SOC estimation of sodium-ion batteries based on EKF

As a new generation of energy storage devices, sodium-ion batteries have promising applications in renewable energy storage and electric vehicles. However, the state-of-charge SOC estimation of sodium-ion batteries is limited by their complex electrochemical properties and dynamic respons-es. In this paper, we propose a SOC estimation method for sodium-ion bat-teries based on extended Kalman filter EKF . Firstly, a second-order RC equivalent circuit model is established for the characteristics of sodium-ion batteries, and the feasibility of the open-circuit voltage-based accurate es-timation of the state of charge of sodium-ion batteries is verified through the open-circuit voltage OCV test experiments, and the experimental data-driven method is adopted for the parameter identification furthermore, a system model is constructed in the Matlab Simulink simulation platform, and the applicability of the model is verified through the online simulation. simulation to verify the applicability of the model. Secondly, the state space equations were constructed based on the model, and the improved EKF algorithm was used to realize the online estimation of SOC. Finally, the effectiveness of the proposed method is verified by the stage discharge condition. The simulation results show that the method can accurately track the SOC changes of sodium-ion batteries, and the estimation error is con-trolled within 2.5 , with high estimation accuracy and robustness. Through accurate parameter identification and model optimization, this pa-per significantly improves the accuracy of SOC estimation of sodium-ion batteries, provides an efficient and reliable solution for the sodium-ion bat-tery management system, and provides important technical support for its promotion in practical applications.