# Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller
## 2 Materials and Methods
### Fuzzy Logic Control  
Fuzzy control derives its strategy as per variation in system parameters as shown in \"Figure 3\". The input and output variable relationship plays an important role in the derivation of rules in the fuzzy logic controller. Therefore, the careful tuning of membership functions improves system performance in diverse realizations. FLC translates a problem with ambiguous information into a well-defined solution. Logic describes the whole functionality within the domain of a mathematically described algorithm [28]. FLC implements the logic derived from the problem statement as if-then rules [29]. General-purpose nonlinear control problems are addressed with the application of fuzzy sets [30].  
Electric scooter left and right gear rapid speed action is controlled by FLC. The electronic differential system sends commands to the FLC for enhanced speed action of the left and right gear [31]. The EV range is estimated by test data implemented on FLC. The FLC predicts the distance covered by EV and maintains the instantaneous consumption of power while the battery state-of-charge remains high for long-distance [32]. The comparison of the P-I controller with FLC for speed variations highlights the robust behavior of FLC. Fuzzy controller approaches reference speed parameter with better transient and steady-state response while maintaining stability [33]. The speed control error in direct torque control and indirect field-oriented control is smaller in FLC as compared with the P-I controller [34]. Hybrid electric vehicle (HEV) energy management fuzzy-control strategy  
Figure 2: Simulink model P–I speed controller with torque saturator.  
is designed for load torque demand [35; 36]. EV based on FLC with vehicle speed and accumulator pressure is considered to optimize speed control operation [37]. A real-life application of the fuzzy model is observed in urban traffic control. The model highlights considerable decay in the length of the traffic queue as compared to a traditional fixed-time control approach [38].  
Advancements in a modern smart system with increased demand for the control of small features has created complications for orthodox methods which are not able to maintain optimum performance for complex control mechanisms. Conversely, real-life applications of the control systems are not easy to formulate correctly due to practical constraints. The exact relationship between inputs and outputs is ambiguous because of the contributions of external factors. To control such complex systems, the latest control approaches are considered to neglect external factors with good precision. A fuzzy control algorithm is one of the modern control approaches designed for complex feedback control systems. The fundamental parts of a fuzzy controller are the fuzzification interface, knowledge base, inference engine, and defuzzification interface.  
Equations (1) and (2) describe the P-I controller for control action. Indirect vector control is further segregated into a P-I controller-based speed controller developed in Simulink as shown in \"Figure 4\". User-defined reference speed is applied to the input of the P-I speed controller. Reference speed is subtracted from the measured speed and an error signal is generated for control action. The gain of proportional and integral gains are tuned at Kp = 15 and Ki = 30. Speed controller generates torque signal. The hysteresis band current regulator compares the current signal generated from the torque command with a three-phase current measured at the input of IM. The regulator supplies six gating pulses to the IGBT inverter. \"Figure 3\" elaborates the flowchart of the Mamdani fuzzy logic controller (MFLC). The indirect vector control of the induction machine model developed in Simulink was elaborated in \"Figure 5\". The vector control block consists of a fuzzy logic controller.  
Figure 3: Simulink model flow chart of Mamdani fuzzy logic controller.