Objective In order to address the issue of inadequate accuracy in estimating the state of charge SOC of lithium batteries using traditional Kalman filtering algorithms this study proposes a fractional order robust unscented Kalman filter-based unscented Kalman filter FORUKF-UKF method for SOC estimation. The estimated value of the lithium battery?? s state of charge is denoted by RSOC . Methods An adaptive genetic algorithm AGA was employed to identify the parameters of a fractional order model FOM of the lithium battery during dynamic stress testing DST . The FORUKF algorithm was proposed by combining the unscented transform UT technique with the H∞ observer based on FOM and the SOC estimation was jointly realized with the UKF. The UKF in the joint estimator realized real-time estimation of the Ohmic resistance R0 in the battery model and fed it back to the FORUKF algorithm to estimate SOC. Finally comparisons and verifications were conducted with extended Kalman filtering EKF and fractional order unscented Kalman filtering FOUKF using Beijing dynamic stress testing BJDST . Results The results showed that the FORUKF-UKF method consistently achieved the highest estimation accuracy in the SOC estimation process compared with EKF FOUKF and FORUKF demonstrating better robustness. Conclusion The FORUKF-UKF method has better accuracy and robustness than the EKF FOUKF and FORUKF algorithms in estimating the SOC of lithium batteries.