With the wide application of lithium-ion battery systems in electric vehicles, the safety issue caused by short-circuit fault of battery pack is becoming more serious. Therefore, the studies on state monitoring of battery pack and fault diagnosis are receiving more attention. To deal with the issues of low generality, poor anti-interference capacity and critical inconsistency of battery pack existed in non-model-based fault diagnosis methods, a short-circuit fault diagnosis method based on statistical analysis and density clustering is proposed for battery packs in this paper. Firstly, the fault information of battery pack is extracted by using the relative entropy of kernel density estimation (KDE) and correlation coefficient, based on a forgetting mechanism. The fault information is used to identify the changes of batteries’ voltage and temperature caused by short-circuit fault. Then, the short-circuit battery can be automatically identified by adopting the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The robustness of the proposed method is validated under conditions of noise interference and serious inconsistency. Furthermore, the effectiveness of the proposed method is verified under different short-circuit degree with 1, 5 and 10 Ω short-circuit resistors, and the accuracy of short-circuit fault diagnosis can reach 92. 17% in the case of a 10 Ω short-circuit resistor. By comparative analysis, the results show that the proposed diagnosis method can effectively detect and locate short-circuit batteries, and the more severe the fault, the shorter the diagnosis time required.