IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v227y2024ics0960148124005214.html
   My bibliography  Save this article

Performance evaluation of a hybrid hydrogen fuel cell/battery bus with fuel cell degradation and battery aging

Author

Listed:
  • Mousavi, Shadi Bashiri
  • Ahmadi, Pouria
  • Raeesi, Mehrdad

Abstract

Public transportation is an effective way to reduce emissions and decrease in fossil fuel consumption challenges. The idea of employing green energy systems in public transportation can significantly change the quality of life in the near future. In this regard, the hydrogen fuel cell bus which uses hydrogen as a main fuel and utilizes small battery systems for the rest of the required energy demands is simulated and investigated in this research study. Additionally, two real driving cycles are employed for dynamic modeling. Therefore, to fill the gaps in the field of public bus performance, heating, ventilation and air conditioning (HVAC) systems, fuel cell degradation, and battery aging, a precise model is presented in the Simcenter Amesim software. Time-dependent thermal loads and variations of passenger numbers are also considered for the HVAC system. Machine learning techniques are employed to predict fuel cell degradation and battery aging. The model is based on calendar and cyclic aging phenomena. Finally, the performance of the hybrid bus is evaluated during operation. Results show that more than 60% of energy is consumed in electric motors and increasing the number of passengers directly affects the amount of energy consumption reaches the maximum of 28 MJ for 40 passengers and additionally affects the final cabin temperature. After 2000 h of operation, the ammount of hydrogen consumption increased by approximately 10% compared to a new bus due to fuel cell degradation. In addition, the capacity loss of the battery reaches 17.6% after a year for the maximum ambient temperature of 36 °C.

