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Data-Driven Modeling and Open-Circuit Voltage Estimation of Lithium-Ion Batteries

Author

Listed:
  • Edgar D. Silva-Vera

    (School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Mexico)

  • Jesus E. Valdez-Resendiz

    (School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Mexico)

  • Gerardo Escobar

    (School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Mexico)

  • Daniel Guillen

    (School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Mexico)

  • Julio C. Rosas-Caro

    (Facultad de Ingenieria, Universidad Panamericana, Zapopan 45010, Mexico)

  • Jose M. Sosa

    (Laboratory of Electrical and Power Electronics, Instituto Tecnológico Superior de Irapuato, Irapuato 36821, Mexico)

Abstract

This article presents a data-driven methodology for modeling lithium-ion batteries, which includes the estimation of the open-circuit voltage and state of charge. Using the proposed methodology, the dynamics of a battery cell can be captured without the need for explicit theoretical models. This approach only requires the acquisition of two easily measurable variables: the discharge current and the terminal voltage. The acquired data are used to build a linear differential system, which is algebraically manipulated to form a space-state representation of the battery cell. The resulting model was tested and compared against real discharging curves. Preliminary results showed that the battery’s state of charge can be computed with limited precision using a model that considers a constant open-circuit voltage. To improve the accuracy of the identified model, a modified recursive least-squares algorithm is implemented inside the data-driven method to estimate the battery’s open-circuit voltage. These last results showed a very precise tracking of the real battery discharging dynamics, including the terminal voltage and state of charge. The proposed data-driven methodology could simplify the implementation of adaptive control strategies in larger-scale solutions and battery management systems with the interconnection of multiple battery cells.

Suggested Citation

  • Edgar D. Silva-Vera & Jesus E. Valdez-Resendiz & Gerardo Escobar & Daniel Guillen & Julio C. Rosas-Caro & Jose M. Sosa, 2024. "Data-Driven Modeling and Open-Circuit Voltage Estimation of Lithium-Ion Batteries," Mathematics, MDPI, vol. 12(18), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2880-:d:1478846
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    References listed on IDEAS

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    1. Lingling Li & Pengchong Wang & Kuei-Hsiang Chao & Yatong Zhou & Yang Xie, 2016. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-13, September.
    2. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    3. Zhu, Qiao & Xu, Mengen & Liu, Weiqun & Zheng, Mengqian, 2019. "A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter," Energy, Elsevier, vol. 187(C).
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