Fast and high-precision online SOC estimation for improved model of lithium-ion battery based on temperature correlation coefficient

In high-energy and high-power applications, thousands of batteries are connected in series and parallel, imposing a substantial computational burden for state of charge (SOC) estimation. The second-order RC equivalent circuit model is often utilized for SOC estimation. However, this model requires the identification of numerous parameters, rendering the calculations complex and computationally intensive. Furthermore, the model often neglects the impact of temperature. To enhance the speed and accuracy of SOC estimation for numerous individual cells, an equivalent circuit model is constructed. This model incorporates temperature correlation coefficients and the electrical characteristics of lithium-ion batteries at various temperatures. Subsequently, a combined forgetting factor recursive least squares and extended Kalman filter algorithm is introduced for battery SOC estimation. The results demonstrate that the improved model significantly reduces SOC estimation time. Compared to the traditional second-order RC model, the improved model reduces the time by 37.8%, 58.3%, and 34% at − 10 °C, 0 °C, and 25 °C, respectively.