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An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles

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  • Hou, Daizheng
  • Sun, Qun
  • Bao, Chunjiang
  • Cheng, Xingqun
  • Guo, Hongqiang
  • Zhao, Ying

Abstract

The interaction between components and energy management is an obstacle for the fuel economy improvement of plug-in hybrid electric buses (PHEBs). Although the systematic method of design, optimization and energy management is recognized as a promising solution, two problems including the robust design of component matching considering the influence of uncertain driving cycles and the integrated design of energy management are still need to be solved. This paper proposes a novelty all-in-one method to address the issues. Firstly, a typical driving cycle construction method for city bus route is proposed to solve the first problem, based on a series of historical driving cycles. Secondly, a co-optimization framework including an outer layer constituted by multi-island genetic algorithm, and an inner layer constituted by dynamic programming (DP) is proposed, to find the best component matching. Specially, in the outer layer, two engines and six motors are taken as discrete design variables, meanwhile, the speed ratios of AMT and final driver are taken as continuous design variables; in the inner layer, the fuel consumption is taken as the objective of the outer layer. Finally, a receding horizon control (RHC)-based energy management strategy together with a predictive model of terminal state of charge (SOC) are proposed to solve the second problem, based on the same DP algorithm in the co-optimization framework. Simulation results demonstrate that the proposed all-in-one method can find the robust component matching; the RHC strategy can realize the real-time control, and its fuel economy is better than the rule-based strategy.

Suggested Citation

  • Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:107
    DOI: 10.1016/j.apenergy.2019.113499
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    References listed on IDEAS

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    Cited by:

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    4. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    5. Cipek, Mihael & Kasać, Josip & Pavković, Danijel & Zorc, Davor, 2020. "A novel cascade approach to control variables optimisation for advanced series-parallel hybrid electric vehicle power-train," Applied Energy, Elsevier, vol. 276(C).
    6. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    7. Wang, Jing & Kang, Lixia & Liu, Yongzhong, 2020. "Optimal scheduling for electric bus fleets based on dynamic programming approach by considering battery capacity fade," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).

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