In today’s world, the integration of renewable energies is essential to meet the surging global energy demand and reduce pollution. Ecuador, with its favorable geographical location, boasts abundant renewable resources. Currently, the country has large-scale renewable energy projects such as “El Aromo” in Manabí, led by Solarpack, and Villonaco in Loja, with its wind power plant. In this study, Homer Pro software is used to simulate two microgrids with solar and wind energy in the mentioned sectors, allowing us to conduct comprehensive economic and energy analyses to determine the most viable configurations.

Lithium-ion batteries have become a beacon in modern energy storage, powering from small electronic devices to electric vehicles (EVs) and critical medical equipment. Since their commercial introduction in the 1990s, significant advancements in materials science and engineering have enhanced battery capacity, safety, and lifespan. However, the complexity of lithium-ion battery dynamics has necessitated the development of advanced charging and control strategies to optimize performance, safety, and longevity. This work proposes a comparative analysis of three advanced control methods for lithium-ion battery charging: reinforcement learning, fuzzy logic, and classic proportional–integral–derivative (PID) control. Traditional charging methods often fail to address the complexities of battery dynamics, leading to suboptimal performance. Our study evaluates these intelligent control strategies using MATLAB-Simulink simulations to enhance charging efficiency, speed, and battery lifespan. The findings indicate that reinforcement learning offers superior adaptability, fuzzy logic provides robust handling of nonlinearity, and PID control ensures reliable performance with minimal computational resources.

One of the critical challenges in mobile robotics is obstacle avoidance, ensuring safe navigation in dynamic environments. In this sense, this work presents a comparative study of two intelligent control approaches for mobile robot obstacle avoidance based on a fuzzy architecture. The first approach is a neuro-fuzzy interface that combines neural networks’ learning capabilities with fuzzy logic’s rule-based reasoning, offering a flexible and adaptable control strategy. The second is a classic Mamdani fuzzy system that relies on human-defined fuzzy rules, providing an intuitive approach to control. A key contribution of this work is the development of a fast comprehensive, model-based dataset for neural network training generated without the need for real sensor data. The results show the evaluation of these two systems’ performance, robustness, and computational efficiency using low-cost ultrasonic sensors on a Pioneer 3DX robot within the Coppelia Sim environment.

The significance of electric vehicles is progressively escalating, underscoring the criticality of technologies underpinning the functionality of their propulsion systems. This particular case study delves into the simulation of field-oriented control, coupled with field weakening, aimed at regulating a salient-pole permanent-magnet synchronous motor (PMSM). This approach involves the utilization of two cascaded loops for current and voltage, each employing PI controllers. The fine-tuning of these controllers’ parameters hinges on the motor characteristics, as well as the desired response bandwidth.

This work presents a new sliding mode control strategy that integrates a Clegg integrator into a Proportional Integral Derivative (PID) sliding surface to address controller saturation (windup) and chattering. The proposed controller was implemented on a Programmable Logic Controller (PLC) to regulate the speed of a three-phase induction motor using a frequency inverter as the final control element. Conventional PID and sliding mode controllers (SMCs) were also implemented for performance comparison. This study included tests for sudden set-point changes and disturbances to evaluate the control strategies. The proposed controller demonstrated a faster response compared to the PID control and a smoother control action compared to the SMC. The results and conclusions discuss the advantages and limitations of the proposed control strategy in comparison to the conventional methods.