Objective Due to the substantial impact of temperature on the capacity of lithium-ion batteries achieving accurate state of charge SOC estimation across a wide temperature range is pivotal for ensuring the secure and efficient operation of lithium-ion batteries. Methods The characteristics of lithium-ion batteries across a wide temperature range were analyzed. Based on a second-order RC equivalent circuit model a temperature-compensated battery model was established. The adaptive forgetting factor recursive least squares AFFRLS method was used for online parameter identification of the battery model. Additionally the adaptive square root cubic Kalman filter ASRCKF algorithm was employed for SOC estimation under different operating conditions across a wide temperature range and the results were compared with those obtained using the cubic Kalman filter CKF algorithm. Results The AFFRLS-ASRCKF joint algorithm showed faster convergence speed and smaller convergence error. In both operating conditions the root mean square error of the SOC estimation using the AFFRLS-ASRCKF joint algorithm remained within 0. 4%. This indicated that the AFFRLS-ASRCKF algorithm has good convergence high accuracy and better robustness. Conclusion The joint algorithm meets the estimation accuracy of the state of charge SOC of lithium-ion batteries over a wide temperature range providing a guarantee for the safe and efficient operation of lithium-ion batteries. In addition this algorithm can be further applied to the research of actual battery packs taking into account the influence of internal and external temperature changes on the state-of-charge SOC estimation of the battery pack. These temperature changes are caused by factors such as the inconsistent states of individual cells the mutual influence between individual cells and the heat dissipation and absorption of the battery pack.