A Multi-Time Scale FFRLS-AEKF Joint Algorithm for Lithium-Ion Battery State of Charge Estimation

Accurate and reliable SOC estimation plays a vital role in the engineering application and development of LIBs. A multi-time scale joint algorithm combining FFRLS and AEKF is introduced in this paper. The FFRLS algorithm is employed for online parameter identification of a second-order resistance-capacitance ECM, while the AEKF algorithm estimates the SOC. To account for the time-varying nature of model parameters and SOC, different sampling periods are selected, enabling the parameter identification and SOC estimation processes to operate on distinct time scales. Experimental results demonstrate that, under constant current conditions at room temperature, the multi-time scale FFRLS-AEKF joint algorithm can maintain a high level of accuracy while reducing the computational burden, with MAE and RMSE values of 0.0111 and 0.0129, respectively. Simultaneously, a public data set is used to prove the application of the algorithm in complex operating conditions, and the computed results of this dataset align with the experimental data. This approach minimizes unnecessary computations in the parameter identification process, thereby conserving computational resources.