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Images of particles made from a promising battery cathode material called NMC

Secondary-Life EV Batteries: Predicting State of Health of Retired EV Batteries using Physics-Informed Machine Learning methods

Bits & Watts Initiative

PI: Simona Onori, Energy Resources Engineering
Students: Gabriele Pozzato, Seongbeom  Lee

The objective of this project is to develop a new technology to assess the health, in terms of actual capacity, energy and impedance, of retired EV batteries. The work will combine the strengths of electrothermal/aging models along with data driven methods to assess, without any historical data information from the device, the remaining life of the battery and optimize its deployment for secondary life. This research will combine fundamental knowledge at the intersection of physics-based modeling, numerical simulations, data-driven approaches and battery experimental competence to address a timely problem such as the one related to lithium-ion battery recycling/repurposing.