Control and Analysis of Regenerative Power
Distribution on Electrical Variable Transmission
Using Fuzzy Logic on HEV System
Abdelsalam Ahmed1*, Shumei Cui2
1
2
Dept. of Electrical Machines and Automation, Harbin Institute of Technology, China
Dept. of Electrical Machines and Automation, Harbin Institute of Technology, China
E-mail: eng.aaaa@yahoo.com
of the studied HEV. Many efforts have been developed for
researching and discussing different aspects of this
series/parallel HEV [12]-[18]. Induction Machines-EVT has
been researched for many years as the drive train concept of
HEV [16]-[18]. Permanent Magnet Synchronous Machines
(PMSM) has been research as the strongest candidate as an
EVT power train for the HEV [12]-[15], [19].
In this paper, by adopting the decision-making property of
the fuzzy logic and at the deceleration time, a regenerative
braking controller has been developed. By this fuzzy logic
controller, the total torque command for the EVT machines is
generated from the vehicle velocity, battery state of charge,
and vehicle’s delivered power.
The paper is organized as follows: Section II presents the
description of the studied HEV with the typical data of the
Toyota prius HEV powered by PMSM-EVT instead of the
THS transmission. Under the supervision of the rule-based
strategy [17], [18], the EMR dynamic model, control and
integration of the subsystems are described in section III.
Then, section IV describes the implementation process of the
proposed regenerative braking control strategy based on fuzzy
logic. The vehicle performance and the power flow through
the ICE, PMSM-EVT machines, and the battery are analyzed
and discussed at different driving cycles in Section VI.
Abstract — In this paper, an intelligent regenerative power
management controller based on fuzzy logic has been presented
to strategize the regenerative process on HEV trained by
Permanent Magnet Synchronous Machines-Electrical Variable
Transmission (PMSM-EVT). Then, the power flow through
EVT-machines as motors and generators during the driving
cycle has been analyzed. This fuzzy logic-based control strategy
is designed based on the most effective variables on the system,
the state of charge of the battery, the power requested at the
wheels and the vehicle velocity. The proposed strategy and the
system performance are validated and tested through the
simulation results with different driving cycles. The proposed
strategy optimizes the distribution of the braking power between
the regenerative and the hydraulic parts. This helps to save the
ratings of the power units of HEV system (engine, battery, and
PMSM-EVT machines), and exploits the EVT machines as
motors and generators on driving the vehicle and charging the
battery, respectively.
I.
INTRODUCTION
A regenerative brake is an energy recovery mechanism
which slows a vehicle by converting its kinetic energy into
another form, which can be either used immediately or stored
until needed. This contrasts with conventional braking
systems, where the excess kinetic energy is converted to heat
by friction in the brake linings and therefore wasted.
In order to provide the appropriate regenerative braking
for the given driving conditions, a control algorithm is
required to organize the distribution process for the braking
energy between the regenerative and the friction. Fuzzy Logic
Control (FLC) is a somewhat intelligent, cost-effective
nonlinear control. It has been successfully applied in Hybrid
Electric Vehicle (HEV) areas of energy management strategy
[1]-[5]. Also, FLC was applied in regenerative braking
distribution in different types of HEVs [6]-[8].
The HEV as a complex system needs robust tool to model
and control its subsystems. Energetic Macroscopic
Representation (EMR) is an interesting tool, allowing a global
overview of the system while taking into account the main
physical properties. Also, it is a graphical description to
organize model and energy management of complex systems.
EMR has successfully been used to model and control HEVs
[9]-[15].
Electrical Variable Transmission (EVT) is the powertrain
II.
DESCRIPTION OF THE PMSM-EVT-HEV
PMSM-EVT, Internal Combustion Engine (ICE), battery,
and final gear are the main components of the studied HEV as
shown in Fig.1.
