TY - JOUR
T1 - Energy management strategy on a parallel mild hybrid electric vehicle based on breadth first search algorithm
AU - Hao, Lei
AU - Wang, Ying
AU - Bai, Yuanqi
AU - Zhou, Qiongyang
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/9/1
Y1 - 2021/9/1
N2 - 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.
AB - 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.
KW - Breadth first search
KW - Energy management strategy
KW - Equivalent consumption minimization strategy
KW - Global optimization
KW - Hybrid electric vehicle
UR - https://www.scopus.com/pages/publications/85108090119
U2 - 10.1016/j.enconman.2021.114408
DO - 10.1016/j.enconman.2021.114408
M3 - 文章
AN - SCOPUS:85108090119
SN - 0196-8904
VL - 243
JO - Energy Conversion and Management
JF - Energy Conversion and Management
M1 - 114408
ER -
