Adaptive energy management strategy for plug-in hybrid electric vehicles based on intelligent recognition of driving cycle

Abstract

In order to enhance the adaptability of energy management strategy (EMS) to complex and changeable driving cycles, this paper proposed an adaptive energy management strategy (A-EMS) based on intelligent recognition of driving cycle (IRDC) by the back propagation neural network (BPNN) and genetic algorithm (GA). Firstly, BPNN is employed to design IRDC. Secondly, the equivalent fuel consumption minimization strategy (ECMS) is derived based on Pontryagin's minimum principle (PMP). Then, GA is used to optimize the MAP of the initial equivalent factor (EF) with the initial state of charge (SOC) and mileage. At the same time, the SOC penalty function and the velocity penalty function are employed to modify the initial EF, and then an adaptive minimum equivalent fuel consumption strategy (A-ECMS) is established. Finally, A-ECMS strategy model based on IRDC is modelled by the Matlab/Simulink software, and its model control effect is verified. Simulation results show that compared with ECMS strategy, A-ECMS strategy can maintain high fuel economy under complex driving cycles, and improve the vehicle's fuel economy up to 3%.