Joint Adaptive Assessment of the State of Charge of Lithium Batteries at Varying Temperatures

The fixed battery model parameters result in poor real-time state of charge (SOC) estimation, and the model-based estimation method of lithium battery SOC ignores the consequences of various working conditions and temperatures with the battery, resulting in low estimation accuracy. Based on multi-new information theories, this work proposes a joint evaluation method for lithium battery state of charge using adaptive extended Kalman filtering (AEKF) and variable forgetting factor recursive least squares (VFFRLS). Through testing at various temperatures and working conditions and a comparison with the conventional joint method, the efficacy of the algorithm presented in this study is confirmed. The findings demonstrate that the maximum root mean square error is kept at 1.57% and that the joint VFFRLS-AEKF technique suggested in this paper can effectively predict the lithium battery SOC. In contrast, the algorithm in this paper takes an average of less than 151 s to converge to within the 2% error range of the true value under various working conditions when the initial SOC value is set incorrectly. It also has good robustness and adaptability to adjust well to complex working conditions, which enhances the ability to predict energy consumption and the battery’s efficiency.