Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses.

—The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS. • An advanced equivalent consumption minimization strategy with segmented equivalent factor (EF) optimization is proposed. • The rational reference state of charge (SOC) trajectory is established using current operating conditions. • The EF is optimized by the grey wolf optimization (GWO) algorithm as to track the reference SOC. • A correction coefficient is created to maximize the superior EF for various road segments within the same cycle. [ABSTRACT FROM AUTHOR]