A fault diagnosis method of battery internal short circuit based on multi-feature recognition

Abstract

The internal short circuit (ISC) fault has been considered as one of the most serious problems, which may pose a threat to the operation safety of the battery system. To solve this problem, this paper proposes an ISC fault diagnosis method based on multi-feature recognition to distinguish aging and ISC fault. The ISC equivalent circuit model is established first. In addition, three characteristic parameters, including the slope of the “rebound” voltage curve, the “valley” ordinate in the differential voltage (DV) curve, and the electric quantity, namely high segment charging capacity (HSCC) between the valley point of the DV curve and the end of charging position, are extracted to distinguish the ISC battery from aging battery. The results show that the proposed method can effectively distinguish between ISC batteries, aging batteries, and normal batteries. Moreover, the ISC resistance is able to be estimated accurately, with an error of less than 5.44%.