Joint Estimation of SOC and SOH for Li-ion Batteries Based on the Dual Kalman Filter Method

With the widespread use of batteries, many of the devices in our lives rely on them. However, due to the variety and cost of batteries and their impact on the environment, we cannot use them in all devices. In order to ensure that the battery pack can work safely and reliably, the state of charge (SOC) and the state of health (SOH) of the battery must be accurately estimated. In this paper, we use lithium iron phosphate batteries as the research object. By analysing and comparing common battery models in the market, we obtain a second-order Thevenin equivalent circuit model suitable for this research and apply the forgetting factor recursive least squares method (FFRLS) to the parameters. In this paper, the SOC and SOH of the Li-ion battery are jointly simulated and validated using the double extended Kalman filter algorithm, the double particle filter algorithm and the double volume Kalman filter algorithm, and the results of the three algorithms are compared and analysed. The final results of the experiments show that, compared with the other two algorithms, the joint estimation of SOC and SOH of Li-ion batteries using the two-particle filter algorithm can accurately follow the true value, and the maximum absolute error and the average absolute error can be kept within 1%, and the estimation accuracy is more accurate compared with the other algorithms, and the performance of the algorithm effect is superior, meeting the accuracy and The performance of the algorithm is superior and meets the requirements of accuracy and stability of lithium-ion battery state estimation, which plays a reference role for future scientific research related to the development of new energy industry.