Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-in Hybrid Electric Vehicles

插件 能源管理 汽车工程 电信 工程类 计算机科学 能量(信号处理) 电气工程 业务 物理 操作系统 量子力学
作者
Dingyi Guo,Guangyin Lei,Huichao Zhao,Fang Yang,Qiang Zhang
出处
期刊:Energies [Multidisciplinary Digital Publishing Institute]
卷期号:17 (24): 6298-6298
标识
DOI:10.3390/en17246298
摘要

This study proposes the use of a Quadruple Deep Q-Network (QDQN) for optimizing the energy management strategy of Plug-in Hybrid Electric Vehicles (PHEVs). The aim of this research is to improve energy utilization efficiency by employing reinforcement learning techniques, with a focus on reducing energy consumption while maintaining vehicle performance. The methods include training a QDQN model to learn optimal energy management policies based on vehicle operating conditions and comparing the results with those obtained from traditional dynamic programming (DP), Double Deep Q-Network (DDQN), and Deep Q-Network (DQN) approaches. The findings demonstrate that the QDQN-based strategy significantly improves energy utilization, achieving a maximum efficiency increase of 11% compared with DP. Additionally, this study highlights that alternating updates between two Q-networks in DDQN helps avoid local optima, further enhancing performance, especially when greedy strategies tend to fall into suboptimal choices. The conclusions suggest that QDQN is an effective and robust approach for optimizing energy management in PHEVs, offering superior energy efficiency over traditional reinforcement learning methods. This approach provides a promising direction for real-time energy optimization in hybrid and electric vehicles.
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