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Net Hydrogen Consumption Minimization of Fuel Cell Hybrid Trains Using a Time-Based Co-Optimization Model

Author

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  • Guangzhao Meng

    (Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China)

  • Chaoxian Wu

    (School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Bolun Zhang

    (Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China)

  • Fei Xue

    (School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

  • Shaofeng Lu

    (Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China)

Abstract

With increasing concerns on transportation decarbonization, fuel cell hybrid trains (FCHTs) attract many attentions due to their zero carbon emissions during operation. Since fuel cells alone cannot recover the regenerative braking energy (RBE), energy storage devices (ESDs) are commonly deployed for the recovery of RBE and provide extra traction power to improve the energy efficiency. This paper aims to minimize the net hydrogen consumption (NHC) by co-optimizing both train speed trajectory and onboard energy management using a time-based mixed integer linear programming (MILP) model. In the case with the constraints of speed limits and gradients, the NHC of co-optimization reduces by 6.4% compared to the result obtained by the sequential optimization, which optimizes train control strategies first and then the energy management. Additionally, the relationship between NHC and employed ESD capacity is studied and it is found that with the increase of ESD capacity, the NHC can be reduced by up to 30% in a typical route in urban railway transit. The study shows that ESDs play an important role for FCHTs in reducing NHC, and the proposed time-based co-optimization model can maximize the energy-saving benefits for such emerging traction systems with hybrid energy sources, including both fuel cells and ESD.

Suggested Citation

  • Guangzhao Meng & Chaoxian Wu & Bolun Zhang & Fei Xue & Shaofeng Lu, 2022. "Net Hydrogen Consumption Minimization of Fuel Cell Hybrid Trains Using a Time-Based Co-Optimization Model," Energies, MDPI, vol. 15(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2891-:d:794154
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    References listed on IDEAS

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    1. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2016. "The key principles of optimal train control—Part 2: Existence of an optimal strategy, the local energy minimization principle, uniqueness, computational techniques," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 509-538.
    2. Scheepmaker, Gerben M. & Goverde, Rob M.P. & Kroon, Leo G., 2017. "Review of energy-efficient train control and timetabling," European Journal of Operational Research, Elsevier, vol. 257(2), pages 355-376.
    3. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2016. "The key principles of optimal train control—Part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 482-508.
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    Cited by:

    1. Mubashir Rasool & Muhammad Adil Khan & Runmin Zou, 2023. "A Comprehensive Analysis of Online and Offline Energy Management Approaches for Optimal Performance of Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-33, April.

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