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On the emerging development of SiC MOSFET, the switching frequency of power converters has been pushed to the order of hundreds of kilohertz or even megahertz to improve power density. However, the state-of-the-art simulations for such high-frequency power electronics circuits are usually time-consuming, blocking the studies of long dynamics such as several seconds. What’s worse, adopting physical models to simulate the turn-on and turn-off transients leads to high stiffness and therefore convergence problems. In this paper, an event-driven approach is proposed to improve the solving speed and simulation stability of SiC based high-frequency converters. A time-scale hierarchical automaton is proposed to model the system, where piecewise analytical transient model for SiC MOSFET is established as the second layer in the automaton to simulate the switching transients with improved numerical stability. Simulation of the system is driven by discrete events in the automaton. A 3.3 kW 79.1 kHz wireless power transfer system for the wireless charging of electric vehicle is studied. Compared with ideal switch model in Simulink and physical switch model in LTspice, 60-fold and 400-fold increases in terms of simulation speed with same level of accuracy are achieved respectively. The simulated results are also attested to by experimental verifications. [ABSTRACT FROM AUTHOR]

We present a compact, continuous, and numerically stable version of a tantalum oxide (TaOx) memristor model which can be employed for robust and reliable simulations of large scale memristor based circuits. The original model contains a piecewise differentiable function in the memductance expression and discontinuous step functions in the state equation. Additionally, the original model does not set a proper upper bound for the state variable and may admit blowing up solutions due to an exponential power term, preventing the use of it for numerically reliable simulations. Considering these drawbacks, we modify the original model so as to i) simplify the memductance function while removing its piecewise differentiable nonlinearity, ii) include a proper window function for the ON state dynamics, which is missing in the original model, iii) modify and bound the exponential power term to prevent an uncontrollable blow-up of the solutions, and iv) apply a process called unification, allowing us to remove the step functions inherent in the model, which is a novelty in state-limited memristor models. We validate the accuracy of the proposed model via DC and transient simulations, dynamic route map analysis and a Spice implementation of an anti-series configuration, showing the applicability of the model. [ABSTRACT FROM AUTHOR]