Online Estimations of Li-Ion Battery SOC and SOH Applicable to Partial Charge/Discharge

Abstract

Estimating the state of health (SOH) and state of charge (SOC) of lithium-ion batteries is crucial for increasing the battery lifetime and performance. Many estimation methods are offline and require large datasets for training. The majority of online estimation methods either take too much time or need a full discharge or charge cycle. In this article, a fast online SOH estimation method that can work with partial charge/discharge is introduced. Only two consecutive partial discharge intervals are used to estimate the battery equivalent circuit model parameters and the open-circuit voltage (OCV). By comparing the estimated OCV curve at each interval with a reference or datasheet OCV curve, the battery capacity and, therefore, its SOH and SOC are accurately estimated. It is shown that updating the OCV reference curve based on temperature readings will provide more accurate results. NASA degradation dataset is used to validate the proposed method and the average reported root-mean-square error is below 1% for SOH and 1.07% for SOC.