Enhancing state-of-charge estimation accuracy for lithium-ion batteries under complex conditions via degradation-aware compensation

Accurately estimating the state of charge (SOC) of lithium-ion batteries (LIBs) is crucial for their safe and reliable operation. However, environmental temperature variations and signal acquisition noise can deteriorate SOC accuracy and capacity estimation. To address these challenges, this study proposes a novel joint estimation strategy integrating the Denoising Adaptive Forgetting Factor Recursive Least Squares (DAFFRLS) algorithm with an Improved Dual Adaptive Extended Kalman Filter (IDAEKF). First, the proposed DAFFRLS algorithm in corporates a denoising mechanism that mitigates the adverse effects of input disturbances on parameter identification, thereby enhancing the precision of model parameter estimation. Second, a temperature correction model based on an improved Arrhenius equation is introduced into the IDAEKF framework, which strengthens SOC robustness and capacity estimation under varying environmental temperatures. Experimental results demonstrate that the estimation errors of SOC and capacity under different temperatures are confined within 1.64 % and 0.62 %, respectively. And under various operating conditions, these errors remain within 0.76 % and 1.32 %, respectively. Moreover, the proposed joint estimation strategy exhibits remarkable anti-interference capability, as evidenced by significantly reduced Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) after denoising. Notably, the verification results in three experimental cases all outperform those of the Control Group, confirming the superior performance and reliability of the proposed method.