0 .
(4.62)
Since during the turn-on transition, the body diode of the switch first conducts, the
energy stored in the parasitic capacitor of the switches is not wasted. Therefore, there is
no output capacitance loss for S5&S6.
PCoss _ S 5  PCoss _ S 6  0 .
The total loss in S5&S6 is the sum of all power losses calculated above.
90
(4.63)
4) Switch S7
The maximum voltage stress on S7 can be derived based on (4.2):
VS 7  Vout  VC1 
1
Vout .
2 D
(4.64)
The rms current of S7 can be calculated based on (4.24) and (4.30):
I S27 _ rms   4 (
 I in  I m 2 1
) 
N
Ts
 ( 4   5 )(

 5T5
0
(
I in I m (Vout  VC1  VC 2 ) 2
 
 t ) dt
N N
N 2 Ls
 I in  I m 2
I
I V V V
 3T 2 V  V  V
)   52Ts ( in  m )( out 2C1 C 2 )  5 s ( out 2C1 C 2 ) 2
N
N N
N Ls
3
N Ls
.
(4.65)
The conduction loss of S7 is calculated as:
Pcon _ S 7  I S27 _ rms RDS .
(4.66)
Since S7 realizes zero-voltage turn-on and turn-off, the switching losses are
approximately zero.
Psw_ S 7  0 .
(4.67)
Since during the turn-on transition, the body diode of the switch first conducts, the
energy stored in the parasitic capacitor of the switch is not wasted. Therefore, there is no
output capacitance loss for S7.
PCoss _ S 7  0 .
The total loss in S7 is the sum of all power losses calculated above.
91
(4.68)
4.6.3 Passive components
1) Capacitor C1 and C2
C1&C2 will be discharged in State IV and V. Assuming that the capacitances are the
same, the total voltage drop will be:
VC1  VC 2 
t4
t5
I
I
I
V V V
I
1
(  ( in  m )dt   ( in  out 2C1 C 2  (t  t4 )  m )dt )
t4
2C1 t3
N N
N
N Ls
N
I
I
1
1

( in ) 4Ts 
( m )(1  D)Ts
2C1
N
2C1
N
.
(4.69)
Based on the required voltage ripple, the capacitance of C1 and C2 can be calculated
based on (4.69).
The rms current can be calculated based on the following equation:
2
2
I rms
_ C 1  I rms _ C 2  1 (
 4 (
I in
I
1
 m )2 
2N 2N
Ts
I in I m 2 1
 ) 
N N
Ts

 5Ts
0
(

 2Ts
0
(
I in
I
V
 m  C21  t ) 2 dt
2 N 2 N 2 N Ls
I in I m Vout  VC1  VC 2 2
 
t ) dt
N N
N 2 Ls
I in
Im 2
I in
Im
VC1
 23Ts2 VC1 2
2
 (1   2 )(

)   2 Ts (

)(
)
(
) . (4.70)
2N 2N
2 N 2 N 2 N 2 Ls
3 2 N 2 Ls
 ( 4   5 )(

I in I m 2
I
I V V V
 )   52Ts ( in  m )( out 2C1 C 2 )
N N
N N
N Ls
 53Ts2 Vout  VC1  VC 2 2
(
)
3
N 2 Ls
The power loss in each capacitor is:
2
PLoss _ C1  PLoss _ C 2  I rms
_ C 1 Resr ,
(4.71)
where Resr is the equivalent series resistance of the capacitor at the operation frequency.
92
2) Capacitor C3
C3 is charged only in State IV. The total voltage drop is:
VC 3  (
I in  I m
T
 I out ) 4 s .
N
C3
(4.72)
Based on the required voltage ripple, the capacitance of C3 can be calculated based on
(4.72).
The rms current can be calculated based on the following equation:
2
2
2
I rms
_ C 3  1 I out   2 I out   4 (
I in I m
1
  I out ) 2 
N N
Ts
2
 (1   2 ) I out
 ( 4   5 )(

