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Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization

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  • Zhou, Quan
  • Zhang, Wei
  • Cash, Scott
  • Olatunbosun, Oluremi
  • Xu, Hongming
  • Lu, Guoxiang

Abstract

This paper firstly proposed a novel HEV sizing method using the Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm and secondly provided a demonstration on sizing a series hybrid electric powertrain with investigations of chaotic mapping strategies to achieve the global optimization. In this paper, the intelligent sizing of a series hybrid electric powertrain is formulated as an integer multi-objective optimization issue by modelling the powertrain system. The intelligent sizing mechanism based on APSO is then introduced, and 4 types of the most effective chaotic mapping strategy are investigated to upgrade the standard APSO into CAPSO algorithms for intelligent sizing. The evaluation of the intelligent sizing systems based on standard APSO and CAPSOs are then performed. The Monte Carlo analysis and reputation evaluation indicate that the CAPSO outperforms the standard APSO for finding the real optimal sizing result with much higher reputation, and CAPSO with logistic mapping strategy is the most effective algorithm for HEV powertrain components intelligent sizing. In addition, this paper also performs the sensitivity analysis and Pareto analysis to help engineers customize the intelligent sizing system.

Suggested Citation

  • Zhou, Quan & Zhang, Wei & Cash, Scott & Olatunbosun, Oluremi & Xu, Hongming & Lu, Guoxiang, 2017. "Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization," Applied Energy, Elsevier, vol. 189(C), pages 588-601.
    • Handle: RePEc:eee:appene:v:189:y:2017:i:c:p:588-601
    DOI: 10.1016/j.apenergy.2016.12.074
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    7. Anselma, Pier Giuseppe, 2022. "Electrified powertrain sizing for vehicle fleets of car makers considering total ownership costs and CO2 emission legislation scenarios," Applied Energy, Elsevier, vol. 314(C).
    8. He, Yinglong & Wang, Chongming & Zhou, Quan & Li, Ji & Makridis, Michail & Williams, Huw & Lu, Guoxiang & Xu, Hongming, 2020. "Multiobjective component sizing of a hybrid ethanol-electric vehicle propulsion system," Applied Energy, Elsevier, vol. 266(C).
    9. Huang, Yanjun & Wang, Hong & Khajepour, Amir & Li, Bin & Ji, Jie & Zhao, Kegang & Hu, Chuan, 2018. "A review of power management strategies and component sizing methods for hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 132-144.
    10. Fei Wang & Lidong Zhou & Hui Ren & Xiaoli Liu, 2017. "Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimizat," Energies, MDPI, vol. 10(12), pages 1-23, November.
    11. Li, Ji & Zhou, Quan & He, Yinglong & Shuai, Bin & Li, Ziyang & Williams, Huw & Xu, Hongming, 2019. "Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
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    13. Zhang, Cetengfei & Zhou, Quan & Hua, Min & Xu, Hongming & Bassett, Mike & Zhang, Fanggang, 2023. "Cuboid equivalent consumption minimization strategy for energy management of multi-mode plug-in hybrid vehicles considering diverse time scale objectives," Applied Energy, Elsevier, vol. 351(C).

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