Title: Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS

Authors: Shaopeng Tian; Qingxing Zheng; Wenbin Wang; Qian Zhang

Addresses: School of Automotive Engineering, Wuhan University of Technology, Wuhan, 430070, China ' School of Automotive Engineering, Wuhan University of Technology, Wuhan, 430070, China ' School of Automotive Engineering, Wuhan University of Technology, Wuhan, 430070, China ' School of Automotive Engineering, Wuhan University of Technology, Wuhan, 430070, China

Abstract: PHEVs have become one of the best market-oriented and industrialised technological routes in the automotive sector owing to fuel economy. To maximise the energy-saving potential of PHEVs, this study proposes an integrated real-time optimal strategy for a "P2+P4" PHEV. First, a rule-based mode-switching strategy was devised based on driving conditions. Second, an offline framework was established to optimise the equivalent factors (EFs) based on the firefly algorithm (FA). A novel EF adaptation law was then proposed based on the SOC feedback and duration of CD mode. Here, AECMS was employed to achieve optimal power allocation during CS mode. Finally, comparative simulations indicate that this PHEV can operate in CD mode for 55 km and 42.66 km under NEDC and WLTP, respectively. In CS mode, FA-AECMS has an approximate global optimal performance and a better charge-sustaining capability. Furthermore, the feasibility of the proposed strategy was validated using a drum experiment.

Keywords: plug-in hybrid electric vehicle; AECMS; adaptive equivalent consumption minimisation strategy; equivalent factors optimisation; firefly algorithm; a novel EF adaptation law.

DOI: 10.1504/IJVD.2024.136239

International Journal of Vehicle Design, 2024 Vol.94 No.1/2, pp.150 - 175

Received: 29 Apr 2022
Received in revised form: 05 Jun 2023
Accepted: 12 Jun 2023
Published online: 23 Jan 2024 *