An Adaptive High-Degree Unscented Kalman Filter for State-of-Charge Estimation of Lithium-Ion Batteries

As a vital important parameter of lithium-ion batteries (LIBs), accurately estimating the state of charge (SOC) holds significant importance in ensuring the reliable and stable operation of power storage equipment. To enhance the accuracy of SOC estimation, an adaptive high-degree unscented Kalman filter (AHUKF) approach is put forward. Firstly, a model of LIBs is established and the corresponding parameters are identified using the adaptive forgetting factor regression least-squares (AFFRLS) approach. The adaptive forgetting factor can augment the features of novel data and gradually diminish the influence of historical data. Subsequently, the AHUKF approach is proposed to strengthen the exactness and consistency of SOC estimation for LIBs. Finally, the effectiveness and robustness of the AHUKF approach are validated through simulation consequences attained under the FUDS and DST conditions.