Session A7
Paper #6225
Disclaimer — This paper partially fulfills a writing requirement for first year (freshman) engineering students at the
University of Pittsburgh Swanson School of Engineering. This paper is a student, not a professional, paper. This paper
is based on publicly available information and may not be provide complete analyses of all relevant data. If this paper is used
for any purpose other than these authors’ partial fulfillment of a writing requirement for first year (freshman) engineering
students at the University of Pittsburgh Swanson School of Engineering, the user does so at his or her own risk.
POWER MANAGEMENT FOR POWER-SPLIT HYBRID POWERTRAINS
Cameron Rendulic, crr60@pitt.edu, Sanchez, 10:00, Steven Milov, sjm141@pitt.edu, Vidic, 2:00
Revised Proposal — As society moves towards a cleaner and
greener future, Hybrid Electric Vehicles (HEV) continue to
serve as a practical means of eco-friendly transportation. Of
the approximately 6.97 billion barrels of petroleum products
used by the United States each year, automobiles alone
account for 72 percent of all consumption. With petroleum
becoming rapidly scarcer and increasingly strict government
emission regulations, it is imperative that automobiles
minimize fuel consumption as well as emissions.
This paper will examine the optimization of power-split
hybrid electric vehicle powertrains through the use of a
control system in order to optimize fuel economy and reduce
emissions. This energy optimization is not only crucial
because of the scarcity of petroleum, but is necessary due to
the nature of hybrid electric vehicles. Unlike conventional
vehicles, hybrids take advantage of a multi-component
powertrain which utilizes an electric motor alongside an
internal combustion engine. With two possible power options,
HEVs are able to benefit from the strengths of both powertrain
components. However, the combination of two powertrain
components in hybrid electric vehicles necessitates an
intelligent power management strategy to maximize fuel
economy while satisfying power demands on the road. This
problem is solved by a control system, a processor capable of
continually making decisions as to which power sources
should be used by employing Model Predictive Control (MPC)
algorithms.
Model Predictive Control is considered to be one of the
most logical strategies for the optimization of complex
dynamic systems such as HEV powertrains. This method of
process control samples current state variables of the
powertrain system allowing for the current timeslot to be
optimized. These variables consist of battery state of charge
(SOC), motor torque, throttle position, power demand, and
vehicle velocity. Given these variables, MPC can predict
future change in dependent variables. This allows the control
system to anticipate future events and to take control actions
accordingly. A successful optimization of a hybrid electric
vehicle’s powertrain effectively utilizes the strengths of the
Internal Combustion Engine (ICE) along with those of the
Electric Motor (EM) all while eliminating the disadvantages
of both. The outcome of this optimization is a powertrain that
is quiet, continually optimized for road demands, and most
importantly fuel efficient. This process is significant not only
University of Pittsburgh Swanson School of Engineering 1
2016/01/29
because it improves the function of the powertrain, but also
reduces the amount of harmful emissions released into the
atmosphere. With the demand for hybrid vehicles increasing,
it is crucial that these vehicles have optimal fuel economy. This
paper will further delineate upon the components of the HEV
powertrain, how the powertrain as a whole can be optimized
through a control system, and the beneficial outcomes
achieved by intelligent power management.
REFERENCES
H. Borhan, A. Vahidi, A. Phillips, et al. (2009). “Predictive
Energy Management of a Power-Split Hybrid Electric
Vehicle.” (online conference article). Proceedings of the
American
Control
Conference.
DOI:
10.1109/ACC.2009.5160451
S. Cairano, D. Bernardini, A. Bemporad, et al. (2014).
“Stochastic MPC with Learning for Driver-Predictive Vehicle
Control and its Application to HEV Energy Management”
(online article). IEEE Transactions on Control Systems
Technology. DOI: 10.1109/TCST.2013.2272179
Y. Leong, A. Razali, G. Priyandoko, et al. (2016). “Review on
Automotive Power Generation System on Plug-in Hybrid
Electric Vehicles & Electric Vehicles.” (online article).
MATEC
Web
of
Conferences.
DOI:
10.1051/matecconf/20163802002
P. Poramapojana, B. Chen. (2012). “Minimizing HEV Fuel
Consumption Using Model Predictive Control.” (online
conference article).
Proceedings IEEE/ASME. DOI:
10.1109/MESA.2012.6275553
D. Rizoulis, J. Burl, J. Beard, “Control strategies for a seriesparallel hybrid electric vehicle,” SAE National. (Online
article). http://papers.sae.org/2001-01-1354/
H. Zhang, Y. Zhu, G. Tian, Q. Chen, Y. Chen. (2004).
“Optimal Energy Management Strategy for Hybrid Electric
Vehicles.”
SAE
National.
