Electric vehicle battery capacity degradation and health estimation using machine-learning techniques: a review

Abstract

Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility. However, predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions. Additionally, state-of-health (SOH) and remaining-useful-life (RUL) predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance. Due to the non-linear behaviour of the health prediction of electric vehicle batteries, the assessment of SOH and RUL has therefore become a core research challenge for both business and academics. This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management, emphasizing state prediction and ageing prognostics. The objective is to provide comprehensive information about the evaluation, categorization and multiple machine-learning algorithms for predicting the SOH and RUL. Additionally, lithium-ion battery behaviour, the SOH estimation approach, key findings, advantages, challenges and potential of the battery management system for different state estimations are discussed. The study identifies the common challenges encountered in traditional battery management and provides a summary of how machine learning can be employed to address these challenges.