Modeling of a controller for an Electric Vehicle

POORANI .S, T.V.S URMILA PRIYA, K. UDAYA KUMAR, S.RENGANARAYANANDepartment of Electrical & Electronics, High Voltage Division,

College Of Engineering, Anna University,Sardar Patel Road, Guindy, Chennai-25.

INDIA

Abstract: - The Electric vehicle uses an electric motor for drive and battery as the energy source. The motor has the capability of operating at variable speed. The power supplied by the battery is DC in nature and it is inappropriate to operate a variable speed DC motor. Therefore, a power converter is used to convert the battery power into a required form to give input to the motor. Voltage is dependent upon the battery’s state of charge and the load current. Hence the motor should be capable of handling these fluctuations in supply voltage in order to drive the vehicle efficiently. This paper is primarily aimed at developing a fault tolerant fuzzy logic controller for the vehicle. This controller takes into account the battery’s state of charge, speed variations, driver command, load conditions, terrain and gear as input parameters and determines the vehicle speed for driving conditions.

Key-Words: - Electric Vehicle, Fuzzy Logic controller (FLC), Driver Speed command, Driver Braking Command, Load, State of charge (SOC), Gear, Terrain, and the Driving Conditions. 1. Introduction Knowledge based intelligent user interfaces are the most popular subject of research. They deal with equipping the user interface with intelligence and associating them with day-to-day activities .The intelligent interface [6], can be so implemented in an electric vehicle to maximize the performance of the vehicle, that the intelligent interface works on the tailored rules, based on the needs of the user. The user interface that is implemented in this battery-operated vehicle [1], [4], is a controller that uses fuzzy logic, which utilizes a rule-based system. The speed response of a conventional PI controller scheme is affected considerably by variation in J, where J is inertia constant of motor[5] and mechanical load [7]. The fuzzy controller [2], thus implemented, takes into consideration more parameters to optimize the vehicle performance for higher efficiency. The controller takes the following parameters such as Acceleration average, State of charge of the battery, Driver braking command, Load of the vehicle, various terrain of the country, Driver Speed command and the various gear in which the vehicle will run. The results will provide the real situation in which the vehicle performs on various

terrain and the roads such as Highway, Urban, Semi Urban, City, Parking, Traffic The driver should first chooses the terrain in which the journey should take place and the controller acts accordingly.

2. Electric vehicle system and controller

The major components of an electric vehicle system are the motor, controller, power source, charger and drive train is shown in fig 1. Lead acid batteries are typically used as a power source for electric vehicle although other promising battery technologies are emerging in the horizon. The controller is needed for controlling the parameters of the vehicle, taking into consideration the overall performance of the vehicle. The motor is of primary importance to the electric vehicle system . A DC shunt motor [5], in which the field windings and the armature may be connected in parallel across a constant voltage supply is used.

Fig 1.Block Diagram of Electric Vehicle

As regards adjustable speed application, since it is used in electric vehicle, the field is connected across an independent adjustable voltage supply. The objective of the paper is to design a fuzzy logic controller taking into account the various parameters like Acceleration average, State of charge, Driver Braking command, Driver speed command, Load of the vehicle, gear of the vehicle, and the terrain in which it will run. Control complications are to be minimized as much as possible. So this new technique has been discussed in the following section. The fuzzy logic controller is needed to control many components in the vehicle. It also adds the capability for the components to work together in harmony while at the same time optimize the operating points of the individual components.2.1 Need for Fuzzy Logic Controller

The electric vehicle system has number of components that have to be optimized before the vehicle is given the command for running. Generally, taking a certain controller will not take care of all the parameters of the vehicle. For e.g. the control action of the electric motor does not take care of the status of the battery temperature, or the charge level of the battery, or the motor temperature of the vehicle. Similarly the control action of the components status such as overdrawing of the motor that operates beyond the normal temperature are taken care of ,either based on the driver action or the sensor that is used to regulate the further control of the next stage. When considering the various uses, the fuzzy logic controller [2]is capable of reasoning all the components status and automatically controlling the speed of the motor, hence,the driving of the vehicle is based on the fuzzy inference system. For e.g. when the charge level of the battery goes low, the FLC does not allow the motor to be driven at the speed selected by the driver, if it is not compatible with the status of the battery. Fig 2 depicts the general fuzzy logic process.[4]2.2. Fuzzy Logic ControllerThis section deals with the fuzzy logic controller [2] that is used to implement the controller.The membership function of each command that is used is presented in

