Hierarchical operation switch schedule algorithm for energy management strategy of hybrid electric vehicle using adaptive dynamic programming

被引:6
|
作者
Li, Fangyuan
Gao, Lefei
Zhang, Yubo [1 ]
Liu, Yanhong
机构
[1] Zhengzhou Univ, Sch Elect & Informat Engn, Zhengzhou 450001, Henan, Peoples R China
来源
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Hybrid electric vehicle (HEV); Energy management; Adaptive dynamic programming (ADP); Optimal switch schedule; Neural networks; POWER-SPLIT; HEVS;
D O I
10.1016/j.segan.2023.101107
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The cost-benefit of the hybrid electric vehicle (HEV) is strongly dependent on the operation of the energy management system (EMS). In this paper, we propose a novel algorithm named hierarchical operation switch schedule (HOSS) algorithm to solve the optimal operation problem in energy management of the HEV. The original energy management problem of EMS is reconstructed as an optimal switch schedule problem of the engine and motor. In the reconstruction, the time -dependent switch cost of the engine and motor is integrated, which is normally considered difficult and challenging for dynamic programming (DP). The proposed HOSS algorithm can get the optimal switch schedule of the engine and motor of the HEV with a closed-loop feature. In this algorithm, once the neural network is trained, it can cope with different initial conditions and disturbances with no need to retrain. In comparison, re-optimization is usually required for other optimization-based strategies when initial conditions vary. We also compare the HOSS algorithm with other algorithms by conducting simulations, and the results show the unique feature and effectiveness of the HOSS algorithm. & COPY; 2023 Published by Elsevier Ltd.
引用
收藏
页数:10
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