170022 20260316092629.0 doi 10.1109/OJIES.2026.3663897 sideral 148551 ART-2026-148551 eng Lahuerta, Oscar Universidad de Zaragoza (orcid)0009-0002-3255-5775 Hybrid-timescale physics-informed neural network for electrical equivalent impedance identification in induction heating systems 2026 This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction heating system. By utilizing observable variables, specifically the voltage and current waveforms from the inductor system, the proposed architecture aims to accurately estimate key electrical parameters, i.e., equivalent resistance and inductance, which vary over time due to the nonlinear magnetic properties of the induction load. To assess the performance of the proposed PINN architecture, a comparison with results obtained using an extended Kalman filter was conducted, which serves as a benchmark for this type of task. In addition, the robustness of both approaches was assessed by introducing varying levels of uncertainty in the observable variables. Finally, the effectiveness of both methods was validated through the analysis of experimental measurements collected from a functional prototype. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/EUR/AEI/CPP2021-008938 info:eu-repo/grantAgreement/ES/DGA/T26-24 info:eu-repo/grantAgreement/ES/DGA/T34-24 info:eu-repo/grantAgreement/ES/MICIU/PDC2023-145837-I00 info:eu-repo/grantAgreement/ES/MICIU/PID2022-136621OB-I00 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Carretero, Claudio Universidad de Zaragoza (orcid)0000-0001-7901-9174 Barragan, Luis Angel (orcid)0000-0003-4633-4551 Navarro, Denis Universidad de Zaragoza (orcid)0000-0002-0795-8743 Acero, Jesus Universidad de Zaragoza (orcid)0000-0001-7207-5536 2002 385 Universidad de Zaragoza Dpto. Física Aplicada Área Física Aplicada 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 7 (2026), 382-392 IEEE open j. ind. electron. soc. IEEE Open Journal of the Industrial Electronics Society 2644-1284 3235746 http://zaguan.unizar.es/record/170022/files/texto_completo.pdf Versión publicada 2391520 http://zaguan.unizar.es/record/170022/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:170022 articulos driver 2026-03-16-08:16:59 ARTICLE