In this work, a methodology to assess the losses related to the main inductor in a Buck DC-DC converter is proposed. The losses are related to the current waveform and the magnetic response of the inductor core. An Artificial Neural Network is used to estimate the losses for given operating conditions of the DC-DC converter. The neural estimator is trained and validated using real data from an experimental workbench, producing as output both the per-period energy loss and an equivalent circuit model useful for inclusion in transfer functions and small signal circuit analysis.
Lozito, G.M., Bertolini, V., Riganti Fulginei, F., Belloni, E., Quercio, M. (2023). Neural Estimator for Inductor Losses in Buck DC-DC Converters Operating in CCM. In EUROCON 2023 - 20th International Conference on Smart Technologies, Proceedings (pp.412-417). Institute of Electrical and Electronics Engineers Inc. [10.1109/EUROCON56442.2023.10198952].
Neural Estimator for Inductor Losses in Buck DC-DC Converters Operating in CCM
Riganti Fulginei F.;Quercio M.
2023-01-01
Abstract
In this work, a methodology to assess the losses related to the main inductor in a Buck DC-DC converter is proposed. The losses are related to the current waveform and the magnetic response of the inductor core. An Artificial Neural Network is used to estimate the losses for given operating conditions of the DC-DC converter. The neural estimator is trained and validated using real data from an experimental workbench, producing as output both the per-period energy loss and an equivalent circuit model useful for inclusion in transfer functions and small signal circuit analysis.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
