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With the increase in the amount of actual operating data on electric vehicles, how to analyze and process useful information from existing battery charging and discharging data and apply it to subsequent state estimation is worthy of in-depth thinking and practice by researchers. This article proposes a collaborative estimation architecture for SOC and SOH based on the 1RC equivalent circuit model, recursive least squares, and adaptive extended Kalman filtering algorithms (AEKF), which combine offline data processing with online applications. By applying offline data processing, OCV–SOC polynomial fitting and average polarization resistance were determined, which reduced the time required for basic data measurement and improved the accuracy of model parameter identification, while a recursive estimation combining micro- and macro-time-scales of AEKF was used for the online real-time estimation of the SOC and actual available capacity of batteries, in order to eliminate interference from measurement and process noise. The results of the simulated and experimental data validation indicate that the proposed algorithm is applicable to the lithium-ion batteries studied in this paper, the average SOC deviation is less than 1.5%, the maximum deviation is less than 2.02%, and the SOH estimation deviation is less than 1% under different driving conditions in the multi-temperature range. This study lays the foundation for further utilizing offline data and improving SOC and SOH collaborative estimation algorithms.
Liu et al. (Thu,) studied this question.