# Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller
## 1 Introduction
### Motivation and Research Gap  
The majority of the research work on the design of the fuzzy logic controllers is focused on flexibility, robustness, control blocks, computation elements, and separate control-driven systems [11]. However, the tuning of the fuzzy logic controller is achieved by input/output scaling factors on (GA, PSO, Cuckoo) optimization algorithms, respectively, [6]. ANFIS  
Figure 1: Phasor diagram of indirect vector control [10].  
-based FOPID shows superior speed controller performance with robustness against internal and external disturbances [8; 12]. Adaptive Takagi-Sugeno-Kang-fuzzy speed controller simulation presents highly robust behavior as compared to traditional control systems [13]. The simulation results of the fuzzy logic controller (FLC) based proportional-integral model waveform shows better settling time and lower peak overshoot [14]. Fuzzy regulator model performance for driverless EV enhances driving speed control in different road scenarios [15; 16]. The FLC controller depicts better in terms of system stability in dynamic driving situations. Mamdani fuzzy interface system (MFIS) can be considered to model an indirect vector control system by taking datasets generated from the P-I speed controller-based indirect vector control EV model. The model was trained on MFIS to develop a logical relationship between the input and output variables. MFIS implements a superior speed controller compared to the traditional fuzzy logic control system (FLCS). It is capable of implementing the same trained model on multiple driving scenarios and abruptly adopt changes. Considering the rise in initial torque, rise time, and overshoot in EV response, a superior speed control solution using FLC at discrete speed levels to visualize smooth drive is necessary.  
The objectives of the research work presented in this paper are as follows:  
* Model development of IM-based indirect vector control of EV in MATLAB (Simulink) with P-I and MFIS speed controllers. Moreover, EV model performance was analyzed in three different speed modes (40, 60, 80 km/h);
* Antecedent and consequent member functions were designed in Mamdani fuzzy inference system (MFIS). The MFIS-based EV model was anticipated to improve time-domain performance indices. The capability of the controller to sustain robustness, reject external disturbance and parameter variation was verified. The controller displays improvement in speed tracking which eliminates the overshoot, and lowers the values of time-domain characteristics.
* Comparison of the proposed speed controller performance parameters with proportional-integral derivative (PID), fuzzy logic control algorithm (FLC), ant colony optimization (ACO), particle swarm optimization (PSO), and genetic algorithm (GA)-based fuzzy FOPID controllers.  
The paper was arranged in the following order: Section 2 highlights the background of studies conducted for the modeling of the electric vehicle P-I and MFLC speed control operation. Section 3 gives a fuzzy MFLC speed control structure with the design information of two-dimensional membership functions and a rule base. This explains the mathematical formulation of the P-I and MFLC control model designed in MATLAB (Simulink). Both models were tested for the same operational parameters. Section 4 presents a detailed analysis of P-I and MFLC waveforms. Furthermore, the MFLC model is compared with existing FLC and fuzzy FOPID optimization algorithms for speed transitions, speed mode tracking, and uncertainties. Conclusion and future prospects are discussed in Section 5.