In a battery management system, the accurate estimation of the battery’s state of health (SOH) and state of capacity (SOC) are vital functions. The traditional estimation methods have limitations. To accurately estimate the SOC and SOH of power battery and improve the performance of the long-term estimation of a battery’s SOC, a joint estimation method based on a Kalman filter is proposed in this work. First, a second-order RC equivalent circuit model of a ternary lithium battery was built, whose parameters were identified online, and the model’s accuracy was verified. Then, the battery data under actual working conditions were collected. The SOC and SOH were estimated based on the Kalman filter algorithm, and the simulation was implemented using MATLAB. Finally, according to a time scale transformation, the battery state was jointly estimated, the SOC was estimated at a short-time scale, the SOH was estimated at a long-time scale, and the SOH estimation results were updated to the model parameters for SOC estimation. The results show that the accuracy of the method is very good, and it can effectively improve estimation accuracy and ensure batteries’ long-term estimation performance.

Accurate estimation of power battery state is an important function in battery management system. In order to accurately estimate power battery SOC and SOH and improve the performance of long-term estimation of battery SOC, a joint estimation method of power battery state based on UKPF was proposed in this paper. The particle filter algorithm was added on the basis of the unscented Kalman filter algorithm, and the particle filter algorithm was optimized by the unscented Kalman filter algorithm, which improved the particle degradation problem and improved the accuracy of battery state estimation. Based on the time scale transformation, the battery state estimation was completed, and the SOC and SOH were estimated at short and long time scales, respectively. The SOH estimation results were updated to the model parameters for SOC estimation. The results show that the joint estimation method can accurately estimate battery SOC and SOH with an error of less than 3%.