Fig. 1 Hybrid electric vehicle system driven with PMSM-EVT
1
Double rotor PMSM (EM1), normal PMSM (EM2) and
two power converters are the components of the split PMSMEVT unit. The inner rotor of EM1 is connected mechanically
to ICE and has distributed windings (stator1) that are
connected to inverter 1 across the brushes and slip-rings. The
rotor of EM2 is connected to the final gear of the vehicle and
the outer rotor of EM1, and the stator windings are connected
to inverter2. Vector control with field weakening strategy is
used to drive the PMSM-EVT machines. So, the PMSMs
have been exploited to optimize the ICE operation via
covering the speed and torque differences between the vehicle
requirements and the optimized output of the engine.
In this paper, the typical ICE torque-speed profile,
capacity of battery pack and other control parameters are
known for the Toyota prius HEV, and are listed in Table 1
[20]. Whereas, the power parameters of the EM1 and EM2 are
designed by the author [21].
III.
45
40
35
30
25
20
15
10
5
0
0
10 0
2 00
30 0
4 00
5 00
60 0
7 00
EMR SIMULATION MODEL AND
CONTROL OF HEV SYSTEM
ICE, inverters, battery, transmission and vehicle dynamics
are modeled by EMR as depicted in Fig. 2 indicating the
global modeling and integration of the PMSM-EVT-HEV
Fig.2 Global EMR of PMSM-EVT-HEV with the common control structure
and FL regenerative controller
components with their controllers. The details of the model of
these plants could be found in [9], [10] and [17] [18]. In this
paper, the model of the PMSM-EVT machines and braking
distribution system and their control are presented for
highlighting the operation process of the proposed controller.
TABLE 1
Simulation parameters of Toyota Prius HEV [20], [21]
Nominal Voltage
288 V
Published Capacity
6.5 Ah (1.5KWh)
Battery
power
21 KW(at 50%SOC)
Maximum Power
43kW @4000rpm
ICE and its Peak Torque
103 Nm@ 4000 rpm
control
Optimum power of ICE
7 : 40 KW
parameters
Optimum speed of ICE
1200 : 3972 rpm
Highest battery state of charge
0.75
Hybrid
Lowest battery state of charge
0.45
Control
strategy
Target SOC
0.6
Vehicle
parameters
EM1
EM2
Final drive gear,
3.93
Gravity,
9.81 m/s^2
Air density,
Total mass,
Maximum velocity,
coefficient of aerodynamic drag,
Maximum acceleration
frontal area of vehicle,
rolling radius of tire,
rolling resistance coefficient,
Slop angle of the road, α
Maximum power, KW
Rated power, KW
Maximum torque data, Nm/rpm
Rated torque data ,Nm/rpm
Maximum speed
Maximum power
Rated power
Maximum torque data, Nm/rpm
Rated torque data, Nm/rpm
Maximum speed
1.2 kg/m^3
1368 Kg
70.75 Km/h
0.3
1.19 m/s2
1.746;(m^2),
0.287;(m),
0.0165
0
17.85 KW
11.6KW
105Nm @ 0-1623rpm
80Nm @ 0-1384rpm
2483rpm
25.2 KW
13KW
110Nm @ 0-2000rpm
60Nm @ 0-2000rpm
4000rpm
A. Chassis Model
The chassis couple the traction force
system
(double triangles).
and the brake
(1)
Moreover the chassis accumulates kinetic energy in the
mass of the vehicle (crossed rectangle). The HEV velocity
is the state variable of this accumulation element,
and the resistant
derived from the total traction force
.
force
(2)
B. Inversion of the Chassis
The first element to invert is the accumulation element
(2). It requires a controller to define the reference force
from the velocity reference
, using a
_
_
rejection of the disturbance
.
_
_
(3)
with C(t) the controller, this inversion represents the
driver in fact. In order to invert the mechanical coupling (1), a
is used to define the
regenerative distribution coefficient
and
reference of the brake and traction forces,
_
from
the
reference
of
the
total
traction
_
force
.
_
2
_
_
_
1
3) Current Controllers: are required to invert current
relationships that deduced from windings equation (6).
(4)
_
C. EMR Model of the PMSM
The EMR model of the PMSM machine has been depicted
in three stages.
