A novel fuzzy adaptive cubature Kalman filtering method for the state of charge and state of energy co-estimation of lithium-ion batteries

The state of charge (SOC) and state of energy (SOE) are the key indices of the battery management system (BMS) of lithium-ion batteries. Based on the second-order resistor-capacitor equivalent circuit model and online parameter identification using variable forgetting factor recursive least square (VFF-RLS), a fuzzy adaptive controller is proposed to improve the convergence speed of the cubature Kalman filter (CKF) for the SOC estimation. Then, the estimated SOC with another fuzzy adaptive controller to correct the estimation and improve the accuracy of the SOE estimation of lithium-ion batteries. The feasibility of the proposed algorithm is verified using two different initial values and working conditions. The verification results show that under simple working conditions, the convergence time of the proposed algorithm for the estimated SOC is 15 s, and the maximum SOE estimation error is 0.0193. Under complex working conditions, the convergence speed of the SOC estimation is increased by 52.17%, and the maximum error of SOE estimation is 0.0463, which is 24.59% less than that of the SOE estimation by the traditional CKF algorithm. The proposed algorithm significantly improves the convergence speed of SOC estimation and the accuracy of SOE estimation, providing a reference for the radical application of lithium-ion batteries.