Rui Esteves Araujo

Abstract
Switched Reluctance Motors (SRMs) are becoming increasingly popular for various applications, including automotive applications. However, challenges such as torque ripple and vibration persist, limiting their performance. This chapter investigates the application of intelligent control strategies, particularly fuzzy logic, to mitigate these issues. Fuzzy logic modeling does not require an accurate mathematical model which is very difficult to obtain from a SRM because of its inherit nonlinearities. In this work a Fuzzy Logic Controller (FLC) applied to the speed control of an SRM, highlighting the advantages of FL over traditional methods in terms of flexibility and performance. A comparison is made between the FLC, a Sliding Mode Control (SMC), and a Proportional Integral (PI) controller. Simulation results using MATLAB/Simulink show that the FLC substantially reduces torque ripple, offering better overall performance in terms of smoothness and robustness under varying operational conditions. The findings demonstrate that FLC offers a more effective solution than conventional approaches for SRM applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Switched reluctance generators (SRG) are one of the machines with huge potential in wind power generation due to their reliability and robust design. However, the inherent characteristics of SRGs lead to significant challenges in achieving high efficiency and low output current and torque ripple simultaneously. The performance of SRGs is hindered by conflicting requirements. To address these issues, this chapter presents an optimization control strategy aimed at improving the static performance of SRGs. The chapter discusses the application of the Particle Swarm Optimization (PSO) technique to optimize the commutation angles, specifically the turn-on (?on) and turn-off (?off) angles, for an 8/6 SRG. The proposed strategy consists of two main steps. First, a Maximum Power Point Tracking (MPPT) algorithm is implemented to maximize power output at varying rotor speeds, combined with a direct power control method to regulate the power generated by the SRG. Second, a multi-objective function is developed to evaluate the SRG performance, considering key factors such as power output, output current ripple, and torque ripple. The simulation results indicate that implementing optimized turn-on and turn-off angles leads to a reduction in torque ripple from -1.78 Nm using the conventional technique to -0.66 Nm with the proposed method, corresponding to an impressive 63% decrease. Furthermore, the optimization strategy effectively maximizes the efficiency of the system employing an MPPT approach, ensuring optimal energy conversion under varying operating conditions. Future research directions include experimental validation of the proposed control system on real hardware to assess its practical feasibility and performance under real-world operating conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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
The objective of this paper is to delineate the ongoing doctoral research work that is focused on the development of a digital model intended to emulate the real-time operation of an electrolyzer that is powered by a DC/DC converter. The digital model of the converter and the proton exchange membrane (PEM) electrolyzer (EL) is presented, and it is based on an electrical equivalent model. A primary contribution of this study is the analysis of the errors resulting from the discretization process. Furthermore, the implementation and development of the digital model requires a comprehensive study of the errors and key affecting factors. Additionally, the formulation of a mechanism to reduce these errors is essential for advancing this topic. Preliminary results obtained using the digital emulator developed demonstrated its capacity to reproduce the voltage and current response applied to the electrolyzer with a reduced error compared to the continuous-time model.

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.