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- Title
基于 LSTM 车速预测和深度确定性策略梯度的增程式 电动汽车能量管理.
- Authors
路来伟; 赵红; 徐福良; 罗勇
- Abstract
In order to improve the energy management of Range Extended Electric Vehicle (REEV), firstly Long ShortTerm 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 hardwarein-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.
- Subjects
RANGE management; DEEP reinforcement learning; REINFORCEMENT learning; ENERGY management; REAL-time control; HYBRID electric vehicles
- Publication
Automobile Technology, 2024, Issue 8, p27
- ISSN
1000-3703
- Publication type
Article
- DOI
10.19620/j.cnki.1000-3703.20231093