A novel fitting polynomial approach for an accurate SOC estimation in Li-ion batteries considering temperature hysteresis

• Introduces a novel FPSOC method for precise SOC estimation in lithium-ion batteries, accounting for hysteresis in the OCV-SOC relationship. • Employs a fifth-degree polynomial fitting to handle wide temperature variations (−10 °C to +80 °C), surpassing the limitations of traditional methods like the CC method. • MATLAB/Simulink simulations demonstrate the FPSOC model's RMSE at 0.93 %, significantly lower than the CC model's 6.77 %. • The FPSOC model shows up to six times higher precision than the CC method at low SOC levels (30 %). • Outperforms the most accurate existing methods in the literature, such as AEKF, IAEKF, FO-IAEKF, and OCV-FO-IAEKF. Lithium-ion batteries are essential to modern technology, requiring accurate estimation of the state of charge (SOC) for optimal performance. Traditional methods such as Coulomb Counting (CC) are ineffective in the face of temperature variations, leading to inaccuracies in SOC estimation, which in turn cause obvious deformation of hysteresis curves. To address this, this paper introduces a novel method called Polynomial Fit State of Charge (FPSOC), for effective SOC estimation. This method incorporates a fifth-degree polynomial fitting that accounts for a wide range of temperature variations (from -10 °°C to +80 °°C), a feature that, according to the authors, has not been offered by all previously published methods. A series of simulation tests using the MATLAB/Simulink tool are conducted under various temperature profiles to evaluate the effectiveness of the FPSOC method. The results demonstrate the notable superiority of the FPSOC model compared to the CC method, with a significantly reduced RMSE of only 0.93 % compared to 6.77 % of the CC model. Particularly effective at low SOC levels (30 %), the FPSOC model demonstrates precision up to six times higher compared to the CC model. Additionally, when evaluated against other recent SOC estimation techniques such as CM, RLSF, EKF, DST, BBDST, ASMO, LPM_H, LSTM-SA Group A and B, and baseline ECM-ID, The FPSOC method proves extremely accurate, with the lowest average error under different temperature conditions.