Novel Multi-Scale joint approach for estimating Lithium-ion battery model parameters and SOC considering hysteresis effect and temperature

Precise estimating of the state of charge (SOC) in lithium-ion (Li-ion) batteries is crucial for effective energy management and safety assurance in electric vehicles. This paper proposes a novel multi-scale joint estimation method for model parameters and SOC to enhance estimation accuracy under temperature variations and hysteresis effects. First, an improved second-order RC equivalent circuit model is developed by incorporating temperature dependencies and hysteresis effects, where the hysteresis parameters are calibrated offline using a data-driven approach. Then, the joint estimation approach employs an adaptive forgetting factor recursive least squares (AFFRLS) algorithm to dynamically update model parameters during SOC estimation, thereby maintaining model fidelity across diverse temperature conditions (−10 °C to 50 °C). Finally, an extended Kalman filter (EKF) is implemented for SOC estimation based on the real-time updated model parameters. Experimental validation under DST, US06, and FUDS conditions demonstrates the effectiveness of the proposed method, achieving a maximum voltage prediction error of 0.0650 and a maximum SOC estimation error of 0.0092 across the full temperature range.