 53Ts2 Vout  VC1  VC 2
3
(
N 2 Ls

 5Ts
0
(
I in I m
V V V
  I out  out 2C1 C 2 t ) 2 dt
N N
N Ls
I in I m
I
I
V V V
  I out ) 2   52Ts ( in  m  I out )( out 2C1 C 2 )
N N
N N
N Ls
)2
.
(4.73)
The power loss in the capacitor is:
2
PLoss _ C 3  I rms
_ C 3 Resr .
(4.74)
3) Input inductor Lin
The inductor power loss includes the copper loss and core loss. Since there are both dc
and ac components in the inductor, the copper loss can be calculated as:
Pcopper _ Lin  I dc2 _ Lin Rdc  I ac2 _ Lin Rac ,
(4.75)
where Rdc is the dc resistance of the winding, and Rac is the ac resistance of the winding
considering the skin effect.
93
Since at different states, the ac components in the input inductor are different. Iac_Lin
can be estimated by the following equation:
I ac _ Lin
V
V V V
Vin  C1
Vin  C1 C 2 out
V
N  T , Vin  T ,
N
 max{
 4Ts , in  5Ts } .
1 s
2 s
Lin  Ls
Lin
Lin  Ls
Lin
(4.76)
The core loss can be calculated as:
Pcore _ Lin  kBacWt ,
(4.77)
where k is a efficiency that can be found in the datasheet and is related to switching
frequency, Bac is the ac flux density, and Wt is the weight of the core.
4) Transformer
The power loss in the transformer is relatively complex to calculate, but in general it is
still composed of the copper loss and core loss. Since there are both dc and ac components
in the windings, the copper loss can be calculated as:
Pcopper _ T  I dc2 _ pri Rdc _ pri  I ac2 _ pri Rac _ pri  I dc2 _ sec Rdc _ sec  I ac2 _ sec Rac _ sec ,
(4.78)
where Rdc_pri and Rac_pri are the dc and ac resistance of the primary side winding, and both
Rdc_sec and Rac_sec are the dc and ac resistance of the secondary side winding.
Similar to the inductor core loss, the core loss of the transformer can be calculated by:
Pcore _ T  kBacWt .
94
(4.79)
4.7 Simulation and Experimental Results
4.7.1 Simulation verification
A simulation model of the circuit is first built in PSIM to verify the circuit analysis.
The circuit parameters and control variables are summarized in Table 4.1.
In the
simulation, the parasitic components are not included.
Table 4.1 Circuit parameters and control variables of the simulation model.
Transformer turns ratio, N
1:3
Transformer mutual inductance, Lm
6 uH
Transformer leakage inductance, Ls
70 nH
Input inductance, Lin
2.3 uH
Capacitors, C1 and C2
10 uF
Capacitor, C3
4.4 uF
Switching frequency, fs
500 kHz
Duty ratio of S5&S6, D
0.667
Duty ratio of State II, δ2
0.067
Duty ratio of State V, δ5
0.034
Input voltage, Vin
40 V
Output resistor, Rout
133 Ohm
The simulation results are shown in Figure 4.12. Comparing the simulation results with
the theoretical waveforms in Figure 4.5, it can be concluded that they are similar to each
other. The simulation results also show that: 1) voltage stress on the secondary side
switches is smaller than the output voltage; 2) voltage stress on the secondary side winding
of the transformer is smaller than the output voltage; 3) The primary side switches realize
95
soft switching at both turn-on and turn-off transition; and 4) The dc offset current of the
transformer can be reduced to a very small value by properly selection of control
parameters.
Further comparisons between calculation results and simulation results are shown in
Table 4.2, which also validate the theoretical analysis and models for circuit design.
Vout
Vsecond_Transformer
600
400
200
0
-200
-400
I_leakage
40
20
0
-20
-40
I_mutual
4
2
0
-2
-4
Vds_S2
Ids_S2
40
30
20
10
0
-10
0.002926
0.002928
0.00293
0.002932
Time (s)
Figure 4.12 Simulation results of the proposed converter.
Table 4.2 Comparison between calculation results and simulation results.
Output voltage, Vout
Calculation Result
Simulation Result
402 V
403
Continued
96
Table 4.2 Continued
Capacitor C1 voltage, Vc1
100.5 V
100.9
Mutual inductor dc offset current, Imutual
0.03 A
0.07
RMS current of switch S1, Irms_S1
24.2 A
24.2 A
RMS current of switch S2, Irms_S2
17.6 A
17.8 A
RMS current of switch S5, Irms_S5
4.0 A
4.0 A
RMS current of switch S7, Irms_S7
5.7 A
5.9 A
RMS current of Capacitor C1, Irms_C1
6.9 A
7.1 A
RMS current of Capacitor C3, Irms_C3
4.8 A
5.0 A
4.7.2 Prototype design and experimental results
A 1.2 kW prototype is built with the parameters in Table 4.1, as shown in Figure 4.13.
At primary side, Si OptiMOSFETs (IPP200N25N3) from Infineon are applied for each
switch. At secondary side, Gallium Nitride (GaN) HEMTs (TPH3006P) from Transphorm
are applied for each switch. Ferret cores E42/21/20 and E32/16/9 are selected for the
transformer and inductor, respectively. Ceramic capacitors are selected for C1, C2, and C3
in the QSC circuit. In the experiments, a NHR 9200 dc power supply is utilized at the PV
input, and resistor banks are used as the load. The control is implemented in a Texas
Instruments DSP (TMS320F2808). The efficiency is measured with a Yokogawa WT3000
power meter with two 701933 current probes.
97
Figure 4.13 Converter prototype.
Figure 4.14 shows the experimental waveform at the operation point where fs=500 kHz,
Vin=45 V, Vout=400 V, and Pout=1.2 kW. It can be noted that the voltage stress on the
secondary side switches is reduced to 300 V, which matches the theoretical analysis. By a
proper control of the shoot through time, the overshoot current on the leakage inductor is
minimized. Compared to the simulation result, the slightly higher input voltage is caused
by the power loss in the circuit. The ringing on the drain-to-source voltage of S1 is caused
by the resonance between the transformer leakage inductor and the parasitic capacitor of
the switch.
98
Figure 4.14 Experimental result at switching frequency of 500 kHz.
Figure 4.15 shows the experimental waveform at the operation point where fs=1 MHz,
Vin=43 V, Vout=400 V, and Pout=1.0 kW. The same operation condition is achieved
compared to the 500 kHz case.
Figure 4.15 Experimental result at switching frequency of 1 MHz.
99
The switching waveforms to show the soft switching characteristics of the proposed
converter are displayed in Figure 4.16. It can be seen that the primary side switch S1
realizes ZVS off, while the secondary side switch S7 realizes both ZVS on and off. Since
the switch current cannot be measured directly in the prototype, the ZCS on of the primary
side switches cannot be observed directly.
Figure 4.16 Soft switching waveforms of the proposed converter.
The efficiency curves of the converter under different switching frequencies are shown
in Figure 4.17. The peak efficiency is 92.7% at 600 W with a switching frequency of 500
kHz. With a switching frequency of 1 MHz, the peak efficiency is 89.0% at 750 W.
Compared to other current-source isolated dc/dc converters in the literature [108]-[128], a
