PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters

TL;DR

PowerGenie is an automated framework that uses analytical and evolutionary methods to discover high-performance reconfigurable power converters, achieving significant efficiency improvements over existing designs without extensive simulations.

Contribution

It introduces a novel analytical approach for performance prediction and an evolutionary finetuning method for large-scale topology discovery in power converters.

Findings

Discovered a novel 8-mode reconfigurable converter with 23% higher FoM.

Achieved average efficiency gains of 10% across modes.

Outperformed existing methods in validity, novelty, and performance metrics.

Abstract

Discovering superior circuit topologies requires navigating an exponentially large design space-a challenge traditionally reserved for human experts. Existing AI methods either select from predefined templates or generate novel topologies at a limited scale without rigorous verification, leaving large-scale performance-driven discovery underexplored. We present PowerGenie, a framework for automated discovery of higher-performance reconfigurable power converters at scale. PowerGenie introduces: (1) an automated analytical framework that determines converter functionality and theoretical performance limits without component sizing or SPICE simulation, and (2) an evolutionary finetuning method that co-evolves a generative model with its training distribution through fitness selection and uniqueness verification. Unlike existing methods that suffer from mode collapse and overfitting, our approach achieves higher syntax validity, function validity, novelty rate, and figure-of-merit (FoM). PowerGenie discovers a novel 8-mode reconfigurable converter with 23% higher FoM than the best training topology. SPICE simulations confirm average absolute efficiency gains of 10% across 8 modes and up to 17% at a single mode. Code will be released upon publication.