detail The input parameters of the vehicle to the fuzzy logic controller [6] are

Fig 2.Fuzzy logic process Algorithm(1) Acceleration average (2) Braking command (3) State of charge of the battery (4) Speed Average (5) Load (6) Terrain and (7) Gear. The output of the fuzzy controller is the set speed of the vehicle based on the component status and the speed predicts the driving conditions of the vehicle. In this controller design, the driving range of the speed of the vehicle is taken as the range of the driving condition. The whole range of the driving condition is divided into six linguistic variables as very low, low medium, medium, high, very high. The trapezoidal membership function is used for the linguistic variables of the driving condition. Here, this membership function is selected such that the driver normal conditions are satisfied, as other functions like exponential if selected will lead to abnormal working condition of the driver which is not advisable. The driver braking condition command range is taken as 0-5 with maximum braking as the value of 5. The range of the braking command is divided into three linguistic variables as short, medium and long. The acceleration average is taken as 0-5,with maximum acceleration as the value of 5. The range of the acceleration average is divided into three linguistic variables as short, medium, and big. The speed average of the vehicle is taken as 0-240, and it is divided into five linguistic variables as very slow, slow, slow medium, medium, and fast. The gear in which the vehicle will run is taken as the range from 0-5,with the maximum speed gear as the fourth gear and the reversing of the vehicle as the fifth parameter. The total range of the state of charge of the battery

is taken as 0-1,and the maximum charge is taken as 1. The range of charge is divided into three variables as low, normal and high. The load of the vehicle, which is taken into account all the possible subsystems, is taken as the range from 0-100. The total range of the load is divided into four linguistic variables as low, low medium, medium and high. Next is the terrain in which the vehicle will run. The total range of the terrain is from 0-4. The different terrains in which it is taken are smooth, rough, uphill and downhill. The vehicle will run at optimum speed on various driving conditions for various terrains.Here the linguistic terms are represented by fuzzy sets. Consider the variable for “state of charge of the battery”. The member ship functions for state of charge is represented in the fig 3.Fuzzy sets “normal” represent the range where the state of charge should be, “low” acts as a buffer between “normal ” and “low” and “high” represent the range in which it is more than sufficient. This is one of the main criteria to be taken into account for moving the vehicle.

Fig 3. Membership function of state of charge of the Battery

Fig 4 represents the membership function for the driver braking command. The total range of the driver braking command is taken as 0-5 and corresponding degree of membership function is also taken care of. Here the linguistic terms “medium” represents the normal operation of the

Fig 4.Membership Function of the Braking driver to run the vehicle. The next term “ long” represents the function of the driver in which the driver has to apply in a long

way as such in a certain driving condition like smooth terrain. The next term “ short” represents the operation in which the driver has to perform in a rough terrain.

Fig 5.Membership function of Driver speed AverageFig 5 represents the membership for the driver speed average command. The total range of the speed average of the vehicle is from 0 –240. Here the term “very slow” represents the range in which it is taking in all the parameters and in a situation the vehicle will run at a lowest range of the starting speed, next the term “slow” represents the range in which the vehicle will run slow in the situation when the braking pedal is utilized more extensively. Next the term “slow medium” represents the situation in which the vehicle will run in a safe mode of the operation. Next the terms “medium” and “fast” represents the situation in which the driver wishes to give more acceleration to increase the cruising speed. Fig 6. represents the membership function for the Acceleration average of the vehicle, which plays one of the main parts in the vehicle-running mode. Here the term “short” represents the range in which the driver is traveling in the city. Next the term “medium” represents the situation in which the driver runs the vehicle in a normal mode. The term “big” represents the situation in which the vehicle runs in a smooth terrain for a long duration without any disturbances ,that is, in a highway driving condition.