comparable efficiency is achieved with a much higher switching frequency.
100
Efficiency (%)
95
93
91
89
87
85
83
81
79
77
75
500 kHz
1 MHz
0
300
600
Pout (W)
900
1200
Figure 4.17 Efficiency curves at different switching frequencies.
4.8 Summary
In this chapter, a full-bridge current-source isolated QSC dc/dc converter is proposed
for PV applications. The operation principle is analyzed in detail, and the design guidelines
are presented. A 1.2 kW, 1 MHz, 40 V/ 400 V prototype utilizing GaN switching devices
is built in the lab. Theoretical analysis is verified by both simulation and experimental
results. A peak efficiency of 92.7% is achieved at 500 kHz, and 89.0% at 1 MHz.
Compared to existing topologies, the features of the proposed circuit include: 1) With
reduced current ripple and improved performance under partial shading, the current-source
input is favorable for PV applications; 2) The high voltage side QSC circuit only needs
three switching devices, while the full-bridge topology needs four switching devices; 3)
The voltage stress in the QSC circuit is reduced to Vout/(2-D), instead of Vout in full-bridge
or half-bridge configurations; 4) All switches in the circuit realize soft turn on and turn off,
101
and a high efficiency can be achieved at a high switching frequency; and 5) A second
voltage source can be integrated in the circuit to realize dual-input operation. In the next
chapter, this feature will be discussed in detail.
102
CHAPTER 5
A FAMILY OF QSC CIRCUIT BASED DUAL-INPUT DC/DC
CONVERTERS FOR INTEGRATION OF PV AND RETIRED EV
BATTERIES
5.1 Introduction
In Chapter 4, the circuit topologies for PV systems are discussed in detail, and a fullbridge current-source isolated dc/dc converter with a reduced number of switches and
voltage stress is proposed. In this chapter, the issue of integration of the PV and the retired
EV batteries will be studied.
To further reduce the total cost of the system with both PV and batteries as input
sources, multiple-input dc/dc converters that have several input ports and one output port
are widely considered. With a reduced number of power electronics devices and passive
components, the multiple-input dc/dc converter can achieve the same power management
capability as several single-input-single-output converters. As previously mentioned, the
proposed isolated dc/dc converter in the last chapter can realize the dual-input operation.
Besides the PV, the retired battery can be set as the secondary input. In addition, a family
of dual-input dc/dc converters can be synthesized based on this topology. In this chapter,
detailed study for this family of dual-input dc/dc converters that integrates PV and retired
batteries is presented.
103
5.2 Overview of the Multiple-Input DC/DC Converter
Existing multiple-input dc/dc converter topologies generally can be divided into two
categories: non-isolated and isolated. The non-isolated topologies usually connect several
basic converters in parallel at the input side. For example, [132], [133] connected several
boost converters at the input side, [134], [135] connected several buck converters at the
input side, [136], [137] utilized several buck boost converters, and [138], [139] utilized
several Z-source converters. There are also some studies combining different basic
converters together to form the multiple-input converter [140] [141]. However, if the nonisolated topologies are implemented at the dc side, a bulky and expensive line frequency
transformer is needed at the ac side. Otherwise, the system will suffer from large leakage
currents between the PV panel and the ground [142].
The isolated topologies of the multiple-input dc/dc converter can help eliminate the line
frequency transformer. It usually is derived from basic isolated dc/dc converters, such as
flyback, half-bridge, and full-bridge converters. These topologies can be divided further
into two different categories. The first category mainly utilizes multi-winding transformers
to connect different input sources [143]-[147]. Since each input source needs a separate
half bridge or full bridge to connect to the transformer winding, the topologies in this
category usually involve a large number of switches.
Besides, the multi-winding
transformer structure also has some drawbacks, such as a larger size and a higher cost.
The other category of isolated topologies utilizes only a normal two-winding
transformer. A dual-input full bridge topology has been presented in [148]. Compared to
the multi-winding structure, this topology decreases the number of active switches in the
104
circuit. However, to realize a bidirectional power flow, eight active switches are still
needed. [149] has proposed a circuit topology with only one active switch for each input
source on the primary side. However, each voltage source also needs a separate inductor.
This will increase the number of passive components in the system. In [150], two flybacktype cells are connected in series to realize a multiple-input operation. The number of
switches is minimized in this topology. However, it is generally agreed that the flyback
converter is not suitable for high power applications.
In this chapter, a family of dual-input dc/dc converters to combine the input of the PV
system with retired EV batteries is proposed. The proposed family of dual-input dc/dc
converters has the following advantages: 1) The voltage stress of switches on the secondary
side QSC circuit is reduced; 2) Compared to voltage-source and current-source H-bridge,
it utilizes less number of switches to realize bidirectional power flow; 3) Compared to other
isolated multiple-input dc/dc converters, the power transfer of one voltage source does not
need to pass through the transformer, so the efficiency of the converter is improved; 4) Soft
switching can be realized for both primary and secondary side switches; 5) Galvanic
isolation is realized between the PV side and the utility side.
5.3 Synthesis of the Dual-Input DC/DC Converter
Based on the circuit topology proposed in Chapter 4, a family of dual-input isolated
dc/dc converters can be synthesized.
At the secondary side of the dual-input dc/dc converter, the topology of the QSC circuit
is used, as shown in Figure 5.1. The control signal for Sc is complementary to that of Sa&Sb
with certain dead time inserted in between, and a variable duty ratio D can be implemented
105
for Sa&Sb. A voltage source can be placed either between points a and b, or between points
b and c, with the different relationships shown below:
Vout 
2 D
1
Vab 
Vac .
1 D
1 D
(5.1)
Vac
Sa
a
Vab
Lm
Sc
C2
C1
VC2
c
C3
Vout
b
Sb
