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Data-driven cost-optimal energy management of postal-delivery fuel cell electric vehicle with intelligent dual-loop battery state-of-charge planner

Author

Listed:
  • Zhou, Yang
  • Chen, Bo
  • Xu, Xianfeng
  • Zhang, Zhen
  • Ravey, Alexandre
  • Péra, Marie-Cécile
  • Ma, Ruiqing

Abstract

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is utilized to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43 % and 7.30 % in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123 ms, less than the sampling time interval 1s, proving its potential of real-time applications.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035351
    DOI: 10.1016/j.energy.2023.130141
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    References listed on IDEAS

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