The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central computation systems that needed extensive communication between the master and the slave module on each cell were presented as a solution for running such a system. However, because of the enormous number of slaves, the bus system created a bottleneck during operation. As an alternative to conventional multilevel inverter systems, which rely on a master–slave architecture for communication, decentralized controllers represent a feasible solution for communication capacity constraints. These controllers operate autonomously, depending on local measurements and decision-making. With this approach, it is possible to reduce the load on the bus system by approximately 90 percent and to enable a balanced state of charge throughout the system with an absolute maximum standard deviation of $1.1\times {10}^{-5}$. This strategy results in a more reliable and versatile multilevel inverter system, while the load on the bus system is reduced and more precise switching instructions are enabled.

Lithium-ion batteries are a key technology for the electrification of the transport sector and the corresponding move to renewable energy. It is vital to determine the condition of lithium-ion batteries at all times to optimize their operation. Because of the various loading conditions these batteries are subjected to and the complex structure of the electrochemical systems, it is not possible to directly measure their condition, including their state of charge. Instead, battery models are used to emulate their behavior. Data-driven models have become of increasing interest because they demonstrate high levels of accuracy with less development time; however, they are highly dependent on their database. To overcome this problem, in this paper, the use of a data augmentation method to improve the training of artificial neural networks is analyzed. A linear regression model, as well as a multilayer perceptron and a convolutional neural network, are trained with different amounts of artificial data to estimate the state of charge of a battery cell. All models are tested on real data to examine the applicability of the models in a real application. The lowest test error is obtained for the convolutional neural network, with a mean absolute error of 0.27%. The results highlight the potential of data-driven models and the potential to improve the training of these models using artificial data.

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.