Experimental Research on Adaptive Equivalent Consumption Minimization Strategy Based on Traffic Information

Energy management strategy (EMS) is one of the key technologies of plug-in hybrid electric vehicle (PHEV). In this paper, an adaptive equivalent consumption minimization strategy (A-ECMS) based on traffic information is proposed to tackle problem of poor environmental adaptability with the traditional equivalent consumption minimization strategy (ECMS). Firstly, the initial equivalent factors (EFs) of different initial state of charge (SOC) and driving distance are adopted by genetic algorithm (GA). Furtherly, a traffic information based dynamic programming (DP) is utilized to obtain the reference SOC trajectory. The adaptive correction of the initial EF is realized by using the proportion integral (PI) controller with reference SOC value as control target. Ultimately, an energy management experiment platform of PHEV based on virtual scene is constructed to validate the proposed strategy. The results show that the proposed strategy is superior to the ECMS without traffic information, with a total consumption reduction by 4.9%.