Post on 02-Oct-2020
Bacterial foraging algorithm for the optimal on-line energy management of a three-
power-source hybrid powertrain
Po-Lin Shih1,a, Yi-Hsuan Hung1,b, Syuan-Yi Chen1,c, Chien-Hsun Wu 2,d
1 Dept. of Industrial Technology, National Taiwan Normal Univ., Taipei, Taiwan
2 Dept. of Vehicle Engineering, National Formosa Univ., Yunlin, Taiwan
ar22263222@gmail.com, bhungyh@ntnu.edu.tw, cchensy@ntnu.edu.tw, dchwu@nfu.edu.tw
Abstract
This research aims for developing the online energy
management optimization for a three-power-source hybrid
powertrain based on Bacterial Foraging Approach (BFA). The
hybrid vehicle dynamics was constructed first by modeling
control-oriented subsystems on the Matlab/Simulink
platform. They are: the spark-ignition engine, traction motor,
lithium battery set, integrated starter generator (ISG),
regenerative braking, transmission, energy management
strategy, and driving cycles. For the rule-based control, 5
modes were developed (system ready,EV,hybrid,range
extension, and regenerative braking) according to the vehicle
speed (or motor rotational speed). For the BFA, it was
established for search two control outputs: the power split
ratios with three given inputs: rotational speed, required
power, and battery state-of-charge (SOC). Three main
procedures for optimal solutions were: 1)chemotaxis,
2)reproduction, and 3)elimination-dispersal. Ten bacteria
were selected for the 2-dimensional optimal search according
to the cost function with physical constraints: the equivalent
fuel consumption of three power sources. To evaluate the
“degree of optimization”, the Equivalent Consumption
Minimization Strategy (ECMS) was developed for two
power-split ratios based on 5 for-loop search (SOC, engine
power, demanded power, motor power, and rotational speed).
Three control laws were integrated into the hybrid
powertrains with a test scenarios: FTP-72 cycle. The
equivalent fuel consumption for three cases (rule-based
control, BFA, ECMS) are: 2100.6g, 1604.1g and 1400.9g.
The equivalent fuel reduction percentage compared to the
rule-based control for BFA and ECMS are [19.8%, 33.3%].
The degree of optimization for BFA compared to ECMS was
87%. It proves that the BFA was suitable for on-line energy
management for hybrid powertrains. Real vehicle verification
for vehicle control unit (VCU) implementation will be
conducted in the future.
Key words: Energy Management, Optimal Control, Bacterial
Foraging Algorithm, and Hybrid Electric Vehicle
Introduction
Hybrid electric vehicles (HEVs) have been widely
produced in international automakers nowadays. The added
high-power electric devices enhances the overall vehicle
performance (max. power, max. torque, acceleration, etc.)
while decreases the pollutants and energy consumption [1-3].
To optimize the vehicle system, the powertrain designs and
control strategies are two main approaches [4], or even the
integration the system design and control [4]. For the control
strategies, recently, the biologically inspired optimization
algorithms have been studied because the highly efficient
computation for on-line control, global optimization, and
applications for various industrial fields [5]. Among them,
BFA is characterized by parallel optimization search,
insensitivity to initial values, and high global optimization
ability [6]. In this study, BFA was thus used for the energy
management of a three-power-source hybrid powertrains.
The implementation in a real vehicle will be conducted in
the future.
Hybrid System Configuration
Figure 1 illustrates the configuration of a three-power-
source HEV. For the control signals, the vehicle control unit
(VCU) receives the commands from the driver and control
units, and sends the power (torque) distribution signals to the
motor control unit (MCU), integrated starter generator control
unit (ISGCU), and engine control unit (ECU) and the battery
management system (BMS). For the electric energy path, the
high-power lithium battery is governed by a battery
management system (BMS) which evaluates the state-of-
charge (SOC). The regulated power is delivered from the
battery to the motor and ISG drivers. The DC power is thus
modified to drive the motor and the ISG. Contrarily, the
recovered power (i.e. regenerative braking power) is sent
back from the two devices as the generators to the battery for
the SOC balance. For the mechanical power flow, the engine
converts the fuel energy into the rotational mechanical power.
