AER adaptive control strategy via energy prediction for PHEV

Abstract

The electrical energy of a plug-in hybrid electric vehicle (PHEV) is provided by the internal combustion engine and grid power. The fuel consumption of a PHEV can be minimised through optimising the operation at all-electric range (AER). The AER may vary with the state of charge (SOC), the expected route characteristic, traffic and the electrical energy available dominated by the forthcoming charge opportunity. This research proposes an AER adaptive energy management strategy based on the equivalent consumption minimisation strategy (ECMS) and the forthcoming energy consumption prediction. The model of the equivalent factor (EF) is developed based on the required energy per unit distance (REPD). The corresponding correction factor of EF is optimised with the particle swarm optimisation and developed as a function of REPD, SOC and the AER. The artificial neural network is used to predict REPD which is applied to update the EF estimated model online. The proposed strategy is validated by the numerical simulation and hardware-in-loop experiment (HIL). The simulation and HIL experiment results demonstrate that the proposed strategy can further improve the fuel economy of PHEVs when compared with the traditional ECMS under different driving cycles.