1) Park Transformation
:
voltages
and stator currents
reference frame.
that expresses stator
in the (d, q) rotating
ΔΩ
as state
.
_
IV.
The kinetic stored energy on the running vehicle could be
converted into two parts, the regenerated part and the friction
losses part. Using a fixed regenerative factor is not suitable
has to be
for the whole driving. At low vehicle speeds,
increased to charge the battery more. But, at high speeds,
increasing this factor will increase the regenerated power
from EM2 and may exceed its limits. So, a regenerative
strategy has been developed intelligently via FLC which
designed carefully to control adaptively the regenerative
factor according to all effective variables. SOC, power stored
on the vehicle, and the vehicle velocity are the most effective
variables used for determining the recovered power from the
vehicle.
(8)
(9)
where,
,
, and the flux linkage and current
components in d-q directions, ω and ω denote the electrical
angular speeds for rotor and stator, respectively, number of
pole pairs, and flux linkage of the permanent magnet.
D. Maximum Control Structure Vector Control of PMSM
B. Description of the Designed FLC
Due to the big necessary of extending the speed rage, field
oriented control with the possibility of field weakening has
been used for both EM1 and EM2. This procedure basically
deals with the magnetic and torque component current.
The FLC system comprises basically from three main
subsystems, FLC input variables, the membership functions
of input and output fuzzy variables, and the fuzzy logic rules.
1) Input/output Membership Functions for Fuzzy Logic
1) The Reference Current _ : the inversion of torque
equation, (7) leads to the reference current
from the
_
torque reference
.
_
The proposed membership functions are developed based
on the pre-calculated limits of vehicle performance, PMSMEVT machines and battery’s SOC. Figure 3 shows the input
and output variables describing their boundaries, and depicts
the shape and ranges of Membership Functions (MFs). For all
input/output variables, the concourses are set as VH, H, M, L,
and VL. The first input, the vehicle stored power, is ranged
from zero at stopping to maximum value at maximum
deceleration rate, which represents worst case of brake. Also,
the range of MFs of the vehicle velocity has been divided
according to the effect of each speed range. Whereas, the
SOC of the battery is organized according to its lowest,
highest, and target values listed in Table 1. Finally, the
(10)
2) The Reference Current _ : Also, the reference
current _
is forced to zero under the base speed and then
forced to negative value related to the speed as soon as the
speed exceeds the base speed [22].
0
ω
ω
ω
IMPLEMENTATION OF FUZZY LOGIC
A. Regenerative Factor and Effective Variables
(7)
ω
(14)
REGENERATIVE BRAKING CONTROL STRATEGY
3) The Electromechanical Conversion: leads to the machine
torque
and the B.EMF
from the stator currents and
the rotation speed as (7), (8) and (9).
1
_
_
where, ,
and
are the stator resistance and d-q
inductances of PMSM respectively.
_
(13)
_
is the speed difference between rotor and stator.
(6)
_
(12)
5) Finally, an Inversion of Park Transformation and Index of
the Inverters: it leads to reference voltages
from the
_
(d-q) voltages and the index of the inverters from the battery.
where , is the rotor position with respect to the stator frame.
_
_
_
_
2) Stator Windings: impose the stator currents i
variables from the stator voltages
and E.M.F.
_
4) Variables Estimation: In practice only stator currents and
rotational speed are measured, and the other variables are
.
estimated such as
_
(5)
_
_
_
(11)
3
membership functions of the regenerative factor
are
carefully shaped according to the power limiits of EVT-EM2.
For , the concourse VL means no regeneraative power
0, while VH means all kinetic power on thhe vehicle
1 will be recovered to battery via EVT machinnes.
The proposed fuzzy rule base was develloped from three
inputs: the vehicle speed, the driver demandeed power, and the
battery state of charge (SOC). These inputs are fuzzified and
mal rule base was
then fed into the fuzzy controller. The optim
found from experimentation with the system. The
is the output vaariable of the
regenerative factor
defuzzification process. In turns, this factorr determines the
magnitude of the regenerative torque for the E
EVT machines as
illustrated in down part of Fig. 2.
