RESEARCH ON ELECTRIC VEHICLE MOTOR DRIVE CONTROL STRATEGIES BASED ON FUZZY-PID ALGORITHM AND GA-FUZZY-PID ALGORITHM

Abstract

With the rapid development of technology and economy, energy and environmental issues have become increasingly prominent. On one hand, global oil resources are becoming scarce, and the import and transportation costs of oil are high, resulting in soaring oil prices. On the other hand, the total number of vehicles worldwide is increasing dramatically, leading to severe emissions from fuel-powered vehicles and the proliferation of smog and other severe weather conditions. To address these problems, many countries have implemented supportive policies in the field of new energy, particularly in the development of electric vehicles (EVs). However, the speed control performance and range of electric vehicle drive systems are currently not satisfactory. Therefore, this paper focuses on the research of the brushless DC motor (BLDC) drive technology for electric vehicles. By building upon traditional control algorithms, the paper proposes a genetic optimization fuzzy PID algorithm and a fuzzy adaptive neural network control algorithm for the drive system, effectively improving the performance of the motor drive.