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A State-of-Health Estimation and Prediction Algorithm for Lithium-Ion Battery of Energy Storage Power Station Based on Information Entropy of Characteristic Data

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Abstract

In order to enrich the comprehensive estimation methods for the balance of battery clusters and the aging degree of cells for lithium-ion energy storage power station, this paper proposes a state-of-health estimation and prediction method for the energy storage power station of lithium-ion battery based on information entropy of characteristic data. This method optimizes and processes attribute data based on specific running segments to form a characteristic data set, and adopts the information entropy value to reflect the orderliness of characteristic data to analyze the balance of battery clusters and the aging degree of cells in it. At the same time, the BP neural network is used to predict the information entropy value to achieve short-term prediction of the station’s health state. The feasibility and effectiveness of the health state estimation and prediction method proposed in this paper are demonstrated using actual data collected from the lithium-ion battery testing platform and the energy storage power station.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 51977014).

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Authors and Affiliations

  1. Department of College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China

    Jiahui Yue, Xiangyang Xia, Yuan Zhang & Tian Xia

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  1. Jiahui Yue
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  2. Xiangyang Xia
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  3. Yuan Zhang
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  4. Tian Xia
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Correspondence to Xiangyang Xia, Yuan Zhang or Tian Xia.

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Yue, J., Xia, X., Zhang, Y. et al. A State-of-Health Estimation and Prediction Algorithm for Lithium-Ion Battery of Energy Storage Power Station Based on Information Entropy of Characteristic Data. J. Electr. Eng. Technol. 18, 1757–1768 (2023). https://doi.org/10.1007/s42835-022-01332-8

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  • DOI: https://doi.org/10.1007/s42835-022-01332-8

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