Figure 5.1 The topology of the QSC circuit with a voltage source.
In addition, it can be concluded from (5.1) that Vab<Vac. Therefore, depending on the
voltage level, the voltage source can be integrated at different positions of the circuit. A
voltage source with lower voltage level can be put between points a and b, and a voltage
source with a higher voltage level can be put between points a and c.
At the primary side of the dual-input dc/dc converter, two current-source based
topologies can be utilized, which can be either a current-source full-bridge circuit or halfbridge circuit. A family of dual input dc/dc converter topologies can be derived by
combining different primary side configurations and secondary side voltage source
positions. A high-frequency transformer is utilized to connect the primary and secondary
106
sides, in the meantime providing galvanic isolation between the primary side voltage
source and the secondary side circuit. There is a total of four different topologies in this
family, as shown in Figure 5.2.
vLin
iLin
Lin
S1
S5
S2
VPV
vC2
C3
Rout
vout
C3
Rout
vout
Rout
vout
1:N
S4
S3
C2
C1
VBatt
S7
S6
(a) Type I.
iPV
S1
S3
S2
VPV
VBatt
L1
S5
vC2
C2
C1
1:N
L2
iL1
iL2
S4
(b) Type II.
VBatt
vLin
iLin
Lin
S1
S5
S2
VPV
C2
C1
S3
S4
S7
vC2
C3
1:N
S6
(c) Type III.
Continued
Figure 5.2 The topologies of the dual input dc/dc converter based on QSC circuit.
107
Figure 5.2 Continued
VBatt
iPV
S1
S3
S2
VPV
vC1
L1
C2
vC2
C3
Rout
vout
1:N
L2
iL1
C1
S5
iL2
S4
(d) Type IV.
5.4 Operation Principle Based on the Half-Bridge Configuration
Since the full-bridge based topology has been described in Chapter 4, in the following
analysis, the half-bridge configuration is used as an example to describe the operation
principle of the converter under the dual-input condition. The equivalent circuit is shown
in Figure 5.3. At the primary side, L1 and L2 connected in series are in parallel with the
mutual inductance of the transformer, Lm. Since the sum of L1 and L2 is much less than Lm,
the mutual inductor is ignored in the equivalent circuit. There is a total of six states in one
switching cycle. The key waveforms in the circuit are shown in Figure 5.4.
108
VBatt
S1
S3
S2
Ls
VPV
L1
S5
C2
C1
L2
S4
Figure 5.3 Equivalent circuit of the half-bridge configuration.
109
C3
Rout
δ1
S1
0
ON
t
t
ON
0
0
ON
ON
S3&S4
S5
δ5 δ 6
ON
0
S2
δ2 δ 3 δ 4
t
ON
ON
t
IL1
0
t
IL2
0
t
ILs
IL2
0
t
-IL1
IS1
0
t
0
t
IS2
IS3&IS4
0
t
-IL2/2N
IS5
0
-IL1/N
t0
t1 t'1 t2 t3
t4 t'4 t5 t6
Figure 5.4 Key waveforms in the circuit.
110
t
5.4.1 State I (t0-t1), parallel charging
In this state, C1&C2 are connected in parallel and charged by the PV and L2. L1 is
charged by the PV, as shown in Figure 5.5.
S1
S2
VPV
L1
S3
Ls
VBatt
C1 C2
S5
C3
Rout
L2
S4
Figure 5.5 Operation State I (t0-t1).
5.4.2 State II (t1-t2), leakage inductor current changing direction
This state starts when S2 is turned on. L1&L2 are charged by the PV. C1&C2 are
connected in parallel to charge the leakage inductor. The current in the leakage inductor
drops to zero at t'1, and then continues to increase in the reverse direction. At the end of
State II (t2), the leakage inductor current will be charged equal to, or a little higher than the
current in L1, as shown in Figure 5.6(a) and (b).
111
S1
S3
S2
Ls
VPV
L1
C1
VBatt
C2
S5
C3
Rout
L2
S4
(a) t1- t'1.
S1
S2
VPV
L1
S3
Ls
C1 C2
VBatt
S5
C3
L2
S4
(b) t'1- t2.
Figure 5.6 Operation State II (t1-t2).
5.4.3 State III (t2-t3), leakage inductor current freewheeling
This state starts when S1 and S3&S4 are turned off and S5 is turned on. The body diode
of S1 is conducting, and the current in the leakage inductor is decreasing rapidly until the
magnitude is equal to that of the L1. Then the body diode of S1 is turned off and this state
ends. In the meantime, C1&C2 are connected in series with the load, as shown in Figure
5.7. This state is usually extremely short.
112
S1
S2
Ls
VPV
L1
VBatt
S3
C1 C2
S5
C3
Rout
L2
S4
Figure 5.7 Operation State III (t2-t3).
5.4.4 State IV (t3-t4), series discharging
In this state, C1&C2, L1, and the PV are connected in series with the load. L2 is charged
by the PV, as shown in Figure 5.8.
S1
S2
VPV
L1
VBatt
S3
Ls
C2
C1
S5
C3
Rout
L2
S4
Figure 5.8 Operation State IV (t3-t4).
5.4.5 State V (t4-t5), leakage inductor current changing direction
This state starts when S1 is turned on. L1&L2 are charged by the PV. C1&C2 and the
load are connected in series to charge the leakage inductor in the reverse direction. The
current in the leakage inductor increases to zero at t'4, then continues to increase in the
113
positive direction. At the end of State V (t5), the leakage inductor current will be charged
equal to, or a little higher than the current in L2, as shown in Figure 5.9(a) and (b).
S1
S2
VPV
L1
VBatt
S3
Ls
C2
C1
S5
C3
Rout
L2
S4
(a) t4- t'4.
S1
S2
VPV
L1
VBatt
S3
Ls
C1
C2
S5
C3
Rout
L2
S4
(b) t'4- t5.
Figure 5.9 Operation State V (t4- t5).
5.4.6 State VI (t5-t6), leakage inductor current freewheeling
This state starts when S2&S5 are turned off, and S3&S4 are turned on. The body diode
of S2 is conducting, and the current in the leakage inductor is decreasing rapidly until the
current magnitude is equal to the current in L2. Then the body diode of S2 is turned off and
this state ends, as shown in Figure 5.10. This state is usually extremely short.
114
S1
S2
VPV
L1
S3
Ls
C1
C2
VBatt
S5
C3
Rout
L2
S4
Figure 5.10 Operation State VI (t5-t6).
In summary, the two switches at the primary side can realize soft switching at both
turn-on and turn-off transitions. For S1&S2, the turning on is ZCS, since Ls limits the switch
current increase rate while the voltages across the switches drop instantly. The turning off
is ZVS, because the turning off is completed after the current flowing through the switch’s
body diode reaches zero.
The three switches at the secondary side can realize soft switching at turn-on transition
(when Iin>Im). Because of the dead time between the control signals of S3&S4 and S5, the
body diode will conduct before the switch is turned on. Therefore, a ZVS can be realized.
The turning off S3&S4 and S5 is hard switching. However, by adding an additional small
capacitor between the drain and source of S3-S5, the voltage across the switch will be
increased slowly during the turn-off transition, and the switch current will be decreased to
zero before the drain-to-source voltage is increased to the steady state value. Therefore, a
ZVS can also be realized during the turning off of S3&S4 and S5.
115
5.5 Circuit Analysis and Power Sharing Strategy
Based on Figure 5.4, one can define the duty ratio of S3&S4 as d, and then the duty ratio
of S5 is (1-d). Therefore:
 d  1   2   6
,

