由于锂离子电池（LIB）在高度复杂和大功率应用中的应用越来越多，以确保系统的安全可靠运行，故障检测/诊断已成为电池管理系统（BMS）的关键功能。长期以来，机器学习（ML）在LIB的BMS中的应用一直被用来对LIB的几个重要状态（例如充电状态，健康状态和剩余使用寿命）进行有效，可靠，准确的预测。受基于 ML 的技术优于传统 LIB 故障检测/诊断方法（如基于模型、基于知识和基于信号处理的技术）的一些有前途的特性的启发，基于 ML 的数据驱动方法已成为主要研究焦点在过去的几年中。本文专门针对基于ML的最新数据驱动的故障检测/诊断技术进行了全面的综述，旨在为研究团体提供准确的参考和指导，以期开发出准确，可靠，适应性强且易于操作的解决方案。实施LIB系统的故障诊断策略。还解释了 LIB 故障诊断的现有策略和未来挑战的当前问题，以便更好地理解和指导。 Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.