IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i12p1849-d1414610.html
   My bibliography  Save this article

An Enhanced Extremum Seeking-Based Energy Management Strategy with Equivalent State for Hybridized-Electric Tramway-Powered by Fuel Cell–Battery–Supercapacitors

Author

Listed:
  • Hoai Vu Anh Truong

    (Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

  • Hoai An Trinh

    (Faculty of Electronics, Telecommunications Saigon University, Ho Chi Minh City 700000, Vietnam)

  • Tri Cuong Do

    (College of Technology and Design, University of Economics, Ho Chi Minh City 700000, Vietnam)

  • Manh Hung Nguyen

    (School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 44610, Republic of Korea)

  • Van Du Phan

    (School of Engineering and Technology, Vinh University, Vinh 43108, Vietnam)

  • Kyoung Kwan Ahn

    (School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 44610, Republic of Korea)

Abstract

This article proposes a novel real-time optimization-based energy management strategy (EMS) for proton membrane exchange fuel cell (PEMFC)-battery-supercapacitors-driven hybridized-electric tramways (HETs). The proposed algorithm is derived based on an enhanced extremum seeking (ES) algorithm, with a new equivalent state-of-charge (SOC) and a new adaptive co-state introduced. Thereby, optimized reference power for each power source can be distributed appropriately when using three components. The workability and prominent of the proposed technique are demonstrated through comparative simulations with fuzzy-rule-based EMS (FEMS) and equivalent consumption minimization strategy (ECMS) in two case studies: with and without considering the supercapacitors, as an important factor in the EMS design to stabilize the SOC of energy storage devices (ESDs). Briefly, under the proposed ES-based method, the PEMFC power can be regulated such that high-efficiency can be performed, approximately by 46.7%. Subsequently, the hydrogen consumption is reduced about 31.2% compared to a comparative fuzzy-based EMS. Besides, the supplements’ SOCs at the end of a driving cycle are also regulated to be equal to the initial ones.

Suggested Citation

  • Hoai Vu Anh Truong & Hoai An Trinh & Tri Cuong Do & Manh Hung Nguyen & Van Du Phan & Kyoung Kwan Ahn, 2024. "An Enhanced Extremum Seeking-Based Energy Management Strategy with Equivalent State for Hybridized-Electric Tramway-Powered by Fuel Cell–Battery–Supercapacitors," Mathematics, MDPI, vol. 12(12), pages 1-22, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1849-:d:1414610
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/12/1849/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/12/1849/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Trovão, João P. & Pereirinha, Paulo G. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2013. "A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach," Applied Energy, Elsevier, vol. 105(C), pages 304-318.
    2. Li, Qi & Wang, Tianhong & Li, Shihan & Chen, Weirong & Liu, Hong & Breaz, Elena & Gao, Fei, 2021. "Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation," Applied Energy, Elsevier, vol. 285(C).
    3. Hoai Vu Anh Truong & Hoang Vu Dao & Tri Cuong Do & Cong Minh Ho & Xuan Dinh To & Tri Dung Dang & Kyoung Kwan Ahn, 2020. "Mapping Fuzzy Energy Management Strategy for PEM Fuel Cell–Battery–Supercapacitor Hybrid Excavator," Energies, MDPI, vol. 13(13), pages 1-27, July.
    4. Caux, Stéphane & Gaoua, Yacine & Lopez, Pierre, 2017. "A combinatorial optimisation approach to energy management strategy for a hybrid fuel cell vehicle," Energy, Elsevier, vol. 133(C), pages 219-230.
    5. Peng, Fei & Zhao, Yuanzhe & Li, Xiaopeng & Liu, Zhixiang & Chen, Weirong & Liu, Yang & Zhou, Donghua, 2017. "Development of master-slave energy management strategy based on fuzzy logic hysteresis state machine and differential power processing compensation for a PEMFC-LIB-SC hybrid tramway," Applied Energy, Elsevier, vol. 206(C), pages 346-363.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jianzhong Li & Hongye Su & Yang Zhu, 2025. "Extremum Seeking for the First Derivative of Nonlinear Maps with Constant Delays via a Time-Delay Approach," Mathematics, MDPI, vol. 13(13), pages 1-15, July.
    2. Haobin Jiang & Yang Zhao & Shidian Ma, 2025. "Dual-Layer Energy Management Strategy for a Hybrid Energy Storage System to Enhance PHEV Performance," Energies, MDPI, vol. 18(7), pages 1-20, March.

