Privacy-preserving State of Health prediction for electric vehicle batteries: A comprehensive review

Abstract

This work presents an in-depth systematic literature review of privacy-preserving machine learning techniques for battery State of Health (SoH) prediction in electric vehicles (EVs). Our review methodically analyses a corpus of 43 research articles and publications spanning from 2012 to 2024 which provides a comprehensive landscape of the field’s evolution and current trends. This review covers a range of machine learning techniques and uniquely addresses first-of-its-kind privacy needs specific to SoH prediction for EVs, with a detailed examination of the integration of privacy-preserving methods. We also delve into the key challenges associated with data processing and feature engineering, and how these challenges are managed. The methodology employed includes not only the identification but also a thematic analysis of the literature, focusing on how these techniques balance data confidentiality with analytical performance. We proposed a privacy preserving system model for SoH prediction. The review is further enriched by various real-time applications and case studies, illustrating the practical implementation and efficacy of the discussed methods. This systematic approach provides a well-rounded view of the existing solutions and highlights gaps for future research, offering invaluable insights for both academic and industrial stakeholders in the domain of electric vehicle technology.