IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v331y2025ics0360544225026556.html

General mathematical characterization and configuration optimization design method for a multi-mode hybrid power system

Author

Listed:
  • Chen, Shuang
  • Shanqi, Guo
  • Hu, Minghui

Abstract

Current offline optimization design methods for hybrid power system (HPS) configurations can only explore the energy-saving potential of specific configurations (e.g., single-planetary gear HPSs or dual-planetary HPSs) through topological derivation. To address this issue, this study investigates three fundamental decoupling forms of HPSs and derives a mapping relationship between seven basic configurations and 127 multi-mode HPS configurations. Additionally, we establish a general mathematical characterization method for multi-mode HPSs. Based on this foundation, an optimization design method for multi-mode HPS configurations is developed. Furthermore, the effects of the configuration type and parameters on fuel economy are explored. It was found that the increase in hybrid modes improves energy-saving potential, but this benefit plateaus once the number of hybrid modes reaches three. Finally, using this method as a theoretical basis, configuration optimization design is performed for two specific cases, resulting in a multi-objective optimal solution set containing nine multi-mode HPS configurations, achieving the forward design of a multi-mode HPS configuration structure, and indicating the effectiveness of this method as a novel approach to facilitate the rapid development of multi-mode HPS configurations.

Suggested Citation

  • Chen, Shuang & Shanqi, Guo & Hu, Minghui, 2025. "General mathematical characterization and configuration optimization design method for a multi-mode hybrid power system," Energy, Elsevier, vol. 331(C).
    • Handle: RePEc:eee:energy:v:331:y:2025:i:c:s0360544225026556
    DOI: 10.1016/j.energy.2025.137013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225026556
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.137013?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Yalian & Li, Pengshuai & Pei, Huanxin & Zou, Yunge, 2022. "Design of all-wheel-drive power-split hybrid configuration schemes based on hierarchical topology graph theory," Energy, Elsevier, vol. 242(C).
    2. Zhou, Xingyu & Qin, Datong & Hu, Jianjun, 2017. "Multi-objective optimization design and performance evaluation for plug-in hybrid electric vehicle powertrains," Applied Energy, Elsevier, vol. 208(C), pages 1608-1625.
    3. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    4. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    5. Anselma, Pier Giuseppe & Biswas, Atriya & Belingardi, Giovanni & Emadi, Ali, 2020. "Rapid assessment of the fuel economy capability of parallel and series-parallel hybrid electric vehicles," Applied Energy, Elsevier, vol. 275(C).
    6. Kapetanović, Marko & Núñez, Alfredo & van Oort, Niels & Goverde, Rob M.P., 2021. "Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trains," Applied Energy, Elsevier, vol. 294(C).
    7. Chen, Shuang & Hu, Minghui & Guo, Shanqi, 2023. "Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles," Energy, Elsevier, vol. 273(C).
    8. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    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. Sun, Xiaodong & Dong, Ziyin & Cai, Yingfeng & Jin, Zhijia & Lei, Gang & Tian, Xiang, 2025. "A comprehensive review of design optimization methods for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).
    2. Zou, Yunge & Yang, Yalian & Zhang, Yuxin & Liu, Changdong, 2024. "Computationally efficient assessment of fuel economy of multi-modes and multi-gears hybrid electric vehicles: A hyper rapid dynamic programming approach," Energy, Elsevier, vol. 313(C).
    3. Zhou, Xingyu & Zhao, Nianhan & Huang, Youliang & Guo, Yuekai, 2025. "Graph deep-learning method for integrated topological and parametric optimization of hybrid electric vehicle powertrain," Energy, Elsevier, vol. 340(C).
    4. 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).
    5. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    6. Xiang Tian & Ma Wan & Xinqiang Chen & Yingfeng Cai & Xiaodong Sun & Zhen Zhu, 2025. "Driver-Oriented Adaptive Equivalent Consumption Minimization Strategy for Plug-in Hybrid Electric Buses," Energies, MDPI, vol. 18(18), pages 1-18, September.
    7. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    8. Zhou, Xingyu & Guo, Yuekai & Hao, Wenmei & Zhang, Xingyong & Wang, Wenwei, 2025. "Kinematic state adaptive based novel powertrain for the next generation of vehicle electrification: Design optimization and simplification," Applied Energy, Elsevier, vol. 395(C).
    9. Ji, Xinzhe & Sun, Kangwen & Liang, Haoquan & Guo, Xiao & Yang, Xixiang & Shan, Chuan, 2025. "Energy management in hybrid energy system for long-duration stratospheric airship mission," Energy, Elsevier, vol. 324(C).
    10. Tang, Wenbin & Jiao, Xiaohong & Zhang, Yahui, 2025. "Hierarchical energy management control for connected hybrid electric vehicles in uncertain traffic scenarios," Energy, Elsevier, vol. 315(C).
    11. Chen, Shuang & Hu, Minghui & Lei, Yanlei & Kong, Linghao, 2023. "Novel hybrid power system and energy management strategy for locomotives," Applied Energy, Elsevier, vol. 348(C).
    12. Fan Wang & Yina Hong & Xiaohuan Zhao, 2025. "Research and Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles: A Review," Energies, MDPI, vol. 18(11), pages 1-28, May.
    13. Pier Giuseppe Anselma, 2022. "Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health," Energies, MDPI, vol. 15(5), pages 1-25, February.
    14. Chen, Shuang & Hu, Minghui & Guo, Shanqi, 2023. "Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles," Energy, Elsevier, vol. 273(C).
    15. Wang, Kunyu & Song, Hao & Guo, Zhiqiang & Zhang, Xuemin, 2025. "Automated multi-dimensional dynamic planning algorithm for solving energy management problems in fuel cell electric vehicles," Energy, Elsevier, vol. 316(C).
    16. Anselma, Pier Giuseppe & Kollmeyer, Phillip & Lempert, Jeremy & Zhao, Ziyu & Belingardi, Giovanni & Emadi, Ali, 2021. "Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation," Applied Energy, Elsevier, vol. 285(C).
    17. Andyn Omanovic & Norbert Zsiga & Patrik Soltic & Christopher Onder, 2021. "Optimal Degree of Hybridization for Spark-Ignited Engines with Optional Variable Valve Timings," Energies, MDPI, vol. 14(23), pages 1-21, December.
    18. Zhou, Xingyu & Sun, Chao & Sun, Fengchun & Zhang, Chuntao, 2023. "Commuting-pattern-oriented stochastic optimization of electric powertrains for revealing contributions of topology modifications to the powertrain energy efficiency," Applied Energy, Elsevier, vol. 344(C).
    19. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    20. Huijun Yue & Jinyu Lin & Peng Dong & Zhinan Chen & Xiangyang Xu, 2023. "Configurations and Control Strategies of Hybrid Powertrain Systems," Energies, MDPI, vol. 16(2), pages 1-18, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:energy:v:331:y:2025:i:c:s0360544225026556. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.