IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p16190-d993155.html
   My bibliography  Save this article

A Novel Multi-Objective Energy Management Strategy for Fuel Cell Buses Quantifying Fuel Cell Degradation as Operating Cost

Author

Listed:
  • Menglin Li

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China
    Hebei Key Laboratory of Special Delivery Equipment, Yanshan University, Qinhuangdao 066004, China)

  • Haoran Liu

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China)

  • Mei Yan

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China
    Hebei Key Laboratory of Special Delivery Equipment, Yanshan University, Qinhuangdao 066004, China)

  • Hongyang Xu

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China)

  • Hongwen He

    (National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Fluctuation in a fuel cell’s output power affects its service life. This paper aims to explore the relationship between power output fluctuation and energy consumption and the cost of the fuel cell system. Hence, based on the actual driving information of vehicles, a novel multi-objective energy management strategy (EMS) for fuel cell buses (FCBs) that quantifies fuel cell life as operating cost is proposed. The actual driving data of FCBs on bus line 727 in Zhengzhou, China, were collected. Based on this, considering the degradation factors of the fuel cell and power battery hybrid energy system, a multi-objective cost framework was established to quantify the life degradation as consumption cost. Furthermore, the influence of different power change limits on the performance of the EMS was analysed based on real-world driving data and the typical Chinese city bus driving cycle, respectively. The simulation results show that the degradation cost of the fuel cell can be effectively reduced when the power change limit is 1 kW, and the simulation results obtained using real-world driving data are very different from those obtained using typical city bus driving cycles. This study provides a reference for the application of a vehicle energy management strategy in real-world scenarios as well as highlights its significance.

Suggested Citation

  • Menglin Li & Haoran Liu & Mei Yan & Hongyang Xu & Hongwen He, 2022. "A Novel Multi-Objective Energy Management Strategy for Fuel Cell Buses Quantifying Fuel Cell Degradation as Operating Cost," Sustainability, MDPI, vol. 14(23), pages 1-16, December.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16190-:d:993155
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/16190/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/23/16190/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ribau, João P. & Silva, Carla M. & Sousa, João M.C., 2014. "Efficiency, cost and life cycle CO2 optimization of fuel cell hybrid and plug-in hybrid urban buses," Applied Energy, Elsevier, vol. 129(C), pages 320-335.
    2. Chen, Huicui & Pei, Pucheng & Song, Mancun, 2015. "Lifetime prediction and the economic lifetime of Proton Exchange Membrane fuel cells," Applied Energy, Elsevier, vol. 142(C), pages 154-163.
    3. 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.
    4. Xu, Liangfei & Mueller, Clemens David & Li, Jianqiu & Ouyang, Minggao & Hu, Zunyan, 2015. "Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles," Applied Energy, Elsevier, vol. 157(C), pages 664-674.
    5. Zhou, Yang & Ravey, Alexandre & Péra, Marie-Cecile, 2020. "Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer," Applied Energy, Elsevier, vol. 258(C).
    6. Shi, Junzhe & Xu, Bin & Shen, Yimin & Wu, Jingbo, 2022. "Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition," Energy, Elsevier, vol. 243(C).
    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. Farhan Mumtaz & Nor Zaihar Yahaya & Sheikh Tanzim Meraj & Narinderjit Singh Sawaran Singh & Md. Siddikur Rahman & Molla Shahadat Hossain Lipu, 2023. "A High Voltage Gain Interleaved DC-DC Converter Integrated Fuel Cell for Power Quality Enhancement of Microgrid," Sustainability, MDPI, vol. 15(9), pages 1-21, April.

    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. 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.
    2. Feroldi, Diego & Carignano, Mauro, 2016. "Sizing for fuel cell/supercapacitor hybrid vehicles based on stochastic driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 645-658.
    3. Jianyun, Zhu & Li, Chen & Lijuan, Xia & Bin, Wang, 2019. "Bi-objective optimal design of plug-in hybrid electric propulsion system for ships," Energy, Elsevier, vol. 177(C), pages 247-261.
    4. Jiajun Liu & Huachao Dong & Tianxu Jin & Li Liu & Babak Manouchehrinia & Zuomin Dong, 2018. "Optimization of Hybrid Energy Storage Systems for Vehicles with Dynamic On-Off Power Loads Using a Nested Formulation," Energies, MDPI, vol. 11(10), pages 1-25, October.
    5. Feng, Yanbiao & Dong, Zuomin, 2020. "Integrated design and control optimization of fuel cell hybrid mining truck with minimized lifecycle cost," Applied Energy, Elsevier, vol. 270(C).
    6. Li, Tianyu & Huang, Lingtao & Liu, Huiying, 2019. "Energy management and economic analysis for a fuel cell supercapacitor excavator," Energy, Elsevier, vol. 172(C), pages 840-851.
    7. Zhu, Jianyun & Chen, Li & Wang, Bin & Xia, Lijuan, 2018. "Optimal design of a hybrid electric propulsive system for an anchor handling tug supply vessel," Applied Energy, Elsevier, vol. 226(C), pages 423-436.
    8. Tang, Xiaolin & Zhou, Haitao & Wang, Feng & Wang, Weida & Lin, Xianke, 2022. "Longevity-conscious energy management strategy of fuel cell hybrid electric Vehicle Based on deep reinforcement learning," Energy, Elsevier, vol. 238(PA).
    9. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    10. Huang, Yanjun & Wang, Hong & Khajepour, Amir & Li, Bin & Ji, Jie & Zhao, Kegang & Hu, Chuan, 2018. "A review of power management strategies and component sizing methods for hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 132-144.
    11. 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.
    12. Liu, Yonggang & Liu, Junjun & Zhang, Yuanjian & Wu, Yitao & Chen, Zheng & Ye, Ming, 2020. "Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization," Energy, Elsevier, vol. 207(C).
    13. Xu, Liangfei & Mueller, Clemens David & Li, Jianqiu & Ouyang, Minggao & Hu, Zunyan, 2015. "Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles," Applied Energy, Elsevier, vol. 157(C), pages 664-674.
    14. 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).
    15. Anselma, Pier Giuseppe & Belingardi, Giovanni, 2022. "Fuel cell electrified propulsion systems for long-haul heavy-duty trucks: present and future cost-oriented sizing," Applied Energy, Elsevier, vol. 321(C).
    16. Pregelj, Boštjan & Micor, Michał & Dolanc, Gregor & Petrovčič, Janko & Jovan, Vladimir, 2016. "Impact of fuel cell and battery size to overall system performance – A diesel fuel-cell APU case study," Applied Energy, Elsevier, vol. 182(C), pages 365-375.
    17. 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.
    18. 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.
    19. Sanghyun Yun & Jinwon Yun & Jaeyoung Han, 2023. "Development of a 470-Horsepower Fuel Cell–Battery Hybrid Xcient Dynamic Model Using Simscape TM," Energies, MDPI, vol. 16(24), pages 1-22, December.
    20. 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.

    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:jsusta:v:14:y:2022:i:23:p:16190-:d:993155. 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.