(Online
article).
http://papers.sae.org/2004-01-0576/
H. Zhao, A. Burke. (2015). “Modelling and Analysis of Plugin Series-Parallel Hybrid Medium-Duty Vehicles.” (I have a
pdf of this)
ANNOTATED BIBLIOGRAPHY
Cameron Rendulic
Steven Milov
H. Borhan, A. Vahidi, A. Phillips, et al. (2009). “Predictive
Energy Management of a Power-Split Hybrid Electric
Vehicle.” (Online conference article). Proceedings of the
American
Control
Conference.
DOI:
10.1109/ACC.2009.5160451
This conference paper, authored by a PhD student at
Clemson University, a Clemson Mechanical Engineering
Professor, as well as three members from Ford’s Research and
Advanced Engineering group, details on how a Model
Predictive Control strategy can optimize hybrid electric
vehicles’ powertrains for improved fuel economy.
Information from this article will help us to provide a clear
description of the power management control system.
D. Rizoulis, J. Burl, J. Beard, “Control strategies for a seriesparallel hybrid electric vehicle,” SAE National. (Online
article). http://papers.sae.org/2001-01-1354/
This paper underlines the key reasons of why we need
Series-Parallel Hybrid Electric Vehicles. In addition to
introducing their purpose, it explains the different strategies
that are being taken into consideration by using previous
simulations and experimental results. By going into each
simulation and discussing its results, this paper will clear up
any misunderstandings and give more insight on the topic.
W. Wang, S. Jia, C. Xiang, K. Huang, Y. Zhao. (2014). “Model
Predictive Control-based Controller Design for a Power-split
Hybrid Electric Vehicle.”
In this article, authored by Weida Wang and colleagues,
introduces another strategy for hybrid optimization. This
strategy, called the predictive control strategy, focuses on fuel
economy using Matlab/Simulink. This article further explains
its inner workings and uses. With this information, we can
compare this efficient strategy to others for a more in-depth
comprehension of series-parallel hybrids.
S. Cairano, D. Bernardini, A. Bemporad, et al. (2014).
“Stochastic MPC with Learning for Driver-Predictive Vehicle
Control and its Application to HEV Energy Management”
(Online article). IEEE Transactions on Control Systems
Technology. DOI: 10.1109/TCST.2013.2272179
This article, authored by IEEE members, examines how a
Model Predictive Control System can effectively model driver
behavior providing an optimization that has practical
applications in HEV power management. The article details
how the control system learns from the driver, resulting in a
user-friendly optimization. Information from this article, will
be of aid while detailing the benefits of Model Predictive
Control Systems.
H. Zhang, Y. Zhu, G. Tian, Q. Chen, Y. Chen. (2004).
“Optimal Energy Management Strategy for Hybrid Electric
Vehicles.”
SAE
National.
(Online
article).
http://papers.sae.org/2004-01-0576/
This paper introduces another preliminary design and indepth analysis for this control system of a power-split hybrid
electric vehicle and explains how its programmed, what factors
are taken into consideration, and what programming method is
used to optimize the vehicle. This paper mostly contributes to
gaining more insight on the different kinds of programming
methods and what common factors they use.
Y. Leong, A. Razali, G. Priyandoko, et al. (2016). “Review on
Automotive Power Generation System on Plug-in Hybrid
Electric Vehicles & Electric Vehicles.” (Online article).
MATEC
Web
of
Conferences.
DOI:
10.1051/matecconf/20163802002
This very current article, authored by faculty of Mechanical
Engineering at Universiti Malaysia Pahang, explains the nonrenewable energy problem, details the role of hybrid vehicles
as a solution to this problem, and looks at the increasing
demand for hybrid vehicles. Information from this article can
aid us in demonstrating how HEVs in general are a vast
improvement over past vehicles, and more specifically the
benefits of powertrain optimization.
H. Zhao, A. Burke. (2015). “Modelling and Analysis of Plugin Series-Parallel Hybrid Medium-Duty Vehicles.” European
Battery, Hybrid and Fuel Cell Electric Vehicle Congress.
(Online conference article)
This professional conference paper presents a very recent
comparison to conventional diesel and full parallel hybrids. By
talking about other hybrids, this paper clearly stresses the
benefits of series-parallel hybrids while simultaneously
revealing the imperfections of this system. This paper is
essential for isolating the hybrid’s pros from the cons in order
to pave the way for future improvements of this system.
P. Poramapojana, B. Chen. (2012). “Minimizing HEV Fuel
Consumption Using Model Predictive Control.” (online
conference article).
Proceedings IEEE/ASME. DOI:
10.1109/MESA.2012.6275553
This conference paper, authored by faculty of Michigan
Technological
University’s
Mechanical
Engineering
Department, details the benefits of Model Predictive Control
as opposed to other methods, as well as explains the functions
of the powertrains components and their relationships with
each other. Information taken from this article will help us
comprehensively explain the planetary gear set, a key
component of a HEV’s powertrain.
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