4.7 Article

Energy management strategy on a parallel mild hybrid electric vehicle based on breadth first search algorithm

期刊

ENERGY CONVERSION AND MANAGEMENT
卷 243, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2021.114408

关键词

Hybrid electric vehicle; Energy management strategy; Global optimization; Breadth first search; Equivalent consumption minimization strategy

资金

  1. Fundamental Research Funds for the Central Universities [xtr012019002]

向作者/读者索取更多资源

The paper presents a new application of the BFS algorithm in the EMS of HEVs, showcasing the potential of global optimization and real-time control in reducing fuel consumption. The results demonstrate significant improvements and potential for future research in this area.
Global optimization plays an important role in the energy management strategies (EMS) of the hybrid electric vehicles (HEV). The fuel consumption of HEV could be reduced significantly with an acceleration of global optimization and application of global result in real-time control. In this paper, a new algorithm called breadth first search (BFS) was first used to realize the global optimization in a parallel mild HEV, which transforms the energy management problem of HEV into optimal path searching. Through simulation and calculation, it was found that the totally identical control strategies and fuel consumption could be obtained with BFS and dynamic programming (DP) respectively, while the calculation time for BFS was just about 50%-60% of that. With BFS results as reference, particle swarm optimization was used to adjust the equivalent factor in real-time and an adaptive equivalent consumption minimization strategy (A-ECMS) based on BFS was proposed. The fuel consumption could be decreased with the proposed A-ECMS by 8-15% in different driving cycles compared with that using rule-based strategies. It is believed that BFS has great potential in the future research on EMS of the HEVs.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据