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Double layer metaheuristic based energy management strategy for a Fuel Cell/Ultra-Capacitor hybrid electric vehicle

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  • Koubaa, Rayhane
  • krichen, Lotfi

Abstract

This paper highlights the use of metaheuristic approaches to perform the energy management of a hybrid Fuel Cell/Ultra-Capacitor Electric Vehicle considering hydrogen consumption, Fuel Cell durability and computational time as key performance criteria. The considered architecture is an integrated rule-based metaheuristic approach that combines the simplicity and the effectiveness of rule based and optimization approaches. Online results compared with benchmark offline Ant Colony Optimization results, present near to optimal performance, with slightly better performance for Particle Swarm Optimization versus Genetic Algorithm. The durability of the main energy source which is a crucial issue for Fuel Cell Hybrid Electric Vehicles is considered by limiting the Fuel Cell power variation rate in both layers providing a high protection level in order to respect its slow dynamics that represent a critical operational limitation. The rule-based layer which is a strategic layer reduces computational effort and time and restricts the search space of the optimization layer allowing a rapid convergence and thus enabling real time applications. Integrated Particle Swarm Optimization algorithm achieved lower computational time with an average of 0.65 ms versus 43.09 ms for the integrated Genetic Algorithm for each time step, which makes it more suitable for real time applications.

Suggested Citation

  • Koubaa, Rayhane & krichen, Lotfi, 2017. "Double layer metaheuristic based energy management strategy for a Fuel Cell/Ultra-Capacitor hybrid electric vehicle," Energy, Elsevier, vol. 133(C), pages 1079-1093.
    • Handle: RePEc:eee:energy:v:133:y:2017:i:c:p:1079-1093
    DOI: 10.1016/j.energy.2017.04.070
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    Cited by:

    1. 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.
    2. Ke Song & Yimin Wang & Cancan An & Hongjie Xu & Yuhang Ding, 2021. "Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method," Energies, MDPI, vol. 14(2), pages 1-31, January.
    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. Niu, Junyan & Zhuang, Weichao & Ye, Jianwei & Song, Ziyou & Yin, Guodong & Zhang, Yuanjian, 2022. "Optimal sizing and learning-based energy management strategy of NCR/LTO hybrid battery system for electric taxis," Energy, Elsevier, vol. 257(C).
    5. Fathabadi, Hassan, 2018. "Novel fuel cell/battery/supercapacitor hybrid power source for fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 143(C), pages 467-477.
    6. Zeng, Tao & Zhang, Caizhi & Hu, Minghui & Chen, Yan & Yuan, Changrong & Chen, Jingrui & Zhou, Anjian, 2018. "Modelling and predicting energy consumption of a range extender fuel cell hybrid vehicle," Energy, Elsevier, vol. 165(PB), pages 187-197.
    7. Balali, Yasaman & Stegen, Sascha, 2021. "Review of energy storage systems for vehicles based on technology, environmental impacts, and costs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Koubaa, Rayhane & Bacha, Seddik & Smaoui, Mariem & krichen, Lotfi, 2020. "Robust optimization based energy management of a fuel cell/ultra-capacitor hybrid electric vehicle under uncertainty," Energy, Elsevier, vol. 200(C).
    9. 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.
    10. Iqbal, Mehroze & Laurent, Julien & Benmouna, Amel & Becherif, Mohamed & Ramadan, Haitham S. & Claude, Frederic, 2022. "Ageing-aware load following control for composite-cost optimal energy management of fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 254(PA).
    11. Li, Lifu & Liu, Qin, 2019. "Acceleration curve optimization for electric vehicle based on energy consumption and battery life," Energy, Elsevier, vol. 169(C), pages 1039-1053.
    12. Hsieh, Chuang-Yu & Pei, Pucheng & Bai, Qiang & Su, Ay & Weng, Fang-Bor & Lee, Chi-Yuan, 2021. "Results of a 200 hours lifetime test of a 7 kW Hybrid–Power fuel cell system on electric forklifts," Energy, Elsevier, vol. 214(C).
    13. 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.
    14. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    15. Nicu Bizon & Valentin Alexandru Stan & Angel Ciprian Cormos, 2019. "Optimization of the Fuel Cell Renewable Hybrid Power System Using the Control Mode of the Required Load Power on the DC Bus," Energies, MDPI, vol. 12(10), pages 1-15, May.
    16. Cong Zhang & Dai Wang & Bin Wang & Fan Tong, 2020. "Battery Degradation Minimization-Oriented Hybrid Energy Storage System for Electric Vehicles," Energies, MDPI, vol. 13(1), pages 1-21, January.
    17. 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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