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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

Author

Listed:
  • Peng, Hujun
  • Chen, Zhu
  • Li, Jianxiang
  • Deng, Kai
  • Dirkes, Steffen
  • Gottschalk, Jonas
  • Ünlübayir, Cem
  • Thul, Andreas
  • Löwenstein, Lars
  • Pischinger, Stefan
  • Hameyer, Kay

Abstract

For a fuel cell hybrid train, offline optimal energy management strategies using the Pontryagin’s minimum principle and dynamic programming are developed and presented in this contribution. The dynamics in the voltages over various parallel resistance-capacitor branches in the batteries are considered. In addition, dynamic limitation of the fuel cell power is taken into account by choosing the fuel cell power rate as the control variable instead of the fuel cell power, as found so far in all literature with related topics. The correctness of the Pontryagin’s minimum principle and the dynamic programming-based strategies are mutually validated. The corresponding results provide more precise references than the offline strategies without the resistance-capacitor branches in batteries taken into account. A damping factor is then introduced into the cost function to reduce unnecessary high dynamic oscillations of the operating points of the fuel cell system without compromising fuel economy. Finally, the results of the offline strategies are validated with measurements on the test bench at the Center for Mobile Propulsion of the RWTH Aachen University. Only a difference of 0.15% was determined between the measured and the offline calculated hydrogen consumption. .

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:282:y:2021:i:pa:s0306261920315610
    DOI: 10.1016/j.apenergy.2020.116152
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    Cited by:

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    4. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    5. Sara Luciani & Andrea Tonoli, 2022. "Control Strategy Assessment for Improving PEM Fuel Cell System Efficiency in Fuel Cell Hybrid Vehicles," Energies, MDPI, vol. 15(6), pages 1-17, March.
    6. Chen, Shuang & Hu, Minghui & Lei, Yanlei & Kong, Linghao, 2023. "Novel hybrid power system and energy management strategy for locomotives," Applied Energy, Elsevier, vol. 348(C).
    7. Qilin Shuai & Yiheng Wang & Zhengxiong Jiang & Qingsong Hua, 2024. "Reinforcement Learning-Based Energy Management for Fuel Cell Electrical Vehicles Considering Fuel Cell Degradation," Energies, MDPI, vol. 17(7), pages 1-19, March.
    8. Zhou, Yang & Chen, Bo & Xu, Xianfeng & Zhang, Zhen & Ravey, Alexandre & Péra, Marie-Cécile & Ma, Ruiqing, 2024. "Data-driven cost-optimal energy management of postal-delivery fuel cell electric vehicle with intelligent dual-loop battery state-of-charge planner," Energy, Elsevier, vol. 290(C).

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