State of health estimation for lithium-ion batteries in real-world electric vehicles

The state of health(SOH) plays a significant role in the mileage and safety of an electric vehicle(EV). In recent years, many methods based on data-driven analysis and laboratory measurements have been developed for SOH estimation. However, most of these proposed methods cannot be applied to real-world EVs. Here, we present a method for SOH estimation based on realworld EV data. A battery-aging evaluation health index(HI) with a strong correlation to the SOH is retrieved from battery-aging data and then modified with thermal factors to depict the former SOH. Afterward, a local weighted linear-regression algorithm is used to qualitatively characterize the declining trend of the HI, which eliminates the local HI fluctuation caused by data noise.Subsequently, a series of features-of-interest(FOIs) is extracted according to the battery consistency, cell-voltage extrema, and cumulative mileage, and validated using the grey relational analysis. Finally, a battery-degradation model is built using the extreme gradient-boosting algorithm with the selected FOIs. The experimental results from real-world data indicate that the proposed method has high estimation accuracy and generalization, and the maximum error is around 2% for batteries in realworld EVs.