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A scalable, causal, adaptive energy management strategy based on optimal control theory for a fuel cell hybrid railway vehicle

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  • Peng, Hujun
  • Li, Jianxiang
  • Löwenstein, Lars
  • Hameyer, Kay

Abstract

A scalable, causal, adaptive optimal control-based energy management strategy for the fuel cell hybrid train is designed. As learned from the results of offline Pontryagin’s minimum principle (PMP)-based strategies, the convexity of the specific consumption curve is emphasized to improve the fuel economy. More important is that the dependency of the co-state on the state of charge (SoC) of batteries and the average fuel cell power is identified the first time. With the help of using the optimal control theory in a reverse way, a quantitative analytical formula is derived to determine the co-state based on the SoC and the average fuel cell power. The accuracy of the estimates, and the effectiveness of this strategy, under different weather, driving, and aging conditions, is validated by comparison to the results of offline PMP-based strategies. Thereby, a maximal deviation of the co-state average value compared to the offline results is 1.8%. An excellent fuel economy under a typical driving cycle of regional railway transports in Berlin, with only 0.03% more consumption for both summer and winter conditions, compared to the results of offline PMP, is resulted. Due to the model-based characteristics, the strategy can be scaled or transferred to other configuration systems or driving conditions without the loss of effectiveness.

Suggested Citation

  • Peng, Hujun & Li, Jianxiang & Löwenstein, Lars & Hameyer, Kay, 2020. "A scalable, causal, adaptive energy management strategy based on optimal control theory for a fuel cell hybrid railway vehicle," Applied Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920304992
    DOI: 10.1016/j.apenergy.2020.114987
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    References listed on IDEAS

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    Cited by:

    1. Hegazy Rezk & Mohammad Ali Abdelkareem & Samah Ibrahim Alshathri & Enas Taha Sayed & Mohamad Ramadan & Abdul Ghani Olabi, 2023. "Fuel Economy Energy Management of Electric Vehicles Using Harris Hawks Optimization," Sustainability, MDPI, vol. 15(16), pages 1-15, August.
    2. Marko Kapetanović & Mohammad Vajihi & Rob M. P. Goverde, 2021. "Analysis of Hybrid and Plug-In Hybrid Alternative Propulsion Systems for Regional Diesel-Electric Multiple Unit Trains," Energies, MDPI, vol. 14(18), pages 1-29, September.
    3. Zhang, Chi & Zeng, Guohong & Wu, Jian & Wei, Shaoyuan & Zhang, Weige & Sun, Bingxiang, 2023. "Integrated optimization of driving strategy and energy management for hybrid diesel multiple units," Energy, Elsevier, vol. 281(C).
    4. Peng, Hujun & Chen, Zhu & Li, Jianxiang & Deng, Kai & Dirkes, Steffen & Gottschalk, Jonas & Ünlübayir, Cem & Thul, Andreas & Löwenstein, Lars & Pischinger, Stefan & Hameyer, Kay, 2021. "Offline optimal energy management strategies considering high dynamics in batteries and constraints on fuel cell system power rate: From analytical derivation to validation on test bench," Applied Energy, Elsevier, vol. 282(PA).
    5. Seydali Ferahtia & Hegazy Rezk & Rania M. Ghoniem & Ahmed Fathy & Reem Alkanhel & Mohamed M. Ghonem, 2023. "Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    6. Jaekwang Jung & Dongeon Kim & Liyue Yang & Namwook Kim, 2024. "Optimal Energy Management Strategy for Repeat Path Operating Fuel Cell Hybrid Tram," Energies, MDPI, vol. 17(7), pages 1-12, March.
    7. Kandidayeni, M. & Macias, A. & Boulon, L. & Kelouwani, S., 2020. "Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies," Applied Energy, Elsevier, vol. 274(C).
    8. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).

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