Supervised Regression using SVR and Neural Networks for Early Prediction of End-of-Life in Lithium-ion Batteries

Early prediction of end-of-life in lithium-ion batteries is a critical factor in managing performance and preventing malfunctions. This work studies various machine learning methods using the lithium-ion battery lifecycle dataset provided by Severson et al. [1].

Support Vector Regression
Multilayer Perceptron
Long-Short-Term-Memory Recurrent Neural Network
Convolutional Neural Network

DATA AVAILABILITY
(https://data.matr.io/1/projects/5c48dd2bc625d700019f3204)

[1] Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., Bazant, M. Z., Harris, S. J., Chueh, W. C., & Braatz, R. D. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4(5), 383–391. (https://www.doi.org/10.1038/s41560-019-0356-8)

[2] Attia, P. M., Severson, K. A., & Witmer, J. D. (2021). Statistical learning for accurate and interpretable battery lifetime prediction. Journal of The Electrochemical Society, 168(9), 090547. (https://www.doi.org/10.1149/1945-7111/ac2704)

[3] Xu, P., & Lu, Y. (2022). Predicting Li-ion Battery Cycle Life with LSTM RNN. arXiv preprint (https://www.arxiv.org/abs/2207.03687)