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Recent advances in high-power applications employing voltage source converters have been primarily fuelled by the emergence of the modular multilevel converter (MMC) and its derivatives. Model predictive control (MPC) has emerged as an effective way of controlling these converters because of its high response. However, the practical implementation of MPC encounters hurdles, particularly in MMCs featuring many sub-modules per arm. This research introduces an approach termed folding model predictive control (FMPC), coupled with a pre-processing sorting algorithm, tailored for modular multilevel converters. The objective is to alleviate a significant part of the computational burden associated with the control of these converters. The FMPC framework combines multiple control objectives, encompassing AC current, DC current, circulating current, arm energy, and leg energy, within a unified cost function. Both simulation studies and real-time hardware-in-the-loop (HIL) testing are conducted to verify the efficacy of the proposed FMPC. The findings underscore the FMPC’s ability to deliver fast response and robust performance under both steady-state and dynamic operating conditions. Moreover, the FMPC adeptly mitigates circulating currents, reduces total harmonic distortion (THD%), and upholds capacitor voltage stability within acceptable thresholds, even in the presence of harmonic distortions in the AC grid. The practical applicability of MMCs, notwithstanding the presence of a large number of sub-modules (SMs) per arm, is facilitated by the significant reduction in switching states and computational overhead achieved through the FMPC approach.

This paper presents a charging architecture for the Reconfigurable Cascaded Multilevel converter, which was specifically designed for electric vehicle (EV) powertrain applications. The RCMC topology is capable of executing power conversion and actively managing battery systems concurrently. The active battery management is achieved using the Reconfigurable Battery Module, which regulates the serial connection of cells via a switch pattern. In this paper, the RCMC is directly interfaced with an AC three-phase power system, facilitating the dynamic control over battery cells charging. Its inherent design allows for the implementation of various charging algorithms, customizable to specific requirements, without necessitating additional intermediary power stages. Firstly, an overview of the RCMC topology is given, and an analysis to define the optimal filter inductance is carried out. Subsequently, after the AC system characteristics are explained, two charging algorithms are presented and described: one prioritizes State of Charge (SOC) balancing among battery cells, while the other focuses on minimizing power losses. Moreover, a time estimation computation for the RCMC is carried out considering a two-level AC charging station. The result is compared with the time required for a conventional battery pack. The results show a reduction of 10 s in charging time for a mere 20% increase in SOC. Finally, the experimental setup is presented and used to validate the efficacy of the proposed algorithms.

For electrical machines with complex structures, the design space of parameters can be large with high dimensions during optimization. Considering the calculation cost and time consumption, it is hard to optimize all the design parameters at the same time. Therefore, in that situation, sensitivity analysis of these design parameters is usually used to sort out crucial parameters. In this paper, the sensitivity analysis-based Taguchi method is applied to optimize the axial-flux permanent magnet (AFPM) machine with yokeless and segmented armature (YASA) topology for an in-wheel traction system. According to the key parameters and their sensitivity analysis, the optimal machine scheme to meet the performance requirements can be formed. In this case study, the machine performance is improved significantly after optimization. Lastly, the experimental results verify the accuracy of the model used in this study.

The integration of artificial intelligence (AI) techniques in power converter-based systems has the potential to revolutionize the way these systems are optimized and controlled. With the rapid advancements in AI and machine learning technologies, this article presents the analysis and evaluation of these powerful tools as well as in computational capabilities of microprocessors that control the converter. This article provides an overview of AI-based controllers, with a focus on online/offline supervised, unsupervised, and reinforcement-trained controllers. These controllers can be used to create surrogates for inner control loops, complete power converter controllers, and external supervisory or energy management control. The benefits of using AI-based controllers are discussed. AI-based controllers reduce the need for complex mathematical modeling and enable near-optimal real-time operation via computational efficiency. This can lead to increased efficiency, reliability, and scalability of power converter-based systems. By using physics-informed methods, a deeper understanding of the underlying physical processes in power converters can be achieved and the control performance can be made more robust. Finally, by using data-driven methods, the vast amounts of data generated by power converter-based systems can be leveraged to analyze the behavior of the surrounding system and thereby forming the basis for adaptive control. This article discusses several other potential disruptive impacts that AI could have on a wide variety of power converter-based systems.

Power electronic systems present a non-linear behavior mainly due to their switching nature. This is often combined with their interaction with non-linear systems, such as other switching converters, diode rectifiers, motor drives, etc. and with possible non linearities of the power grid in the case of grid connected systems. The major effect of these non-linear interactions is the generation of harmonic distortion on voltages and currents (both in DC and AC), which needs to be compensated to achieve high power quality systems. The use of passive filters is often the simplest and most immediate solution; however, this decreases converter efficiency and increases its weight and volume. Thus, the use of a control strategy capable of tracking periodic signals, rejecting periodic disturbance and largely improving steady state behavior and harmonic distortion with a limited bandwidth is a very desirable feature. Repetitive Control (RC) represents an extremely practical and efficient solution for the aforementioned issues, and it is widely employed in many different applications. This paper focuses on state of the art of RC used in power electronics and drives. RC basic concepts, different control structures, design methods, fixed and variable frequency operating conditions, etc. are investigated. Furthermore, many example applications and existing control approaches developed in recent years for power electronics and drive systems based on RC, have also been discussed in detail.

The scientific literature acknowledges cascaded H-bridge (CHB) converters as a viable alternative to two-level inverters in electric vehicle (EV) powertrain applications. In the context of an electric vehicle engine connected to a DC charger, this study introduces a state of charge (SOC)-governed method for charging li-ion battery modules using a cascaded H-bridge converter. The key strength of this algorithm lies in its ability to achieve balanced charging of battery modules across all three-phase submodules while simultaneously controlling the DC charger, eliminating the need for an additional intermediate converter. Moreover, the algorithm is highly customizable, allowing adaptation to various configurations involving different numbers of submodules per phase. Simulative and experimental results are presented to demonstrate the effectiveness of the proposed charging algorithm, validating its practical application.

This paper presents a comprehensive review of multi-port power electronics converters used for application in AC, DC, or hybrid distribution systems in an Internet of Energy scenario. In particular, multi-port solid-state transformer (SST) topologies have been addressed and classified according to their isolation capabilities and their conversion stages configurations. Non-conventional configurations have been considered. A comparison of the most relevant features and design specifications between popular topologies has been provided through a comprehensive and effective table. Potential benefits of SSTs in distribution applications have been highlighted even with reference to a network active nodes usage. This review also highlights standards and technical regulations in force for connecting SSTs to the electrical distribution system. Finally, two case studies of multi-port topologies have been presented and discussed. The first one is an isolated multi-port bidirectional dual active bridge DC-DC converter useful in fast-charging applications. The second case of study deals with a three-port AC-AC multi-level power converter in H-Bridge configuration able to replicate a network active node and capable of routing and controlling energy under different operating conditions.