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DDPG Energy Management of Extended-Range Electric Vehicle Based on LSTM Speed Prediction
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Laiwei Lu, Hong Zhao, Fuliang Xü, Yong Luo
Automobile Technology | 2024, (8) : 27 - 37
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Automobile Technology | 2024, (8): 27-37
DDPG Energy Management of Extended-Range Electric Vehicle Based on LSTM Speed Prediction
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Laiwei Lu, Hong Zhao, Fuliang Xü, Yong Luo
Affiliations
  • Qingdao University, Qingdao 266071
Published: 2024-08-24 doi: 10.19620/j.cnki.1000-3703.20231093
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In order to improve the energy management of Range Extended Electric Vehicle (REEV), firstly Long Short-Term Memory (LSTM) neural network was used to predicate vehicle speed, then calculates the demand power in the prediction time domain, and the demand power in the prediction time domain and the demand power at the current moment were jointly inputted to the Deep Deterministic Policy Gradient (DDPG) agent, which outputted the control quantity. Finally, the hardware-in-the-loop simulation was carried out to verify the real-time performance of the control strategy. The validation results show that using the proposed LSTM-DDPG energy management strategy reduces the equivalent fuel consumption by 0.613 kg, 0.350 kg, and 0.607 kg compared to the DDPG energy management strategy, the Deep Q-Network (DQN) energy management strategy, and the power-following control strategy, respectively, under the World Transient Vehicle Cycling (WTVC) conditions, which is only 0.128 kg different from that of the dynamic planning control strategy when the dynamic planning control strategy is used.

Extended-range electric vehicle  /  Long Short-Term Memory (LSTM) neural network  /  Deep Reinforcement Learning (DRL)  /  Deep Deterministic Policy Gradient (DDPG)
Laiwei Lu, Hong Zhao, Fuliang Xü, Yong Luo. DDPG Energy Management of Extended-Range Electric Vehicle Based on LSTM Speed Prediction[J]. Automobile Technology, 2024 , (8) : 27 -37 . DOI: 10.19620/j.cnki.1000-3703.20231093
Year 2024 volume Issue 8
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doi: 10.19620/j.cnki.1000-3703.20231093
  • Online Date:2025-12-22
  • Published:2024-08-24
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    Qingdao University, Qingdao 266071
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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