State of charge estimation framework for lithium-ion batteries based on square root cubature Kalman filter under wide operation temperature range

Due to the significant influence of temperature on battery charging and discharging performance, exact evaluation of state of charge (SOC) under complex temperature environment becomes increasingly important. This paper develops an advanced framework to estimate the SOC for lithium-ion batteries with consideration of temperature variation. First, an accurate electrical model with wide temperature compensation is established, and a series of experiments are carried out under wide range time-varying temperature from -20 degrees C to 60 degrees C. Then, the genetic algorithm is leveraged to identify the temperature-dependent model parameters. On this basis, the battery SOC is accurately estimated based on the square root cubature Kalman filter algorithm. Finally, the availability of the proposed method at different temperatures is validated through a complicated mixed working cycle test, and the experimental results manifest that the devised framework can accurately evaluate SOC under wide time-varying temperature range with the maximum error of less than 2%.

Automated summary

This paper proposes an advanced framework to estimate SOC for lithium-ion batteries considering temperature variation. By establishing an accurate electrical model and optimizing model parameters with genetic algorithm, combined with Kalman filter algorithm, it can accurately evaluate battery SOC under complex temperature environments.