Research on methods for extracting aging characteristics and health status of lithium-ion batteries based on small samples

Abstract

The number of aging samples is small with regard to the small sample characteristics of lithium-ion battery aging data, and the extraction of effective aging features is difficult to perform, which reduces the generalization of the battery aging behavior model as well as the accuracy of battery state of health (SOH) estimation. According to the incremental capacity analysis, a gray relational analysis (GRA) method based on PCA (principal component analysis) was proposed in this study, which was combined with AdaBoost-SVR (support vector regression) to achieve accurate SOH measurement under small sample conditions. Accordingly, the combination of GRA and PCA was able to fully mine the aging characteristic information from limited battery aging samples while improving the generalization ability of the battery aging behavior model. Moreover, based on AdaBoost-SVR, it was able to perform adaptive weighted sampling on small data samples, and through multiple iterations of the SVR training model, the full use of small sample information was realized, ensuring the accuracy of SOH estimation. Finally, by employing multiple sets of battery aging training sets and test sets for experimental verification, the joint algorithm based on GRA-PCA and AdaBoost-SVR was proven to achieve the accurate extraction of battery aging characteristics and precise tracking of capacity decline.