This study proposes a novel single-phase switched capacitor multilevel inverter structure for symmetrical and asymmetrical operation. The proposed configuration requires two DC sources, twelve switches, two flying capacitors, and a single diode to generate 9-level and 21-level output voltage. The proposed configuration features inherent self-voltage balancing without sensors, ensuring stable operation under varying conditions. The switched-capacitors enhance voltage gain (Vo=10Vdc) while reducing component count and switch stress. A single-carrier pulse width modulation (SC-PWM) method is utilized to minimize computation time and produce gating signals. The proposed topology is designed and simulated in PLECS software, incorporating thermal modeling to assess its efficiency and overall performance. The experimental setup was prepared, and a thorough comparative evaluation was carried out using dual-source-based topologies. The proposed topology demonstrates superior performance and enhanced design characteristics.

This paper presents a multi-objective control strategy for a four-port solid-state transformer (SST) interfacing photovoltaic (PV) system, battery energy storage (BESS), utility grid, and a 48V data center load. The proposed control approach simultaneously optimizes multiple objectives, including power allocation and load voltage regulation. The main objectives of the proposed approach are to supply a constant 48V to the data center while maximizing renewable energy utilization, and balancing the demand and supply by dynamically injecting the extra available power into the grid or the battery and consuming it from the grid or the battery if there is no sufficient renewable energy availability. The proposed approach is experimentally implemented and its effectiveness is validated through a set of different case studies.

This paper proposes a DC plasma power supply topology with systematic design and robust voltage control approach. The proposed topology consists of a three-port solid-state transformer (SST), comprising one port for the input source, a second port to deliver high voltage (HV) required for plasma ignition, and a third port for supplying a constant low voltage (LV) for sustained plasma reactor operation. A comprehensive design methodology is presented to determine all passive elements, including the resonant tank components and transformer’s turns ratios. Moreover, a seamless transition algorithm between ignition and operation modes is presented. To achieve micro-scale dynamic response, an inverse model predictive control (IMPC) strategy is employed to identify the necessary switching frequency in feedforward fashion. In parallel, a PI controller is used to eliminate the deviation and steady state error of the output voltage and ensure asymptotic error convergence, even in the presence of parameter uncertainties. The proposed circuit, seamless transition algorithm, and the control technique are implemented on a lab-scale test-bed with voltage range between 0-1200 V.

The rapid advancement in electric vehicle (EV) technology stimulated the development of fast chargers, with innovative solutions in power electronics (PEs), communication, protection, and control. This literature review explores the evolving landscape of fast EV charging infrastructures, and focuses on their topologies, advanced PEs solutions, and associated challenges. In addition, this article explores the multifaceted aspects of fast charging systems. In addition, the article discusses the protection systems, communication systems, and control techniques for facilitating seamless, safe, and reliable interaction between the grid, fast EV chargers (FEVCs), and the EV. Furthermore, the review investigates the interaction between the grid and FEVCs through ancillary services, and highlights their potential to enhance grid stability and reliability. Although a significant research is conducted in the literature and a significant progress is achieved, a notable gap persists between the available academic literature and commercially deployed solutions. Investigating this gap is crucial for accelerating the adoption of FEVC technologies and maximizing their benefits for both consumers and grid operators. Therefore, a reference design is synthesized that combines the recommended elements identified throughout the literature.

Digital twins (DTs) are emerging as effective tools for power electronic converters, addressing important objectives such as real-time monitoring, fault detection and predictive maintenance. Among these power converters, the dual active bridge (DAB) stands out for DC-DC conversion applications. However, the non-linear dynamics of DABs pose considerable challenges to their accurate modeling. To address these challenges, this paper investigates the use of machine learning (ML) and deep learning (DL) models for DT implementation in DABs. Specifically, XG-Boost and dense neural network (DNN) models are employed to construct DTs compatible with low-cost microcontrollers. These DTs are operated in parallel with the physical DAB converter to predict its output voltage in real-time. Experimental results demonstrate that both of the models perform well in terms of accuracy and applicability to low-cost microcontroller. However, the DNN is more reliable and the XGBoost is simpler.

This paper presents a grid voltage sensorless model predictive control (MPC) technique for a single-phase fivelevel active neutral point clamped flying capacitor (ANPC-FC) rectifier. The control strategy utilizes MPC to regulate the grid current and ensure equilibrium in the voltages of the flying and DC side capacitors, without measuring the grid voltage. A state feedback observer (SFO) is responsible to provide the grid voltage estimation to the MPC. Achieving this involves integrating all controlled parameters into a finite control set MPC (FCS-MPC). Moreover, the reference grid current is generated to regulate the DC link voltage based on a power equilibrium technique supported by a feed-forward loop. The proposed controller is implemented and evaluated using MATLAB/Simulink. Simulation results are carried out to show that the proposed MPC technique effectively achieves control objectives, while ensuring accurate tracking, and resisting voltage distortions.

