Intelligent energy management control for extended range electric vehicles based on dynamic programming and neural network

Abstract

The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC-NN) is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC-NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC-NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC-NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC-NN had better fuel economy than the charging deplete/charging sustain (CD/CS) algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC-NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.