A cruise control system for an electric vehicle has been modeled in MATLAB Simulink. A PI controller controls torque and a PID controller controls speed. The effect of the controllers and different inputs were analyzed. With both controllers, the system became stable, while it was unstable with no controllers. Step, ramp, and sine wave inputs all stabilized. Key parameters like peak time, rise time, and settling time were calculated from the output.

1. CONTROL SYSTEMS
308301
SEMESTER PROJECT REPORT
CRUISE CONTROL SYSTEM FOR AN
ELECTRIC VEHICLE
Submitted By:
Shahzaib Anwar 180501008
M. Teham Tahir 180501016
Hafiz M. Shahid Tariq 180501018
Hamza Irfan 180501020
Submitted to: Ma’am Ruqia Ikram
Department: Mechanical (07)
Institute of Space Technology
Date of Submission: 7th
April 2021

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Table of Contents
ABSTRACT................................................................................................................................ 3
INTRODUCTION ...................................................................................................................... 4
LITERATURE REVIEW...........................................................................................................6
OBJECTIVES ............................................................................................................................ 7
PROBLEM STATEMENT.........................................................................................................7
METHODOLOGY ..................................................................................................................... 8
SIMULINK MODEL.................................................................................................................. 8
ANALYSIS AND RESULTS.......................................................................................................9
Effect of Controllers................................................................................................................ 9
Effect of Inputs ..................................................................................................................... 13
System Parameters................................................................................................................ 16
DISCUSSION........................................................................................................................... 18
CONCLUSION......................................................................................................................... 18
REFERENCES......................................................................................................................... 19

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ABSTRACT
In this project a cruise control system for electric vehicle has been modelled using MATLAB
Simulink. A PI controller has been used to control torque and a PID controller to control
speed. In the present automobiles are manufactured with automatic control systems already
installed. One of these is the cruise control system used to control and maintain constant
speed of the vehicle without constant user interference. The system has been modelled using
transfer function blocks and controllers. After modelling the system the effect of the
controllers, and of different inputs has been studied along with other parameters.

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CRUISE CONTROL SYSTEM FOR AN
ELECTRIC VEHICLE
INTRODUCTION
Electric vehicles (EV) are one of the most widely used vehicles these days. It is based as
electric propulsion system in which all the power is provided through electric batteries.
Hence, no internal combustion engine is required. Its main advantage is the increased
efficiency. Moreover, emission elimination, low operating cost and superior controllability
over the powertrain are other factors which have made electric vehicles a preferred choice.
Construction
 Battery
 Charge port
 DC/DC Convertor
 Electric traction motor
 Onboard charger
 Power electronics controller
 Thermal system
 Traction battery pack
 Transmission.
The EV powertrain consists of an electric motor, single or double speed transmission and the
final drive unit.

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Cruise control system is developed for driving with constant speed on long stretched roads.
This system performs as a speed-tracking controller and autonomously follows a pre-set
vehicle speed. For instance, a well-tuned and robust cruise control system is an essential
component of adaptive cruise control systems. The control logic of the cruise controller can
be designed by employing different types of controllers, such as a proportional-integral-
derivative (PID) controller
The robust controller minimizes the effect of uncertainties being encountered in the control
system. These uncertainties can occur, due to the simplification of plant’s model or the
surroundings effects, such as the temperature fluctuation, pressure fluctuation, noise, etc.
Several methods have been developed to manage the uncertainties present in processes and
improve the robustness and the disturbance rejection of PID controller
Robust proportional integral (PI) torque and PID speed controllers are designed using the
numerical optimization technique for the cruise controller of the EV. Simulation results show
that the controller has sufficient robustness to compensate any disturbances, for the case of
road grades, and presents a superior speed-tracking behavior. The major contribution of this
research is to model a cruise controller and develop its complex design procedure with multi-
layer controllers. The model performance and simulation results were verified with those
reported in the literature, and showed an overall improvement of the cruise controller
performance. Applicability of the developed control design procedure is carried out for an
automotive system consisting of two-layer control loops with both PI and PID controllers.
One of the widely used types of electric motors for the EV powertrains is the DC motor. The
main objective of a cruise control system is to momentarily track the desired speed of the
vehicle. In an EV, speed can be actively controlled by continuously adjusting the torque of
the electric motor. In this research, a robust optimized control system, consisting of both PID
and PI controllers, has been utilized to constantly track the desired speed.

