Online Estimation of the State of Health for the Lithium-Ion Battery Based on Open Circuit Model

The accurate prediction of the state of health (SOH) for lithium-ion batteries is of significant importance in proactively avoiding unsafe behavior. The commonly used indicator of the SOH is the ratio of available capacity to rated capacity based on its equivalent circuit model (ECM) where the open circuit voltage (OCV) is obtained through large numbers of offline battery experiments. In this research, a method based on the ECM is proposed to estimate available capacity and OCV online. Firstly, the Thevenin model of the lithium-ion battery is established, of which the parameters are identified online by the recursive least squares with forgetting factor algorithm. Then, the OCV model is set up based on the first-order Taylor’s formula where the first-order partial derivation of OCV against state of charge (SOC) is modeled as unmodeled noise. The adaptive unscented Kalman filter is leveraged to estimate the available capacity and OCV online, both of which are adopted to estimate SOC reconstructing the relationships between the SOC and OCV in the whole lifespan. The results indicate that the available capacity estimated with the proposed method converges quickly to the vicinity of the true value under accelerated aging and normal aging conditions. The relative errors under these two aging conditions are respectively confined to below 3% and 2.3%. This demonstrates good estimation accuracy and robustness against wrong initial values. The proposed method provides valuable guidance for online state estimation throughout the entire lifecycle of the lithium-ion battery.