Climate change is a fact, and electric vehicles play a key role in contributing to mitigate its effects. The Switched Reluctance Motor (SRM) is one of the best candidates for use in electric mobility systems due to its rare-earth-free composition, high performance, low cost, simple and robust structure and fault tolerance capability. However, one of its major disadvantages is the torque ripple. The goal of this study is to evaluate two control techniques applied to an $8 / 6 \text{SRM}$ to determine which one provides better operating conditions. The analytical method is applied to implement the mathematical model comprehensively. Pulse Width Modulation (PWM) and Direct Torque Control (DTC) control strategies are implemented in Simulink, and detailed results of the comparison between the two controls techniques are shown, evaluating various performance metrics such as torque ripple, dynamic characteristics, speed error and efficiency.

The Switched Reluctance Motor (SRM) is a strong candidate for high-performance industrial drives and electric vehicle (EV) propulsion due to its robust, magnet-free construction and high fault tolerance. However, its main drawback lies in its nonlinear behavior, which produces significant torque ripple and acoustic noise, thereby hindering its widespread adoption. In recent years, Finite Control Set Model Predictive Control (FCS-MPC) has emerged as a promising alternative to mitigate these issues. Nevertheless, existing implementations typically rely on an eight-vector set comprising both single-phase and dual-phase excitations with unequal magnitudes, resulting in a nonuniform distribution in the αβ-plane. Unlike the conventional square-shaped distribution of vectors where excitation alternates between one and two phases, this study proposes a novel vector set that consistently energizes two phases in each selection. This approach achieves a uniform circular distribution in the αβ-plane, enabling the voltage magnitude to remain constant. The proposed eight-vector set leads to smoother current transitions, reduced torque ripple, and improved dynamic behavior. The strategy is validated on the MATLAB/Simulink platform, with detailed comparative results presented against the conventional method. The findings demonstrate a torque ripple reduction of up to 58% and an acceleration time improvement of up to 64%. These results highlight the strong potential of the proposed method for scalable SRM performance enhancement in demanding applications such as EV propulsion systems.

The Switched Reluctance Motor (SRM) is a strong candidate for high-performance industrial drives and electric vehicle (EV) propulsion due to its robust, magnet-free construction, high fault tolerance. The main drawback of the switched reluctance motor is the nonlinear behavior producing high torque ripple and noise these factors significantly hinder their widespread adoption. A novel alternative that is being investigated the last years to mitigate this problem is FCS-MPC (Finite Control Set Model Predictive Control), nevertheless the existing implementation use an eight-vector set as a base including single phase and dual phase excitation that don’t have equal magnitude, representing a nonuniform distribution in the αβ-plane. Therefore, this article, propose a novel eight vector set, that excites two phases in every vector with equal magnitude distributed in the αβ-plane, this new set of vectors result in smother current transitions, and reduce the torque ripple improve the dynamic behavior, this strategy is validated on the MATLAB/Simulink platform present detailed results comparing the proposed and the conventional method. The findings indicate a reduction in torque ripple by up to 58%, an improvement in acceleration time by up to 64%. The method demonstrates strong potential for scalable SRM performance enhancement in demanding applications such as EV drives.

Switched reluctance motors (SRMs) are attractive for fault-tolerant drives because their rare-earth-free rotor and intrinsic phase isolation support continued operation after a converter fault. Realising this requires a post-fault control policy that preserves both torque tracking and per-phase thermal balance, with the latter being a safety-relevant design consideration motivated by—though not herein verified against—ISO 26262. This paper proposes and evaluates, by simulation, a three-layer fault-tolerant finite-control-set model predictive control (FCS-MPC) architecture for a four-phase 8/6 SRM under a single open-phase converter fault. The layers are (i) a vector-set reconfiguration from the eight healthy, active vectors to the twenty-six admissible post-fault vectors, which restores controllability of the reduced converter; (ii) soft commutation expressed as a position-dependent penalty inside the MPC cost; and (iii) asymmetry-aware balancing that evens out the accumulated thermal load across the three healthy phases. We additionally analyse an activated-on-demand max-penalty thermal limiter and show, both analytically and in simulation, that it shares its optimiser with the variance-based balancing term and therefore confers no measurable benefit over it; it is consequently retained only as an optional on-demand limiter rather than a separate layer. The architecture is benchmarked against a fault-blind baseline, a rule-based hard fault-tolerant reference (Hard-FT), and intermediate configurations through a deterministic ablation across three critical operating points, complemented by a robustness assessment under measurement noise and parameter mismatch. A six-criteria fault-tolerance scorecard is reported as a methodological observation on the transferability of healthy-mode SRM specifications to post-fault operation.

The purpose of this research is to analyze the monthly energy consumption by the users of the PEC program (Energy Efficiency Program for induction cooking and electric water heating) of ELEPCO SA (Empresa Eléctrica Provincial Cotopaxi Sociedad Anónima) before and after the curfew measures dictated by the national “COE” (Emergency Operations Center) in response to the global pandemic caused by COVID-19 disease. Carried out by taking into account four strata distributed according the energy consumption levels, its variance in relation to the year 2019 and applying the linear regression method to estimate the average monthly demand in relation to the curfew time, resulting in that the rate The increase in the period April-May 2019 versus April-May 2020 is 5.17% and analyzing only stratum B in the period March-July 2019 in relation to March-July 2020 is 8.02%, estimating that for the month of August 2020 the will have an average monthly consumption of 215,441 KWh in stratum A

This research aims to analyze the relationship between monthly energy consumption and age of users of Barrio San Agustin in Machachi Canton in the province of Pichincha in the EEQ by its Spanish acronym, Empresa Electrica de Quito, before and after the curfew measures issued by the national COE (by its Spanish acronym) in response to the global COVID-19 pandemic. It is carried out taking into account a stratum distributed by the level of energy consumption, conducting age surveys of users in the area, determining its variation in relation to the years 2018, 2019 2020 and 2021, applying the curvilinear regression method to estimate the average monthly demand in relation to the average age of users, and as a result thereof, the group that reaches the peaks of power consumption in the period from March to November 2020, corresponds to an average age ranging from 27 to 33 years old and the average age groups that present the lowest average consumption are those ranging from 20 to 25 and from 40 to 45 years old. Esta investigación tiene como objetivo analizar la relación entre el consumo energético mensual y la edad de los usuarios pertenecientes al Barrio San Agustín en el Cantón Machachi provincia de Pichincha en la EEQ (Empresa Eléctrica Quito) antes y después de las medidas de toque de queda dictadas por el COE nacional en respuesta a la pandemia mundial por COVID-19. Se lleva a cabo tomando en cuenta un estrato distribuido por el nivel de consumo energético, levantando encuestas de edad de usuarios de la zona determinando su variación en relación al año 2018, 2019 2020 y 2021 aplicando el método de regresión curvilineal para estimar la demanda promedio mensual en relación a la edad promedio de los usuarios, resultando que el grupo que alcanza los picos de consumo de energía eléctrica en este periodo de marzo a noviembre del año 2020 pertenecen a una edad promedio de 27 a 33 años y el grupo de edades promedio que presenta el menor consumo promedio es de 20 a 25 años y de 40 a 45 años.