Fig 6. Membership function of Acceleration Average

At last the function for the various loads of the vehicle is taken, which is the one of the main criteria. Here the linguistic variable “high” represents the range in which the vehicle should be driven carefully so that it can reach the required distance within the limited time. Next the term “ medium” represent the range in which the prior situation of the vehicle can be made safer . Next the term “low medium” represents the range in which it is more than sufficient to move the vehicle. Next the term “low” represents the range in which the vehicle will run very smoothly without any hindrance. Fig 7. represents the membership function of the Terrain in which the vehicle has to take up its journey. Here the linguistic variable “smooth” represent the range, in which the vehicle can ride without any disturbances in its path. The most comfortable journey can be obtained in this range achieving the maximum speed. The next term “rough”, as the name implies will be much difficult to ride the vehicle and the range of speed in which it travels will be very low compared to the previous situation. The next term “uphill” represents the situation in which the vehicle will be taking up acceleration in the slope and it can travel with minimum speed with the safer mode during this process. The range in which it travels will be much low compared to the previous case but with safe operating situation. Next the term “downhill” depicts that the vehicle is taking deceleration with the minimum operating speed during its passage . The range of speed in which it travels will be of less speed compared to other parameters, taking into account the safety.

Fig 7.Membership function of TerrainFig 8. represents the membership function of the Gear, which is another important function in which the vehicle will get the

the drive train. The linguistic term “first gear” is the minimum speed in which the vehicle will start and try to reach the

Fig 8. Membership function of Gearspeed. The term “second gear” represents the state in which the vehicle has increased its speed and the term “third gear” represents the state when the vehicle will be running at the speed next to the range of the previous range. The term “fourth gear” is the maximum gear in which it will be traveling in a situation of smooth terrain. In “ reverse gear” as the name implies, the vehicle will be moving in the backward direction with a safe speed. Thus the linguistic terms of the gear are helpful in achieving the speed range of the vehicle. TheFig9.represents the membership function of the driving condition in which it will travel through its whole journey. The terms “very low”, “low”, “slow medium”, “medium”, “high”, and “very high” represent the various driving conditions in which the vehicle operates while parking or traveling in city, highway, urban and semi urban areas and negotiating traffic. The term “very low” represents the situation in which vehicle is at parking and “low” represents the situation where the vehicle can take off at certain speed of low acceleration taking into consideration driving conditions like traffic and movement in a city .

Fig 9. Membership function of driving condition

The term “ slow medium” indicates, general or cruising speed range of the vehicle when traveling in a city. Next the term “medium” represents the situation,

which is in semi-urban and urban condition. The term “very high” i.e. high acceleration and high speed represents the range in which the vehicle will be running in a highway . The whole range and the membership function are totally taken care of.3. Control action of the flc

The control action of the FLC is mainly based on the component status. According to the design of the FLC, when the vehicle is to be started, the driver initially has to decide the terrain in which the vehicle has to take up its journey. After taking care of all the conditions of the parameters of the vehicle, that is ,when the charge level of the battery is high, load of the vehicle is very low, the acceleration average is good enough, the speed average is fast enough, the gear is properly engaged, then the vehicle can run at optimum speed. When any one of these parameters changes, the controller will adapt to the required variations. In this case the controller FLC will predict the speed in which it can run. A set of different rules has been developed for each terrain. The FLC output is based on the evaluation of the rules by the fuzzy inference system. The following table 1 indicate how the rule base has been created for some conditions. The values indicated inside the parentheses represent the weights of the rules. The rules set are divided into three categories, 1) to represent the normal conditions with no acceleration and braking, 2) to represent the driving conditions on acceleration, 3) to represent the driving condition on braking. All the rules in the rules set are evaluated in parallel.Table-1 shows the rule base when Terrain is smooth, Speed Average is fast and the gear is in the fourth state. Table – 1 When Terrain – Smooth, SA – Fast, Gear – 4 Similarly the rule base has been formed when Terrain is smooth, Braking is long, Speed Average is fast and the gear is in the fourth state.When Terrain is Rough, Speed Average is fast and the gear is in the fourth state, When Terrain is Hill Up, Speed Average is small medium and the gear is in the fourth state. And finally when Terrain is

down hill, Speed Average is slow and the gear is in the second state. Since the vehicle is running in gear two which is in slow driving conditions no matter what the SOC is either high or normal and Load is either L or LM. So the same rules have been taken for the respective cases.Table 1Soc/Load L LM M H

VH(0.075)

H (0.05)

VH(0.03)

H (0.05)

H (0.3)

M(0.015)

H (1)

M (0.5)

Normal H (0.35)

M (0.07)

H (0.5)

M (0.25)

H (0.23)

M (0.2)

H (0.2)

M (0.3)