The ISG downstream the engine is directed connected to the
engine. The traction motor on the other side is linked to the
transmission as well. Therefore, the power flows (torques) of
the three power sources are directly combined to drive the
final transmission, and then to accelerate the 1st-ordered
vehicle dynamics [7-8]. The related dynamic equations have
been studied in [9]
Bacterial Foraging Algorithm on HEV
Figure 2 illustrates the process of the BFA for the energy
management optimization among three power sources. Two
variables: and are defined as the power split
ratios, which represents the ratios of engine power
and motor power divided by the demanded power,
respectively.
dmde PP /P;/P (1)
The process of the BFA is separated into three main parts.
1)Chemotactic loop: in this step, the bacterium conducts
tumble or swim actions. The new position after a tumble
action can be expressed as:
)()(),,(),,1( rrClkjlkj rr (2)
where )(r is the random direction of a tumble action,
which is defined as
)()(
)()(
rr
rr
T
(3)
The fitness (cost) function is define as follows: 𝑭𝑰𝑻 = 𝟏/[�̇�𝒇𝒖𝒆𝒍 + ƒ(𝑺𝑶𝑪)�̇�𝒈 + ƒ(𝑺𝑶𝑪)�̇�𝒎] + 𝜷𝒑 = 𝟏/[�̇�𝒇𝒖𝒆𝒍 +
ƒ(𝑺𝑶𝑪)𝑩𝑺𝑭𝑪𝒂𝒗𝒈
𝟑𝟔𝟎𝟎× 𝑷𝒊𝒔𝒈 + ƒ(𝑺𝑶𝑪)
𝑩𝑺𝑭𝑪𝒂𝒗𝒈
𝟑𝟔𝟎𝟎× 𝑷𝒎] + 𝜷𝒑 (4)
The physical meaning of Eq. (4) is the inverse of the
summation of the engine fuel and the equivalent “battery
fuel” [10].
2)Reproduction loop: after the bacteria search for the fitness
function, those who have fitness values in the lower half will
be cancelled, while the remaining bacteria split into two
bacteria that are placed in the same location.
3) Elimination-dispersal loop: in the step, the bacteria will be
cancelled and dispersed to a new location in the search space
if a random probability is higher than a predefined threshold.
It is to prevent the local optimization occur.
Fig. 1 Configuration of a Three-Energy-Source EV
Fig. 2 Mechatronics of the Three-Energy-Source EV
Simulation Results and Discussion
The vehicle that we chose is a 1700kg vehicle. The
maximal power of the engine, motor, and ISG are 70kW,
65kW, and 40 kW, separately. The electric capacity of the
350 V battery moduleis 7 kW. For the BFA setting, the
bacteria number is 10. The elimination-dispersal loop number
is 30, while the chemotactic loop number is 1. The simulation
program was coded on the Matlab/Simulink platform. For the
comparison of control strategies, the equivalent consumption
minimization strategy (ECMS) was organized by 5 for-loops.
For the rule-based control, 5 modes (IG mode, EV mode,
hybrid mode, Range-Extended mode, Reg mode) were
designed [11-16]. The driving cycle that we chose is FTP
cycle, where the total operation time is xx seconds. The max.
speed is 90.72 km/hr.
Fig. 3 Demanded speed of FTP driving cycle
After the BFA process, the simulation results are shown
below. Fig. 4 illustrates the two power split ratios for BFA.
Fig.4 Two power split ratios by BFA
Helpful Hints
Fig. 5 Comparison of engine output power of three control strategies
Fig. 6 Comparison of motor output power of three control strategies
Fig. 7 Comparison of ISG output power of three control strategies
Fig. 8 Comparison of battery SOC of three control strategies
Fig. xx Comparison of energy and equivalent fuel
TABLE I
COMPARISON OF THE FUEL CONSUMPTION OF THE THREE
CASES
Rule-based BFA ECMS
FC (ml) 1306.11 1188.11 981.3
Equivalent
FC (g) 1887.66 1512.83 1273.21
Equivalent
FC
Improvement
(%)
-- 19.8 33.3
Conclusion This study
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