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

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.