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Online cell-by-cell SOC/SOH estimation method for battery module employing extended Kalman filter algorithm with aging effect consideration

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Abstract

As the number of series connections of battery cells increases, individual cells are operating in different temperature profiles, and the aging patterns of the cells become dissimilar from each other. Thenceforth, individual state-cell-characteristics should be tracked online for higher safety. Although Kalman-filter-based battery state estimation is one of the most popular methods, it is sensitive to the accuracy of the battery model parameters and difficult to be applied to every cell. This work proposes an online cell-by-cell state-of-charge (SOC)/state-of-health (SOH) estimation method to mitigate this limitation. The aging patterns of the individual cells are predicted by introducing a combination of a switch-matrix flying capacitor and electrochemical impedance spectroscopy (EIS) model parameter scanning techniques. Accordingly, the accuracy of the SOC estimation for individual cells is enhanced. The proposed method is verified by a real-time simulation platform, where the SOC and SOH levels of the cells are individually estimated within a 1.24% error.

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Acknowledgements

This work was partly supported by the Technology Development Program (S3207312) funded by the Ministry of SMEs and Startups (MSS, Korea) and the National Research Foundation of Korea (NRF-2020R1A2C2009303) grant funded by the Korea government (MSIT).

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Authors and Affiliations

  1. Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, Republic of Korea

    Ngoc-Thao Pham, Phuong-Ha La & Sung-Jin Choi

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  1. Ngoc-Thao Pham
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Correspondence to Sung-Jin Choi.

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Pham, NT., La, PH. & Choi, SJ. Online cell-by-cell SOC/SOH estimation method for battery module employing extended Kalman filter algorithm with aging effect consideration. J. Power Electron. 22, 2092–2099 (2022). https://doi.org/10.1007/s43236-022-00526-7

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