Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization

Pei Zhang, Xianpan Wu, Changqing Du, Hongming Xu and Huawu Wang
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Pei Zhang: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
Xianpan Wu: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
Changqing Du: Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
Hongming Xu: Department of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UK
Huawu Wang: Dongfeng Commercial Vehicle Technical Center of DFCV, Wuhan 430056, China

Energies, 2020, vol. 13, issue 20, 1-20

Abstract: The accurate determination and dynamic adjustment of key control parameters are challenges for equivalent consumption minimization strategy (ECMS) to be implemented in real-time control of hybrid electric vehicles. An adaptive real-time ECMS is proposed for hybrid heavy-duty truck in this paper. Three efforts have been made in this study. First, six kinds of typical driving cycle for hybrid heavy-duty truck are obtained by hierarchical clustering algorithm, and a driving condition recognition (DCR) algorithm based on a neural network is put forward. Second, particle swarm optimization (PSO) is applied to optimize three key parameters of ECMS under a specified driving cycle, including equivalent factor, scale factor of penalty function, and vehicle speed threshold for engine start-up. Finally, combining all the above two efforts, a novel adaptive ECMS based on DCR and key parameter optimization of ECMS by PSO is presented and validated through numerical simulation. The simulation results manifest that proposed adaptive ECMS can further improve the fuel economy of a hybrid heavy-duty truck while keeping the battery charge-sustainability, compared with ECMS and PSO-ECMS under a composite driving cycle.

Keywords: hybrid heavy-duty vehicle; particle swarm optimization; equivalent consumption minimum strategy; driving condition recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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