Research on the SOH Prediction Based on the Feature Points of Incremental Capacity Curve

Abstract

The accurate prediction of the state of health (SOH) is an important basis for ensuring the normal operation of the lithium-ion battery (LIB). The accurate SOH can extend the life-span, ensure safety, and improve the performance of LIBs. The charging voltage curve and incremental capacity (IC) curve of the LIB in different SOH are obtained through experiments. The location parameters of each feature point on IC curve are closely related to battery aging, to characterize the SOH of the LIB with the location of feature points. To solve the difficulty in identifying feature points due to the oscillation in solving IC curves with a traditional numerical analytic method, the piecewise polynomial fitting method is adopted to smooth IC. To discuss the law between the location change of all feature points on the IC curve and the capacity attenuation, a capacity prediction regression model is established after the dimensionality reduction of the coordinate data of feature points on the IC curve with the principal component analysis method. The proposed method can rapidly estimate the online SOH of LIBs during the charging process of electric vehicles and the results show the maximum error is 0.63AH (3.15%).