Novel Design Methodology for DC-DC Converters Applying Metaheuristic Optimization for Inductance Selection
Source abstract
Abstract
Nowadays in modern industrial applications, where the power supply efficiency is more important than the output noise performance, DC-DC converters are widely used in order to fulfill the requirements. Yet, component selection and precise estimation of parameters can improve the converter’s performance, leading to smaller and more efficient designs. Hence, metaheuristic optimization algorithms can be applied using the mathematical model of DC-DC converters, in order to optimize their performance through an optimal inductance selection. Therefore, this work presents a novel design methodology for DC-DC converters, where the inductance selection is optimized, in order to achieve an optimal relation between the inductance size and the required energy. Moreover, a multi-objective metaheuristic optimization is presented through the Earthquake Algorithm, for parameter estimation and component selection, using the inductance of a buck DC-DC converter as a case study. The experimental results validate the design methodology, showing ripple improvement and operating power range extension, which are key features to have an efficient performance in DC-DC converters. Results also confirm the Small-Signal Model of the circuit, as a correct objective function for the parameter optimization, achieving more than 90% of accuracy on the presented behavior.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced DC-DC Converters · Advanced Battery Technologies Research · Innovative Energy Harvesting Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 02860, Mexico
References (32)
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