Abstract
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.
Highlights
As the energy crisis and climate deterioration become increasingly serious problems, plug-in hybrid electric vehicles (PHEVs) have been regarded as one of the research focuses for how to reduce petroleum demand and exhaust emission
For PHEVs, recent studies have shown that the cost of the whole vehicle is highly sensitive to battery health
These mappings will be embedded in the energy management control system of PHEV in actual operation, and the management control strategy is generated online by looking up the mappings according to the current system states such as states of charge (SOC), power demand and road information
Summary
As the energy crisis and climate deterioration become increasingly serious problems, PHEVs have been regarded as one of the research focuses for how to reduce petroleum demand and exhaust emission. In order to improve the fuel economy of the vehicle, a wide range of optimal control methods has been proposed to solve the energy management problem of PHEVs. Examples include rule-based control [1], deterministic dynamic programming [2], stochastic dynamic programming [3], the equivalent consumption minimization strategy (ECMS) [4], Pontryagin’s minimum principle [5]. When the battery life is attenuated to a certain extent, the performance of the battery will be changed This could lead to different results in the energy management and even may cause the pre-designed controller to fail to achieve the desired target [13]. In order to predict the dynamics of the battery cell more accurately, reduced electrochemical battery models are proposed Based on these models, the optimization control strategies with the consideration of battery health are developed.
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