A Dynamic ECMS Method Considering Vehicle Speed Pattern and Minimum Engine Operation Time for a Range-Extender Electric Vehicle (Jan. 2022)

Abstract

A new equivalent consumption minimization strategy (ECMS) is proposed for a hybrid electric vehicle in which the equivalent factor varies dynamically with the current speed and the minimum engine operation time that leads to improvement in vehicle fuel economy and battery performance. To construct the strategy, a self-organizing map neural-network was utilized to recognize and group the speed patterns. The driving cycles corresponding to these speed patterns were reconstructed for solving the corresponding optimal equivalent factors. When applying the strategy on the road, the current vehicle speed pattern is recognized by using the historical vehicle speed data, and the corresponding optimal equivalent factor is adopted based on that pattern. Furthermore, to constrain the battery state of charge (SOC) to avoid exceeding the allowable values and to consider the hysteresis of engine starting, a fuzzy logic controller was used to dynamically adjust the minimum engine operation time. The simulated results indicated that the proposed ECMS improved the vehicle fuel economy by 7.87% and 6.23% over the conventional ECMS and the rule-based single-point strategy, respectively. Furthermore, the peak battery charging-current was reduced by 42.94% and 27.73%, and the peak discharging-current by 62.19% and 56.97%, respectively. In addition, the proposed strategy could lead to better SOC evolution of the battery.