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Real-Time Integrated Energy Management Strategy Applied to Fuel Cell Hybrid Systems

Author

Listed:
  • Matthieu Matignon

    (ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Saclay, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France)

  • Toufik Azib

    (ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Saclay, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France)

  • Mehdi Mcharek

    (ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Saclay, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France)

  • Ahmed Chaibet

    (Laboratoire DRIVE Nevers, Université de Bourgogne, 58027 Nevers, France)

  • Adriano Ceschia

    (ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Saclay, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France)

Abstract

Integrating hydrogen fuel cell systems (FCS) remains challenging in the expanding electric vehicle market. One of the levers to meet this challenge is the relevance of energy supervisors. This paper proposes an innovative energy management strategy (EMS) based on the integrated EMS (iEMS) concept. It uses a nested approach combining the best of the three EMS categories (optimization-based (OBS), rules-based (RBS), and learning-based (LBS) strategies) to overcome the real-time operating condition limitations of the fuel cell hybrid electric vehicle (FCHEV). Through a fuel cell/battery hybrid architecture, the purpose is to improve hydrogen consumption and manage the battery state of charge (SOC) under real-time driving conditions. The proposed iEMS approach is based on an OBS with optimal control to make the energy-optimal decision. However, it requires the adaptations of real-time operating conditions and a dynamic SOC horizon management. These requirements are supported by combining an RBS based on expert and fuzzy rules to compute the SOC target on each sliding window and an LBS based on fuzzy C-mean clustering to enhance the cooperative environment data processing and adapt it to the FHCEV topology. Our approach obtained simple and realistic system behaviors while having an acceptable computing time suitable for real time constraint. It was then designed and validated using a 27-h real-time measured database. The results show the effectiveness of the proposed iEMS concept with an excellent performance close to the optimal offline strategy (an under 2% consumption gap).

Suggested Citation

  • Matthieu Matignon & Toufik Azib & Mehdi Mcharek & Ahmed Chaibet & Adriano Ceschia, 2023. "Real-Time Integrated Energy Management Strategy Applied to Fuel Cell Hybrid Systems," Energies, MDPI, vol. 16(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2645-:d:1094220
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    References listed on IDEAS

    as
    1. Daniel Ritzberger & Christoph Hametner & Stefan Jakubek, 2020. "A Real-Time Dynamic Fuel Cell System Simulation for Model-Based Diagnostics and Control: Validation on Real Driving Data," Energies, MDPI, vol. 13(12), pages 1-20, June.
    2. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    3. 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.
    4. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.
    5. Gaizka Saldaña & José Ignacio San Martín & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Analysis of the Current Electric Battery Models for Electric Vehicle Simulation," Energies, MDPI, vol. 12(14), pages 1-27, July.
    6. Changqing Du & Shiyang Huang & Yuyao Jiang & Dongmei Wu & Yang Li, 2022. "Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 15(12), pages 1-25, June.
    7. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
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