@inproceedings{1d1400e20d1443399a1888921f58673c,
title = "Application of Digital Twin Concept in Condition Monitoring for DC-DC Converter",
abstract = "This paper presents a digital twin-based condition monitoring method for DC-DC power converters, which features non-invasive and without additional hardware. To demonstrate it, a buck converter is applied as a case study with theoretical analysis and experimental verification. The digital twin of the buck converter is established, which includes the power stage, sampling circuit, and close-loop controller. Particle Swarm Op-timization (PSO) algorithm is applied to minimize the difference between the digital twin and its physical counterpart. Compare to conventional methods, the proposed method is able to monitor the health indicators of the key components in the buck converter: capacitor and MOSFET, without adding extra measurement circuits. Moreover, because the digital twin is a replica of the physical buck converter, accessing to the internal buck converter is unnecessary, which is non-invasive.",
keywords = "Component, DC-DC, Digital twin, Health condition, Parameter identification",
author = "Yingzhou Peng and Huai Wang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 11th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2019 ; Conference date: 29-09-2019 Through 03-10-2019",
year = "2019",
month = sep,
doi = "10.1109/ECCE.2019.8912199",
language = "British English",
series = "2019 IEEE Energy Conversion Congress and Exposition, ECCE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2199--2204",
booktitle = "2019 IEEE Energy Conversion Congress and Exposition, ECCE 2019",
address = "United States",
}