Optimization method of electric vehicle energy system based on machine learning

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.