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Theoretical Framework for Health Estimation Using Machine Learning
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DOI：10.1007/978-3-319-03527-7_8.png)
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Abstract
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Real-time prediction of Remaining Useful Life (RUL) is an essential feature of a robust battery management system (BMS). However, due to the complex nature of the battery degradation, physics-based degradation modeling is often infeasible. Data-driven approaches provide an alternative when physics-based modeling is infeasible. In this chapter, we investigate some of the most popular machine learning-based data-driven approaches used by the Lithium-ion battery community. The chapter first introduces basic concepts of classification and regression, followed by a generic framework for its solution. Finally, we introduce some machine learning algorithms for the solution of this generic framework.
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