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Lifecycle assessment of diesel, diesel-electric and hydrogen fuel cell transit buses with fuel cell degradation and battery aging using machine learning techniques

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  • Ahmadi, Pouria
  • Raeesi, Mehrdad
  • Changizian, Sina
  • Teimouri, Aidin
  • Khoshnevisan, Alireza

Abstract

Dynamic simulation and a regional lifecycle assessment of a hydrogen fuel cell vehicle (FCV), a hybrid electric/diesel, and a diesel-powered transit bus are conducted in this research. Since operating conditions such as driving patterns, number of passengers, ambient temperature, and road angle significantly affect the bus performance, these factors are considered in this study. An actual driving cycle for the Victoria City, British Columbia , Canada, is assessed, and a dynamic simulation is performed. One of the challenges in the widespread deployment of FCV and electric vehicles is fuel cell degradation and battery aging. Thus, this study deals with considering fuel cell degradation and battery aging to investigate the bus performance. Results show that in the degraded fuel cell bus, with an increase in the number of passengers from 5 to 60, the fuel consumption increases by more than 24%, which is the highest fuel consumption among the other buses. Besides, machine learning techniques are used to evaluate the impacts of the degradation phenomenon in the fuel cell stack on the hydrogen fuel cell buses fuel consumption. Moreover, battery aging on diesel-electric hybrid bus fuel consumption is analyzed at various ambient temperatures. Results illustrate that battery aging can significantly affect the battery performance by a 5.56% drop in battery pack capacity annually. Eventually, LCA analysis is carried out to evaluate each bus life cycle's total environmental effects. Based on LCA analysis, fuel cell buses have the best performance considering environmental aspects.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:259:y:2022:i:c:s0360544222019004
    DOI: 10.1016/j.energy.2022.125003
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    Cited by:

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    2. Chen, Ben & Deng, Qihao & Yang, Guanghua & Zhou, Yu & Chen, Wenshang & Cai, Yonghua & Tu, Zhengkai, 2023. "Numerical study on heat transfer characteristics and performance evaluation of PEMFC based on multiphase electrochemical model coupled with cooling channel," Energy, Elsevier, vol. 285(C).
    3. Yongtao Liu & Chunmei Zhang & Zhuo Hao & Xu Cai & Chuanpan Liu & Jianzhang Zhang & Shu Wang & Yisong Chen, 2023. "Study on the Life Cycle Assessment of Automotive Power Batteries Considering Multi-Cycle Utilization," Energies, MDPI, vol. 16(19), pages 1-24, September.
    4. 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).
    5. Izadi, Mohammad Javad & Hassani, Pourya & Raeesi, Mehrdad & Ahmadi, Pouria, 2024. "A novel WaveNet-GRU deep learning model for PEM fuel cells degradation prediction based on transfer learning," Energy, Elsevier, vol. 293(C).
    6. 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).

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