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Schematic diagram of the EV powertrain including cruise control system.
Because of its simplicity and accurate control over the electric motors, EV power train are
more reliable. Furthermore, the regenerative braking of EV powertrain makes it more elegant
over other systems.
LITERATURE REVIEW
Automobiles are now equipped with enhanced and efficient systems to control systems
automatically. Cruise Control system is designed to control and maintain constant speed in a
car without the driver pushing the accelerator paddle constantly. The applications of this
control system are widely use in newly designed electronic cars and it is quite useful during
long journeys on wide stretched roads. This system performs as a speed-tracking controller
without any human observer. It monitors a pre-defined constant speed at which the car is
supposed to move. A well-tuned and robust cruise control system is an essential component
of adaptive cruise control systems. The control logic of the cruise controller can be designed
by employing different types of controllers, such as a proportional-integral-derivative (PID)
controller. (Diba, Arora, and Esmailzadeh 2014)
Cruise control system is to maintain the output speed of the system as set by input signal.
This can be achieved by various methods of controller such as using proportional-integral
derivatives (PID) controller, state-space controller, and many more. Modelling is a task that
requires simplification and ideal environment. A complex model of a car with dampers,
springs and masses can be reduced to much simpler form of model such as moving cart.
Modelling a system cruise control, will take into accounts all of the important parameters,
including those that are due to disturbances which directly or indirectly affect the overall
performance of the system. After modelling the cruise control system, the design of the
controller such as PID control can be applied and the stability analysis based on linear state-
space model or transfer function is analyzed. (Osman, Rahmat, and Ahmad 2009)
A PID controller is an instrument used in industrial control applications to regulate
temperature, flow, pressure, speed and other process variables. PID controllers use
a control loop feedback mechanism to control process variables and are the most accurate and

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stable controller. The gains of this controller can be tuned using different control theories,
such as the robust control theory. The robust controller minimizes the effect of uncertainties
being encountered in the control system. Robust control is an approach to controller design
that deals with uncertainty, it is developed to function properly in environments with
uncertain parameters or disturbances. The robust controller can be designed by using
numerical optimization techniques, which involve fewer calculations and gives stability to the
system.
The robust proportional integral (PI) torque and PID speed controllers are designed using the
numerical optimization technique for the cruise controller of the electric car simulation. The
project aims to model a cruise controller and develop its complex design that simulates a
controller that has satisfactory robustness to compensate any disturbances, and presents a
good speed-tracking behavior.
Tuning methods based on optimization approach have recently received more attention in the
literature, with the aim of ensuring good stability robustness of the controlled system.
However, these new methods are not easy to use for the operating engineer who is the main
user of the PI/PID controller. Considering PI structure adaptive control design, an approach is
presented by Xu and Loannu (1994).The design is based on a linearized vehicle model, while
a reference model generates the reference velocity signal. The adaptation guarantees the
handling of model parameter variations. A model-free control design approach is applied to
design an intelligent PI controller in Menhour et al.(2013).The longitudinal control input s are
traction/braking wheel torques, and the longitudinal positioning error of the vehicle is
improved using the method. The method is robust with respect to modeling error and
parametric uncertainty
OBJECTIVES
The objectives of this project are:-
1. To model cruise control system in MATLAB Simulink.
2. To find the percentage overshoot, rise time, peak time, and settling time of the system.
3. To observe the effect the speed and torque controllers have on the system.
4. To observe the effect different inputs have on the system.
PROBLEM STATEMENT
To increase the speed of a vehicle, drivers must push the accelerator and, on the highways,
for maintaining a constant speed of the vehicle the pressure on the pedal has to be maintained
constant for a long period of time. To support the drivers in driving, modern electric cars are
equipped with cruise control systems. The cruise control systems can maintain the desired
speed of a car set by the drivers, without using an accelerator and without the interference of
the driver. Thus, the driver’s task will be reduced to steer the car and drive carefully to avoid
any accident.

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METHODOLOGY
A cruise control system for electric vehicles has been modelled using MATLAB
Simulink. The system consists of a block diagram made up of multiple transfer function blocks.
The system has been modelled using a speed controller which is a PID controller and a torque
controller which is a PI controller. It also consists of an actuator, and an armature circuit.
Feedback from system is back emf, friction torque, current sensor sensitivity, and speed sensor
sensitivity. Using scope the speed has been plotted against time and settling time peak time,
rise time, percentage overshoot calculated, and behavior of system studied for different inputs
to the system. The effect of the controllers on the system has also be studied.
SIMULINK MODEL
Total drive ratio = itot = 4.875
Total Inertia = Jtot = 8.6 kg.m2
Armature gain constant = Ra = 1
Armature time constant =Ta = 0.1
Actuator gain constant = KA = 30
Actuator time constant = TA = 0.03
Speed sensor Sensitivity = Kss = 0.02
Current Sensor sensitivity = Kcs = 0.03
Back emf gain = Kb = 2
Friction torque gain = Kf = 0.1
Actuator =
𝐾𝐴
1+𝑠𝑇𝐴
Armature Circuit =
1
𝑅𝑎
1+𝑠𝑇𝑎
Gain5= 𝑛𝑡𝑜𝑡𝐾𝑡
Transfer Fcn2 =
1
𝐽𝑡𝑜𝑡 𝑠
Transfer Fcn3 =
𝑟
𝑖𝑡𝑜𝑡
Gain1 = Kf
Gain3 = Kb
Speed Sensor Sensitivity = Kss
Current Sensor sensitivity = Kcs

