Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter

The accurate State of Charge (SOC) estimation in a Li-ion battery requires a suitable model of the cell behavior. In this work an enhanced closed loop estimator based on Extended Kalman Filter (EKF) is proposed, considering a precise model of the cell dynamics valid for different current profiles and SOCs, and a complete model of the Open Circuit Voltage (OCV) which takes into account the hysteresis influence. The employed model and proposed estimator are validated with experimental results obtained from the response of a 40 Ah NMC Li-ion cell to several current profiles. These tests include current pulses, FUDS driving cycles, residential lift profiles, and specially designed profiles which demand an accurate modeling of the transitions between OCV boundaries. In each case, it is demonstrated that the enhanced model can reduce the estimation error nearly by half compared to an estimator ignoring the hysteresis effect. Furthermore, the good performance of the cell dynamics model allows an accurate and stable estimation over different conditions.