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A combinatorial optimisation approach to energy management strategy for a hybrid fuel cell vehicle

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  • Caux, Stéphane
  • Gaoua, Yacine
  • Lopez, Pierre

Abstract

Hybrid Electric Vehicles are becoming more and more prevalent for economic and environmental reasons. Many studies have been conducted in order to improve Hybrid Electric Vehicle performance by increasing their autonomy while respecting the power demand of the electric motor and various constraints. Focusing on the Hybrid Electric Vehicle energy management problem, different approaches and strategies already exist based on non-linear modelling, selection of adequate architecture and source design or the expertise of the manufacturer in the domain. In this paper, a new combinatorial approach is presented to optimally manage offline Hybrid Electric Vehicle energy distribution, composed of two energy sources: a fuel cell as a main source and a super-capacitor for energy storage. New mathematical modelling has been developed that reflects the functioning of the Hybrid Electric Vehicle energy chain, using an exact method to provide an optimal solution that corresponds to hydrogen consumption. Simulations were performed on different realistic mission profiles that showed a significant gain in solution quality and computation time compared with other approaches presented in the literature. Since the quality of solutions depends on the reliability of input data, including disruptions, a robustness study also is carried out.

Suggested Citation

  • Caux, Stéphane & Gaoua, Yacine & Lopez, Pierre, 2017. "A combinatorial optimisation approach to energy management strategy for a hybrid fuel cell vehicle," Energy, Elsevier, vol. 133(C), pages 219-230.
    • Handle: RePEc:eee:energy:v:133:y:2017:i:c:p:219-230
    DOI: 10.1016/j.energy.2017.05.109
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    Citations

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    Cited by:

    1. Li, Tianyu & Liu, Huiying & Wang, Hui & Yao, Yongming, 2020. "Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles," Energy, Elsevier, vol. 198(C).
    2. Li, Tianyu & Liu, Huiying & Ding, Daolin, 2018. "Predictive energy management of fuel cell supercapacitor hybrid construction equipment," Energy, Elsevier, vol. 149(C), pages 718-729.
    3. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    4. Yao, Yongming & Wang, Jie & Zhou, Zhicong & Li, Hang & Liu, Huiying & Li, Tianyu, 2023. "Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles," Energy, Elsevier, vol. 262(PA).
    5. Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2021. "Real-time energy management of fuel cell hybrid electric buses: Fuel cell engines friendly intersection speed planning," Energy, Elsevier, vol. 226(C).
    6. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    7. Rudravaram Venkatasatish & Dhanamjayulu Chittathuru, 2023. "Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    8. Bao, Shuyue & Tang, Shifa & Sun, Ping & Wang, Tao, 2023. "LSTM-based energy management algorithm for a vehicle power-split hybrid powertrain," Energy, Elsevier, vol. 284(C).
    9. Gao, Renjing & Zhou, Guangli & Wang, Qi, 2024. "Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC," Energy, Elsevier, vol. 295(C).
    10. Bizon, Nicu & Thounthong, Phatiphat, 2018. "Real-time strategies to optimize the fueling of the fuel cell hybrid power source: A review of issues, challenges and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1089-1102.
    11. Lei, Fei & Gu, Ke & Du, Bin & Xie, Xiaoping, 2017. "Comprehensive global optimization of an implicit constrained multi-physics system for electric vehicles with in-wheel motors," Energy, Elsevier, vol. 139(C), pages 523-534.
    12. Yan, Yan & Xu, Zhan & Han, Feng & Wang, Zhao & Ni, Zhonghua, 2022. "Energy control of providing cryo-compressed hydrogen for the heavy-duty trucks driving," Energy, Elsevier, vol. 242(C).
    13. Wu, Jinglai & Zhang, Yunqing & Ruan, Jiageng & Liang, Zhaowen & Liu, Kai, 2023. "Rule and optimization combined real-time energy management strategy for minimizing cost of fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    14. Liu, Teng & Wang, Bo & Yang, Chenglang, 2018. "Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning," Energy, Elsevier, vol. 160(C), pages 544-555.
    15. Bouguenna, Ibrahim Farouk & Azaiz, Ahmed & Tahour, Ahmed & Larbaoui, Ahmed, 2019. "Robust neuro-fuzzy sliding mode control with extended state observer for an electric drive system," Energy, Elsevier, vol. 169(C), pages 1054-1063.

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