Designing Intelligent, Data-Driven Motor Control Strategies for Electric Vehicles Using Advanced Deep Learning Architectures

Abstract

Electric Vehicles (EVs) require efficient and adaptive motor control strategies to ensure optimal performance under various driving conditions. Traditional control methods lack the ability to dynamically adapt to such conditions, leading to suboptimal efficiency and ride quality. This paper presents the design and implementation of intelligent, data-driven motor control strategies that leverage advanced Deep Learning (DL) architectures, particularly long short-term memory (LSTM) networks and Convolutional Neural Networks (CNNs). The proposed control strategy dynamically adjusts the motor input parameters based on real-time sensor data and predicted torque demands. The simulation results show significant improvements in energy efficiency and torque ripple reduction compared to traditional PID and Field-Oriented Control (FOC) strategies.