Optimization method of electric vehicle energy system based on machine learning

Abstract

Introduction: To enhance energy management in electric vehicles (EVs), this study proposes an optimization model based on reinforcement learning. Methods: The model integrates gated recurrent units (GRU) with double deep Q-networks (DDQN) to improve time-series data processing and action value estimation. Results: Results show that the model achieves the lowest estimation bias (0.017 in training, 0.018 in testing) and the highest cumulative reward (97.1) among all compared methods. In real-world highway scenarios, it records the lowest total energy consumption at 14.2 kWh, achieving a range of 503 km and an energy efficiency of 87.6%. Discussion: These findings suggest that the proposed model offers a more efficient and reliable solution for EV energy optimization with strong application potential.