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Battery State of Health Estimation from Discharge Voltage Segments Using an Artificial Neural Network

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Abstract

Battery state of health (SOH) estimation is imperative for preventive maintenance, replacement, and end-of-life prediction of lithium ion batteries. Herein, we introduce a data-driven approach to state of health (SOH) prediction for battery cells using a Deep Neural Network (DNN). Our DNN model, trained on short discharge curve segments, outperforms Multilayer Perceptron (MLP) and Support Vector Regression (SVR) models. The Mutual Information (MI) score guides the selection of voltage range and width for model training, reflecting nonlinear degradation characteristics. A transfer learning strategy is applied for outlier cells, initially training on normal cells and fine-tuning with outlier cells, resulting in improved SOH predictions, particularly at higher cycles. The study finds that increasing the segment width reduces SOH prediction error, with the smallest segment of 0.05 V demonstrating good performance (RMSE of 0.0246), decreasing to 0.0142 at a width of 0.2 V. For outlier cells, transfer learning leads to a 48% reduction in RMSE. The partial segment-based approach offers potential for rapid SOH prediction in laboratory and field applications, enhancing efficiency in the development process.

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References

  1. Schmuch, R., et al. (2018). Performance and cost of materials for lithium-based rechargeable automotive batteries. Nature Energy, 3(4), 267–278.

    Article  Google Scholar 

  2. Pinson, M. B., & Bazant, M. Z. (2013). Theory of SEI formation in rechargeable batteries: Capacity fade, accelerated aging and lifetime prediction. Journal of the Electrochemical Society, 160(2), A243–A250.

    Article  Google Scholar 

  3. Christensen, J., & Newman, J. (2005). Cyclable lithium and capacity loss in Li-ion cells. Journal of the Electrochemical Society, 152(4), A818–A829.

    Article  Google Scholar 

  4. Lombardo, T., et al. (2022). Artificial intelligence applied to battery research: Hype or reality? Chemical Reviews, 122(12), 10899–10969.

    Article  Google Scholar 

  5. Li, W. H., et al. (2021). Online capacity estimation of lithium-ion batteries with deep long short-term memory networks. Journal of Power Sources, 482, 228863.

    Article  Google Scholar 

  6. Sulzer, V., et al. (2021). The challenge and opportunity of battery lifetime prediction from field data. Joule, 5(8), 1934–1955.

    Article  Google Scholar 

  7. Nejad, S., Gladwin, D. T., & Stone, D. A. (2016). A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. Journal of Power Sources, 316, 183–196.

    Article  Google Scholar 

  8. Keil, J., & Jossen, A. (2020). Electrochemical modeling of linear and nonlinear aging of lithium-ion cells. Journal of the Electrochemical Society, 167(11), 110535.

    Article  Google Scholar 

  9. Ng, M. F., et al. (2020). Predicting the state of charge and health of batteries using data-driven machine learning. Nature Machine Intelligence, 2(3), 161–170.

    Article  Google Scholar 

  10. Severson, K. A., et al. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4(5), 383–391.

    Article  Google Scholar 

  11. Tao, L. F., et al. (2017). A review of stochastic battery models and health management. Renewable & Sustainable Energy Reviews, 80, 716–732.

    Article  Google Scholar 

  12. Chu, A., et al. (2020). Stochastic capacity loss and remaining useful life models for lithium-ion batteries in plug-in hybrid electric vehicles. Journal of Power Sources, 478, 228991.

    Article  Google Scholar 

  13. Xu, X., et al. (2019). State-of-health estimation for lithium-ion batteries based on wiener process with modeling the relaxation effect. IEEE Access, 7, 105186–105201.

    Article  Google Scholar 

  14. Feng, H. L., & Yan, H. M. (2022). State of health estimation of large-cycle lithium-ion batteries based on error compensation of autoregressive model. Journal of Energy Storage, 52, 104869.

    Article  Google Scholar 

  15. Lin, M. Q., Zeng, X. P., & Wu, J. (2021). State of health estimation of lithium-ion battery based on an adaptive tunable hybrid radial basis function network. Journal of Power Sources, 504, 230063.

