Online Parameter Identification for Lithium-Ion Batteries: An Adaptive Moving Window Size Design Methodology for Least Square Fitting

Abstract

Online state-of-charge estimations are critical to lithium-ion batteries in electric vehicles for safe and reliable operations. To ensure accurate online state estimations, an online battery parameter identification algorithm, such as the least square fitting approach, along with a shifting moving window, was developed and implemented, while the decision of the moving window size for general purpose is a technical issue not yet been well-addressed. This paper proposes an adaptive moving window size design methodology that adjusts the moving window size considering three important factors: load profile, state-of-charge to open-circuit voltage profile, and condition of data. The simulation results show that the proposed methodology helps to identify the parameters of the battery electric circuit model with high accuracy, where the results are compatible to the results using fine-tuned fixed window sizes. Also, the proposed methodology is general to driving profiles from moderate to severe without extra algorithm redesign. In the three standard driving profile cases, the parameter identification error is reduced by 59.15%, 69.02%, and 75.14%, respectively, compared to the worst case in experiments, which are compatible with the results after fine-tuning the fixed window sizes. With accurately identified parameters, the modeling error is minimized, and three typical linear SOC estimation algorithms thus render accurate SOC estimation, rendering an average of 13.4%, 64.43% 57.48% reduction from the worst case in experiments, respectively.