A Comparison Study of Machine Learning Enabled Filtering Methods for Battery Management

Abstract

Prognostics and health management has become a prominent field for the analyses of dynamic system degradation. Specifically, methods for forecasting remaining useful life have been studied extensively, including some hybrid approaches that have indicated successful results. Mainly, a combination of machine learning and filtering techniques have shown to be the most effective. Currently, there exists a need to determine an optimal general method for remaining useful life estimation in complex systems. This paper focuses on a comparison between successful hybrid approaches. The methods are applied to modeling capacity degradation in lithium-ion batteries, with the NASA dataset utilized for this study.