LAUSR

As the use of energy storage systems (ESSs) and electric vehicles (EVs) increases, the importance of lithium-ion (Li-ion) batteries is also growing. The accurate capacity estimation of a battery is useful for detecting the degradation and end-of-life of the battery for scheduled maintenance and replacement, thereby improving the reliability of battery-powered systems. However, many of the capacity estimation methods developed thus far are offline methods and are not suitable for online monitoring. In this study, a novel capacity estimation method for Li-ion batteries is proposed, based on the enhanced coulomb counting (ECC) method. Typically, the battery capacity calculated by ECC has a large error owing to the error in the state of charge estimation. Therefore, additional means are required to obtain an accurate value for the battery capacity. The Kalman filter is an optimal estimator of the hidden state with Gaussian noise and is applied to the capacity values calculated by the ECC method. The performance of the proposed method in estimating capacity is verified through experiments with a Li-ion battery operated using a standard vehicle driving schedule. The results show that the online capacity estimation using the proposed method is performed successfully within 10 h with a 1.7% error.