Early diagnosis of internal short circuit (ISC) of lithium-ion batteries can effectively prevent thermal runaway. Based on the charging curve of lithium-ion battery module, a quantitative diagnosis algorithm of ISC based on the remaining charge capacity (RCC) is proposed in this study. The simulation and experimental verification of the algorithm are carried out under the conditions of different voltage acquisition accuracies, sampling periods, temperatures, and aging degrees. The results show that the proposed algorithm can accurately and quantitatively diagnose the ISC under certain conditions: (1) For the serious ISC of 10 Ω
level, high diagnosis accuracy can be obtained even under the conditions of 10 mV acquisition accuracy, 10 s sampling period and variable temperature. For the early ISC of 100 Ω level, the ISC resistance is smaller than the actual value, and the diagnosis time is longer. To improve the accuracy and timeliness of early ISC diagnosis, the voltage acquisition accuracy and sampling frequency should be higher than 1mV and 1Hz, respectively; (2) Battery aging will reduce the accuracy of ISC diagnosis, but it has little effect on 10 Ω level ISC, and the diagnostic error of ISC resistance is less than 6% even at extreme low temperature (- 20 ℃). The conclusions are of great significance to improve the accuracy of quantitative diagnosis of ISC for lithium-ion batteries.