Suggested Citation

  • Mousavi, Shadi Bashiri & Ahmadi, Pouria & Raeesi, Mehrdad, 2024. "Performance evaluation of a hybrid hydrogen fuel cell/battery bus with fuel cell degradation and battery aging," Renewable Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:renene:v:227:y:2024:i:c:s0960148124005214
    DOI: 10.1016/j.renene.2024.120456
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124005214
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.120456?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).
    2. İnci, Mustafa & Büyük, Mehmet & Demir, Mehmet Hakan & İlbey, Göktürk, 2021. "A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    3. Ahmadi, Pouria & Raeesi, Mehrdad & Changizian, Sina & Teimouri, Aidin & Khoshnevisan, Alireza, 2022. "Lifecycle assessment of diesel, diesel-electric and hydrogen fuel cell transit buses with fuel cell degradation and battery aging using machine learning techniques," Energy, Elsevier, vol. 259(C).
    4. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    5. 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).
    6. Kalghatgi, Gautam, 2018. "Is it really the end of internal combustion engines and petroleum in transport?," Applied Energy, Elsevier, vol. 225(C), pages 965-974.
    7. Weckerle, C. & Nasir, M. & Hegner, R. & Bürger, I. & Linder, M., 2020. "A metal hydride air-conditioning system for fuel cell vehicles – Functional demonstration," Applied Energy, Elsevier, vol. 259(C).
    8. 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).
    9. Izadi, Ali & Shahafve, Masoomeh & Ahmadi, Pouria & Hanafizadeh, Pedram, 2023. "Design, and optimization of COVID-19 hospital wards to produce Oxygen and electricity through solar PV panels with hydrogen storage systems by neural network-genetic algorithm," Energy, Elsevier, vol. 263(PA).
    10. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2017. "A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique," Energy, Elsevier, vol. 141(C), pages 1402-1415.
    11. Hu, Jianjun & Wang, Yangguang & Zou, Lingbo & Wang, Zhouxin, 2023. "Adaptive rule control strategy for composite energy storage fuel cell vehicle based on vehicle operating state recognition," Renewable Energy, Elsevier, vol. 204(C), pages 166-175.
    12. Sahraei, Mohammad Ali & Duman, Hakan & Çodur, Muhammed Yasin & Eyduran, Ecevit, 2021. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, Elsevier, vol. 224(C).
    13. Mousavi, Shadi Bashiri & Ahmadi, Pouria & Adib, Mahdieh & Izadi, Ali, 2023. "Techno-economic assessment of an efficient liquid air energy storage with ejector refrigeration cycle for peak shaving of renewable energies," Renewable Energy, Elsevier, vol. 214(C), pages 96-113.
    14. Ajanovic, A. & Glatt, A. & Haas, R., 2021. "Prospects and impediments for hydrogen fuel cell buses," Energy, Elsevier, vol. 235(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. Rahmani, Ebrahim & Moradi, Tofigh & Ghandehariun, Samane & Naterer, Greg F. & Ranjbar, Amirhossein, 2023. "Enhanced mass transfer and water discharge in a proton exchange membrane fuel cell with a raccoon channel flow field," Energy, Elsevier, vol. 264(C).
    2. Zou, Weitao & Li, Jianwei & Yang, Qingqing & Wan, Xinming & He, Yuntang & Lan, Hao, 2023. "A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle," Applied Energy, Elsevier, vol. 334(C).
    3. Zhao, Lei & Yuan, Hao & Xie, Jiaping & Jiang, Shangfeng & Wei, Xuezhe & Tang, Wei & Ming, Pingwen & Dai, Haifeng, 2023. "Inconsistency evaluation of vehicle-oriented fuel cell stacks based on electrochemical impedance under dynamic operating conditions," Energy, Elsevier, vol. 265(C).
    4. 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).
    5. Halder, Pobitra & Babaie, Meisam & Salek, Farhad & Shah, Kalpit & Stevanovic, Svetlana & Bodisco, Timothy A. & Zare, Ali, 2024. "Performance, emissions and economic analyses of hydrogen fuel cell vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    6. Ahmadi, Pouria & Raeesi, Mehrdad & Changizian, Sina & Teimouri, Aidin & Khoshnevisan, Alireza, 2022. "Lifecycle assessment of diesel, diesel-electric and hydrogen fuel cell transit buses with fuel cell degradation and battery aging using machine learning techniques," Energy, Elsevier, vol. 259(C).
    7. Zuo, Jian & Steiner, Nadia Yousfi & Li, Zhongliang & Hissel, Daniel, 2024. "Health management review for fuel cells: Focus on action phase," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    8. Wu, Yunna & Liu, Fangtong & He, Jiaming & Wu, Man & Ke, Yiming, 2021. "Obstacle identification, analysis and solutions of hydrogen fuel cell vehicles for application in China under the carbon neutrality target," Energy Policy, Elsevier, vol. 159(C).
    9. Li, Yanfei & Taghizadeh-Hesary, Farhad, 2022. "The economic feasibility of green hydrogen and fuel cell electric vehicles for road transport in China," Energy Policy, Elsevier, vol. 160(C).
    10. Kale, Aneesh Vijay & Krishnasamy, Anand, 2023. "Experimental study of homogeneous charge compression ignition combustion in a light-duty diesel engine fueled with isopropanol–gasoline blends," Energy, Elsevier, vol. 264(C).
    11. Wenxian Duan & Chuanxue Song & Silun Peng & Feng Xiao & Yulong Shao & Shixin Song, 2020. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 13(23), pages 1-19, December.
    12. 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.
    13. Thananya Janhuaton & Vatanavongs Ratanavaraha & Sajjakaj Jomnonkwao, 2024. "Forecasting Thailand’s Transportation CO 2 Emissions: A Comparison among Artificial Intelligent Models," Forecasting, MDPI, vol. 6(2), pages 1-23, June.
    14. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    15. Li, Yihuan & Li, Kang & Liu, Xuan & Li, Xiang & Zhang, Li & Rente, Bruno & Sun, Tong & Grattan, Kenneth T.V., 2022. "A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements," Applied Energy, Elsevier, vol. 325(C).
    16. Ma, Yan & Hu, Fuyuan & Hu, Yunfeng, 2023. "Energy efficiency improvement of intelligent fuel cell/battery hybrid vehicles through an integrated management strategy," Energy, Elsevier, vol. 263(PE).
    17. Ma, Zetai & Xie, Wenping & Xiang, Hanchun & Zhang, Kun & Yang, Mingyang & Deng, Kangyao, 2023. "Thermodynamic analysis of power recovery of marine diesel engine under high exhaust backpressure by additional electrically driven compressor," Energy, Elsevier, vol. 266(C).
    18. Zhang, Nan & Lu, Yiji & Kadam, Sambhaji & Yu, Zhibin, 2023. "A fuel cell range extender integrating with heat pump for cabin heat and power generation," Applied Energy, Elsevier, vol. 348(C).
    19. Nithin Isaac & Akshay Kumar Saha, 2023. "Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    20. Georgios Varvoutis & Athanasios Lampropoulos & Evridiki Mandela & Michalis Konsolakis & George E. Marnellos, 2022. "Recent Advances on CO 2 Mitigation Technologies: On the Role of Hydrogenation Route via Green H 2," Energies, MDPI, vol. 15(13), pages 1-38, June.

    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:renene:v:227:y:2024:i:c:s0960148124005214. 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/renewable-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.