In recent years, adaptive network-based fuzzy inference systems (ANFIS) applied in various engineering fields have demonstrated their ability to combine the learning capability of artificial neural networks and the representation of fuzzy inference systems, achieving promising results, and attracting the research community's interest. This article provides a valuable overview of ANFIS applications. Focusing on the field of electronic engineering, in particular for power electronics applications, the use of ANFIS has proven to be a very effective and reliable tool for applications requiring maximum power point tracking and power quality improvement. For this reason, this article presents an analysis of the review of 59 papers published between 2013 and 2023, of particular interest the applications of ANFIS for power quality improvement and harmonic content decrease in multilevel converters. This article applied a methodology for the systematic literature review that included identification, selection, eligibility, and inclusion criteria that allowed the selection of publications from the various relevant journals and databases consulted. Subsequently, the surveyed publications were analyzed and classified, highlighting the applications, characteristics of the ANFIS employed, their training algorithms, and the platforms or digital cards used. After analyzing the information from the surveyed papers, it can be concluded that using ANFIS has obtained promising results in improving inverter performance and power quality indexes in the face of variations. ANFIS offers flexibility and adaptability by performing adaptive and real-time control by continuously adjusting the inverter operation.

Recently, electric vehicles (EVs) have proven to be a practical option for lowering greenhouse gas emissions and reducing reliance on fossil fuels. Lithium-ion batteries, at the core of this innovation, require efficient thermal management to ensure optimal performance, safety, and durability. This article reviews current scientific studies on controlling the temperature of lithium-ion batteries used in electric vehicles. Several cooling strategies are discussed, including air cooling, liquid cooling, the use of phase change materials (PCMs), and hybrids that combine these three types of cooling, with the primary objective of enhancing the thermal performance of the batteries. Additionally, the challenges and proposed solutions in battery pack design and energy management methodologies are explored. As the demand for electric vehicles increases, improving battery thermal management systems (BTMSs) is becoming increasingly important. Implementing and developing better BTMSs will help increase the autonomy and safety of electric vehicles in the long term.

This work aims to obtain the switching angles to reduce the total harmonic distortion (THD) and compare the different sets of angles obtained by modifying the modular index (Mi) in a seven-level multilevel inverter using the differential evolution (DE) algorithm. A seven-level cascaded multilevel inverter with RL load was simulated in MATLAB Simulink. The Mi was varied (0.6 to 1.27) to check which set of switching angles is optimal to reduce the THD. These sets of angles are then experimentally validated on a test bench. A brief discussion of the results is presented, comparing the minimum THD found without considering Mi, versus the minimum THD obtained if Mi is less than 1.

Uninterruptible Power Supplies (UPSs) protect electronic equipment by delivering consistent power. Among the core components of a UPS is the inverter, which converts stored DC energy from batteries into AC power. This work focuses on a cascaded multilevel inverter topology for its ability to reduce voltage Total Harmonic Distortion (THD), which is essential for maintaining UPS efficiency and power quality. Using an ANFIS (Adaptive Neuro-Fuzzy Inference System) model, enhanced with the Particle Swarm Optimization (PSO) algorithm, the switching angles were optimized to minimize THD. This work focused on an online UPS with a seven-level inverter structure powered by three LifePo4 S17 batteries, with critical load levels (100%, 95%, 50%, 15%, and 5%) represented in 35 experimental cases. The experimental design allowed the ANFIS-PSO model to adapt to varying voltages, achieving robust THD reduction. The results demonstrated that this combination of ANFIS and PSO provided effective angle optimization, with a low standard deviation of 0.06 between the training and simulated %THD, highlighting the process’s accuracy. The analysis showed that, in most cases, the simulated THD values closely aligned with, or even improved upon, the calculated values, with discrepancies not exceeding 0.2%. These findings support the ANFIS-PSO model’s potential in enhancing power electronics applications, particularly in critical sectors like renewable energy and power transmission, where THD minimization is crucial.

The developing environmental consequences of excessive dependence on fossil fuels have pushed many countries to invest in clean and renewable energy sources. Mexico is a country that, due to its geographic and climatic diversity, can take advantage of this potential in renewable energy generation and reduce its dependence on fossil fuels while developing strategies to improve its energy system. This study investigated the feasibility of the autonomous use of two hybrid renewable energy systems and a photovoltaic system to power homes in a remote location. With the help of HOMER Pro Version 3.14.5 software, a model was made to evaluate the operation of three systems for one year, and the demand was predicted according to a given scenario. In addition, the optimal configuration of the components of each system was determined. The results showed that the simultaneous use of solar systems with a converter and a backup system consisting of a diesel generator and batteries would be the most viable and reliable option for generating renewable energy at the selected location, offering electricity with a renewable fraction of more than 80%.

The main contribution of this paper is to present a simple algorithm that theoretically and numerically assesses the switching angles of an inverter operated with the SPWM technique. This technique is the most widely used for eliminating harmonics in DC-AC converters for powering motors, renewable energy applications, household appliances, etc. Unlike conventional implementations of the SPWM technique based on the analog or digital comparison of a sinusoidal signal with a triangular signal, this paper mathematically performs this comparison. It proposes a simple solution to solve the transcendental equations arising from the mathematical analysis numerically. The technique is validated by calculating the total harmonic distortion (THD) of the generated signal theoretically and numerically, and the results indicate that the calculated angles produce the same distribution of harmonics calculated analytically and numerically. The algorithm is limited to single-phase inverters with unipolar SPWM.