Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles
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 en...
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6298 |
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| author | Dingyi Guo Guangyin Lei Huichao Zhao Fang Yang Qiang Zhang |
| author_facet | Dingyi Guo Guangyin Lei Huichao Zhao Fang Yang Qiang Zhang |
| author_sort | Dingyi Guo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4ac6f5b5064e406eb4076b40433a3e1c |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-4ac6f5b5064e406eb4076b40433a3e1c2025-08-20T02:00:41ZengMDPI AGEnergies1996-10732024-12-011724629810.3390/en17246298Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric VehiclesDingyi Guo0Guangyin Lei1Huichao Zhao2Fang Yang3Qiang Zhang4Institute of Future Lighting, Academy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaInstitute of Future Lighting, Academy for Engineering and Technology, Fudan University, Shanghai 200433, ChinaGeneral Research and Development Institute, China FAW Corporation Limited, Changchun 130011, ChinaGeneral Research and Development Institute, China FAW Corporation Limited, Changchun 130011, ChinaGeneral Research and Development Institute, China FAW Corporation Limited, Changchun 130011, ChinaThis 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.https://www.mdpi.com/1996-1073/17/24/6298energy management strategyreinforce learningdouble deep Q-networkquadruple deep Q-network |
| spellingShingle | Dingyi Guo Guangyin Lei Huichao Zhao Fang Yang Qiang Zhang Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles Energies energy management strategy reinforce learning double deep Q-network quadruple deep Q-network |
| title | Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles |
| title_full | Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles |
| title_fullStr | Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles |
| title_full_unstemmed | Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles |
| title_short | Quadruple Deep Q-Network-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicles |
| title_sort | quadruple deep q network based energy management strategy for plug in hybrid electric vehicles |
| topic | energy management strategy reinforce learning double deep Q-network quadruple deep Q-network |
| url | https://www.mdpi.com/1996-1073/17/24/6298 |
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