Model DC/DC converters with CCM/DCM boundaries and loss estimation in C++.
npx skills add https://github.com/lgili/skillex --skill power-electronics
Please help me install this Agent Skill. Skill: power-electronics Source: https://github.com/lgili/skillex/tree/main/skills/power-electronics Command: npx skills add https://github.com/lgili/skillex --skill power-electronics
This skill provides structured guidance and reference material for building and analyzing discrete-time power-electronics converter models, including accurate CCM/DCM boundaries, state-space averaging, and loss estimation in a C++ simulator.
Initialize a starter converter model in the simulator and validate CCM/DCM behavior.
High-intent search queries and answers about installing and using this skill.
You model CCM/DCM boundaries for DC/DC converters by applying state-space averaging techniques within a C++ simulator to ensure accurate discrete-time converter behavior. This approach validates topology performance across non-isolated and isolated variants.
State-space averaging is a modeling technique that linearizes switched-mode converter behavior to derive small-signal transfer functions. It enables accurate stability analysis and controller design for topologies like buck, boost, and LLC converters.
You estimate power electronics losses by including parasitic resistances like ESR, DCR, and Rds_on directly in your converter models. This parasitic inclusion allows precise loss accounting and performance optimization across buck, boost, and DAB topologies.
Yes, you can simulate isolated converter topologies like flyback, LLC, and DAB. The modeling approach explicitly handles both non-isolated and isolated variants, providing accurate loss estimation and small-signal analysis for design verification.
The best way to validate small-signal transfer functions is to use reference materials on CCM/DCM analysis alongside state-space averaging in a C++ simulator. This ensures consistent simulation results for design verification and academic study.
Converter simulations mismatch expected CCM and DCM behavior when state-space averaging lacks precise boundary handling or ignores parasitic elements. Including Rds_on, ESR, and DCR ensures accurate loss estimation and consistent transfer functions.
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