(a)
2) Fuzzy Logic Rules with 125 Rules
The performance of the FLC depends heaavily on its fuzzy
rules. The rule base for the 125 rules is built tto relate the three
inputs with the output factor. Each of the innputs and output
has 5 linguistic variables. The relation betweeen the input and
the output variables can be clearly seen in the surface plot
which is shown in Fig. 4 (a, b). These rules ffor managing the
regenerative process are explained below:
, the eengine is turned
• If SOC is VH, i.e.
OFF and
is VL i.e.
0 (no regenerating power
required from the EVT machines) whatever the velocity and
stored braking power are.
• If SOC is VL, i.e. SOC SOC , the engiine is turned ON
and K is VH i.e. K
max (maximum poower is recovered
from the braking power via the EM2 machinne and no friction
braking), this for all velocities and for storeed braking power
except two cases. The first exception is whenn the vehicle runs
at very low velocity, K has to be decreased because it is not
preferable to operate the EVT machines as ggenerators at very
low speeds. The second exception is when thhe vehicle runs at
very high speed, K has to be changed from H to VL
gradually according to the SOC and the brakinng power.
0.4
Regenerative factor
0.35
Deg. MF
VH
M
H
L
0.1
0
80
Deg. MF
0
60
-1
40
-2
20
Velocity (Km/h)
(b)
0
-4
-3.5
5
-3
-0.5
-1.5
-2.5
4
x 10
Vehicle power (W)
Effect of vehicle power and velocity on the
t FLC variables
Fig. 4 Surface plot for the
• When SOC within its operating raange, i.e.
, K is adaptively conttrolled, and its value is
automatically changed in the rangee of (0-1) according to the
required operation points. Controlling on K is conditioned to
sustaining the SOC at its optimum
m, range and exploiting the
saved kinetic energy in the vehicle to recharge the battery
o PMSM machines and
with saving the power limits of
maximum capacity of the battery.
VL
-3.5
-3
-2.5
-2
-1.5
Vehicle stored power (W)
-1
-0.5
V. SIMULATION RESULT
TS AND ANALYSIS
OF REGENERATIVE POWE
ER DISTRIBUTION
ON PMSM-E
EVT
0
4
x 10
1.5
1
VL
L
H
M
VH
0.5
A. Simulation Results
0
0
10
20
30
40
50
Velovity of vehicle (Km/h)
60
70
80
90
The simulation results are carrried out on three different
driving cycles to ensure the effecctiveness of the proposed
regenerative FL strategy at medium
m and high power driving.
The main specifications of these driving
d
cycles are listed on
Table 2. The simulation results seen
n in Fig. 5 indicate that the
simulation speed can tracking the driving cycle profile, in a
way which indicates that the drivee ability is satisfied. Also,
the variation of the regenerativee factor
is depicted.
Working the rule base control sttrategy with the proposed
regenerative mechanism, the SOC
C is controlled within its
1.5
Deg. MF
0.2
0.15
0.5
0
-4
VL
1
L
M
H
0.5
SOC
0.6
0.7
VH
0.5
0
0
0.1
0.2
0.3
0.4
0.8
0.9
1
0.9
1
1.5
Deg. MF
0.3
0.25
0.05
1.5
1
Effect of SOC and vehiicle velocity on the
1
VL
L
H
M
VH
0.5
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Regenerative factor (Kd)
0.7
0.8
Fig. 3 Input and output membership functions of the prooposed FL controller:
VL=very low, L=low, M=medium, H=high, VH
H=very high.
4
required range as shown in Fig. 6 for the three driving cycles.
Also, Fig. 6 shows the charging and discharging mechanisms
of the battery indicating that with the appropriate regenerative
mechanism, the battery power rating could be decreased. Fig.
7 shows the torque-speed profile of the engine at different
driving cycles ensuring that the ICE is fully controlled to
operate in its high efficient region.
