Abstract: Accurate estimation of the state of charge (SOC) in lithium-ion batteries is crucial for ensuring their safe and stable operation. To address the issue of insufficient SOC estimation accuracy caused by inaccurate model parameters, this paper proposes an online joint estimation method for model parameters and SOC based on the adaptive unscented Kalman filter (AUKF). The method first establishes a state equation that includes both the model parameters and SOC. Then, it performs online estimation of the model parameters and SOC using real-time measurements of current and voltage. Finally, experimental validation under DST conditions demonstrates the accuracy and adaptability of the proposed method. The experimental results show that, compared to traditional offline methods, the proposed joint estimation approach reduces the Root Mean Square Error (RMSE) of the model accuracy by 48.387%, and the RMSE of SOC estimation is reduced by 36.957%.