A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method. (19th August 2020)

• Record Type: Journal Article
• Authors: Deng, Tao; Tang, Peng; Luo, JunLin
• Other Names: Nižetić Sandro guestEditor.
• Is Part Of: International journal of energy research. Volume 45:Number 1(2021)
• Page Start: 626
• Page End: 641
• Publication Date: 2020-08-19
• Subjects: Adaptive equivalent consumption minimum strategy (A‐ECMS) -- equivalent factor (EF) -- fast searching method -- hardware‐in‐loop (HIL) -- plug‐in hybrid electric vehicle (PHEV); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.5794
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: The universal adaptive equivalent consumption minimization strategy (A‐ECMS) has the potential of being implemented in real‐time for plug‐in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a long‐term future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real‐time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real‐time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A‐ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a three‐dimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A‐ECMS, the computational time of A‐ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware‐in‐loop results prove that the proposed novel energy management strategy A‐ECMS2 has better real‐time performance and less computing burden than the general A‐ECMS. Abstract : 1. To eliminate the sensitivity of velocity to the updating of EF, a novel PI adaption law for calculating the dynamic optimal EF is proposed using instantaneous information of current speed and SOC. 2. A fast searching method of optimal engine torque based on its characteristic parameters is utilized to improve computational efficiency and control performance of A‐ECMS.