Electric vehicle (EV) charging stations have been evolving to offer better and more efficient power delivery methods. A key area of research in this field involves predicting the power consumption of EV charging stations. Many researchers have addressed this issue using machine learning and deep learning methods, however, forecasting models struggle to predict individual charging sessions with high resolutions. In this paper, a deep neural network (DNN) is constructed to forecast the EV charging current profile and state of charge (SoC). Charging current and SoC are mathematically formalized and embedded into the DNN’s loss calculation to build a physics-informed neural network (PINN). The performance of the models is assessed through analysis of their prediction error, utilizing actual data from real EV charging sessions.

Droop-based control is widely used in islanded microgrids to regulate power sharing among distributed generators (DGs) according to their ratings, without the need for communication. However, simultaneous and accurate active and reactive power sharing regulation represents a key challenge for droop-based techniques. This issue arises due to the inconsistent voltage drops in feeder lines and the distinct local loads distributed across the microgrid DGs. To this end, this paper introduces a new solution for droop-based approaches to enhance power sharing accuracy in the context of local loads, without relying on any communication means between DGs. The proposed solution effectiveness is demonstrated through MATLAB/Simulink and is validated for conventional, inverse, and angle droop control.

This paper proposes a novel sensorless control method for a simplified single-stage rectifier (SSSR) based electric vehicle (EV) charger. The proposed control strategy consists of three cascaded control loops, PI controller for DC link voltage regulation, another PI controller for grid current regulation, and a model predictive control (MPC) for inductor current regulation. Additionally, a novel state observer technique is proposed specifically for this topology, offering the advantages of simplicity, robustness, reduced number of tuned parameters, and compatibility with low-cost industrial microcontrollers. The effectiveness of the proposed control approach and observer technique is validated through extensive simulations across various scenarios and operating conditions, including boost operation, buck operation, and operation under uncertainties. The obtained results demonstrate the efficacy of the proposed method in achieving precise regulation of DC link voltage, grid current, and inductor current without the need for explicit inductor current sensor feedback.

This paper proposes a method for optimizing the charging process of electric vehicles (EVs) within the context of renewable energy integration. The proposed method continuously monitors the maximum available power from the renewable energy sources (RES), grid conditions, and EV’s requirements. Upon receiving reference charging or power signals from the connected EV, the system computes the surplus or required power and adjusts grid interaction accordingly. The proposed method offers several advantages. Firstly, it optimizes energy harvesting from RESs by dynamically adjusting grid and EV interactions based on real-time monitoring. Secondly, it enhances grid stability by intelligently controlling power flow, injecting or consuming surplus power in response to the grid condition. Thirdly, it leverages the growing number of EVs connected to the grid, effectively utilizing them as distributed energy storage systems. To validate the effectiveness and efficiency of the proposed method, extensive simulation tests are conducted under diverse scenarios.

The Controller Area Network (CAN) Bus is a commonly used communication protocol for different applications including vehicle controllers and power electronics converters which are the application focus in this paper. One drawback of CAN-bus is the lack of security measures in the protocol, which can be problematic if the communication line is exposed to attackers. This paper proposes a novel method to secure CAN-bus communication that is light-weight and applicable for power converters and microgrid applications. The proposed solution aims to protect against eavesdropping, false data injection, and replay attacks. The security scheme is implemented and deployed on a low-cost microcontroller that is used to control power sharing rectifiers. Experimental results are carried out to show that the proposed method effectively secures the channel against attacks with minimal time and space overhead.

This paper presents a model predictive control (MPC) technique for a three-phase five-level active neutral point clamped flying capacitor (ANPC-FC) rectifier. The proposed control algorithm depends on a single loop control to regulate the DC link voltage, grid currents, and to balance the voltages of the DC side capacitors. This is achieved by incorporating all controlled states into a multi-objective cost function (MOCF), which encompasses the tracking errors of AC currents, DC link voltage, and DC side capacitor voltage balance. The proposed controller is implemented and tested in MATLABI Simulink environment. Investigations are conducted to prove that the proposed MPC successfully meets the control objectives, enabling precise tracking and ability to deal with abnormal grid conditions, including uncertainties, load variations, voltage sags and swells, as well as unbalanced grid voltages.

In this paper, a novel multi-mode converter (MMC) for battery storage application is proposed. The MMC consists of a non-inverting Cuk converter to feed a load, and a bidirectional buck-boost converter to regulate the battery voltage during charging and discharging. The proposed MMC is used to control the power flow between an input source, a battery, and a load. Furthermore, an adaptive selection algorithm is proposed to enable battery charging or discharging based on the source voltage, the state of charge (SoC), and a reference SoC. The proposed converter has many advantages such as simplicity, and supporting many modes of operation. The proposed MMC is controlled using a sliding mode controller and its performance is described theoretically and validated through simulations. The final version of the paper will contain experimental results.

This paper proposes a self-tuning current-control algorithm for a three-phase three-level T-type controlled rectifier. The aim of this algorithm is to control the AC-side current without any previous knowledge about the parameters of the system. The dynamic model of the rectifier is assumed as a black box, where all internal parameters are considered unknowns. The proposed algorithm depends on Recursive Least Squares Algorithm (RLSA) to estimate the state-space model of the system. Then, the estimated model is used to derive the gains optimally for a Linear Quadratic Tracker controller (LQT). The obtained results prove the ability of the proposed algorithm to estimate the entire dynamic model accurately and to control the system with an accurate, smooth, and rapid response. In addition to zero tracking error and no overshot.