基于DL-MOPSO算法的等效燃油消耗最小能量管理策略优化研究*

doi:10.19562/j.chinasae.qcgc.2018.09.002

摘要/Abstract

摘要： 等效燃油消耗最小能量管理策略(ECMS)的优化问题,是一个不连续、非可导的内外层嵌套多目标优化问题,为进一步提高整车燃油经济性,同时使电池具有良好的电量保持性能,提出一种内外层嵌套的双层多目标粒子群算法(DL-MOPSO)对充放电等效因子和功率分配方式同时进行寻优。仿真结果表明,与传统的穷举法相比,DL-MOPSO算法寻优获得的ECMS可提高整车燃油经济性10.28%,且SOC终值与目标值差为0.001 9,有效保持电量平衡。最后分析了惩罚函数中β参数对ECMS寻优的影响,对β参数的取值具有一定指导意义。

关键词: 等效燃油消耗最小策略, 能量管理, 双层粒子群算法, 惩罚函数

Abstract: The optimal design of equivalent fuel consumption minimization strategy (ECMS) is for discontinuous and non-derivable multi-objective optimization. In order to improve the vehicle fuel economy and realize good power retention performance of the battery, a novel double-loop multi-objective particle swarm optimization (DL-MOPSO) algorithm is proposed to optimize the charging and discharging equivalent factor and power allocation mode simultaneously. Simulation results show that compared with the traditional exhaustion method, the ECMS obtained by the DL-MOPSO algorithm can improve the vehicle fuel economy by 10.28%, and the difference between the SOC final value and the target value is reduced to 0.0019, effectively maintaining power balance. Finally, the influence of the parameter β in penalty function on ECMS optimization is analyzed, which is of guiding significance to parameter selection

Key words: equivalent fuel consumption minimization strategy, energy management, double-loop multi-objective particle swarm optimization, penalty function

石琴,仇多洋,吴冰,刘炳姣,陈一锴. 基于DL-MOPSO算法的等效燃油消耗最小能量管理策略优化研究^*[J]. 汽车工程, 2018, 40(9): 1005-1013.

Shi Qin, Qiu Duoyang, Wu Bing, Liu Bingjiao & Chen Yikai. A Research on Equivalent Fuel Consumption Minimization Strategy Optimization#br# Based on Double-loop Multi-objective Particle Swarm Optimization Algorithm[J]. , 2018, 40(9): 1005-1013.

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基于DL-MOPSO算法的等效燃油消耗最小能量管理策略优化研究^*

A Research on Equivalent Fuel Consumption Minimization Strategy Optimization#br# Based on Double-loop Multi-objective Particle Swarm Optimization Algorithm

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