Prediction of Remaining Useful Life of Batteries Using Machine Learning Models

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Author(s): Jaouad Boudnaya (Moulay Ismail University, Morocco), Hicham Laacha (Moulay Ismail University, Morocco), Mohamed Qerras (Moulay Ismail University, Morocco)and Abdelhak Mkhida (Moulay Ismail University, Morocco)
Copyright: 2024
Pages: 20
Source title: Enhancing Performance, Efficiency, and Security Through Complex Systems Control
Source Author(s)/Editor(s): Idriss Chana (ESTM, Moulay Ismail University of Meknès, Morocco), Aziz Bouazi (ESTM, Moulay Ismail University of Meknès, Morocco)and Hussain Ben-azza (ENSAM, Moulay Ismail University of Meknes, Morocco)
DOI: 10.4018/979-8-3693-0497-6.ch017

Keywords: Artificial Intelligence / Information Science Reference / Information Security & Privacy / Security & Forensics

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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.

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Prediction of Remaining Useful Life of Batteries Using Machine Learning Models

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