The following example shows how the controller will act under the various driving conditions and the various terrains during its course of travel. The results have been taken in the running mode as follows.Case 1- Terrain is Smooth (0.5) : In normal mode, when state of charge (SOC) is 0.9,load is 10, driver speed average is 200 and gear is 3.5 then FLC set speed is 201.When the driver raises the speed (i.e.) acceleration average is 4, state of charge (SOC) is 0.9,load is 10, driver speed average is 200 and gear is 3.5 then FLC set speed is 217. When the driver reduces the speed (i.e.) braking is 4, state of charge (SOC) is 0.9,load is 10, driver speed average is 200 and gear is 3.5 then FLC set speed is 187.Case 2- Terrain is Rough (1.5) : When the driver raises the speed (i.e.) acceleration average is 2.5, state of charge (SOC) is 0.9,load is 10, driver speed average is 200 and gear is 3.5 then FLC set speed is 175. When the driver reduces the speed (i.e.) braking is 4.5, state of charge (SOC) is 0.9,load is 10, driver speed average is 200 and gear is 3.5 then FLC set speed is 150.Case 3- Terrain is Up Hill (2.5) : When the driver raises the speed (i.e.) acceleration average is 2.5, state of charge (SOC) is 0.5,load is 50, driver speed average is 120 and gear is 2.5 then FLC set speed is 111. When the driver reduces the speed (i.e.) braking are 2.5, state of charge (SOC) is

0.5,load is 50, driver speed average is 120 and gear is 2.5 then FLC set speed is 89.Case 4 - Terrain is Down Hill (3.5): When the driver raises the speed (i.e.) acceleration average is 1, state of charge (SOC) is 0.79, load is10, driver speed average is 50 and gear is 1.5 then FLC set speed is 71. When the driver reduces the speed (i.e.) braking is 2.5, state of charge (SOC) is 0.79,load is 10, driver speed average is 50 and gear is 1.5 then FLC set speed is 60.4. Simulation ResultsIn this section, the simulation realization of modeled electric Vehicle system with the assistance of Mat lab Simulink.[3]Table 2. Result

Terrain Vehicle Speed

Motor Speed

(Rad/sec)

Motor Current(Amps)

Smooth 155 170 29-31Rough 100 130 29-31Up Hill 50 70 29-31Down Hill 30 47 29-31

The table 2. clearly depicts the result of motor speed and vehicle speed for the corresponding FLC output. 5. CONCLUSIONIn this paper, the capability of the fuzzy logic based controller for the electric vehicle has been presented. This fuzzy logic controller optimizes the major components of the electric vehicle like (1) acceleration average (2) braking command (3) state of charge of the battery (4) speed average (5) load (6) terrain of the situation (7) gear of the vehicle. Taking into consideration all the parameters, the flc predicts the set speed of the vehicle at which it can run. The fuzzy logic controller ensures that the driving conditions are satisfied consistently, the battery is sufficiently charged at all times, and all the other parameters are satisfied and maintained properly to keep the vehicle in the running mode. In future research, the robustness of the fuzzy logic controller will be investigated. Adaptive/learning elements will be added to the controller to enable on-line controller optimization and moreover, flc can also be implemented in the fpga model to run in real time situations.

References

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[3] Karen L.Butler,Mehrdad Ehsani, Preyas Kamath, "A Matlab-Based Modeling and Simulation Package for Electric and Hybrid Electric Vehicle Design”, IEEE Transactions on Vehicular technology,Vol.48,No.6,pp1770-1778,Nov 1999.

[4] Niels J.Schouten, Mutasim A. Salman, and Naim A. Kheir , “Fuzzy Logic Control for Parallel Hybrid Vehicles” IEEE Transactions on Control Systems Technology, Vol 10, No. 3, pp 460-468, May 2002.

[5] Philip d.olivier,”Feedback linearization of dc motors”, IEEE transactions on industrial electronics,vol 38,no 6,pp 498-501,dec 1991

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[7]Tien-Chi-Chen,Tsong-TerngSheu, “Model Reference Neural Network Controller for Induction Motor Speed Control “,IEEE Transactions on Energy Conversion,Vol 17,No 2,June 2002.

List of Abbreviations Used.

SA – Speed Average;VL – Very Low VSlow – Very Slow ;BR – Braking; L – Low ;AA – Acceleration Average ; LM – Low Medium ; M – Medium ; H – High ;VH-Very High