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 manuscript investigates the torque-speed characteristics of a synchronous reluctance motor drive with an open-end winding (OEW) configuration. The machine is powered by standard two-level voltage source inverters (VSI), one supplied by a DC power source while the other connected to a floating capacitor (FC). The analysis considers the flux maps of a synchronous reluctance motor identified through experiments to account for the effect of the self-and cross-saturation phenomenon. The study reveals that the dual-inverter (DI) configuration with a FC can significantly extend the constant torque speed range and achieve unity power factor. In addition, the analysis demonstrates that the torque in the constant power speed range can be noticeably increased compared to the standard single-inverter (SI) configuration. With the full model of the OEW drive plant, the FC voltage can be purposefully selected to modify the drive torque speed characteristic to benefit the specific application. The advantages of the investigated synchronous drive architecture have been experimentally validated in different operating conditions with a purposely built set-up.

This paper presents an advanced control strategy for a Synchronous Reluctance Machine (SyRel) in an Open-End Winding (OEW) configuration. The goal is to enhance performance and control flexibility in dual-inverter systems, which present strong multivariable interactions and nonlinear dynamics. To address these challenges, a Finite-Control-Set Model-Predictive Control (FCS-MPC) method is proposed, as it can directly handle multiple control objectives and system constraints within a unified framework. A single cost function is used to regulate motor torque, the auxiliary converter Floating Capacitor (FC) voltage, and the power factor at the main side. This enables coordinated control of both converters without cascaded loops. The predictive controller uses an experimentally identified magnetic model of the SyRel machine, which accounts for magnetic nonlinearities, improving prediction accuracy under dynamic response. Experimental validation confirms the effectiveness of the proposed strategy. The system shows fast dynamic response, robust regulation of control objectives, and consistent unity power factor at the main converter. A comparison with conventional PI-based control highlights the advantages of the proposed method in terms of power factor regulation and dynamic performance, particularly under strong nonlinearities introduced by the FC voltage control. These results confirm that the proposed cost function formulation, with its specific control terms, effectively enables regulation of the overall system.

This paper proposes a Tree-Based Finite-Control-Set Model Predictive Control (TBFCS-MPC) strategy for a Cascaded H-Bridge (CHB) inverter, integrated with an adaptive online Look-Up Table (LUT) for real-time battery voltage balancing. The proposed TBFCS-MPC reduces computational complexity by limiting the number of evaluated voltage vectors to a maximum of 22, compared to 127 in conventional FCS-MPC approaches. This reduction enables real-time execution on embedded hardware, achieving a cost function evaluation time of only $\bf {4.2\ \mu s}$, compared to $\bf {24.1\ \mu s}$ with state-of-the-art solutions. The LUT dynamically selects switching states based on instantaneous battery voltages, ensuring active balancing. Experimental results validate the method's fast current reference tracking, effective balancing, and significantly reduced computational burden, making it a practical solution for high-performance, battery-fed multilevel inverter applications.

With the increasing integration of renewable energy sources (RESs) into power systems, batteries are playing a critical role in ensuring grid reliability and flexibility. Among them, vanadium redox flow batteries (VRFBs) have emerged as a promising solution for large-scale storage due to their long cycle life, scalability, and deep discharge capability. However, achieving optimal control and system-level integration of VRFBs requires accurate, real-time modeling and parameter estimation, challenging tasks given the multi-physics nature and time-varying dynamics of such systems. This paper presents a lightweight physics-informed neural network (PINN) framework tailored for VRFBs, which directly embeds the discrete-time state-space dynamics into the network architecture. The model simultaneously predicts terminal voltage and estimates five discrete-time physical parameters associated with RC dynamics and internal resistance, while avoiding hidden layers to enhance interpretability and computational efficiency. The resulting PINN model is integrated into a modulated model predictive control (MMPC) scheme for a dual-stage DC-AC converter interfacing the VRFB with low-voltage AC grids. Simulation and hardware-in-the-loop results demonstrate that adaptive tuning of the PINN-estimated parameters enables precise tracking of battery parameter variations, thereby improving the robustness and performance of the MMPC controller under varying operating conditions.

This paper presents an extensive experimental comparison of different predictive current control strategies applied to a two-level voltage source inverter supplying a resistive - inductive load. All controllers were implemented on the same hardware platform and evaluated under identical operating conditions at a controller sampling frequency of $\text{20}~\text{kHz}$. The evaluated methods include a conventional model-based (MB) predictive controller, an auto-regressive model with exogenous input and recursive least squares estimation (ARX-RLS), and three first-order ultra-local (UL) formulations: the linear extended state observer (LESO), the least absolute shrinkage and selection operator-based estimator (LASSO) and the extended-affine (EA). Experimental results confirm that the MB achieved the lowest computational time, whereas the ARX-RLS controller attained the lowest total harmonic distortion (THD). The EA-UL method provided the best overall trade-off between harmonic performance, transient dynamics and computational effort. The study highlights that model-free-predictive controllers (MF-PC), particularly ARX-RLS and EA-UL, can deliver performance comparable to or better than MB while significantly reducing dependence on system modeling. These findings confirm the potential of MF-PC approaches as efficient and robust solutions for next-generation power electronic converters.