This paper provides a comprehensive review of model-based fault diagnosis methods for lithium-ion batteries (LIBs). It discusses the classification of battery models into physics-based electrochemical models (EMs) and electrical equivalent circuit models (ECMs). A general state-space representation is introduced to describe the electrical dynamics of a faulty battery, including the formulation of state vectors and parameter matrices. The paper elaborates on fault mechanisms, including battery faults (overcharge/overdischarge, connection faults, short circuit) and sensor faults (voltage and current sensor faults). It also addresses modeling uncertainties such as modeling errors, measurement noises, aging effects, and measurement outliers. The design and implementation of state observers, including online and offline observers, are discussed, along with their algorithms for battery fault diagnosis. The paper also covers the complete fault diagnosis procedure, including fault detection, identification, and estimation. It highlights the importance of fault diagnosis in ensuring the safety and reliability of LIBs in various operating conditions. The review emphasizes the role of model-based methods in early detection of battery faults and outlines future research directions.This paper provides a comprehensive review of model-based fault diagnosis methods for lithium-ion batteries (LIBs). It discusses the classification of battery models into physics-based electrochemical models (EMs) and electrical equivalent circuit models (ECMs). A general state-space representation is introduced to describe the electrical dynamics of a faulty battery, including the formulation of state vectors and parameter matrices. The paper elaborates on fault mechanisms, including battery faults (overcharge/overdischarge, connection faults, short circuit) and sensor faults (voltage and current sensor faults). It also addresses modeling uncertainties such as modeling errors, measurement noises, aging effects, and measurement outliers. The design and implementation of state observers, including online and offline observers, are discussed, along with their algorithms for battery fault diagnosis. The paper also covers the complete fault diagnosis procedure, including fault detection, identification, and estimation. It highlights the importance of fault diagnosis in ensuring the safety and reliability of LIBs in various operating conditions. The review emphasizes the role of model-based methods in early detection of battery faults and outlines future research directions.