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Speed Control of Electric Vehicle Using Fuzzy Logic
Controller
Surabhi Agrawal
Dr. Vivek Shrivastava
Electrical Engineering Department
Rajasthan Technical University Kota
Rajasthan, India
surabhi.agrawal93@gmail.com
Electrical Engineering Department
Rajasthan Technical University Kota
Rajasthan, India
shvivek@gmail.com
Abstract— In this paper, the utilization of regenerative
braking energy to control the speed of electric vehicle
based on fuzzy logic controller is discussed. A fuzzy logic
controller established is sugeno type. The output of motor
controller of electric vehicle with fuzzy logic controller is
acting as feedback to motor and improvement in speed of
vehicle is observed. Under condition of stability and
braking safety, the proposed strategy includes two types of
braking force, one is regenerative and another one is
frictional. So that a large amount of kinetic energy can be
converted into electrical energy form and this electrical
energy can be stored in the storage device like battery.
This strategy is used to use the motor’s regenerative
braking characteristics. Modeling of FLC (fuzzy logic
controller) is done under the MATLAB/Simulink software.
Simulation was done with different cycles and FLC is
examined with SOC (state of charge) of a battery, speed of
motor and required force to drive vehicle.
Keywords—Fuzzy Logic Controller; Battery; Brushless DC
Motor; SOC; Regenerative braking; FPGA.
I. INTRODUCTION
In recent time, Government agencies are following standard
strategies for fuel consumption and emission against
hazardous environmental conditions, specially those are
related to fossil fuel vehicle emissions [1]. Electric vehicles
are considered as the best solution for energy problems in
individual transportation system [2]. Energy efficiency is an
important factor for battery electric vehicle.
But there is still a gap of carrying into execution between
conventional fossil fuel vehicle and electric vehicles. This gap
exists with respect to energy saving, safety, power train
efficiency and driving range [3].
To overwhelm the shortcomings of electric vehicle, the
application of regenerative braking method is an operative
way. In case of a conventional fossil fuel vehicle, a
considerable amount of energy is consumed in braking [4,5],
But in electric vehicle this braking energy can be utilized.
In regenerative braking energy method, electrical motors can
be operated as generators in order to convert the potential
energy and kinetic energy in electrical energy form and can be
stored in a storage device [6].
This unique method assures the braking safety. It extends the
driving range and improves economic performance of electric
vehicle. However, this method is an uncertain method with
external disturbances and non-linear parameter perturbation.
Therefore, a regenerative braking force controller is designed.
This controller is based on fuzzy logic consisting of three
inputs i.e. vehicle’s speed, braking force applied by driver and
battery’s %SOC and one output i.e. the ratio between
regenerative braking force and total braking force [7].
A MATLAB/ Simulink environment is developed for FLC
model. A simulation result shows the effective and feasible
control strategy of FLC. Then FLC is implemented with
electric vehicle model and output speed of motor controller is
observed.
II. PRINCIPLE OF REGENRATIVE BRAKING ENERGY
When vehicle gets start, the driving motor utilizes the
electrical energy stored in storage device (like battery) to get
the speed [8-10] and when driver applies brakes, the motor
starts working as a generator. It converts a large amount of
mechanical energy into electrical energy and this can be saved
in storage device and again this energy can be utilized to drive
a driving motor. This is the basic principle of regenerative
braking energy. The schematic block diagram of behavior of
regenerative braking between parts of vehicle is shown in
Fig.1.
Driven
shaft
Motor
Energy
storage
device
Fig.1 The regenerative Braking Method
III. DESIGN OF FLC
Due to some effective advantages like employs adaptive
technique, high operational efficiency and co-ordination with
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linear control theory, a sugeno fuzzy controller is selected
here. FLC system basically consists of input fuzzy
membership function, output fuzzy membership function and
fuzzy rules. In this FLC, the input variables are braking force,
battery’s SOC and vehicle speed. The output variable is ratio
of regenerative energy braking force to total braking force
[11]. The FLC structure designed is shown in Fig.2.
