Four-Phase Interleaved Synchronous Buck Converter Multi-Parameter Identification Based on Digital Twin

This paper proposes a digital twin-based condition monitoring framework for the four-phase interleaved synchronous buck converter (ISBC), which enables non-intrusive identification of parameters. A nonlinear dynamic model of the ISBC is first established as the digital replica. Then, a particle-swarm-optimization routine updates the model parameters by minimizing the time-domain error between measured and simulated responses. Simulation results validate the effectiveness of the approach, which shows accurate estimation of component parameters and robust convergence.