Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

Lihe Xi,Chuanyang Sun,Jibao Zhang,Xin Zhang,Xiaosen Hou,Zexing Wang

doi:10.3390/en10111871

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.

Highlights

Air pollution in large cities is predominantly caused by the exhaust emissions of gasoline vehicles [1]
With known driving distance information, the battery state of charge (SOC) controlled by IEMC_NN can just reach the lower bound as the extended range electric vehicle (EREV) arrives at the charging station, which was feasible when the driver changed the destination during the trip
According to the description of the IEMC_NN in Section 4, the IEMC_NN can intelligently select an NN controller based on the target driving distance information

Summary

Introduction

Air pollution in large cities is predominantly caused by the exhaust emissions of gasoline vehicles [1]. In order to achieve the goals of low-carbon green cities, it is necessary to transform traditional fuel vehicles into new energy vehicles [2,3]. The electric vehicle plays an important role in this transition, with its advantages of no pollution or emissions. Technical limitations, such as low battery power and short driving range, significantly affect the promotion of electric vehicles [4]. Extended range electric vehicles (EREVs) have the characteristics of low emissions and pollution, and extend the endurance mileage of vehicles [5].

Objectives

Results

Conclusion