1  d   3   4   5
(5.2)
where δ1 is the duty ratio of State I, δ2 is the duty ratio of State II, etc.
Based on the voltage-second balance of L1, it can be derived that:
VC1  VC 2  Vout
)   4  VPV   5  VPV   6  0
N
, (5.3)
2 4VC1   4Vout

0
N
VPV  1  VPV   2  VPV   3  (VPV 
 VPV
where N is the transformer turns ratio.
Based on the voltage-second balance of L2, it can be derived that:
(VPV 
VC1
)  1  VPV   2  VPV   3  VPV   4  VPV   5  VPV   6  0
N
.
VC1
 VPV  1
N
(5.4)
Combine the above two equations:
VC1

VPV  1 N
.

Vout  (2  1 )VC1
4

(5.5)
Assume that the current ripple in the two input inductors is negligible, and the average
current is IL1 and IL2, respectively. Then at the end of State III, the current in the leakage
inductor is -IL1. Therefore:
116
3 
VC1
( I L1  I L 2 ) NLs
2 
.
(Vout  VC1  VC 2 )
(Vout  VC1  VC 2 )Ts
(5.6)
where Ls is the leakage inductance and Ts is the switching period.
Similarly, at the end of State VI, the current in the leakage inductor is IL2. Therefore:
6 
(Vout  VC1  VC 2 )
( I  I ) NLs
 5  L1 L 2
.
VC1
VC1Ts
(5.7)
Similar to the circuit analysis in Chapter 4, to simplify the control, the following
relationship is satisfied:
2
d
=
.
5 1  d
(5.8)
Combine (5.5) - (5.8), and notice that IL1+IL2=IPV, it can be derived that:
(1  d )Vout
I L

VPV =
(d  2 2 )  PV s

(2  d ) N
Ts

.

