An Enhanced Recursive Least Square Method with A Forgetting Factor for On-Line Parameter Identification of Equivalent Circuit Model for Sodium-Ion Battery

Sodium-ion batteries (SIBs) make their marks in energy storage and electric vehicles due to their abundant reserves, cost-effectiveness, environmental resilience, and high safety. However, maintaining high battery performance in intricate operating conditions is challenging, which necessitates precise control based on timely and accurate acquisition of operation parameters, especially for the state of charge (SOC). Equivalent circuit model (ECM) is the most widely used in the evaluation of SOC. In this work, a 2nd-order resistor-capacitor ECM (2ORC-ECM) is chosen because of its balance between accuracy and computational efficiency. Furthermore, dynamic parameters in the 2ORC-ECM are accurately identified online by introducing an enhanced recursive least squares method with a forgetting factor. Finally, the proposed method is carried out based on the measured data of commercial SIBs. The results show that the proposed method can mitigate data saturation effectively while ensuring high accuracy and robust real-time performance. The highest accuracy is achieved when the forgetting factor is 0.98 and the resulting voltage error is smaller than 0.02 V. This work proves the correctness and high accuracy of online identification of parameters for 2ORC-ECM of SIBs by the recursive least squares method with a forgetting factor, facilitating the development and application of SIBs.