    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. Sheng, Chuang & Guo, Ziang & Lei, Jingzhi & Zhang, Shuyu & Zhang, Wenxuan & Chen, Weiming & Jiang, Xuefeng & Wang, Zhuo & Li, Xi, 2024. "Optimal energy management strategies for hybrid power systems considering Pt degradation," Applied Energy, Elsevier, vol. 360(C).
    2. Li, Tianyu & Liu, Huiying & Wang, Hui & Yao, Yongming, 2020. "Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles," Energy, Elsevier, vol. 198(C).
    3. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    4. Peng, Fei & Zhao, Yuanzhe & Li, Xiaopeng & Liu, Zhixiang & Chen, Weirong & Liu, Yang & Zhou, Donghua, 2017. "Development of master-slave energy management strategy based on fuzzy logic hysteresis state machine and differential power processing compensation for a PEMFC-LIB-SC hybrid tramway," Applied Energy, Elsevier, vol. 206(C), pages 346-363.
    5. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    6. Bảo-Huy Nguyễn & João Pedro F. Trovão & Ronan German & Alain Bouscayrol, 2020. "Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation," Energies, MDPI, vol. 13(21), pages 1-19, October.
    7. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    8. 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.
    9. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    10. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    11. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    12. Wu, Jinglai & Zhang, Yunqing & Ruan, Jiageng & Liang, Zhaowen & Liu, Kai, 2023. "Rule and optimization combined real-time energy management strategy for minimizing cost of fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    13. De Filippo, Giovanni & Marano, Vincenzo & Sioshansi, Ramteen, 2014. "Simulation of an electric transportation system at The Ohio State University," Applied Energy, Elsevier, vol. 113(C), pages 1686-1691.
    14. 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).
    15. Pu, Yuchen & Li, Qi & Zou, Xueli & Li, Ruirui & Li, Luoyi & Chen, Weirong & Liu, Hong, 2021. "Optimal sizing for an integrated energy system considering degradation and seasonal hydrogen storage," Applied Energy, Elsevier, vol. 302(C).
    16. Most Nahida Akter & Md Apel Mahmud & Amanullah Maung Than Oo, 2017. "A Hierarchical Transactive Energy Management System for Energy Sharing in Residential Microgrids," Energies, MDPI, vol. 10(12), pages 1-27, December.
    17. Shen, Xiaojun & Li, Xingyi & Yuan, Jiahai & Jin, Yu, 2022. "A hydrogen-based zero-carbon microgrid demonstration in renewable-rich remote areas: System design and economic feasibility," Applied Energy, Elsevier, vol. 326(C).
    18. Badji, Abderrezak & Abdeslam, Djaffar Ould & Chabane, Djafar & Benamrouche, Nacereddine, 2022. "Real-time implementation of improved power frequency approach based energy management of fuel cell electric vehicle considering storage limitations," Energy, Elsevier, vol. 249(C).
    19. Nicu Bizon & Phatiphat Thounthong, 2020. "Energy Efficiency and Fuel Economy of a Fuel Cell/Renewable Energy Sources Hybrid Power System with the Load-Following Control of the Fueling Regulators," Mathematics, MDPI, vol. 8(2), pages 1-22, January.
    20. Chen, Dongfang & Pei, Pucheng & Meng, Yining & Ren, Peng & Li, Yuehua & Wang, Mingkai & Wang, Xizhong, 2022. "Novel extraction method of working condition spectrum for the lifetime prediction and energy management strategy evaluation of automotive fuel cells," Energy, Elsevier, vol. 255(C).

    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:gam:jmathe:v:12:y:2024:i:12:p:1849-:d:1414610. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.