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Auto-Adaptive Filtering-Based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

Author

Listed:
  • Jamila Snoussi

    (National Engineering School of Monastir (ENIM), Monastir University, Monastir 5000, Tunisia)

  • Seifeddine Ben Elghali

    (Laboratory of Information and Systems (UMR CNRS 7020 LIS), Aix-Marseille University, Marseille 13397, France)

  • Mohamed Benbouzid

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, Brest 29238, France and with the Shanghai Maritime University, Shanghai 201306, China)

  • Mohamed Faouzi Mimouni

    (National Engineering School of Monastir (ENIM), Monastir University, Monastir 5000, Tunisia)

Abstract

The global need to solve pollution problems has conducted automotive engineers to promote the development and the use of electric vehicle technologies. This paper focuses on the fuel cell hybrid electric vehicle which uses a proton exchange membrane fuel cell as a main source associated to hybrid storage device: lithium ion battery and ultracapacitors. A common interest in such technology is to spread out the energy flow between its different sources in order to satisfy the power demand for any requested mission. However, the challenging task stills the optimization of this split to reduce hydrogen consumption and respect, at the same time, the system limitations such as admissible limits of storage system capacities and battery current variation. An adaptive filtering-based energy management strategy is proposed in this paper to ensure an optimum distribution of the energy between the sources taking into account dynamic and energetic constraints of each device. For more performance, a fuzzy logic system is used to adapt the frequency of separation with the system state evolution. A sliding mode control is applied to control electric characteristics (voltage and currents) in the considered hybrid power supply. Simulation results, obtained under MATLAB ® /SimPowerSystems ® for four driving cycles are presented. The proposed strategy achieved good performances by respecting the ultracapacitors state of charge while preserving the battery lifetime under various driving missions.

Suggested Citation

  • Jamila Snoussi & Seifeddine Ben Elghali & Mohamed Benbouzid & Mohamed Faouzi Mimouni, 2018. "Auto-Adaptive Filtering-Based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2118-:d:163717
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    References listed on IDEAS

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    2. Hoai-Linh T. Nguyen & Bảo-Huy Nguyễn & Thanh Vo-Duy & João Pedro F. Trovão, 2021. "A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles," Energies, MDPI, vol. 14(12), pages 1-23, June.
    3. Krisztián Kun & Lóránt Szabó & Erika Varga & Dávid István Kis, 2024. "Development of a Hydrogen Fuel Cell Prototype Vehicle Supported by Artificial Intelligence for Green Urban Transport," Energies, MDPI, vol. 17(7), pages 1-17, March.
    4. Mehdi Sellali & Alexandre Ravey & Achour Betka & Abdellah Kouzou & Mohamed Benbouzid & Abdesslem Djerdir & Ralph Kennel & Mohamed Abdelrahem, 2022. "Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-17, February.
    5. Vishnu P. Sidharthan & Yashwant Kashyap & Panagiotis Kosmopoulos, 2023. "Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles," Energies, MDPI, vol. 16(3), pages 1-26, January.
    6. Mohsen Kandidayeni & Alvaro Macias & Loïc Boulon & João Pedro F. Trovão, 2020. "Online Modeling of a Fuel Cell System for an Energy Management Strategy Design," Energies, MDPI, vol. 13(14), pages 1-17, July.
    7. Arkadiusz Adamczyk, 2020. "Sizing and Control Algorithms of a Hybrid Energy Storage System Based on Fuel Cells," Energies, MDPI, vol. 13(19), pages 1-15, October.
    8. Zhiwen Zhang & Jie Tang & Jiyuan Zhang & Tianci Zhang, 2024. "Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle," Energies, MDPI, vol. 17(6), pages 1-22, March.
    9. Carmen Raga & Andres Barrado & Henry Miniguano & Antonio Lazaro & Isabel Quesada & Alberto Martin-Lozano, 2018. "Analysis and Sizing of Power Distribution Architectures Applied to Fuel Cell Based Vehicles," Energies, MDPI, vol. 11(10), pages 1-30, September.
    10. Ahmed Al Amerl & Ismail Oukkacha & Mamadou Baïlo Camara & Brayima Dakyo, 2021. "Real-Time Control Strategy of Fuel Cell and Battery System for Electric Hybrid Boat Application," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
    11. Wang, Yichun & Zhang, Yuanzhi & Zhang, Caizhi & Zhou, Jiaming & Hu, Donghai & Yi, Fengyan & Fan, Zhixian & Zeng, Tao, 2023. "Genetic algorithm-based fuzzy optimization of energy management strategy for fuel cell vehicles considering driving cycles recognition," Energy, Elsevier, vol. 263(PF).
    12. Tom Fletcher & Kambiz Ebrahimi, 2020. "The Effect of Fuel Cell and Battery Size on Efficiency and Cell Lifetime for an L7e Fuel Cell Hybrid Vehicle," Energies, MDPI, vol. 13(22), pages 1-18, November.
    13. Fathy, Ahmed & Ferahtia, Seydali & Rezk, Hegazy & Yousri, Dalia & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid," Energy, Elsevier, vol. 247(C).
    14. Ioan-Sorin Sorlei & Nicu Bizon & Phatiphat Thounthong & Mihai Varlam & Elena Carcadea & Mihai Culcer & Mariana Iliescu & Mircea Raceanu, 2021. "Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies," Energies, MDPI, vol. 14(1), pages 1-29, January.
    15. Sadam Hussain & Muhammad Umair Ali & Gwan-Soo Park & Sarvar Hussain Nengroo & Muhammad Adil Khan & Hee-Je Kim, 2019. "A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-24, December.

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