1
V 
Vout
Batt

2d

(5.9)
To further simplify the circuit analysis, it is assumed that State III and State VI can be
ignored. Therefore, it can be derived that
2 
(2  d ) NI PV Ls
(2  d ) NI PV Ls
,  3  0,  5 
, 6  0 .
(1  d )TsVout
dTsVout
(5.10)
The expression for VPV then can be simplified as:
VPV 
I L
d (1  d )
Vout  PV s .
N (2  d )
Ts
(5.11)
From (5.11), it can be noted that there are two control freedoms in this converter. One
is the duty ratio of S3&S4, d, and the other is the switching period, Ts.
117
Since the battery voltage is relatively stable, it is used as the reference to control both
the PV and output voltages. Based on (5.9), the output voltage and power can be
determined as:
Vout  (2  d )VBatt , Pout 
2
Vout
V2
 (2  d ) 2 Batt ,
Rout
Rout
(5.12)
where Rout is the load resistance.
Therefore, though Rout or Vbatt may change from time to time, the output power Pout can
be adjusted by controlling the duty ratio, d. Or if needed, the converter can achieve a
constant output voltage when the output power changes.
As for the output power of the PV, from (5.11)
PPV  VPV I PV 
Ts d (1  d )
2
(
VPV VBatt  VPV
).
Ls
N
(5.13)
From (5.13), it can be noted that, by adjusting the switching period, Ts, the PV can be
controlled to track its MPP. In addition, the PV output power is not related to the load
condition. Furthermore, if another bidirectional voltage source is installed instead of the
PV, a bidirectional power flow can be achieved.
The output power of the battery is controlled indirectly by the following equation
PBatt  Pout  PPV .
(5.14)
5.6 Simulation and Experimental Results
In this section, the simulation and experimental results from both half-bridge and fullbridge dual-input dc/dc converters are presented to verify the theoretical analysis.
118
5.6.1 The QSC circuit with its secondary side voltage source between points a and b
The operation of the QSC circuit is first tested with only the secondary side voltage
source. In the experimental result shown in Figure 5.11, the voltage source was put
between points a and b of Figure 5.1, which corresponded to the secondary side topologies
shown in Figure 5.2(a) and (b). The input voltage was set as 100 V, and the duty ratio was
0.67. The output voltage was boosted to 400 V.
Figure 5.11 QSC circuit with secondary voltage source put between points a and b.
It can be concluded that the output voltage was boosted higher than the input voltage,
and the relationship follows (1). The voltage stress on the secondary side switch was
reduced to 290 V, compared to the output voltage of 400 V. This verifies the function and
merits of the QSC circuit.
119
5.6.2 The half-bridge dual-input converter
A simulation model of the half-bridge dual-input converter is first built in PSIM with
the parameters shown in Table 5.1.
Table 5.1 Circuit parameters and control variables of the half-bridge simulation model.
Transformer turns ratio, N
3:8
Transformer mutual inductance, Lm
63 uH
Transformer leakage inductance, Ls
140 nH
Input inductance, L1&L2
3.5 uH
Capacitors, C1 C2, and C3
11 uF
Switching frequency, fs
500 kHz
Duty ratio of S3&S4, d
0.62
Input voltage, Vin
23 V
Battery voltage, Vbatt
290 V
Two cases are simulated to demonstrate the basic circuit operation principle and power
sharing strategy, as shown in Figure 5.12. In both cases, the PV is controlled to operate at
the MPP, which is 600 W. In Case One, the load is also 600 W. Therefore, only the PV is
supplying power to the load and the output power of the battery is zero. In Case Two, the
load is 1200 W. The PV is still operated at the MPP and outputs 600 W; the battery outputs
the other 600 W to the load.
120
Vds_S1
Vds_S2
Vds_S3
Vds_S5
80
40
0
300
200
100
0
I_Leakage
20
10
0
-10
-20
Ipv
28
26
24
Ibatt
0.4
0
-0.4
-0.8
I_load
1.4997
1.4996
1.4995
0.00955
0.009551
Time (s)
0.009552
(a) Ppv= 600 W, PLoad= 600 W, and Pbatt= 0 W.
0.009553
Continued
From top to bottom: 1) Voltage across S1&S2; 2) Voltage across S3&S5; 3) Leakage inductor current; 4) PV
current; 5) Battery current; 6) Load current.
Figure 5.12 Simulation results of the half-bridge dual-input converter under different load conditions.
121
Figure 5.12 Continued
Vds_S1
Vds_S2
Vds_S3
Vds_S5
80
40
0
300
200
100
0
I_Leakage
20
10
0
-10
-20
Ipv
28
26
24
Ibatt
2.4
2
1.6
I_load
3.0084
3.008
3.0076
0.009549
0.00955
0.009551
Time (s)
0.009552
0.009553
(b) Ppv= 600 W, PLoad= 1200 W, and Pbatt= 600 W.
A 1.3 kW circuit prototype is built in the lab, as shown in Figure 5.13. In the primary
side, two Infineon Si OptiMOSFETs (IPP075N15N3) are used in parallel for each switch.
In the secondary side, the CREE Silicon Carbide (SiC) MOSFET (CMF20120D) is used.
A planar transformer is built with N=3:8, Lm=64 uH, and Ls=140 uH (all referring to the
primary side). Two planar inductors are built for L1 and L2, with L1=L2=3.5 uH. Ceramic
capacitors are used for C1 C2, and C3, with C1=C2=C3=11 uF. The size of the prototype is
290 mm × 120 mm × 30 mm.
122
Figure 5.13 Prototype of the half-bridge dual-input dc/dc converter.
In the experiments, the PV was emulated by a NHR 9200 dc power supply, and the
retired battery was emulated by a California Instruments MX30 bidirectional dc power
supply. Resistor banks were used as load. The control algorithm was implemented in a
DSP from Texas Instruments (TMS320F2808). In the test, the PV voltage was 23 V and
the battery voltage was 290 V. The switching frequency was fixed at 500 kHz and the duty
ratio, d, was fixed at 0.62. The output voltage was boosted to 400 V. The experimental
results with RLoad=320 Ω are shown in Figure 5.14, and the experimental results with
RLoad=240 Ω are shown in Figure 5.15, respectively. Because of the limitation on the
measurement point, the leakage inductor current was measured at the secondary side.
123
(a) From top to bottom: 1) Battery voltage; 2) Output voltage; 3) Battery current; 4) PV current.
(b) From top to bottom: 1) Voltage across S1; 2) Voltage across S2; 2) Voltage across S5; 4) Leakage
inductor current.
Figure 5.14 Experimental results of the dual-input half-bridge converter with RLoad=320 Ω.
124
(a) From top to bottom: 1) Battery voltage; 2) Output voltage; 3) Battery current; 4) PV current.
(b) From top to bottom: 1) Voltage across S1; 2) Voltage across S2; 2) Voltage across S5; 4) Leakage
inductor current.
Figure 5.15 Experimental results of the dual-input half-bridge converter with RLoad=240 Ω.
It can be seen from Figure 5.14(a) that when RLoad=320 Ω, the PV output power was
