Fuzzy Logic Controller and Its Application in Brushless DC Motor (BLDC) in Electric Vehicle-A Review

Brushless DC motor (BLDC) is one type of electric motor that is widely used, especially in automotive systems. This motor is widely used as a driving force in electric vehicles. BLDC motor is chosen because it has the characteristics of high efficiency, reliability, and a wide speed range. Besides, BLDC motors require less maintenance and can operate quieter than DC motors. Even though it has many advantages, in its application the use of BLDC motors in electric vehicles is often less than optimal. The use of a conventional control system proportional integral derivative (PID) still has many weaknesses, especially in response to changes in load and track conditions. In this study, a control system was designed to regulate the speed of the BLDC motor, using a combination of Fuzzy and PID methods. Based on the results of the tests that have been done, the Fuzzy-PID control can provide better and more stable performance than using the conventional PI control. Keywords— Fuzzy Logic, Brushless DC motor (BLDC), proportional integral derivative (PID)


I. INTRODUCTION
The use of electric vehicles in this increasingly modern era is a must. The current transportation system uses more fossil fuels, whose sources are depleting and causing pollution to the environment. The increasing awareness of the environment and attention to fossil fuels in the future has encouraged research on alternative energy sources [1]. One of the results of this research is the development and marketing of electric vehicles for the public. Electric vehicles are an alternative transportation system that is environmentally friendly and reliable in supporting community mobility.
Electric vehicles move by using electrical energy to rotate the motor. Unlike conventional vehicle systems, the driving motor used in electric vehicles is an electric motor. One type of electric motor that is often used is a brushless DC motor (BLDC). BLDC motor is chosen because it has the characteristics of high efficiency, reliability, and a wide speed range [2]. These types of DC motors do not have brushes and commutators, so BLDC motors require less maintenance and can operate quieter than DC motors [3].
BLDC motors move by utilizing 3-phase alternating (AC) electrical energy. To be able to operate, this motor requires a motor drive to control the rotational speed. BLDC motor drives use an inverter for the commutation process in the brush [4]. Unlike conventional DC motors, BLDC motors require electronic commutator switching in the form of a converter for proper operation [5]. In operation, the BLDC motor requires information about the position of the rotor for the correct change in each phase of the inverter [6]. This information can be obtained with a hall effect sensor attached to the BLDC motor to monitor the position of the rotor.
Even though it has many advantages, in its application the use of BLDC motors in electric vehicles is often less than optimal. Variations in setpoint and dynamic load conditions must be considered in electric vehicles [7]. In conventional control systems, these two aspects cannot be applied in controlling electric vehicles. This makes the electric vehicle system unable to provide maximum performance. To overcome this problem, several studies have been carried out to produce an optimal control system for electric vehicles. One system that is often used is fuzzy logic control. In this paper, there will be a review of several studies that have been carried out regarding the application of fuzzy logic in BLDC motors in electric vehicles.

A. BLDC motor
BLDC motor is a type of synchronous motor that uses a type of permanent magnet in its rotor. This motor is included in the direct current (DC) motor category, but uses a 3-phase alternating (AC) power source to supply to move the rotor. One of the differences between a BLDC motor and a conventional DC motor lies in the addition of the phase which affects the overall result of the BLDC model [8]. The schematic of a brushless DC motor model can be seen in the following figure [8]: The dynamic equation for modeling a BLDC motor can be seen in the following equation [9]: Where Van, Vbn, and Vcn are the voltages of each phase on the stator and R is the stator resistance in each phase. Ia, ib, and ic are the phase currents in the stator, M is the mutual inductance and L is the armature inductance. Meanwhile ea, eb, and ec is the back EMF of each phase, which can be calculated using the formula below [9]: The mathematical model of the BLDC motor used in this study can be observed in table 1.

B. Basic Fuzzy Logic
Fuzzy logic is a branch of Artificial Intelligence (AI) that has been used since 1965 until now. Fuzzy is still chosen because of its reliability to solve complex and nonlinear problems, its flexibility to various problems and can be combined with other control methods to produce a more optimal system [10] [2]. Fuzzy logic uses basic rules to produce fuzzy output, namely the IF-THEN rule, where IF is the antecedent and THEN is the consequence [11]. In the fuzzy method, there are 4 main components, namely [2]:

Fuzzification
Fuzzification is a stage to change the input from crisp (definite) variables to fuzzy variables based on the membership function and determine the degree of each crisp input to the fuzzy set [10]. The input membership function in this study can be observed in table 2:   Fuzzy Inference System (FIS) contains a set of rules used for decision making. This sequence is the result of a combination of the input membership function, which produces several rules as in the following table:

De-Fuzzification
Defuzzification produces control decisions to set the best non-fuzzy output value that represents the control decision from different rules [10]. To calculate the crisp thickness value, the center of area (COA) method is used with the following formula [10]: Where µ is the total number of rules, µ (∆Cemi) is the membership class for rule I, and ∆Cemi is the singleton position in rule-i.