.
Fig.4: Input variable ‘speed’
Fig. 2 The structure design of FLC
A. Relationship Between Applied Braking Force and
Regenrative Braking Force:
If the braking force command given by driver is small, then a
large amount of regenerative braking force to the vehicle can
be provided. If the braking force applied by driver is large, it
means we should reduce the part of regenerative braking force
and braking force command should be kept at medium,
proportion of regenerative braking force will be enhanced. The
linguistic variables of driver’s braking force are expressed as
High, Medium, Low and universe of discourse are [0, 2000].
Its membership functions are shown are shown in Fig.3.
C. Relationship Between Battery’s % SOC and Regenrative
Braking Force:
When battery’s SOC is below 10%, then battery is unsuitable
for charging and part of regenerative braking force should be
low and when SOC is between 10% and 90%, then charging
of battery can be done with large amount. When SOC is more
than 90% [12], the proportion of regenerative braking force
should be improved. The SOC is set at very high, high,
medium, low, very low and the universe of discourse is [0,1].
Its membership functions are in Fig.5.
Fig.5 Input variable ‘SOC’
D. Output:
We favor the concourse of the ratio as: Mf = {Mf0, Mf1, Mf2,
Mf3, Mf4, Mf5, Mf6, Mf7, Mf8, Mf9, Mf10} = (0, 0.1, 0.2, 0.3,
0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0).
Fig.3 Input variable ‘force’
B. Relationship Between Vehicle’s Speed and Regenrative
Braking Force:
When the speed of vehicle is low, the regenerative braking
force proportion should have low value. When speed is high,
the ratio of generative braking force should be increased to
very high value. The linguistic variables of vehicle speed are
High, Medium, Low. The universe of discourse is [0,100] and
membership functions are shown in Fig.4.
E. Rules:
The execution of FLC depends on fuzzy rules. Here fuzzy
rules are built by co-relating the three inputs and one output
factor.
The conditional sentence used to build the fuzzy rules is: IfġĩS
is SiĪ,ġ F is Fi炸and炷SOC is SOCi炸then 炷ratio is Mfi. The
fuzzy rules are shown in Table I.
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S. No.
TABLE I.
S. No.
Si
THE FUZZY RULE
Fi
SOCi
Ratio
Si
Fi
SOCi
Ratio
37.
High
High
Very Low
Mf0
38.
High
Medium
Very Low
Mf1
1.
High
High
Very High
Mf0
39.
High
Low
Very Low
Mf1
2.
High
Medium
Very High
Mf1
40.
Medium
High
Very Low
Mf1
3.
High
Low
Very High
Mf1
41.
Medium
Medium
Very Low
Mf1
4.
Medium
High
Very High
Mf1
42.
Medium
Low
Very Low
Mf2
5.
Medium
Medium
Very High
Mf1
43.
Low
High
Very Low
Mf1
6.
Medium
Low
Very High
Mf2
44.
Low
Medium
Very Low
Mf2
7.
Low
High
Very High
Mf1
45.
Low
Low
Very Low
Mf2
8.
Low
Medium
Very High
Mf2
9.
Low
Low
Very High
Mf2
10.
High
High
High
Mf5
11.
High
Medium
High
Mf8
12.
High
Low
High
Mf10
13.
Medium
High
High
Mf5
14.
Medium
Medium
High
Mf7
15.
Medium
Low
High
Mf9
16.
Low
High
High
Mf1
17.
Low
Medium
High
Mf2
18.
Low
Low
High
Mf3
19.
High
High
Medium
Mf6
20.
High
Medium
Medium
Mf8
21.
High
Low
Medium
Mf10
22.
Medium
High
Medium
Mf5
23.
Medium
Medium
Medium
Mf7
24.
Medium
Low
Medium
Mf9
25.
Low
High
Medium
Mf1
26.
Low
Medium
Medium
Mf2
27.
Low
Low
Medium
Mf3
28.
