A novel approach to estimate the state of charge for lithium-ion battery under different temperatures incorporating open circuit voltage online identification

The open circuit voltage (OCV) is inherently related to the state of charge (SoC) and their relationships under different temperatures are crucial for accurate SoC estimation for the lithium-ion battery based on the equivalent circuit model (ECM), which requires long time-consuming offline OCV tests. In this research, an online closed-loop SoC estimation without conducting OCV tests is put forward. Firstly, the parameters of the Thevenin model for the lithium-ion battery are identified online through the adaptive recursive least square with forgetting factor (AFFRLS). Afterwards, the adaptive unscented Kalman filter (AUKF) is applied to achieve the online closed-loop SoC estimation with the identified parameters. Subsequently, the relationships between the OCV and SoC under different temperatures have been reconstructed online. The proposed method is validated under different temperatures. The research reveals this method can accurately estimate the SoC and is robust to the initial SoC values in wide temperature range.