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Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization

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  • Zhihao Li
  • Ping Xiao
  • Jiabao Pan
  • Wenjun Pei
  • Aoning Lv

Abstract

In order to solve the problem of poor adaptability and robustness of the rule-based energy management strategy (EMS) in hybrid commercial vehicles, leading to suboptimal vehicle economy, this paper proposes an improved dung beetle algorithm (DBO) optimized multi-fuzzy control EMS. First, the rule-based EMS is established by dividing the efficient working areas of the methanol engine and power battery. The Tent chaotic mapping is then used to integrate strategies of cosine, Lévy flight, and Cauchy Gaussian mutation, improving the DBO. This integration compensates for the traditional dung beetle algorithm’s tendency to fall into local optima and enhances its global search capability. Subsequently, fuzzy controllers for the driving charging mode and hybrid driving mode are designed under this rule-based EMS. Finally, the improved DBO is used to obtain the optimal control of the fuzzy controller by taking the fuel consumption of the whole vehicle and the fluctuation change of the battery state of charge (SOC) as the optimization objectives. Compared to traditional rule-based energy management strategies, the optimized fuzzy control using the enhanced DBO continuously adjusts the torque distribution between the engine and motor based on the vehicle’s real-time state, resulting in a 9.07% reduction in fuel consumption and a 3.43% decrease in battery SOC fluctuations.

Suggested Citation

  • Zhihao Li & Ping Xiao & Jiabao Pan & Wenjun Pei & Aoning Lv, 2025. "Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-27, January.
    • Handle: RePEc:plo:pone00:0313303
    DOI: 10.1371/journal.pone.0313303
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    References listed on IDEAS

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    3. Xue, Jiaqi & Jiao, Xiaohong & Yu, Danmei & Zhang, Yahui, 2023. "Predictive hierarchical eco-driving control involving speed planning and energy management for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 283(C).
    4. Haicheng Zhou & Zhaoping Xu & Liang Liu & Dong Liu & Lingling Zhang, 2018. "A Rule-Based Energy Management Strategy Based on Dynamic Programming for Hydraulic Hybrid Vehicles," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, October.
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