Precise Model Extraction for Li-Ion Batteries Using Segmented Columb Counting and Kalman Filtering

With the rapid development of new energy industries, batteries have been widely used. Different types of batteries are used in industry. The usage of Li-Ion batteries is increased in recent applications due to their more charge density, cost efficiency and etc. One of the major problems of the li-ion batteries is to specify the battery health (SOH) at the time of purchase because used batteries may be sold instead of new one. The SOH can be recognized by battery’s residual Capacity or R 0. The battery model is so important in the design of the battery balancing circuits. An accurate state of charge (SOC) estimation is needed to improve the battery modeling. In this paper methods for estimating SOC using mixture of Kalman filter and Columb counting method and SOH with measuring input resistance of the battery are proposed based on the battery’s model which is composed using data extracted from a board that we designed for testing batteries.