Kalman filter method with correction of open-circuit voltage curve for estimating SOC of lithium-ion batteries

This study presents a method for accurately estimating the state-of-charge (SOC) of lithium-ion batteries. By leveraging the correlation between open-circuit voltage (OCV) curves of batteries, the method generalized a customized OCV curve for the battery under test, enhancing the accuracy of SOC estimation. The equivalent circuit model parameters of the battery were identified using a particle swarm optimization with a genetic algorithm (GA-PSO). The SOC was then estimated using a cubature Kalman filter. Experimental results demonstrate that the method achieves a mean absolute error of less than 0.70% and a maximum error of less than 6.45%. The method is applicable to various types of lithium-ion batteries, temperature conditions and working conditions. Furthermore, this study shows that the method significantly improves the accuracy at low sampling rates, achieving an approximate 50% improvement in accuracy. Consequently, this method can be effectively integrated into the battery management systems of electronic devices.