In this paper, an artificial intelligence (AI)-integrated direct torque control (DTC) scheme is developed for an electric vehicle (EV or eCAR) propulsion motor drive. In addition, a comparison is made between adaptive neural network (ANN) and genetic algorithm (GA)-based torque controllers. The integration of AI into EVs has attracted the attention of many researchers in terns if drive control, dynamic stability, speed estimation, and energy management strategies. Amidst the various motor drive control strategies, DTC schemes with space vector pulse width modulation (SVPWM) have gained prominence due to its fast torque (speed) control capability. The smooth control of a DTC-eCAR propulsion motor is accomplished by the use of AI algorithms. The applications of ANN and GA algorithms for tuning the torque controller are tested and the behavior of an eCAR in terms of drive range, percentage of state of charge (SOC), and energy consumption for different driving conditions is observed using MATLAB simulations.

在本文中，为电动汽车（EV 或 eCAR）推进电机驱动开发了一种人工智能 (AI) 集成直接扭矩控制 (DTC) 方案。此外，还对基于自适应神经网络 (ANN) 和基于遗传算法 (GA) 的转矩控制器进行了比较。将人工智能集成到电动汽车中，在驱动控制、动态稳定性、速度估计和能源管理策略等领域引起了许多研究人员的关注。在各种电机驱动控制策略中，具有空间矢量脉宽调制 (SVPWM) 的 DTC 方案因其快速的转矩（速度）控制能力而备受瞩目。DTC-eCAR 推进电机的平稳控制是通过使用 AI 算法实现的。