Recent advances in model-based fault diagnosis for lithium-ion batteries: A comprehensive review

Lithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods for LIBs in advanced BMSs. This paper provides a comprehensive review on these methods. Different from the existing reviews focusing on the minute details of the methods, this review systematically explores the model-based fault diagnosis framework along with an in-depth examination of its critical components. Based on a general state-space battery model, the study elaborates on the formulation of state vectors, the identification of model parameters, the analysis of fault mechanisms, and the evaluation of modeling uncertainties. Following this foundational work, various state observers and their algorithm implementations are designed for fault diagnosis, with a focus on design characteristics, the importance of selecting appropriate observers for specific applications, and highlighting the advantages and limitations of different fault diagnosis methods in practical applications. Finally, the paper discusses the challenges and outlook in model-based fault diagnosis methods, envisioning their possible future research directions. • Systematic exploration of the model-based fault diagnosis framework. • Examination of state vectors, model parameters, fault mechanisms and modeling uncertainties. • Detailed analysis of observer designs and their algorithm implementations. • Provision of challenges and outlook on real-world applications and integration with cloud.