SBIR Phase I: Rapid Used Battery Testing System

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to enable the reuse of electric vehicle (EV) batteries to create lower-cost, more sustainable energy storage systems on a large scale. The costs of renewable energy have declined dramatically, and more than one-third of Americans now live in a community committed to procuring 100% renewable or low-carbon electricity. However, low-cost energy storage is key to achievement of these bold goals and to the growth of clean energy jobs. To meet this need while mitigating dependence on foreign miners of scarce battery materials, hard-to-recycle EV battery waste may be given a “second life” in lower-cost, more sustainable energy storage systems. However, second-life batteries must be safe, reliable, and significantly cheaper than new battery alternatives to gain traction. The cost advantage and performance of second-life batteries hinges on fast and accurate battery testing. Successful development of the proposed rapid used battery testing system would enable widespread deployment of second-life batteries at 30% to 70% lower cost than new battery alternatives. As a result, the innovation has the potential to enhance the reliability, affordability, and sustainability of the nation’s energy system. This Small Business Innovation Research Phase I project proposes to dramatically reduce the time required to accurately measure the state of health (SoH) of used EV batteries. Existing methods either require several hours to fully charge and discharge batteries or highly granular data on historical battery usage. The objective of the proposed research and development effort is to measure the SoH of used EV batteries at any state of charge (SoC) with less than 2% error in less than 75 seconds. The proposed method involves training neural networks to recognize subtle patterns in used batteries’ responses to brief pulses of electric current and ultrasound. The team has collected promising preliminary data, which suggest the test methodology produces highly accurate SoH estimates for batteries at a specific SoC. To maximize the accuracy and robustness of SoH predictions for batteries at any SoC, the team will compare various profiles of electric current and ultrasonic pulses, expand its neural network training datasets, and refine the architecture of its neural networks. By dramatically reducing used battery test time and expense, this innovation will reduce the cost of safely repurposing used EV batteries in second-life energy storage systems. 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.