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Intelligent Learning Method for Capacity Estimation of Lithium-Ion Batteries Based on Partial Charging Curves

Author

Listed:
  • Can Ding

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Qing Guo

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Lulu Zhang

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Tao Wang

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

Abstract

Lithium-ion batteries are widely used in electric vehicles, energy storage power stations, and many other applications. Accurate and reliable monitoring of battery health status and remaining capacity is the key to establish a lithium-ion cell management system. In this paper, based on a Bayesian optimization algorithm, a deep neural network is structured to evaluate the whole charging curve of the battery using partial charging curve data as input. A 0.74 Ah battery is used for experiments, and the effect of different input data lengths is also investigated to check the high flexibility of the approach. The consequences show that using only 20 points of partial charging data as input, the whole charging profile of a cell can be exactly predicted with a root-mean-square error (RMSE) of less than 19.16 mAh (2.59% of the nominal capacity of 0.74 Ah), and its mean absolute percentage error (MAPE) is less than 1.84%. In addition, critical information including battery state-of-charge (SOC) and state-of-health (SOH) can be extracted in this way to provide a basis for safe and long-lasting battery operation.

Suggested Citation

  • Can Ding & Qing Guo & Lulu Zhang & Tao Wang, 2024. "Intelligent Learning Method for Capacity Estimation of Lithium-Ion Batteries Based on Partial Charging Curves," Energies, MDPI, vol. 17(11), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2686-:d:1406744
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    References listed on IDEAS

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    1. Hongyuan Yuan & Jingan Liu & Yu Zhou & Hailong Pei, 2023. "State of Charge Estimation of Lithium Battery Based on Integrated Kalman Filter Framework and Machine Learning Algorithm," Energies, MDPI, vol. 16(5), pages 1-16, February.
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