An Adaptive Equivalent Consumption Minimization Strategy for Plug-In Hybrid Electric Vehicles Based on Energy Balance Principle

Abstract

Energy management strategies can directly determine the dynamic performance and fuel economy of plug-in hybrid electric vehicles (PHEVs). In this paper, an adaptive equivalent consumption minimization strategy (A-ECMS) is proposed based on the energy balance principle of the hybrid powertrain of the target vehicle, by which a pair of boundary equivalent factors can be determined according to the future transportation information. Then, the equivalent factor is calculated in real time based on the energy variation in the powertrain system during the operation. Consequently, the torque distribution between the engine and the motor can be determined by solving the Hamilton function according to the dynamically adjusted equivalent factor, and thus, the energy management control is adaptively realized. The simulations were conducted considering three typical driving conditions, different battery aging statuses, and inaccurate road information. The results manifest that the proposed algorithm is feasible to improve the fuel economy with attainable adaptivity and robustness compared with the typical ECMS.

Keywords

• Plug-in hybrid electric vehicles (PHEVs)
• adaptive equivalent fuel consumption minimum strategy (A-ECMS)
• energy balance principle
• equivalent factor
• probability factor