Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

Energies

卷:10

Xiaohong Jiao¹  Zitao Sun¹  Ping Li¹  Yuying Wang¹ 

[1] Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;

关键词: plug-in hybrid electric vehicle (PHEV);    battery health;    energy management strategy;    stochastic dynamic programming (SDP);    particle swarm optimization (PSO);   

DOI  :  10.3390/en10111894

来源: DOAJ

【 摘 要 】

This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.

【 授权许可】

Unknown