Because of their advantages of high energy and power density

low self-discharge rate

and long lifespan

lithium-ion batteries (LIBs) have been widely used in many applications such as electric vehicles

energy storage systems

smart grids

etc. However

lithium-ion battery systems (LIBSs) frequently malfunction because of complex working conditions

harsh operating environment

battery inconsistency

and inherent defects in battery cells. Thus

safety of LIBSs has become a prominent problem and has attracted wide attention. Therefore

efficient and accurate fault diagnosis for LIBs is very important. This paper provides a comprehensive review of the latest research progress in fault diagnosis for LIBs. First

the types of battery faults are comprehensively introduced and the characteristics of each fault are analyzed. Then

the fault diagnosis methods are systematically elaborated

including model-based

data processing-based

machine learning-based and knowledge-based methods. The latest research is discussed and existing issues and challenges are presented

while future developments are also prospected. The aim is to promote further researches into efficient and advanced fault diagnosis methods for more reliable and safer LIBs.