Abstract: Fault diagnosis is key to enhancing the performance and safety of battery storage systems. However, it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algorithm is limited and the features of the different faults are similar. The model-based method has been widely used for degradation mechanism analysis, state estimation, and life prediction of lithium-ion battery systems due to the fast speed and high development efficiency. This paper reviews the mainstream modeling approaches used for battery diagnosis. First, a review of the battery’s degradation mechanisms and the external factors affecting the aging rate is presented. Second, the different modeling approaches are summarized, from microscopic to macroscopic scales, including density functional theory, molecular dynamics, X-ray computed tomography technology, electrochemical model, equivalent circuit model, distributed model and neural network algorithm. Subsequently, the advantages and disadvantages of these model approaches are discussed for fault detection and diagnosis of batteries in different application scenarios. Finally, the remaining challenges of model-based battery diagnosis and the future perspective of using cloud control and battery intelligent networking to enhance diagnostic performance are discussed.