State-of-Charge Estimation of Battery Modular Multilevel Inverter Systems Based on Extended and Dual Extended Kalman Filter

A lot of research is presently being done on Modular Multilevel Inverter-based battery systems because of their diverse possibilities of potential applications and their encouraging efficiency results. Due to the special changeable structure employed in the Modular Multilevel Inverter, each individual cell load displays unique properties when compared to a battery system with a conventional inverter. State evaluation and interpretation are challenging for lithium-ion batteries, as they are intricate electrochemical systems. Therefore, one of the biggest challenges is accurately assessing the condition of a lithium-ion battery, like the State-of-Charge. The distinctive and varied load on the different battery cells in a Modular Multilevel Inverter system impedes the task significantly. Using a modification of the conventional and widely used State-of-Charge estimating approach, the extended Kalman filter and dual extended Kalman filter, this work demonstrates a robust solution for the State-of-Charge estimation in a Modular Multilevel Inverter system.