Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter

Accurate estimation for state-of-charge of the battery is very important for energy storage systems in electric vehicles and smart grids. To improve the accuracy and reliability of state-of-charge estimation, accurate model equations and a set of robust algorithm are necessary. Different from the commonly used method, this paper adopts a polynomial based on Gaussian function to build up the open circuit voltage function, and proposes an adaptive fifth-degree cubature Kalman filter algorithm to estimate the battery state-of-charge. Two typical driving cycles, including the dynamic stress test and the Worldwide harmonized Light Vehicles Test Cycle are applied to evaluate the performance of the proposed estimator. The results indicate that compared with the unscented Kalman filter and the adaptive cubature Kalman filter, the adaptive fifth-degree cubature Kalman filter can achieve higher state-of-charge estimation accuracy and better overcome the impact of large measurement error and initial error.