Application of Digital Twin Concept in Condition Monitoring for DC-DC Converter

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.