SBIR Phase I: Predictive Analytics for Battery Formation

SBIR 第一阶段：电池化成的预测分析

• 批准号: 2015127
• 负责人: Robert Masse
• 金额: $ 22.5万
• 依托单位: ASTROLABE ANALYTICS, INC.
• 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: 2020-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015127&HistoricalAwards=false
• 关键词: SBIR; Phase; Predictive; Analytics; Battery

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the acceleration and improvement of battery manufacturing and production. Forecasting battery safety and lifetime is largely an unsolved problem in the battery industry. For manufacturers, this uncertainty increases cell cost through control measures during production as well as the precautions taken to avoid warranty events. This project proposes "data science-as-a-service" for battery formation to address both issues. By streamlining the battery formation, test, and grading process, manufacturers benefit from reduced work-in-progress (WIP) inventory waiting for final inspection, reducing facility space requirement to store WIP cell, and reducing scrap rates and increasing manufacturing yields. The impact of these improvements will potentially enable wider spread adoption of electric vehicle applications, a major driver for battery demand.This Small Business Innovation Research (SBIR) Phase I project focuses on developing information technology infrastructure and algorithms for the prediction of battery performance during cell production. By combining state-of-the-art machine learning techniques with data management and manufacturing execution systems, battery cell manufacturers will greatly reduce the cost to operate and manage cell formation and test - an environment which has been largely underserved for innovation. The proposed project objectives will be achieved through two developing battery classification and prediction machine learning algorithms to improve early detection of battery failures. Novel implementation of the proof-of-concept algorithms in battery production environments will improve the key performance indicators of these battery manufacturers. Regression and clustering models will be used as often as possible, and the bulk of the technical work will be dedicated to the feature engineering required to elucidate changes in the change and discharge voltage profile during the first few cycles. New features will be developed by a) modelling physical processes (e.g. growth of the solid-electrolyte interphase layer) expected for a given cluster group or b) employing dynamical systems techniques like time-delay embeddings.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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将是加速和改进电池制造和生产。预测电池的安全性和寿命在很大程度上是电池行业中尚未解决的问题。对于制造商来说，这种不确定性通过生产期间的控制措施以及为避免保修事件而采取的预防措施增加了电池成本。该项目提出了电池形成的“数据科学即服务”，以解决这两个问题。通过简化电池组的形成、测试和分级过程，制造商可以减少等待最终检查的在制品（Work-in-progress，简称WIP）库存，减少存储电池的设施空间需求，降低废品率并提高制造产量。 这些改进的影响可能会使电动汽车应用更广泛地采用，这是电池需求的主要驱动力。这个小企业创新研究（SBIR）第一阶段项目的重点是开发信息技术基础设施和算法，用于预测电池生产过程中的电池性能。通过将最先进的机器学习技术与数据管理和制造执行系统相结合，电池制造商将大大降低运营和管理电池形成和测试的成本-这是一个在很大程度上缺乏创新的环境。拟议的项目目标将通过两个开发电池分类和预测机器学习算法来实现，以改善电池故障的早期检测。 在电池生产环境中实现新的概念验证算法将改善这些电池制造商的关键性能指标。 将尽可能经常地使用回归和聚类模型，并且大部分技术工作将致力于阐明前几个循环期间变化和放电电压曲线的变化所需的特征工程。新的功能将开发a）模拟物理过程（如固体电解质界面层的增长）预期为一个给定的集群组或B）采用动态系统技术，如时间延迟embeddings.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。