Abstract
This work focuses on designing nonlinear control algorithms for dual half-bridge converters (DHBs). We propose a two-layer controller to regulate the current and voltage of the DHB. The first layer utilizes a change in the control variable to obtain a quasi-linear representation of the DHB, allowing for the application of simple linear controllers to regulate current and power flow. The second layer employs a nonlinear control allocation algorithm to select control actions that fulfill (pseudo) power setpoints specified by the first control layer; it also minimizes peak-to-peak currents in the DHB and enforces voltage balance constraints. We apply the DHB and this new control strategy to manage power flow in a hybrid energy storage system comprising of a battery and supercapacitors. Numerical simulation results demonstrate that, in comparison with state-of-the-art approaches, our control algorithm is capable of maintaining good transient behavior over a wide operating range, while reducing peak-to-peak current by up to 80%.

Abstract

Abstract
As electric vehicle (EV) adoption accelerates, residential buildings—particularly multi-dwelling structures—face increasing challenges to electrical infrastructure, notably due to conservative sizing practices of electrical feeders based on maximum simultaneous demand. Current sizing methods assume all EVs charge simultaneously at maximum capacity, resulting in unnecessarily oversized and costly electrical installations. This study proposes an optimized methodology to estimate accurate coincidence factors, leveraging simulations of EV user charging behaviors in multi-dwelling residential environments. Charging scenarios considering different fleet sizes (1 to 70 EVs) were simulated under two distinct premises of charging: minimization of current allocation to achieve the desired battery state-of-charge and maximization of instantaneous power delivery. Results demonstrate significant deviations from conventional assumptions, with estimated coincidence factors decreasing non-linearly as fleet size increases. Specifically, applying the derived coincidence factors can reduce feeder section requirements by up to 86%, substantially lowering material costs. A fuzzy logic inference model is further developed to refine these estimates based on fleet characteristics and optimization preferences, providing a practical tool for infrastructure planners. The results were compared against other studies and real-life data. Finally, the proposed methodology thus contributes to more efficient, cost-effective design strategies for EV charging infrastructures in residential buildings. © 2025 Elsevier B.V., All rights reserved.

Abstract
In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system.

Abstract
Core loss estimation in switched reluctance motor is a complex task, due to non-linear phenomena and non-sinusoidal flux density waveforms. Several methods have been developed for estimating it (e.g. empirical, and physical-mathematic models), each one with merits and limitations. This paper proposes a new method for core losses estimation based on Finite Element Method Magnetics software. The main idea is using the machine phase-current harmonics as input for estimating core losses. In addition, a comparative study is carried out, where the proposed approach is faced up to a different one, based on Fourier decomposition of the flux density waveforms in the machine sections. In order to systematically analyze and compare the applied estimation cores loss techniques, a case study of a three-phase 6/4 SRM for different simulation scenarios is introduced. The outcomes of both methods are discussed and compared, where core loss convergence is found for limited speed and load ranges.