almost equal to the load power, so the battery power was around zero. While in Figure
125
5.15(a), the PV output power did not change much. However, the load power was
increased, so the battery power was also increased.
Comparing the results shown in Figure 5.14(a) and Figure 5.15(a), it can be noted that
though the load conditions were different, both the output voltage and the PV current were
kept almost the same. This is because the control variables, which were the switching
frequency and duty ratio, did not change during the operation. While the difference
between the PV power and the output power was compensated by the battery automatically.
From Figure 5.14(b) and Figure 5.15(b), it can be noted that the voltage stress on the
primary side switches and the current in the leakage inductor did not change much.
Although the output power was changed, the operation condition of the PV did not change.
The ringing in the primary side switch voltages and the leakage inductor current was caused
by the oscillation between the output capacitance of the primary side switches and the
leakage inductor of the transformer.
5.6.3 The full-bridge dual-input converter
A secondary input is added to the full-bridge current-source isolated QSC dc/dc
converter prototype presented in Chapter 4, so the dual-input operation of the topology can
be tested. The circuit parameters and control variables remain the same as shown in Table
4.1, but the output power is increased from 1.2 kW to 2 kW.
In the dual-input operation test, the PV was emulated by a NHR 9200 dc power supply,
and the retired battery was emulated by a California Instruments MX30 bidirectional dc
power supply. Resistor banks were used as load. The control algorithm was implemented
in a DSP from Texas Instruments (TMS320F2808).
126
Figure 5.16 shows the experimental waveform of the dual-input operation at Vpv=44 V,
Vbatt=300 V, Vout=400 V, fs=500 kHz, Ppv=800 W, and Pout=2 kW. It can be seen that the
waveform is similar to the PV single input case shown in Chapter 4, which indicates that
the dual-input operation inherits all the merits of the circuit under the single input
condition. The voltage stress on the secondary side switches is reduced to 300 V. Through
proper control of the shoot-through time, the overshoot current on the leakage inductor is
minimized. The ringing on the drain-to-source voltage of S1 is caused by the resonance
between the transformer leakage inductor and the parasitic capacitor of the switch.
Figure 5.16 Experimental results of the dual-input full-bridge converter under fs=500 kHz.
Figure 5.17 demonstrates the capability of the converter to adjust the voltage of the
battery. In the test, the duty ratio of S5 was reduced to 0.4. The output voltage was kept at
400 V. The battery voltage, which was the same as the voltage of S7, was reduced from
300 V to 250 V, which verifies the theoretical analysis.
127
Figure 5.17 Experimental results of the dual-input full-bridge converter with a different battery voltage.
Figure 5.18 shows the dynamic load response of the dual-input full-bridge converter.
In the test, Vpv=41 V, Vbatt=300 V, Vout=400 V, fs=1 MHz, and Ppv=600 W. The output
power was changed from 550 W to 1000 W. Since the control signals did not change
during the load dynamics, the output power of the PV was kept the same. The battery
automatically balanced the power between the PV and the load. This verifies the proposed
power sharing strategy.
128
Figure 5.18 Experimental results of the dual-input full-bridge converter under load dynamics .
The efficiency curves of the converter under different switching frequencies are shown
in Figure 5.19. During the tests, the operation condition of the circuit was fixed. For fs=500
kHz, Vin=44 V, Vbatt=300 V, and Vout=400 V. For fs=1 MHz, Vin=41 V, Vbatt=300 V, and
Vout=400 V. At the first test point, only the PV was outputting power. Then the load was
increased gradually and the battery started to output power. The peak efficiency was 95.9%
at 1.95 kW with a switching frequency of 500 kHz. With a switching frequency of 1 MHz,
the peak efficiency was 93.5% at 1.88 kW. Compared to other isolated multiple-input
dc/dc converters in the literature [143]-[150], the proposed converter achieves a higher
efficiency at a much higher switching frequency.
129
98
Efficiency (%)
96
94
92
90
88
500 kHz
1 MHz
86
84
500
1000
1500
Output Power (W)
2000
Figure 5.19 Efficiency curves of the dual-input full-bridge converter under different switching frequencies.
5.7 Summary
In this chapter, a family of dual-input dc/dc converters is proposed for integration of
the PV and the battery. This family of converters utilizes a full-bridge or half-bridge
current-source topology as the primary side, and the QSC topology as the secondary side.
Depending on the power level of the primary side and voltage level of the battery, different
topologies can be selected. The operation principle is analyzed in detail based on a halfbridge topology, which also can be applied to the full-bridge topology. Simulation and
experimental results for both half-bridge and full-bridge dual-input dc/dc converters are
presented to verify the theoretical analysis. A peak efficiency of 95.9% is achieved at 500
kHz, and 93.5% at 1 MHz for the full-bridge prototype.
130
Compared to existing multiple-input dc/dc converter topologies, the features of the
proposed family of dual-input converters include: 1) The voltage stress of switches on the
secondary side QSC circuit is reduced to Vout/(2-D), instead of Vout in full-bridge or halfbridge configurations; 2) Compared to the voltage-source and the current-source H-bridge,
it has less number of switches to realize a bidirectional power flow; 3) Compared to other
isolated multiple-input dc/dc converters, the power transfer of one voltage source does not
need to pass through the transformer, so the efficiency of the converter is improved; 4) All
the switches can realize a soft turn on and turn off through the proposed control method,
and a high efficiency can be achieved at high switching frequency; 5) Galvanic isolation is
realized between the PV side and the output side.
131
CHAPTER 6
CONCLUSIONS AND FUTURE WORK
6.1 Conclusions
In this dissertation, both system and circuit level study are performed to implement
retired EV batteries with PV systems in Microgrid applications. The major contributions
of the work include:
