Impact Factor:4.7

DOI number:10.1007/s11431-022-2220-y

Journal:SCIENCE CHINA Technological Sciences

Key Words:lithium-ion battery; electric vehicle; state of health; extreme gradient boosting

Abstract: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 real-world 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 real-world EVs.

Indexed by:Journal paper

Discipline:Engineering

Document Type:J

Volume:66

Issue:1

Page Number:46-56

Translation or Not:no

Date of Publication:2023-01-01

Included Journals:SCI

Links to published journals:https://www.sciengine.com/SCTS/doi/10.1007/s11431-022-2220-y