Valdez Resendíz, Jesús ElíasRosas Caro, Julio CésarSilva Vera, Edgar Daniel2025-01-152024-11Silva Cera, E. D. (2024). A data-driven modeling approach for energy storage systems [Tesis maestría]. Instituto Tecnológico y de Estudios Superiores de Monterrey. Recuperado de: https://hdl.handle.net/11285/703040https://hdl.handle.net/11285/703040https://orcid.org/0000-0002-1889-1353This disertation presents a versatile data-driven modeling methodology designed for various energy systems, including battery-based power systems, DC-DC power electronic converters, Lithium-Ion batteries, and Proton-Exchange Membrane Fuel Cells (PEMFC). The proposed approach captures the non linear dynamics of each system by leveraging fundamental measurements and operational data, thus eliminating the need for explicit theoretical models and significantly simplifying the modeling process. Specifically, the methodology allows for the identification of essential parameters by constructing state-space representations that describe both fast and slow system dynamics, which are crucial for accurately modeling transient behaviors and implementing adaptive control strategies. The models were validated across different applications, showing their ability to replicate real system behaviors with high precision. For instance, in the case of DC-DC converters, the models demonstrated an average error deviation of approximately 2% for current signals and 4% for voltage signals, confirming their capacity to track the actual converter dynamics. Similarly, the Lithium-Ion battery models enabled accurate estimation of state of charge (SoC) and opencircuit voltage using a modified recursive least-squares algorithm, achieving close alignment with real discharge curves. In the PEMFC stack modeling, the methodology utilized real-physic model operational data to refine model accuracy, yielding improved predictive capabilities over traditional approaches. These results underscore the efficacy and robustness of the data-driven approach in enhancing the design, control, and optimization of diverse energy systems. By providing a framework that can be readily adapted to different components and configurations, this methodology supports advancements in sustainable energy technologies, enabling the interconnection of multiple energy storage and conversion systems with minimal computational cost and measurement requirements.TextoengopenAccesshttp://creativecommons.org/licenses/by-nc/4.0INGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA ENERGÉTICA::GENERACIÓN DE ENERGÍATechnologyTesis de doctoradoDebido a que existe información dentro del documento la cual es parte de dos trabajos de investigación. Estos trabajos se encuentran en revisión y aún no se han publicado.https://orcid.org/0009-0009-6338-3831Data driven modelBlack boxBattery modelPEMFC modelsRenewable energy