Fuzzy based Direct Torque Control of Induction Motor for Electric Vehicles

Abstract

Introduction: The growing emphasis on sustainable transportation, driven by climate change awareness, is accelerating the adoption of electric vehicles (EVs). A critical challenge is the precise control of induction motors (IMs) used in EVs. Traditional control methods like Field Oriented Control (FOC) and Direct Torque Control (DTC) suffer from parameter sensitivity and high torque ripple, reducing efficiency. This research proposes a Fuzzy DTC scheme to address these limitations.

Objectives: The primary objectives of this research are to develop and implement a Fuzzy-based Direct Torque Control (DTC) scheme for Induction Motor (IM) drives in Electric Vehicles (EVs), specifically designed to overcome the limitations of conventional DTC methods. This entails achieving a significant reduction in torque ripple, a common issue in traditional DTC, which directly impacts the smoothness and efficiency of the EV's operation. Furthermore, the research aims to enhance the dynamic response of the IM drive system, enabling faster and more precise control of the motor's torque and speed, crucial for the dynamic driving conditions experienced by EVs. Ultimately, the successful implementation of the Fuzzy DTC scheme should lead to an overall improvement in the efficiency and robustness of the IM speed control within the EV system, ensuring reliable and high-performance operation across all driving scenarios, including acceleration, deceleration, and constant speed maintenance.

Methods: The methodology employed in this research centers around the development and implementation of a Fuzzy-based Direct Torque Control (DTC) scheme for Induction Motor (IM) drives. Departing from traditional DTC, which relies on hysteresis bands and a switching table, this approach integrates a Fuzzy Logic Switching Controller (FLSC) to optimize inverter switching decisions. The FLSC takes as inputs the torque error, stator flux error, stator flux angle, and the count of switching updates, providing a more refined control mechanism. A Mamdani fuzzy inference system (FIS) is utilized, employing triangular and trapezoidal membership functions to fuzzify these input variables. The output of the fuzzy controller dictates the switching state, selected from seven possible states represented by crisp triangular membership functions. This fuzzy logic-based approach allows for a more nuanced and adaptive control strategy, enabling the system to respond effectively to the nonlinearities and uncertainties inherent in IM drives. The fuzzy rules, developed based on engineering expertise and practical experience, guide the selection of the optimal switching state. The research leverages simulations using MATLAB/Simulink to model the IM drive system and evaluate the performance of both conventional and Fuzzy DTC schemes under various operating conditions. This allows for a comparative analysis of torque ripple, dynamic response, and overall efficiency, validating the effectiveness of the proposed fuzzy-based control strategy.

Results: The simulation results presented in this paper demonstrate the superior performance of the proposed Fuzzy-based Direct Torque Control (DTC) scheme compared to conventional DTC methods for Induction Motor (IM) drives in Electric Vehicles (EVs). Across various operating conditions, including different load and speed combinations, the Fuzzy DTC consistently exhibited a significant reduction in torque ripple. This reduction translates to a smoother and more efficient motor operation, crucial for enhancing the driving experience and overall performance of EVs. Furthermore, the Fuzzy DTC showed improved dynamic response, characterized by lower overshoot and faster settling times. These findings indicate that the fuzzy logic-based control strategy enables more precise and rapid control of the IM's torque and speed, effectively addressing the limitations of traditional DTC. Specifically, the data presented in Table 3 and Figures 18, 19, and 20 highlight the quantifiable improvements in parameters such as torque ripple percentage, slew rate, and overshoot. The comparative analysis consistently favored the Fuzzy DTC scheme, validating its effectiveness in achieving robust and efficient IM speed control under the dynamic operating conditions typical of electric vehicles.

Conclusions: This paper has investigated the application of fuzzy based DTC to induction motor (IM) drives in electric vehicles (EVs). The proposed Fuzzy DTC approach addresses the limitations of conventional technique of DTC, including high ripple of torque by integrating fuzzy logic into the control scheme. Simulation results show the proposed Fuzzy DTC effectively achieves precise and robust speed control under various EV operating conditions. The approach optimizes switching decisions based on fuzzy rules, resulting in improved performance compared to traditional DTC methods. The proposed Fuzzy DTC scheme offers reduced torque ripple, improved efficiency, enhanced dynamic performance, and a smoother driving experience.