State-of-charge estimation for onboard LiFePO4 batteries with adaptive state update in specific open-circuit-voltage ranges

Accurate estimation of the state-of-charge (SOC) is crucial for efficient and safe battery applications. However, existing SOC estimation methods fail to provide accurate SOC estimation for LiFePO4 batteries that have a flat voltage-SOC relationship. The analysis of the voltage-SOC characteristics shows that the failure of the present model-based methods can be ascribed to their inability to simultaneously accommodate the differences in voltage characteristics between different open-circuit-voltage (OCV) ranges. To overcome this limitation, an adaptive recursive square root algorithm is used to online identify OCV and other battery model parameters. Then, the parameters of the extended Kalman filter are adaptively updated in different OCV ranges, which are distinguished based on the identified OCV. Additional filtering methods are employed to enhance the stability of the estimation. Large-scale experiments are conducted at different temperatures with various driving profiles for method validation. While conventional methods fail to converge, the proposed method ensures both high accuracy and stability, with a maximum absolute error of <2%. The viability of the proposed method is further verified using data collected from real battery systems. Our work lays a foundation for the reliable management of LiFePO4 batteries in electric vehicles.

Automated summary

Accurate estimation of state-of-charge (SOC) is crucial for efficient and safe battery applications. However, existing SOC estimation methods fail for LiFePO4 batteries due to their flat voltage-SOC relationship. To address this, an adaptive algorithm is used to identify open-circuit voltage (OCV) and update parameters for the extended Kalman filter based on different OCV ranges. Additional filtering methods improve the stability of estimation. Experimental validation shows high accuracy and stability with a maximum absolute error of <2%. Real battery data further confirms the viability of the proposed method, laying a foundation for reliable LiFePO4 battery management in electric vehicles.