SBIR Phase II: Real-time predictive battery pack diagnostics and algorithms

SBIR 第二阶段：实时预测电池组诊断和算法

• 批准号: 2026198
• 负责人: Steven Chung
• 金额: $ 100万
• 依托单位: Rejoule Incorporated
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026198&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Real; time

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to support the sustainability of lithium-ion battery (LIB) technologies. LIB use is growing and has consequently led to the potential of increased battery waste. The Phase II project will develop novel diagnostic technologies that enable automakers and energy storage providers to quickly and accurately measure how their batteries perform and degrade in the field. It will further use machine learning to develop more accurate battery models and predictive health algorithms for field applications. These algorithms will have the capability to make health predictions even without historical information for the specific system, and the hardware will be portable for different applications. A more accurate assessment of battery degradation in real time can inform on-board algorithms, improve overall battery pack efficiency, and reduce costs. This Small Business Innovation Research Phase II project addresses the challenge of measuring and predicting degradation of large-format batteries, blending new hardware with machine learning and electrochemistry. The project will conduct a comprehensive battery aging study to quantify leading and lagging indicators of battery degradation. Leading indicators include utilization, calendar aging, and environmental effects, and lagging indicators consist of AC impedance, DC internal resistance, and a battery’s specific charge/discharge patterns. Anticipated results include the ability to accurately predict the remaining useful life of a battery in under two minutes, even in the absence of historical data.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.

小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力是支持锂离子电池(LIB)技术的可持续性。LiB的使用正在增长，因此可能会增加电池浪费。第二阶段项目将开发新的诊断技术，使汽车制造商和储能供应商能够快速准确地测量他们的电池在现场的性能和降解情况。它将进一步利用机器学习为现场应用开发更准确的电池模型和预测健康算法。这些算法将能够在没有特定系统历史信息的情况下进行健康预测，并且硬件将可以针对不同的应用进行移植。更准确的电池劣化实时评估可以为车载算法提供信息，提高整体电池组效率，并降低成本。这个小型企业创新研究第二阶段项目解决了测量和预测大型电池退化的挑战，将新硬件与机器学习和电化学相结合。该项目将进行一项全面的电池老化研究，以量化电池退化的领先和滞后指标。领先指标包括利用率、日历老化和环境影响，滞后指标包括交流阻抗、直流内阻和电池的特定充放电模式。预期结果包括能够在不到两分钟的时间内准确预测电池的剩余使用寿命，即使在没有历史数据的情况下也是如此。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。