Prediction of Remaining Useful Life of Batteries Using Machine Learning Models

Abstract

Predictive maintenance is a maintenance strategy based on monitoring the state of components to predict the date of future failure. The objective is to take the appropriate measures to avoid the consequences of this failure. For this reason, the authors determine the remaining useful life (RUL) which is the remaining time before the appearance of the failure on the component. It is an important approach that allows the prediction of aging mechanisms likely to lead components to failure. In this chapter, a new methodology for predicting the remaining useful life of components is proposed using a data-driven prognosis approach with the integration of machine learning. This approach is illustrated in a battery case study to predict the remaining useful life.