    Article  Google Scholar 

  16. Kim, S. W., et al. (2022). Recent advances of artificial intelligence in manufacturing industrial sectors: A review. International Journal of Precision Engineering and Manufacturing, 23, 111–129.

    Article  Google Scholar 

  17. Kim, E., et al. (2023). Data-driven methods for predicting the state of health, state of charge, and remaining useful life of li-ion batteries: A comprehensive review. International Journal of Precision Engineering and Manufacturing, 24, 1281–1304.

    Article  Google Scholar 

  18. Klass, V., Behm, M., & Lindbergh, G. (2014). A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation. Journal of Power Sources, 270, 262–272.

    Article  Google Scholar 

  19. Shao, L. Y., et al. (2023). A review of remaining useful life prediction for energy storage components based on stochastic filtering methods. Energies, 16(3), 1469.

    Article  Google Scholar 

  20. Roman, D., et al. (2021). Machine learning pipeline for battery state-of-health estimation. Nature Machine Intelligence, 3(5), 447–456.

    Article  Google Scholar 

  21. Shi, M. J., Xu, J., Lin, C. P., & Mei, X. S. (2022). A fast state-of-health estimation method using single linear feature for lithium-ion batteries. Energy, 256, 124652.

    Article  Google Scholar 

  22. Birkl, C. (2017). Oxford Battery Degradation Dataset 1, U.o. Oxford, Editor.

  23. Shen, S., et al. (2020). Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries. Applied Energy, 260, 114296.

    Article  Google Scholar 

  24. Eddahech, A., et al. (2012). Behavior and state-of-health monitoring of Li-ion batteries using impedence spectroscopy and recurrent neural networks. International Journal of Electrical Power & Energy Systems, 42(1), 487–494.

    Article  Google Scholar 

  25. Lin, M., Wu, J., Meng, J., Wang, W., & Wu, J. (2023). State of health estimation with attentional long short-term memory network for lithium-ion batteries. Energy, 268, 126706.

    Article  Google Scholar 

  26. Bao, X. Y., et al. (2023). Hybrid deep neural network with dimension attention for state-of-health estimation of lithium-ion batteries. Energy, 278, 127734.

    Article  Google Scholar 

  27. Meng, Z., et al. (2023). PINN-FORM: A new physics-informed neural network for reliability analysis with partial differential equation. Computer Methods in Applied Mechanics and Engineering, 414, 116172.

    Article  MathSciNet  Google Scholar 

  28. Hofmann, T., et al. (2023). Physics-informed neural networks for state of health estimation in lithium-ion batteries. Journal of the Electrochemical Society, 170(9), 090524.

    Article  Google Scholar 

  29. Murphy, K. P. (2022). Probabilistic machine learning: An introduction. MIT Press.

    Google Scholar 

  30. Pan, S. J., & Yang, Q. A. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

    Article  Google Scholar 

  31. Toyota Research Institute Experimental Data Platform. 2019.

  32. Wan, X., Wang, W. Q., Liu, J. M., & Tong, T. J. (2014). Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Medical Research Methodology, 14, 135.

    Article  Google Scholar 

  33. Gommers, R. (2023). Available from: https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.InterpolatedUnivariateSpline.html

  34. Shu, X., et al. (2021). State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives. Iscience, 24(11), 103265.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1I1A3073674) and Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20224000000150).

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Authors and Affiliations

  1. School of Mechanical Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea

    Muhammad Usman Javaid & Sung Yeol Kim

  2. Department of Statistics, Kyungpook National University, Daegu, 702-701, Republic of Korea

    Jaewon Seo

  3. School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea

    Young-Kyoon Suh

  4. Department of Hydrogen and Renewable Energy, School of Convergence, Kyungpook National University, Daegu, 702-701, Republic of Korea

    Sung Yeol Kim

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  1. Muhammad Usman Javaid
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Correspondence to Sung Yeol Kim.

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Javaid, M.U., Seo, J., Suh, YK. et al. Battery State of Health Estimation from Discharge Voltage Segments Using an Artificial Neural Network. Int. J. of Precis. Eng. and Manuf.-Green Tech. (2024). https://doi.org/10.1007/s40684-024-00602-2

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  • DOI: https://doi.org/10.1007/s40684-024-00602-2

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