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

Dingyi Guo (), Guangyin Lei (), Huichao Zhao, Fang Yang and Qiang Zhang
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Dingyi Guo: Institute of Future Lighting, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
Guangyin Lei: Institute of Future Lighting, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
Huichao Zhao: General Research and Development Institute, China FAW Corporation Limited, Changchun 130011, China
Fang Yang: General Research and Development Institute, China FAW Corporation Limited, Changchun 130011, China
Qiang Zhang: General Research and Development Institute, China FAW Corporation Limited, Changchun 130011, China

Energies, 2024, vol. 17, issue 24, 1-21

Abstract: 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.

Keywords: energy management strategy; reinforce learning; double deep Q-network; quadruple deep Q-network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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