Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction
نویسندگان
چکیده
Data-driven methods for battery lifetime prediction are attracting increasing attention applications in which the degradation mechanisms poorly understood and suitable training sets available. However, while advanced machine learning deep promise high performance with minimal data preprocessing, simpler linear models engineered features often achieve comparable performance, especially small sets, also providing physical statistical interpretability. In this work, we use a previously published dataset to develop simple, accurate, interpretable data-driven prediction. We first present "capacity matrix" concept as compact representation of electrochemical cycling data, along series feature representations. then create number univariate multivariate models, many highest-performing dataset. These provide insights into these cells. Our approaches can be used both quickly train new benchmark more methods.
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ژورنال
عنوان ژورنال: Journal of The Electrochemical Society
سال: 2021
ISSN: ['0013-4651', '1945-7111']
DOI: https://doi.org/10.1149/1945-7111/ac2704