Entropy-driven online open circuit voltage identification for precise state estimation in lithium-ion batteries

The open circuit voltage (OCV)-state of charge (SOC) curve of lithium-ion batteries is affected by battery inconsistency and degradation. Compared to lab methods, which are time-consuming, using operation data of electric vehicles (EVs) to identify OCV-SOC curve online attracts increasing attention. Considering that many operating conditions of EVs cannot sufficiently excite the dynamic voltage response of battery, leading to significant uncertainty in identification results, the Shannon entropy of measured signal and terminal voltage error calculated by the identified parameters are used to assess the accuracy of the identified OCV in this work. Then the identified OCV is used to interpolate the start point of ampere-hour counting in the constructed OCV-SOC segment, to guarantee the accuracy of SOC. Validation results show that the maximum deviation of the online constructed OCV-SOC curve is below 22 mV. When applied to SOC estimation, an error of less than 2.2% can be achieved.