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

Abstract

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

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

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