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Experimentally Validated Coulomb Counting Method for Battery State-of-Charge Estimation under Variable Current Profiles

Author

Listed:
  • Bachir Zine

    (Mechanical Engineering Department, Faculty of Technology, University of Eloued, El Oued 39000, Algeria)

  • Haithem Bia

    (Mechanical Engineering Department, Faculty of Technology, University of Eloued, El Oued 39000, Algeria)

  • Amel Benmouna

    (ESTA, School of Business and Engineering, 90000 Belfort, France
    Femto-ST, FCLAB, University Bourgogne Franche-Comte, CNRS, UTBM, 91110 Belfort, France)

  • Mohamed Becherif

    (Femto-ST, FCLAB, University Bourgogne Franche-Comte, CNRS, UTBM, 91110 Belfort, France)

  • Mehroze Iqbal

    (Femto-ST, FCLAB, University Bourgogne Franche-Comte, CNRS, UTBM, 91110 Belfort, France)

Abstract

Battery state of charge as an effective operational indicator is expected to play a crucial role in the advancement of electric vehicles, improving the battery capacity and energy utilization, avoiding battery overcharging and over-discharging, extending the battery’s useful lifespan, and extending the autonomy of electric vehicles. In context, this article presents a computationally efficient battery state-of-charge estimator based on the Coulomb counting technique with constant and variable discharging current profiles for an actual battery pack in real time. A dedicated experimental bench is developed for validation purposes, where pivotal measurements such as current, voltage, and temperature are initially measured during the charging/discharging cycle. The state of charge thus obtained via these measurements is then compared with the value estimated through the battery generic model. Detailed analysis with conclusive outcomes is finally presented to exhibit the flexible nature of the proposed method in terms of the precise state-of-charge estimation for a variety of batteries, ranging from lead–acid batteries for domestic applications to Li-ion batteries inside electric vehicles.

Suggested Citation

  • Bachir Zine & Haithem Bia & Amel Benmouna & Mohamed Becherif & Mehroze Iqbal, 2022. "Experimentally Validated Coulomb Counting Method for Battery State-of-Charge Estimation under Variable Current Profiles," Energies, MDPI, vol. 15(21), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8172-:d:960799
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    References listed on IDEAS

    as
    1. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
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    3. 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).
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    Cited by:

    1. Luigi Rubino & Guido Rubino & Raffaele Esempio, 2023. "Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station," Energies, MDPI, vol. 16(3), pages 1-17, January.

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