Abstract

Hardware-in-the-loop (HIL) simulations of power converters must achieve a truthful representation in real time with simulation steps on the order of microseconds or tens of nanoseconds. The numerical solution for the differential equations that model the state of the converter can be calculated using the fourth-order Runge–Kutta method, which is notably more accurate than Euler methods. However, when the mathematical error due to the solver is drastically reduced, other sources of error arise. In the case of converters that use deadtimes to control the switches, such as any power converter including half-bridge modules, the inductor current reaching zero during deadtimes generates a model error large enough to offset the advantages of the Runge–Kutta method. A specific model is needed for such events. In this paper, an approximation is proposed, where the time step is divided into two semi-steps. This serves to recover the accuracy of the calculations at the expense of needing a division operation. A fixed-point implementation in VHDL is proposed, reusing a block along several calculation cycles to compute the needed parameters for the Runge–Kutta method. The implementation in a low-cost field-programmable gate arrays (FPGA) (Xilinx Artix-7) achieves an integration time of 1μs. The calculation errors are six orders of magnitude smaller for both capacitor voltage and inductor current for the worst case, the one where the current reaches zero during the deadtimes in 78% of the simulated cycles. The accuracy achieved with the proposed fixed point implementation is very close to that of 64-bit floating point and can operate in real time with a resolution of 1μs. Therefore, the results show that this approach is suitable for modeling converters based on half-bridge modules by using FPGAs. This solution is intended for easy integration into any HIL system, including commercial HIL systems, showing that its application even with relatively high integration steps (1μs) surpasses the results of techniques with even faster integration steps that do not take these events into account.

Highlights

  • The error curves of the other values of R do not resemble a line with a slope of 4. This points to the fact that the miscalculation of i L during deadtimes offsets the high accuracy provided by the fourth-order Runge–Kutta method

  • The magnitudes at dt = 1 μs are very similar to the errors of the 64-bit floating point. This indicates that a fixed-point implementation of the hardware-in-the-loop simulation is feasible, reaching the same accuracy as using floating-point arithmetics

  • Fourth-order Runge–Kutta methods allow for accurate modeling of switched circuits

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Summary

Introduction

The HIL model usually must operate in real time and must provide acceptable accuracy The former implies that calculation of the state variables is performed with integration times between a few microseconds and tens of nanoseconds, in order to provide a very small calculation delay and be able to emulate medium- to high-frequency converters. Some examples of these sources are small electrical losses [22] and resolution issues in the calculation of variables [23] Another issue is found in [24], where a synchronous buck converter model does not benefit from 4th-order methods when the current reaches zero during the switching deadtime.

Buck Converter Modeling
Generic Sources of Errors
Model-Specific Sources of Error
Time Step Subdivision with a Linear Approximation
Floating-Point Simulations
Fixed-Point Implementation
Pipelined Architecture
K-Calculator Generic Block
Synthesis Results
Conclusions
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