I-Corps: Machine learning-driven design of lithium-ion batteries

I-Corps：机器学习驱动的锂离子电池设计

• 批准号: 2028630
• 负责人: Andres Tovar
• 金额: $ 5万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028630&HistoricalAwards=false
• 关键词: Corps; Machine; learning; driven; design

The broader impact/commercial potential of this I-Corps project is the development of high-performance, cost-effective, and safe lithium-ion batteries (LIBs) based on components optimized with a novel computational platform that can minimize the discovery and design-cycle time scales. The new simulation and design technologies have a direct impact on segments including developers in LIB companies and electric vehicle companies, and university/laboratory researchers. This technology can accelerate adoption of electric vehicles, which can reduce emissions and achieve better fuel economy.This I-Corps project is based on the development of geometrically accurate numerical models of particles, binders, and conductivity enhancers in LIB electrodes. Finding optimal LIB electrode architectures and corresponding LIB configurations that maximize energy and power densities, rate capability, and safety during operation presents significant challenges. This project proposes use of Bayesian Machine Learning (BML) algorithms. The proposed BML-driven design algorithms incorporate statistical microstructure characterization and reconstruction methods to support the LIB electrode design, multitask Gaussian Processes (GPs) to integrate experimental data with multi-fidelity and multiscale simulations, and a multi-objective acquisition function to achieve the LIB multi-objective design. The use of a Bayesian optimization approach allows the systematic exploration of the design space. The result is an LIB electrode architecture optimizing the trade space of performance, cost, and safety.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.

该I-Corps项目的更广泛的影响/商业潜力是开发高性能，具有成本效益和安全的锂离子电池（LIBS），该电池（LIBS）基于使用新颖的计算平台优化的组件开发，该平台可以最大程度地减少发现和设计周期的时间量表。新的模拟和设计技术对包括LIB公司和电动汽车公司以及大学/实验室研究人员在内的细分市场有直接影响。 这项技术可以加速采用电动汽车，从而减少排放并获得更好的燃油经济性。该I-Corps项目基于LIB电极中颗粒，粘合剂和电导率增强器的几何数值模型的开发。找到最佳的LIB电极体系结构和相应的LIB配置，以最大程度地提高能量和功率密度，速率能力和安全性，这带来了重大挑战。该项目建议使用贝叶斯机器学习（BML）算法。所提出的BML驱动设计算法结合了统计微观结构表征和重建方法，以支持LIB电极设计，多任务高斯流程（GPS），以将实验数据与多效率和多识别性模拟以及多目标模拟功能集成在一起，以实现LIB多目标设计。使用贝叶斯优化方法可以系统地探索设计空间。结果是LIB电极体系结构优化了绩效，成本和安全性的贸易空间。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。