Machine Learning-based Prediction of the Remaining Life of Sodium-ion Batteries

SHI Yongsheng; ZHAI Xinran; HU Yujun

doi:10.6054/j.jscnun.2023031

Journal of South China Normal University (Natural Science Edition) > 2023 > 55(3): 17-24. > DOI: 10.6054/j.jscnun.2023031

SHI Yongsheng, ZHAI Xinran, HU Yujun. Machine Learning-based Prediction of the Remaining Life of Sodium-ion Batteries[J]. Journal of South China Normal University (Natural Science Edition), 2023, 55(3): 17-24. DOI: 10.6054/j.jscnun.2023031

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Machine Learning-based Prediction of the Remaining Life of Sodium-ion Batteries

School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China

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Received Date: May 14, 2023
Available Online: August 25, 2023

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Abstract

Sodium-ion battery capacity degradation exhibits nonlinearity and complexity, making accurate prediction of remaining useful life (RUL) extremely difficult. Therefore, this paper proposes an RUL prediction model combining Singular Spectrum Analysis (SSA) optimization algorithm with Gradient Boosting Regression Tree (GBRT). Firstly, experimental data is filtered and smoothed, and Incremental Capacity (IC) curves are plotted. The health indicators (HI) with high correlation to capacity decay are extracted from the IC curves. Secondly, PCA algorithm is used to reduce data redundancy, and the processed data is inputted into the GBRT model. SSA algorithm is used to search for the optimal hyperparameters to improve prediction accuracy. Finally, multiple sets of aging experimental data are used for validation, with RMSE, MAPE, and MAE all below 15.2, 7%, and 11.2, respectively. The results show that the proposed model has high prediction accuracy and robustness and outperforms other mainstream algorithms.
- sodium-ion batteries,
- remaining useful life,
- incremental capacity curve,
- Gradient boosting regression tree

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