Integrated MPPT and DC-DC Converter Design: Metaheuristic Optimization from Theory to Application

This project showcases the integration of all my major contributions as a Ph.D. student at Tecnológico de Monterrey—ranging from the development of an enhanced Maximum Power Point Tracking (MPPT) algorithm based on metaheuristic optimization, to the creation of a novel design methodology for DC-DC converters.

I introduced the first simulated version of the improved MPPT algorithm using the Earthquake Optimization Algorithm (EA) in a peer-reviewed journal article, demonstrating the algorithm’s potential for a range of power electronics applications. Building on this, I contributed a book chapter proposing a reinitialization technique for MPPTs based on a perceptron artificial neural network (ANN), enabling greater robustness in metaheuristic-based tracking algorithms. Finally, I consolidated these efforts in a comprehensive peer-reviewed journal publication, which experimentally validated both the enhanced MPPT algorithm and the optimally designed DC-DC converter that served as its hardware testbed.

You can find bellow these publications:

• Journal Paper – Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm.

• Book Chapter – Solar Irradiation Changes Detection for Photovoltaic Systems Through ANN Trained with a Metaheuristic Algorithm.

• Journal Paper – Experimental Validation of an Enhanced MPPT Algorithm and an Optimal DC–DC Converter Design Powered by Metaheuristic Optimization for PV Systems.

This project reflects my ability to integrate advanced optimization techniques with practical power electronics and real-time embedded systems. It also validates my hands-on expertise in designing, fabricating, and testing custom PCBs for functional, field-deployable energy systems—demonstrating a complete pipeline from theoretical modeling to physical implementation.

Earthquake Optimization Algorithm:
Development, Validation, and Real-Time Application

As a Ph.D. student at Tecnológico de Monterrey, I developed the Earthquake Optimization Algorithm, a novel metaheuristic designed for applications with broad search spaces, including multi-objective optimization problems. I validated its effectiveness through a case study involving DC motor model identification and PID controller tuning (through MATLAB and Simulink), comparing its performance to analytical methods (AM) for both modeling and control tasks.

The entire system was implemented and tested in real-time using NI FPGA hardware through LabVIEW, demonstrating the algorithm's reliability in embedded applications. I also fully developed the testbed and the electrical circuit design for the motor driver and instrumentation.

This work was first presented at the NanoFim-2018 IEEE conference in Mexico City, Mexico. An extended version was later published in a peer-reviewed journal, showcasing the algorithm’s capability as a training method for Artificial Neural Networks (ANNs), with successful applications in mobile phone usage detection and logic gate emulation.

Here you can find both publications:

• IEEE Conference Paper – Electric machines control optimization by a novel geo-inspired earthquake metaheuristic algorithm.

• Extended Journal Paper – Mobile Phone Usage Detection by ANN Trained with a Metaheuristic Algorithm.

This project highlights my ability to bridge optimization techniques, control systems, and real-time embedded deployment, strengths I continue to apply across my field-deployable solutions.