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

这个I-Corps项目更广泛的影响/商业潜力是开发电力电子技术，以自动化复杂的电路设计。虽然在推进建模仿真和验证电力转换器方面已经取得了重大进展，但设计这种设备的过程在时间和成本方面仍然效率低下。功率变换器的电路设计主要依赖于人类的经验和直觉来选择最优拓扑和搜索设计参数。 这个过程可能非常耗时、低效和劳动密集型。所提出的软件可以帮助电力电子工程师在选择工程最佳架构进行高保真设计和评估之前，更快速，更经济地考虑各种新概念。这个I-Corps项目基于技术的发展，集成了机器学习，电力电子，数据分析，仿真软件，和优化以使电力转换器的电路设计自动化。 该技术还可以促进将所提出的软件工具集成到现有的功率转换器设计工作流程中。该技术旨在通过基于物理的降阶模型自动生成和评估电源转换器设计：自动地朝着最佳系统配置演进体系结构概念并自动地生成，在可接受的性能不确定性范围内评估和优化体系结构，同时满足期望的输出。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准。

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.