期刊
WORLD ELECTRIC VEHICLE JOURNAL
卷 13, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/wevj13070110
关键词
plug-in hybrid electric vehicle; hybrid energy storage system; particle swarm optimization; component sizing; all electric range; cost
资金
- Applied Technology R&D Projects of Beilin District in 2020 [GX2012]
- Natural Science Basic Research Program of Shaanxi [2021JM-363]
This paper proposes an approach based on the particle swarm optimization algorithm to simultaneously determine the sizes of the engine, motor, battery, and supercapacitor in a plug-in hybrid electric vehicle (PHEV) with a hybrid energy storage system (HESS). The effects of battery type and drive cycle on the optimization results are analyzed and compared.
For plug-in hybrid electric vehicle (PHEV), using a hybrid energy storage system (HESS) instead of a single battery system can prolong the battery life and reduce the vehicle cost. To develop a PHEV with HESS, it is a key link to obtain the optimal size of the power supply and energy system that can meet the load requirements of a driving cycle. Since little effort has been dedicated to simultaneously performing the component sizing of PHEV and HESS, this paper proposes an approach based on the particle swarm optimization (PSO) algorithm to simultaneously determine the sizes of the engine, motor, battery and supercapacitor (SC) in a PHEV with HESS. The drivetrain cost is minimized in a different all-electric range (AER)-and depends on the battery type-while ensuring the driving performance requirements. In addition, the effects of the power system and drive cycle on the component sizes were analyzed and compared. The simulation results show that the cost of the PHEV drivetrain with the Ni-MH battery/SC HESS is reduced by up to 12.21% when compared to the drivetrain with the Li-ion battery/SC HESS. The drivetrain cost is reduced by 8.79% when compared to analysis-based optimization. The type of power supply system and drive cycle can significantly affect the optimization results.
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