Research on Intelligent Algorithm for Optimization Control Strategy of Drive Motor of New Energy Vehicle

This paper presents an optimal control strategy for a new type of EV, which combines reinforcement learning with DNN. In this strategy, a reinforcement learning algorithm is introduced, and a deep neural network is proposed for adaptive motor control. Based on various driving conditions, it can adjust the motor's control parameters in real-time, so as to optimize the power output, the energy efficiency and the stability of the motor. The proposed optimization control strategy is superior to traditional control methods in multiple performance indicators. Specifically, the control strategy based on intelligent algorithm improves energy efficiency by 15%, motor response speed by 20%, and system stability by 18%. Through the reinforcement learning model, the motor can dynamically adjust the control strategy according to real-time feedback during driving, which improves the adaptability of the motor under complex working conditions. Moreover, the adaptive learning capability of this model makes it possible to make rapid optimization under changing circumstances, thus guaranteeing the high efficiency of the motor. This research provides a new type of intelligent optimization solution for the new energy vehicle's driving motor, which is particularly suited to handle complicated driving conditions and to improve the performance of the vehicle.