Collaborative state estimation of lithium‐ion battery based on multi‐time scale low‐pass filter forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm

Abstract

AbstractFor the lithium battery management system and real‐time safety monitoring, two issues are of great significance, namely, the ability to accurately update the model parameters in real time and to accurately estimate the state of charge and health. In this context, this thesis adopts the second‐order RC equivalent circuit model and the forgetting factor recursive least squares ‐ double extended Kalman filtering (FFRLS‐DEKF) algorithm with multi‐time scales and low‐pass filter. Forgetting factor recursive least squares is applied to conduct online parameter identification, and the traditional double extended Kalman filtering algorithm is optimized to evaluate the state of charge and model parameters in the micro‐scale and macro‐scale. In this way, the error caused by two different characteristics is reduced, and a low‐pass filter is added to optimize the fluctuation problem of the estimated value of the model parameters. According to the experiment results, the maximum error between the model simulation value and the actual value of the terminal voltage is 0.0459 V. If the initial value of the state of charge deviates from the actual value, the maximum errors under BBDST and HPPC conditions record 0.0235 and 0.0048, respectively, the forgetting factor recursive least squares ‐ double extended Kalman filtering algorithm with multi‐time scales and low‐pass filter is able to track the true value within 40 s. Furthermore, the lithium‐ion battery state of health both reaches 98% under the two conditions. In summary, the experimental analysis shows that the algorithm helps reduce the influence of initial values on the results, thereby reducing error accumulation and improving the robustness.