ORIGINAL RESEARCH article
Front. Mech. Eng.
Sec. Engine and Automotive Engineering
Volume 11 - 2025 | doi: 10.3389/fmech.2025.1597558
Optimization Method of Electric Vehicle Energy System Based on Machine Learning
Provisionally accepted
- Shanghai Jianqiao University, Shanghai, China
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To enhance energy management in electric vehicles (EVs), this study proposes an optimization model based on reinforcement learning. The model integrates gated recurrent units (GRU) with double deep Q-networks (DDQN) to improve time-series data processing and action value estimation. 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%. These findings suggest that the proposed model offers a more efficient and reliable solution for EV energy optimization with strong application potential.
Keywords: Electric Vehicles, GRU, DQN, Energy optimization, Reinforcement learning 1. Introduction
Received: 21 Mar 2025; Accepted: 16 Jul 2025.
Copyright: © 2025 Ren. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Huanmei Ren, Shanghai Jianqiao University, Shanghai, China
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