CYC-1015-6PRIUS
CYC-1015-6PRIUS-Modified
Velocity (Km/h)
100
50
0
50
50
0
0
Regenerative factor
100
90
70
B. Power Flow Analysis of PMSM-EVT Machines
Referring to Fig. 8, showing the power of ICE, EM1 and
EM2 at different driving cycles, the power flow is analyzed
below. ICE delivers power all time with the lowest limit equal
to the minimum optimal power, and with the increase of the
requested power at the wheels, the ICE delivers more power
within its optimum boundaries. For EM1, it operates as a
generator when the speed of engine is greater the speed of
vehicle (NEM1>NEM2), while operates as motor when the speed
of the vehicle is greater than the speed of the engine
(NEM2>NEM1). For EM2, it operates as a generator when the
torque of engine is greater the torque of vehicle (TEM1>TEM2),
while operates as a motor when the torque of the vehicle is
greater than the speed of the engine (TEM2>TEM1). The power
flows through the ICE, EM1, EM2 and battery reaching to the
wheels of the vehicle have been studied via three different
driving cycles. With the proposed FL regenerative strategy
and field weakening vector control of the PMSM-EVT
machines, the simulation results show that the power of plants
into their predetermined boundaries.
CYC-UDDS
100
90
200
400
600
0
0
200
400
600
1
1
1
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0
0
0
200
400
600
0
500
1000
0
500
1000
0
0
200
400
Time (sec)
600
Fig. 5 Simulation results under tested driving cycles with the resulting
regenerative factor
0.75
Battery SOC
0.7
0.65
0.6
0.55
0.5
0.45
CYC-UDDS
CYC-1015-6PRIUS-Modified
CYC-1015-6PRIUS
0.4
0
x 10
100
200
300
400
500
600
700
4
2
CONCLUSION
Battery power (W)
VI.
An intelligent regenerative controller has been designed
using Mamdani-type of a fuzzy logic to strategize the amount
of regenerative power. The controller receives the battery
SOC, vehicle velocity and the power that is requested at the
wheels and determines the value of regenerative factor and
then the value of the reference torque applied to the PMSMEVT machines. The effectiveness of the proposed FL
controller has been demonstrated via the global modeling
integrating of the subsystems of HEV using an efficient
modeling tool, EMR. Also, FOC of EM1 and EM2 has been
developed with field weakening method to extend the
constant power operation and to save the power boundaries of
the machines. The simulation results at different
circumstances demonstrate that the proposed strategy is able
to recover power according to nature of the driving cycles and
power limits of PMSM-EVT machines, and also the required
power from the driver has been satisfied with the complete
HEV system.
TABLE 2
Specifications of the used driving cycles
Max.
Max.
CYC
velocity Acceleration
( m/s^2)
(Km/h)
1015-6PRIUS
70.75
1.19
Modified-1015-6PRIUS 90
1.5
UDDS
91.25
1.48
1
0
-1
CYC-UDDS
CYC-1015-6PRIUS-Modified
CYC-1015-6PRIUS
-2
0
100
200
300
400
Time (sec)
500
600
700
Fig. 6 Battery power and SOC at different driving cycles
120
Maximum torque line
0.45
100
0.4
Engine torque (Nm)
80
0.35
CYC-UDDS
60
1015-6PRIUS-Modified
1015-6PRIUS
40
0.3
0.25
20
Max.
Deceleration
( m/s^2)
1.45
1.9
1.48
0.2
0.15
0
500
1000
0.15
1500
2000
2500
Engine speed (rpm)
3000
3500
Fig. 7 ICE torque-speed profile at different driving cycles showing the
optimal operating points
5
4000
4
CYC-1015-6PRIUS
Power (W)
x 10
ICE
2
EM1
EM2
0
-2
0
100
200
4
300
400
600
700
CYC-1015-6PRIUS-Modified
x 10
4
Power (W)
500
ICE
EM1
EM2
2
0
-2
0
100
200
4
300
400
600
700
CYC-UDDS
x 10
4
Power (W)
500
ICE
EM1
EM2
2
0
-2
0
200
400
600
800
Time (sec)
1000
1200
1400
Fig. 8 Power of ICE, EM1 and EM2 at different driving cycles
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