Simulation Research of the Fuzzy Torque Control for Hybrid Electrical
Vehicle Based on ADVISOR
Bo-jun Zhang, Yu Wang, Jia-tian Guo
Department of Automotive and Transportation, Tianjin University of Technology and Education, Tianjin, China
(zhangbojun808@126.com)
Abstract - The simulation model of the super-mild
hybrid electrical vehicle is established through the
simulation software ADVISOR. The fuzzy logic torque
distribution controller of motor and engine is designed. The
drive cycle selects Urban Dynamometer Driving
Schedule(UDDS). The simulation results show that the fuzzy
torque controller of motor and engine can properly
distribute the torque. The fuel economy and emission
performance are improved.
Key words - ADVISOR, hybrid electrical vehicle,
simulation, torque control
I. VEHICLE SIMULATION MODEL
The simulation model of the super-mild hybrid
vehicle is established through the simulation software
ADVISOR. This Model is shown in Fig. 1.
The backward simulation and forward simulation
can be used in ADVISOR. The backward simulation can
Fig. 1.
calculate the engine and motor output power. Forward
simulation is to be after the backward simulation, it can
use the engine and motor power along with backward
simulation passing in the opposite direction. The actual
speed of vehicle is calculated by the forward simulation
[1]
. Every module in the vehicle simulation model contains
a simulink simulation module. We can modify the
parameters in the M-file for data input [2].
The parameters in the M-file of vehicle module are
modified according to the parameters of the entire
vehicle, such as:
veh_gravity=9.81;
% m/s^2
veh_air_density=1.2;
% kg/m^3
veh_mass = 865;
% (kg)
vehicle_height = 1.380;
% (m)
vehicle_width = 1.590;
% (m)
veh_CD=0.38;
veh_FA=1.7;
% (m^2)
veh_wheelbase=2.365;
% (m)
The super-mild hybrid vehicle simulation model
II. TORQUE FUZZY LOGIC CONTROL
The super-mild hybrid electrical vehicle has no pure
electric operating mode. Motor only be used for idle
starting, stopping, power compensation and recycling
braking energy, so the motor torque control will affect the
vehicle performance [3-6].
The output torque of the engine can be divided into
two parts：One is used to drive the vehicle, the other is
used to drive the generator for the battery charging. The
input/output torque of the motor can balance the
relationship between engine torque and vehicle required
torque, which controls the engine working point along the
economic curve [7] [8]. When the engine output torque is
lower than the vehicle required torque, the motor will
make up the difference. When the engine output torque is
higher than the vehicle required torque or the vehicle is in
deceleration braking condition, the extra engine output
torque or the recovery of deceleration energy can drive
generator for battery charging [9-13]. The fuzzy controller
not only improves the vehicle fuel economy but also
makes the battery SOC value to be in the high efficiency
range.
Take the differential values between the vehicle
required torque and the engine output torque (Tc), and
battery SOC values to be input variables. Take the torque
adjustment parameter to be output variables. The
membership function is shown in Fig. 2.
Fig. 4.
Torque control
III. SIMULATION RESULT
The simulation results are shown in Fig. 5.
Fig. 2.
Membership function
Input and output variables of the fuzzy sets are as
follows:
The fuzzy set for the SOC is: {L, PL, W, PH, H}.
The fuzzy set for the Tc is:{FD, FX, W, ZX, ZD}.
The fuzzy set for the V is: {X, PX, W, PD, D}.
Fig.5
Simulation results
Where: L represents low, PL represents partial low, W
represents moderate, PH represents partial high, H
The engine fuel economy and emission values are
represents high, FD represents negative bigness, FX
shown
in Table I.
represents negative smallness, ZX represents positive
TABLE I
smallness, ZD represents positive bigness, X represents
ENGINE FUEL ECONOMY AND EMISSIONS
smallness, PX represents partial smallness, PD represents
fuel
partial bigness, D represents bigness.
Emissions(grams/mile)
Torque
economy(
25 fuzzy control rules are designed to describe the
controller
L/100km)
HC
CO
NOX
relationship between input and output. There are some rules
to illustrate its function, IF is the premise and THEN is the
Without
conclusion:
fuzzy
0.47
0.422
10.82
0.113
controlier
a. IF(Tc is W) THEN (k is W)
b. IF(SOC is L)and(Tc is FX) THEN (k is W)
With
c. IF(SOC is PL)and(Tc is FD) THEN (k is X)
fuzzy
0.35
0.33
6.456
0.113
d. IF(SOC is H)and(Tc is ZX) THEN (k is X)
controller
e. IF(SOC is PH)and(Tc is ZD) THEN (k is PD)
…
The simulation results show that the battery SOC value
The modules of < cl > and <tc> realize the fuzzy logiccan be maintained in the high efficiency range. And the fuel
torque control, as shown in Fig.3 and Fig. 4.
economy of the engine has been improved, the HC and CO
emissions have been lower.
IV. CONCLUSION
Fig.3
Clutch control
The ADVISOR simulation model and the fuzzy logic
torque controller are established. The fuzzy logic torque
controller is realized through the clutch and torque control
module. The fuzzy torque control strategy can more
effectivly distribute the motor and the engine operating
range. The fuel economy of the vehicle has been improved
and the emissions have been lower.
ACKNOWLEDGMENT
This dissertation is under the project support of Natural
Science Foundation of Tianjin (09JCYBJC04800).
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