Deep Reinforcement Learning Algorithm based PMSM Motor Control for Energy Management of Hybrid Electric Vehicles

Abstract

Hybrid electric vehicles (HEV) have great potential to reduce emissions and improve fuel economy. The application of artificial intelligence-based control algorithms for controlling the electric motor speed and torque yields excellent fuel economy by reducing the losses drastically. In this paper, a novel strategy to improve the performance of an electric motor-like control system for Permanent Magnet Synchronous Motor (PMSM) with the help of a sensorless vector control method where a trained reinforcement learning agent is used and provides accurate signals which will be added to the control signals. Control Signals referred to here are direct and quadrature voltage signals with reference quadrature current signals. The types of reinforcement learning used are the Deep Deterministic Policy Gradient (DDPG) and Deep Q Network (DQN) agents. Integration and implementation of these control systems are presented, and results are published in this paper. The advantages of the proposed method over the conventional vector control strategy are validated by numerical simulation results.