IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0313303.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0313303
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0313303&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0313303?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zeyu Chen & Rui Xiong & Kunyu Wang & Bin Jiao, 2015. "Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(5), pages 1-18, April.
    2. Changqing Du & Shiyang Huang & Yuyao Jiang & Dongmei Wu & Yang Li, 2022. "Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 15(12), pages 1-25, June.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    3. Lihua Ye & Zixing Zhang & Qinglong Zhao & Xu Zhao & Zhou He & Aiping Shi, 2025. "Research on Energy Management Strategies for Fuel Cell Hybrid Vehicles Based on Time Classification," Energies, MDPI, vol. 18(8), pages 1-29, April.
    4. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    5. Lin, Xinyou & Zhou, Qiang & Tu, Jiayi & Xu, Xinhao & Xie, Liping, 2024. "Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles," Applied Energy, Elsevier, vol. 376(PA).
    6. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    7. Enyong Xu & Mengcheng Ma & Weiguang Zheng & Qibai Huang, 2023. "An Energy Management Strategy for Fuel-Cell Hybrid Commercial Vehicles Based on Adaptive Model Prediction," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    8. Hsiu-Ying Hwang & Jia-Shiun Chen, 2020. "Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization," Energies, MDPI, vol. 13(9), pages 1-18, May.
    9. Tong, He & Chu, Liang & Zhang, Yuanjian & Zhao, Di & Hu, Jincheng & Xie, Zhihao & Liu, Ming, 2024. "Towards sustainable high-speed cruising: Optimizing energy efficiency of plug-in hybrid electric vehicle via intelligent pulse-and-glide strategy," Energy, Elsevier, vol. 311(C).
    10. Matthieu Matignon & Toufik Azib & Mehdi Mcharek & Ahmed Chaibet & Adriano Ceschia, 2023. "Real-Time Integrated Energy Management Strategy Applied to Fuel Cell Hybrid Systems," Energies, MDPI, vol. 16(6), pages 1-21, March.
    11. Zheng, Weiguang & Ma, Mengcheng & Xu, Enyong & Huang, Qibai, 2024. "An energy management strategy for fuel-cell hybrid electric vehicles based on model predictive control with a variable time domain," Energy, Elsevier, vol. 312(C).
    12. Benmouna, Amel & Becherif, Mohamed & Depernet, Daniel & Ebrahim, Mohamed A., 2018. "Novel Energy Management Technique for Hybrid Electric Vehicle via Interconnection and Damping Assignment Passivity Based Control," Renewable Energy, Elsevier, vol. 119(C), pages 116-128.
    13. Ons Sassi & Ammar Oulamara, 2017. "Electric vehicle scheduling and optimal charging problem: complexity, exact and heuristic approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 519-535, January.
    14. Konrad Prajwowski & Wawrzyniec Golebiewski & Maciej Lisowski & Karol F. Abramek & Dominik Galdynski, 2020. "Modeling of Working Machines Synergy in the Process of the Hybrid Electric Vehicle Acceleration," Energies, MDPI, vol. 13(21), pages 1-20, November.
    15. Shantanu Pardhi & Sajib Chakraborty & Dai-Duong Tran & Mohamed El Baghdadi & Steven Wilkins & Omar Hegazy, 2022. "A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions," Energies, MDPI, vol. 15(24), pages 1-55, December.
    16. Li Zhang & Wenfang Zhang & Jinxin Liu & Tong Zhao & Liang Zou & Xinghua Wang, 2017. "A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network," Energies, MDPI, vol. 10(12), pages 1-13, December.
    17. Jingxian Hao & Zhuoping Yu & Zhiguo Zhao & Peihong Shen & Xiaowen Zhan, 2016. "Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm," Energies, MDPI, vol. 9(12), pages 1-24, November.
    18. Li, Shuangqi & He, Hongwen & Zhao, Pengfei, 2021. "Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective," Energy, Elsevier, vol. 230(C).
    19. Laura Zecchi & Giulia Sandrini & Marco Gadola & Daniel Chindamo, 2022. "Modeling of a Hybrid Fuel Cell Powertrain with Power Split Logic for Onboard Energy Management Using a Longitudinal Dynamics Simulation Tool," Energies, MDPI, vol. 15(17), pages 1-18, August.
    20. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0313303. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.