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A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems

Author

Listed:
  • Valentina Lucaferri

    (Department of Industrial Engineering, Electronics and Mechanics, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy
    These authors contributed equally to this work.)

  • Michele Quercio

    (Department of Industrial Engineering, Electronics and Mechanics, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy
    These authors contributed equally to this work.)

  • Antonino Laudani

    (Department of Electrical, Electronic and Computer Engineering (DIEEI), University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
    These authors contributed equally to this work.)

  • Francesco Riganti Fulginei

    (Department of Industrial Engineering, Electronics and Mechanics, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy
    These authors contributed equally to this work.)

Abstract

Battery state estimation is fundamental to battery management systems (BMSs). An accurate model is needed to describe the dynamic behavior of the battery to evaluate the fundamental quantities, such as the state of charge (SOC) or the state of health (SOH). This paper presents an overview of the most commonly used battery models, the equivalent electrical circuits, and data-driven ones, discussing the importance of battery modeling and the various approaches used to model lithium batteries. In particular, it provides a detailed analysis of the electrical circuit models commonly used for lithium batteries, including equivalent circuit and thermal models. Furthermore, a comprehensive overview of data-driven approaches is presented. The advantages and limitations of each type of model are discussed. Finally, the paper concludes with a discussion of current research trends and future directions in the field of battery modeling.

Suggested Citation

  • Valentina Lucaferri & Michele Quercio & Antonino Laudani & Francesco Riganti Fulginei, 2023. "A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems," Energies, MDPI, vol. 16(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7807-:d:1288765
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    References listed on IDEAS

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    1. Aldo Canova & Federico Campanelli & Michele Quercio, 2022. "Flywheel Energy Storage System in Italian Regional Transport Railways: A Case Study," Energies, MDPI, vol. 15(3), pages 1-15, February.
    2. Tian, Jinpeng & Xiong, Rui & Shen, Weixiang & Lu, Jiahuan, 2021. "State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach," Applied Energy, Elsevier, vol. 291(C).
    3. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
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