C. PID Control
PID control is composed of 3 main parameters, namely proportional (P), integral (I), and derivative (D) [12]. PID control has the characteristics of increasing the rise time, reducing the steady-state error, and reducing oscillations [7]. The block diagram of the PID control can be seen in the following figure: Where Kp is proportional gain, Kd is derivative gain and Ki is integral gain.

D. Fuzzy Application on BLDC Motors
To increase the reliability of the electric vehicle system, several studies have been carried out by designing an electric vehicle control system. One of the most developed control methods is fuzzy logic. The research was conducted by designing a fuzzy-based control system that is implemented in electric vehicles. The fuzzy control system is also combined with several other methods to increase the efficiency, performance, and accuracy of the system to produce a more optimal electric vehicle. Some of these studies can be seen in the The proposed Fuzzy-PID control system can produce the desired output when compared to the usual PID control.
The proposed system is proven to be efficient, high resilience, and easy to implement in sensor applications.
[20] The design of a BLDC motor speed control system using online self-tuning fuzzy PID. The type of motor drive used is an Outer-rotor brushless DC motor.

The
proposed system can produce an overshoot of 1.5% and an error of speed of 3 r / min compared to conventional PID with an overshoot of 3% and an error of 6 r / min. The system can adjust PID parameters in real-time, reducing overshoot and increasing dynamic performance.
[21] The design of the BLDC motor speed control system uses the fuzzy-PID method based on vector control. Using a BLDC motor with a number of poles 1, R = 2.875 Ω, Ld = Lq = 0.835e -3 H, J = 0.8e -3 kg.m 3 The proposed fuzzy-PID control system can provide a good BLDC motor performance. The test was carried out by providing a variation of the set points of 5000 rpm and 3000 rpm. This system can reduce overshoot, This system also provides good and stable dynamic performance.

E. Fuzzy-PID based BLDC Motor Speed Control
This research was conducted using MATLAB / Simulink software. The plant of this circuit is a 48V 1kW brushless DC motor with a maximum speed of 700 rpm. The design of the series of this project is as shown below. In this circuit, 3 Fuzzy Logic Control blocks are used, each of which has the same input, namely error, and delta-error. While the output of each fuzzy block is Kp, Ki, and Kd. This output is then processed using the PID control to regulate the output voltage from the power source that supplies the BLDC motor. The block diagram of the Fuzzy-PID control can be seen in the following figure:    The simulation is carried out by providing a speed setpoint input of 650 rpm. The simulation was carried out by bending 2 methods, namely Fuzzy-PID Logic and the conventional Pi method which was carried out for 1 second. The simulation results can be seen in the following graph:

Membership function for Kd
A. Simulation with Fuzzy-PID control 1. Rotor Speed    Based on the review of 23 papers that have been conducted, several control systems are obtained that combine the fuzzy method with other methods to control the speed of a BLDC motor on an electric vehicle. This combination is intended to obtain an optimal, effective and reliable BLDC motor control system. Of the several proposed methods, the combined method that has the highest usage quantity is the fuzzy-PID method. Fuzzy-PID is considered to provide good control performance, which has many advantages such as: reducing overshoot, minimizing speed errors, increasing control precision. Seeing this fact, in this study, the author has tested the BLDC motor speed control system using the fuzzy-PID method.

Stator Current and back EMF
From the test results, it can be observed that the Fuzzy-PID control can provide a good response to reach the desired set point. By using the Fuzzy-PID method, steady-state speed can be achieved at 0.3 seconds without significant overshoot and fluctuation. While the conventional PI method can reach a steady-state point at 0.2 seconds but with very high fluctuations and experiencing overshoot up to 830 V.
For electromagnetic torque, using the Fuzzy-PID method produces a stable torque graph, where the torque decreases with the increase in speed of the motor. The torque reaches a constant point at 5 Nm when the motor reaches a steady-state speed. Meanwhile, the PI method provides an unstable graph of torque with a high enough fluctuation and an overshoot of up to 35 Nm before the steady-state speed is reached.
The current and reverse voltage resulting from the use of Fuzzy-PID is quite stable during the motor operation. Meanwhile, if you use the PI method, there are fluctuations in the current and reverse voltage EMF on the stator. This resulted in system instability and resulted in a lot of losses.
Based on the results of the tests that have been done, the Fuzzy-PID control can provide better and more stable performance than using the conventional PI control. The use of Fuzzy-PID control can reduce speed fluctuation and torque stability so that the BLDC motor can operate more efficiently and reliably.