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
Battery degradation remains a major challenge in electric vehicle (EV) adoption, directly affecting long-term performance, cost, and user satisfaction. This paper proposes a data-driven charging strategy that reduces battery wear while meeting the user's daily range needs. By integrating manufacturer guidelines, battery aging models, and thermal dynamics, the proposed optimization algorithm dynamically adjusts the charging current and timing to minimize stressors, such as high temperatures and prolonged high state of charge (SoC). The methodology is responsive to user inputs such as departure time and required driving range, enabling personalized charging behavior. Simulation results show that this approach can reduce battery degradation by up to 2.7% over a 30-day period compared to conventional charging habits, without compromising usability. The framework is designed for integration into Battery Management Systems (BMS), with applications for both private EV users and fleet operators. We address EV battery aging driven by high core temperature and prolonged high state of charge (SoC) during overnight/home charging. Given a user-specified departure time and required driving range, we schedule charging power over time to minimize predicted degradation exposure while still meeting the range requirement. The scheduler optimizes charging timing/current under SoC dynamics, thermal constraints, and charger/ BMS limits.

Abstract
In this paper, a robust nonlinear Super-Twisting Sliding Mode Controller (STSMC) is proposed to minimize torque ripple in Switched Reluctance Motor (SRM) drive systems, thereby reducing acoustic noise and vibration. To optimize torque ripple, the firing angles (theta(on) and theta(off)) are dynamically adjusted based on the instantaneous torque and speed error. To demonstrate its superiority, the performance of the STSMC is compared with conventional linear and Sliding Mode Control (SMC) regulators. The results confirm the robustness and effectiveness of the proposed controller. The torque ripple with PSO-optimized firing angles and STSMC is reduced by around 50% compared to conventional fixed switching angles. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
This paper presents a new power chain and its control scheme that provides highly flexible low voltage ride through (LVRT) capabilities for power converters that feed the stack of Polymer Electrolyte Membrane (PEM) hydrogen electrolyzers. It introduces an intermediate power stage with a new adaptive feedforward controller, that isolates the electrolyzer stack from grid-side disturbances. An RMS model of the whole solution is developed and validated. The system was developed in MATLAB/SIMULINK and PLECS environments. Furthermore, the system was tested in DC and AC grids by subjecting it to a fault reducing the input voltage magnitude down to 0.2 pu. The system demonstrated its ability to ride through the fault whilst maintaining the power set-points and supply quality at the electrolyzer stack connection point. © 2025 Elsevier B.V., All rights reserved.