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ANALYSIS AND RESULTS
Effect of Controllers
Without both controllers:-

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With Torque Controller (PI controller) only:-

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With Speed Controller (PID controller) only:-

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With both Speed and Torque Controllers:-

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Effect of Inputs
Step Input

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Ramp Input

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Sine Wave Input

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System Parameters
Peak time
The peak time as seenin signal statistics is 100 seconds.

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Rise time
11.45-1.492=9.958
The rise time as can be seenfrom cursor measurements is 9.958 seconds
Settling time

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As can be seenfrom cursor measurements the settling time for the systemcomes out as
19.954 seconds.
Percentage Overshoot
There is no percentage overshoot as systembecomes stable without exceeding the peak
value.
DISCUSSION
The results from the analysis show that the system is unstable with no clear signs of settling
when neither controller is implemented in the system. With the torque controller only, which
is a PI controller, the system is still not showing any significant change except the change in
magnitude. With the speed controller only, which is a PID controller, the system has starting
to show signs of settling and is starting to become stable. When both controllers have been
implemented the system has become stable.
Next the effect of different inputs was observed. For the step input the system after settling
had a zero gradient and hence a constant magnitude, which is the same signal shape as the
input. For the ramp input after settling had a constant gradient and a constantly increasing
magnitude, which was again the same shape as the input. For the sine wave input the output
was a sinusoidal wave with each wave having the same maximum and minimum amplitude
after the system had stabilized. All of the inputs stabilized during the time interval observed.
The system parameters found were peak time, rise time, settling time, and percentage
overshoot. The peak occurred at 100 seconds although the system started to have an almost
constant magnitude after 30 seconds. The rise time was calculated equal to 9.958 seconds, as
system had 10% value at 1.492 seconds, and 90% value at 11.45 seconds. The settling time of
the system was found to be at 79.219seconds where the signal magnitude had reached 98% of
its value. There was no percentage overshoot as system did not exceed the value it stabilized
at making it a critically damped system.
CONCLUSION
In this project, a cruise control system to achieve smooth and steady motion of electric cars
on highways has been designed. These cruise control systems make long highway journeys
easy and reliable for drivers. Firstly, a block diagram of a closed-loop cruise control system
including inputs, outputs, and feedback has been obtained, and then according to that the
cruise control system for velocity and torque control has been designed using different
controllers. The working of the control system is demonstrated using MATLAB and
Simulink. Using scope the peak time, rise time, settling time, and percentage overshoot has
been found as shown above. The effect of both controllers and different inputs has also been
observed.

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REFERENCES
Diba, Fereydoon, Ankur Arora, and Ebrahim Esmailzadeh. 2014. “Optimized Robust Cruise
Control System for an Electric Vehicle.” Systems Science & Control Engineering 2 (1): 175–
82. https://doi.org/10.1080/21642583.2014.891956.
Osman, Khairuddin, Mohd. Fuaad Rahmat, and Mohd Ashraf Ahmad. 2009. “Modelling and
Controller Design for a Cruise Control System.” 2009 5th International Colloquium on Signal
Processing & Its Applications, March. https://doi.org/10.1109/cspa.2009.5069228.
Diba, F., Arora, A., & Esmailzadeh, E. (2014). Optimized robust cruise control system for an
electric vehicle. Systems Science & Control Engineering, 2(1), 175–182.
https://doi.org/10.1080/21642583.2014.891956
Menhour, L., d'Andréa-Novel, B., Fliess, M., & Mounier, H. (2013, December).
Multivariable decoupled longitudinal and lateral vehicle control: A model-free design. In
52nd IEEE Conference on Decision and Control (pp. 2834-2839). IEEE.
Robust H‘ Design of an Automotive Cruise Control System. (2015). IFAC-PapersOnLine,
48(15), 341–346. https://doi.org/10.1016/j.ifacol.2015.10.049
Lu, X. Y., & Hedrick, J. K. (2005). Heavy-duty vehicle modelling and longitudinal control.
Vehicle System Dynamics, 43(9), 653-669.