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

The energy management strategy used to split the energy flow among different energy resources of hybrid electric vehicles plays a critically important role in achieving fuel economy. Additionally, battery degradation and high production cost lead to the necessary consideration of the battery lifetime in the energy management strategy design for a plug-in hybrid electric vehicle (PHEV). This paper investigates the PHEV energy management problem taking into consideration battery lifetime on how to distribute power between the engine and the electric equipment during the driving cycle to achieve the whole economy for a commuter PHEV. Shortest path stochastic dynamic programming (SP-SDP) is employed to address this energy management problem, which is formulated as a stochastic optimal control problem with the minimization of a weighted combination of the fuel and electricity consumption and the battery degradation rate for a stochastic process model with the statistic characteristics captured from the historical traffic speed profiles. The solution of this optimization problem, derived from a modified policy iteration algorithm, is a time-invariant, state-dependent power split strategy, which can be directly applied on the actual running vehicle. Simulation results carried on a PHEV Prius model in MATLAB/Simulink environment over some driving cycles are presented to demonstrate the effectiveness of the proposed energy management strategy.

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