SOC estimation of lithium-ion battery based on FOASOUPKF algorithm

The problem of estimation accuracy of lithium-ion battery state of charge (SOC) for electric vehicles is addressed. An equivalent network of the second-order RC fractional-order model is established and parameter identification is performed using the adaptive genetic algorithm identification parameter (AGA) method. Based on this model, adaptive Schmidt orthogonalization of untraceable particle filtering (ASOUPKF) is applied to estimate the SOC. The accuracy of the estimation of SOC is improved by four aspects, such as improving the model, automatic matching of noise information covariance, reducing the problems arising from the effects of battery aging, and reducing the number of sampling points, thus realizing the accurate joint estimation of SOC and SOH. Under the New European Driving Cycle (NEDC) conditions and the Urban Driveway Cycle (UDDS) conditions, compared with the EKF estimation algorithm, the joint estimation algorithm based on the fractional-order model ASOUPKF reduces the average error of the SOC by about 1.5%, and the maximal errors of the final SOCs of the fractional-order model ASOUPKF algorithm are all less than 2.2%, and the average errors are all less than 0.8%, providing an excellent estimation method for the state estimation of the battery.