Quantitative short circuit identification for single lithium-ion cell applications based on charge and discharge capacity estimation

Abstract

Micro short circuit (MSC) is a potential risk of thermal runaway in batteries. It is essential to prevent thermal runaway and improve the safety of batteries via short-circuit detection. Traditional detection methods take the healthy cells in the battery pack as a reference, which use statistical characteristics to perform qualitative or quantitative MSC diagnosis. However, for the application scenarios of one single cell, the existing methods cannot determine short circuit due to the lack of healthy batteries as a reference. Therefore, a quantitative diagnosis method for single lithium-ion cell applications is proposed in this paper. The core idea of the method is that the estimated capacity of the short-circuit cell during the discharging process is smaller than the normal value, while the estimated capacity during the charging process is larger than the normal value. Hence, by comparing the historical capacity variation characteristics under the charging and discharging cycle, the fault can be diagnosed quantitatively. The experimental results show when the short-circuit resistance is 5Ω for large-capacity cells, the short-circuit resistance estimation accuracy can reach 2.5%. And the capacity estimation error of the short-circuit cell is within 1.5% after the capacity is compensated. • The method can realize the quantitative short-circuit diagnosis. • The method is suitable for single lithium-ion cell applications. • It can be diagnosed through the longitudinal comparison of historical data. • It is based on the comparison of the historical charge and discharge capacity. • It can improve the accuracy of fault diagnosis.