Real-Time Performance Validation With Hardware-in-the-Loop and Energy Optimization of Electric Vehicle Motor Drives Using Fuzzy Logic Controller

Abstract

As the world advances toward energy decarbonization, efficient energy management in electric vehicle motors is critical for extending range, efficiency, and advancing sustainability. This study evaluates a six-phase permanent magnet synchronous motor using a fuzzy-logic-based adaptive proportional-integral-derivative (PID) tuning control algorithm to optimize energy consumption. Comparative analysis of traditional PID and fuzzy logic controllers highlights the superior performance in drive cycle tracking and energy efficiency of fuzzy. Experiments across switching frequencies (1kHz, 10kHz, 20kHz) and samples of 20s and 35s reveal a notable 22.19% energy consumption reduction with the fuzzy logic controller. Drive cycle tests (FTP75 and HWFET) confirm its advantage in energy efficiency and tracking accuracy, particularly under low switching frequencies. This adaptive strategy dynamically tunes PID parameters in real-time, responding to varying conditions and enhancing motor control. Results are validated using a high-fidelity testbed integrating an STM32F7 microcontroller and a Speedgoat hardware-in-the-loop system. The findings underscore the potential of fuzzy logic controllers to improve the dynamic response and energy efficiency of electric vehicle motors, supporting the transition to more sustainable mobility solutions and contributing to global decarbonization efforts.