High
High
Low
Mf9
29.
High
Medium
Low
Mf8
30.
High
Low
Low
Mf10
31.
Medium
High
Low
Mf5
32.
Medium
Medium
Low
Mf7
33.
Medium
Low
Low
Mf9
34.
Low
High
Low
Mf1
35.
Low
Medium
Low
Mf2
36.
Low
Low
Low
Mf1
The output surfaces based on fuzzy rules are shown in Fig. 6,
7 and 8:
Fig.6: The relation between vehicle’ speed, battery’s SOC and output ratio
Fig. 7: The relation between braking force, battery’s SOC and output ratio
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Fig.8: The relation between braking ratio, vehicle’s speed and output ratio
Fig.9: The Basic Structure of Electirc Vehicle
IV. IMPLIMENTATION OF ELECTRIC VEHICLE WITH
FLC
A model of electric vehicle mainly consists of BLDC
(brushless DC motor), battery and an inverter. Inverters are
fed by BLDC. Due the efficient characteristics of BLDC (such
as wide speed range, high efficiency and good power
densities), these motors are highly preferred for electric
vehicles [13]. BLDC information expects six discrete rotor
positions for operation of inverter. These are generated by
Hall Effect sensors. These sensors provide the information of
position, those are needed to synchronize the excitation of
stator with position of rotor and this synchronization is done to
produce constant torque [14, 15]. This sensor control is
implemented by adopting the FPGA technique [16].
When a speed input is given to BLDC motor, BLDC motor
starts working and provides input to universal bridge i.e.
inverter. This inverter is gated by FPGA. FPGA is getting
signal from motor output. The universal bridge provides its
output voltage to vehicle battery and vehicle battery starts
functioning by providing SOC of battery. The decision of
selecting a battery should be used depends on the charging and
discharging behavior of battery [17]. The battery SOC, speed
of vehicle and braking force behaves as input to FLC. By
following ranges of membership functions and fuzzy rules,
FLC provides output. This output is ratio between regenerative
braking force and total braking force. This ratio acts as
feedback for BLDC at the torque input of motor and output
speed of BLDC gets improved as it is using regenerative
braking energy as well.
In the Fig. 9 shown below, when speed as an input is given
BLDC motor, it provides sensor signal to IGBT inverter using
FPGI. Then inverter provide output signal to battery and
battery produces SOC. This SOC is given to FLC as input
along with braking force of vehicle and vehicle’ speed and
FLC provide a ratio, which acts as feedback for BLDC and
BLDC provide improved speed.
V. SIMULATION RESULTS
Fig. 10: The output speed of vehicle
As shown in Fig.10 the waveform of vehicle’s speed. Before
using the FLC, the speed obtained is negative, which is not the
required result. But, when FLC is used, vehicle’s speed
increases in positivce direction and shows the improved
results.
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Fig.11: The output torque of BLDC Motor along with Error and Reference
values
As in the figure above shown, After using the FLC, output
torque of BLDC is improved. Before using the FLC, the
output torque was almost zero and error was very high. But, as
we use FLC, the error is reduced to very less and acceptable
Value.
VI. CONCLUSION
This paper presents the utilization of regenerative braking
energy in improvement of vehicle speed, in improvement of
output torque of BLDC motor, in reduction of error and in
enhancment of efficiency of vehicle. The output of Motor with
FLC acts as feedback for motor. Vehicle speed, SOC of
battery and Braking force of vehicle acts as input for FLC and
output of FLC is the ratio of regenrative braking force to total
braking force of vehicle. Using a Sugeno type fuzzy logic
controller, FLC structure is established. Before using the FLC,
the speed of vehicle was in range of -3000 rad/sec and speed
error was around 10,000. But after using FLC, the speed
increased from zero and become constant at 120 rad/sec and
speed error is reduced to 1200 from 10,000, which improves
the overall effocoency of electric vehicle. Using this electric
energy, a vehicle can be driven again. MATLAB/Simulink is
used established the FLC and model of vehicle.
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