I-Corps: Machine Learning Enhanced Automated Circuit Configuration and Evaluation of Power Converters

这个i-Corps项目的更广泛的影响/商业潜力是电力电子的发展，使复杂的电路设计自动化。虽然在推进建模、仿真和验证电力转换器方面取得了重大进展，但设计此类设备的过程在时间和成本方面仍然效率低下。最先进的功率变流器电路设计在很大程度上依赖于人类专家来选择最优的拓扑结构，并根据人类的经验和直觉寻找设计参数。这一过程可能非常耗时、低效和劳动密集型。建议的软件可以帮助电力电子工程师在选择高保真设计和评估的工程最佳架构之前，更快、更具成本效益地考虑广泛的新概念。这个I-Corps项目基于技术的开发，该技术集成了机器学习、电力电子、数据分析、仿真软件和优化方面的最新突破，以自动化电力转换器的电路设计。该技术还可促进将拟议的软件工具整合到现有的功率转换器设计工作流程中。该技术旨在使用基于物理的降阶模型自动生成和评估功率转换器设计：自动向最佳系统配置演化体系结构概念，并在满足预期输出的同时，在可接受的性能不确定范围内自动生成、评估和优化体系结构。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of power electronics to automate complex circuit design. While significant progress has been made in advancing modeling simulation and verifying electrical power converters, the process of designing such devices remains inefficient in terms of time and cost. The state-of-the-art circuit design of power converters relies heavily on human experts to select the optimal topology and search for design parameters with human experience and intuitions. This process can be very time-consuming, inefficient, and labor-intensive. The proposed software may help power electronics engineers consider a wide range of novel concepts more rapidly and cost-effectively before selecting an engineering-optimal architecture for high-fidelity design and evaluation.This I-Corps project is based on the development of technology that integrates recent breakthroughs in machine learning, power electronics, data analytics, simulation software, and optimization to automate the circuit design of electrical power converters. The technology may also facilitate the integration of the proposed software tools into existing power-converter design workflows. The technology seeks to automatically generate and evaluate power converter designs with physics-based Reduced Order Models: automatically evolving architecture concepts toward the optimal system configurations and automatically generating, evaluating and optimizing architectures within acceptable performance uncertainties while satisfying the desired outputs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.