New SOC estimation method under multi-temperature conditions based on parametric-estimation OCV

Abstract

A new state of charge (SOC) estimation method under multi-temperature conditions is presented. The model parameters and SOC value of a lithium-ion battery cell are calculated based on the parametric-estimation open circuit voltage (OCV). The main efforts of this study are as follows. First, the OCV value is obtained based on an equivalent circuit model. Second, the model parameters and states are estimated using an adaptive joint extended Kalman filter. Third, the parametric OCV mapping model (OCVPE–SOC) is put forward to reduce systematic errors. Lastly, the online SOC value is estimated based on the battery model, the model parameters and the OCVPE–SOC mapping model. The noise covariance matrix is updated iteratively using an innovation sequence. The mapping model is established under a dynamic stress test cycle test and the verification results are obtained under the federal urban driving schedule cycle test. The obtained experimental results indicate that the proposed method can effectively reduce the system error and that a more accurate SOC estimation value can be obtained under different temperatures.