1) An algorithm is proposed to determine the optimal usage profile of the retired EV
battery for residential applications. Since the cost of PV systems is high, the size of the
PV system is also optimized with the consideration of both load condition and the BESS.
As part of the algorithm, an Energy Management Strategy (EMS) is developed to minimize
the daily system operation cost. The proposed optimization algorithm is verified by
simulation results.
2) A reconfigurable hybrid Microgrid testbed is designed and developed. The testbed
features a PHIL system that provides a near-to-real test condition for the system under test
(SUT) and makes the electric power network in the testbed reconfigurable. The testbed
also features a real-time SCADA system with SITL based reconfigurable communication
network. Based on the setup, it is able to exchange information with the electrical power
system, intentionally introduce and accurately control different communication
132
phenomena in the testbed, such as protocols, latency and bandwidth requirements, loss of
package, cyber attack, and data management. In addition, the testbed contains multiple
power electronics circuits and programmable power sources/loads to emulate different
hardware components in a Microgrid, such as renewable energy resources and nonlinear
loads. In the experiment, the testbed was utilized to study the implementation of retired
EV batteries with PV systems in a Microgrid. The experimental results have verified the
functionality of the testbed.
3) A full-bridge current-source isolated QSC dc/dc converter is proposed for PV
applications. It has reduced input current ripple and improved performance under partial
shading. The high voltage side QSC circuit has reduced number of switches and reduced
voltage stress. Soft switching can be realized for both primary side and secondary side
switches. Furthermore, a second voltage source can be integrated in the secondary side of
the circuit to realize dual-input operation. A 1.2 kW, 1 MHz, 40 V/ 400 V prototype
utilizing GaN switching devices is built in the lab. A peak efficiency of 92.7 % is achieved
at 600 W with a switching frequency of 500 kHz. With a switching frequency of 1 MHz,
the peak efficiency is 89.0% at 750 W. Compared to other current-source isolated dc/dc
converters in the literature, a comparable efficiency is achieved with a much higher
switching frequency.
4) A family of dual-input dc/dc converter is proposed for PV/battery systems. This
family of converters utilizes a full-bridge or half-bridge current-source topology as the
primary side, and the QSC topology as the secondary side. A high frequency transformer
is utilized to provide galvanic isolation between the primary and secondary side.
133
Depending on the power level of the primary side and voltage level of the battery, different
topologies can be selected. The proposed converters inherit all the merits of the full-bridge
current-source isolated QSC PV converter, which include reduced input current ripple and
improved performance under partial shading, reduced number of switches and voltage
stress on the high voltage side QSC circuit, and soft switching for both primary side and
secondary side switches. In addition, compared to other isolated multiple-input dc/dc
converters, power transfer of one voltage source does not need to pass through the
transformer, so the efficiency of the converter is improved. A 1.3 kW half-bridge circuit
prototype based on SiC switching devices and a 2 kW full-bridge circuit prototype based
on GaN switching devices are built in the lab. As for the full-bridge prototype, a peak
efficiency of 95.9% is achieved at 1.95 kW with a switching frequency of 500 kHz. With
a switching frequency of 1 MHz, the peak efficiency is 93.5% at 1.88 kW. Compared to
other isolated multiple-input dc/dc converters in the literature, the proposed converter
achieves a higher efficiency at a much higher switching frequency.
6.2 Recommendations for Future Work
1) For the optimization algorithm proposed in Chapter 2, the battery-aging model can
be included in the cost function for future study. Since the daily charging and discharging
profile can directly influence the lifetime of the battery, the yearly-prorated
installation/maintenance cost will change with different usage profiles. This will greatly
increase the complexity of the model, but the optimization results will be more practical if
an accurate aging model can be found.
134
2) For the developed testbed in Chapter 3, it can be used to study broad topics related
to Microgrids, including circuit topology, control algorithm, and communication related
issues. Other studies on the functions of the retired battery can also be conducted, such as
voltage regulation, power quality improvement, etc. In addition, the testbed can also be
served as a teaching and educational platform, and provide demonstration and hands-on
experience in the operation of Microgrids for undergraduate and graduate students.
3) For the full-bridge current-source isolated QSC dc/dc converter proposed in Chapter
4, the experiment used a dc voltage source at the input side. It would be good to connect
the converter with PV panels or a PV simulator to test the MPPT capability of the circuit.
4) For the family of dual-input dc/dc converters proposed in Chapter 5, it is worthwhile
to study the implementation of the converter in the cascaded multilevel converter topology,
as shown in Figure 6.1.
135
PV
H-Bridge
Battery
PV
H-Bridge
Battery
Medium Voltage
Grid
PV
H-Bridge
Battery
Proposed dual-input dc/dc
converter
Cascade Multilevel Converter
Figure 6.1 Topology of the cascade multilevel converter combined with dual-input dc/dc converter for
PV/battery systems.
This topology combines the advantage of cascaded multilevel converter and dual-input
dc/dc converter, and is suitable for medium or large-scale PV systems integrated with
distributed ESS. With the help of the cascaded multilevel converter and the high frequency
transformer in the dc/dc converter, a direct medium-voltage grid connection can be realized
to eliminate the line frequency transformer, at the same time limit the ground leakage
current in the system. Furthermore, it realizes a cost-effective combination of PV and
battery inputs with the help of the dual-input dc/dc converter. This topology could be a
promising candidate for future utility level PV/battery systems.
136
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