Power Circuit Simulation Using Laplace Model

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Solution Overview

Problem

Existing electronic circuit simulation methods, such as the Modified Nodal Analysis (MNA) framework, require numerical polynomial approximation for long-term predictions, increasing computational complexity, and the State-Space Analysis (SSA) framework struggles with automation for complex circuits, limiting accurate and efficient waveform prediction.

Innovation Solution

A computer-implemented method that translates the MNA model into a Laplace model, determining characteristic frequencies and transfer functions to predict circuit waveforms without approximation, allowing for exact solutions and reduced computational complexity by processing the Laplace model matrix.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If polynomial approximation is used to solve the MNA model for long-term predictions, then the solution accuracy can be controlled by increasing the order of approximation, but the computational complexity increases

Engineering Contradiction:
Improvesolution accuracyVSAvoidcomputational complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces the traditional polynomial approximation method (mechanical/mathematical system) with an exponential function-based approach. By expressing circuit waveforms as sums of exponential functions with characteristic frequencies, the method achieves exact solutions without requiring high-order polynomial approximations, thereby reducing computational complexity while maintaining or improving accuracy.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the fundamental parameter representation from polynomial coefficients to characteristic frequencies and exponential decay constants. This parameter transformation allows the system to capture long-term behavior more efficiently, as exponential functions naturally model the transient and steady-state responses of electrical circuits without requiring increasing polynomial orders.

Inventive Principle:
Principle #35Parameter changes

2Duration of action of moving object

If the simulation duration is segmented into multiple consecutive steps, then the need for large polynomial orders is reduced, but the computational complexity for simulating the electronic circuit still increases

Engineering Contradiction:
Improvesimulation durationVSAvoidcomputational complexity
Core Design Contradiction:
Duration of action of moving objectVSDevice complexity

Solution Approach 1:

The patent performs preliminary calculation of the characteristic frequencies of the circuit once, and then uses these frequencies to directly compute waveforms at any future time point. This eliminates the need for sequential time-stepping simulation, allowing long-term predictions to be made in a single computational step rather than requiring multiple consecutive simulation steps.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent transitions from a time-domain sequential simulation approach to a frequency-domain approach by utilizing characteristic frequencies. This dimensional change allows the system to predict circuit behavior at any time t directly from the frequency domain representation, bypassing the need for step-by-step time domain integration.

Inventive Principle:
Principle #17Another dimension (Dimensionality change)

3Measurement precision

If the State-Space Analysis framework is used, then the solution can be obtained without approximation, but the framework struggles with automation for complex circuits

Engineering Contradiction:
Improvesolution accuracyVSAvoidautomation capability
Core Design Contradiction:
Measurement precisionVSExtent of automation

Solution Approach 1:

The patent develops a universal methodology based on modified nodal analysis that can automatically handle any circuit topology. By formulating the problem in terms of MNA matrices and characteristic frequencies, the approach provides a unified framework that works for both simple and complex circuits without requiring manual intervention or specialized treatment for different circuit configurations.

Inventive Principle:
Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent enables the simulation system to automatically determine its own characteristic frequencies and exponential mode structures without requiring external guidance or manual model formulation. The method self-adapts to any circuit configuration by extracting the relevant parameters directly from the circuit's MNA representation, making the automation process robust and general-purpose.

Inventive Principle:
Principle #25Self-service

Data Source

PatentEP4462302A1Method and computing system for simulating a power circuit
Publication Date: 2024.11.13 MITSUBISHI ELECTRIC R&D CENTRE EUROPE BV
  • EP4462302A1 patent drawingFigure 1~2
  • EP4462302A1 patent drawingFigure 3~4
  • EP4462302A1 patent drawingFigure 5~6d

AI summary

The present disclosure relates to a computer-implemented method (20) for simulating a power circuit (10), wherein the method comprises: - determining (S20) a modified nodal analysis, MNA, model of the power circuit, - translating (S21) the MNA model into a Laplace model which describes relations between circuit waveforms and power sources waveforms with a matrix of Laplace functions, - determining (S22) characteristic frequencies of the power circuit based on the matrix of the Laplace model, - determining (S23) transfer functions from one circuit waveform to each of the other circuit waveforms, based on the matrix of the Laplace model, - determining (S24) weighting coefficients for characteristic functions of the characteristic frequencies, based on an initial state of passive energetic components of the power circuit and based on the transfer functions, - predicting (S25) the circuit waveforms of the power circuit based on the characteristic frequencies, the transfer functions and the weighting coefficients.