Lithium-Ion Battery Modelling and Adaptive Extended Kalman Filter Implementation for BMS Application Software Development

A custom lithium-Ion battery was built for the payload system on a single-engine two-seaters glider. The stages of software development in forming the Battery Management System as a way to provide security in the charging and discharging processes need some parameters to indicate the conditions of the battery. Therefore, in this study, the process of the State of Charge estimation which used in the Electrical Equivalent Circuit Methods and Adaptive Extended Kalman Filter was carried out in a 42 Ah Li-ion battery. As the results, a Mean Absolute Error, and a Root Mean Square Error in which the value of less than 1%. In the actual process, the real error value has never been discovered. The noise is given to determine the adaptive ability between the extended Kalman Filter and the Adaptive Extended Kalman Filter (AEKF) algorithms. In addition, the primary SoC is set at a certain value to see the estimation accuracy.