A novel adaptive dual extended Kalman filtering algorithm for the Li‐ion battery state of charge and state of health co‐estimation

Abstract

Accurate prediction of the state of health (SOH) of Li-ion battery has an important role in the estimation of battery state of charge (SOC), which can not only improve the efficiency of battery usage but also ensure its safety performance. The battery capacity will decrease with the increase of charge and discharge times, while the internal resistance will become larger, which will affect battery management. The capacity attenuation characteristics of Li-ion batteries are analyzed by aging experiment. Based on the equivalent circuit model and online parameter identification, a novel adaptive dual extended Kalman filter algorithm is proposed to consider the influence of the battery SOH on the estimation of the battery SOC, and the SOC and SOH of the Li-ion battery are estimated collaboratively. The feasibility and accuracy of the model and algorithm are verified by experiments. The results show that the algorithm has good convergence and tracking. The maximum error in the estimation of the SOC is 2.03%, and the maximum error of the Ohmic resistance is 15.3%. It can better evaluate the SOH and SOC of Li-ion battery and reduce the dependence on experimental data, providing a reference for the efficient management of Li-ion batteries.