With the aim of economy improvement, emission reduction and prolonging the battery service life, an adaptive parameter optimal energy management strategy is proposed for range extended electric vehicle and a method of multi-objective optimization (MOO) is proposed. Firstly, two strategies based on different threshold parameter types, namely velocity-switch-based multi-operation-point control strategy (MCS_v–b) and power-switch-based multi-operation-point control strategy (MCS_p–b) are designed. Then, the oil-electric conversion loss rate, comprehensive exhaust emission, and battery capacity loss rate are selected as the optimization objectives. The barebones multi-objective particle swarm optimization is applied in MCS_v–b and MCS_p–b for solving the MOO problem. The simulation results show a clear conflict that three optimization objectives cannot be optimal under the same solution. And then, the individual with optimal comprehensive objective is taken as the final optimization solution to evaluate the performance of the proposed methodology. As expected, the proposed MCS_p–b has a positive effect on prolonging the battery service life while ensuring high fuel economy and low emission. Experimental test results thoroughly validate the proposed approach and this result can be used to improve comprehensive performance levels.

以提高经济性、减排和延长电池使用寿命为目的，提出了一种适用于增程式电动汽车的自适应参数优化能量管理策略，并提出了多目标优化（MOO）方法。首先，基于不同阈值参数类型的两种策略，即基于速度切换的多操作点控制策略（MCS _v–b）和基于功率切换的多操作点控制策略（MCS _p–b）是设计的。然后选择油电转换损失率、尾气综合排放和电池容量损失率作为优化目标。准系统多目标粒子群优化应用于 MCS _v-b和 MCS _p-b用于解决 MOO 问题。仿真结果表明，三个优化目标在同一个解下不可能是最优的，存在明显的冲突。然后，将具有最优综合目标的个体作为最终优化解来评估所提出方法的性能。正如预期的那样，所提出的 MCS _p-b对延长电池使用寿命同时确保高燃油经济性和低排放具有积极影响。实验测试结果彻底验证了所提出的方法，该结果可用于提高综合性能水平。