SBIR Phase II: Forecasting Battery Health and Maintenance using Data-Driven Predictive Analytics

SBIR 第二阶段：使用数据驱动的预测分析来预测电池健康状况和维护

• 批准号: 2243671
• 负责人: Robert Masse
• 金额: $ 100万
• 依托单位: ASTROLABE ANALYTICS, INC.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243671&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Forecasting; Battery

The broader impact of this Small Business Innovation Research (SBIR) Phase II project includes enhancing US economic competitiveness, improving the health and welfare of the American public, and developing the US technical workforce. The success of this project will have a direct impact on the manufacturers, integrators, and operators of battery-powered assets. Empowering battery engineering teams with predictive analytics across their product life cycle will be a crucial competitive advantage to accelerating the scale-up of domestic battery technology development and deployment. Bringing better battery technology to market faster and ensuring a long, safe operating life will, in turn, catalyze the transition away from fossil fuels and towards electric vehicles, grid-scale energy storage, and other clean technologies. The social and economic implications include clean energy jobs, improved environmental quality, and ubiquitous low-cost energy. The potential commercial impact of this project will help accelerate the development and deployment of new battery-powered vehicles, energy storage systems, and other assets. It will allow the company to serve the wider battery industry by de-risking operation and extending service life of battery assets, thereby increasing customer revenue and avoiding costly warranty events. This Small Business Innovation Research (SBIR) Phase II project's goal is to de-risk the deployment, operation, and maintenance of battery energy storage systems. It will combine results from the Phase I with data from partners to forecast system maintenance and inform warranty design, thereby lowering the total cost of ownership and minimizing liability. Access to cell testing, outgoing quality control, and field data will allow for a deep dive across the product life cycle to identify how known degradation mechanisms manifest in the real-world battery data. Physics-informed feature engineering will be used to extend models to incorporate these insights and then implement these models at scale in the cloud. Criteria for success include: 1) correlating real-world operating conditions with known Lithium-ion battery degradation pathways, 2) engineering new features that are correlated with physics- and electrochemical-based insights, 3) accurately estimating remaining useful life to within 5% of total cycle life, and 4) implementing data-driven model in a scalable cloud environment.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)第二阶段项目的更广泛影响包括提高美国的经济竞争力，改善美国公众的健康和福利，以及发展美国的技术劳动力。该项目的成功将对电池供电资产的制造商、集成商和运营商产生直接影响。为电池工程团队提供产品生命周期内的预测分析，将是加快国内电池技术开发和部署规模的关键竞争优势。将更好的电池技术更快地推向市场，并确保长时间、安全的运行寿命，反过来将催化从化石燃料向电动汽车、电网规模的储能和其他清洁技术的过渡。其社会和经济影响包括清洁能源就业、环境质量改善和无处不在的低成本能源。该项目的潜在商业影响将有助于加快新电池动力汽车、储能系统和其他资产的开发和部署。这将使该公司能够通过降低运营风险和延长电池资产的使用寿命来服务于更广泛的电池行业，从而增加客户收入并避免代价高昂的保修事件。该小型企业创新研究(SBIR)第二阶段项目的目标是降低电池储能系统的部署、运营和维护风险。它将把第一阶段的结果与合作伙伴的数据结合起来，以预测系统维护并为保修设计提供信息，从而降低总拥有成本并将责任降至最低。访问电池测试、传出质量控制和现场数据将允许深入了解产品生命周期，以确定已知的退化机制如何在实际电池数据中表现出来。将使用物理信息功能工程来扩展模型，以纳入这些见解，然后在云中大规模实施这些模型。成功的标准包括：1)将实际运行条件与已知的锂离子电池退化路径相关联；2)设计与基于物理和电化学的见解相关的新功能；3)准确估计剩余使用寿命不到总循环寿命的5%；4)在可扩展的云环境中实施数据驱动模型。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。