The broader impact of this SBIR Phase I project is enabling middle and low income car buyers to fully participate in the electrification of transportation, ensuring that all Americans can benefit from lower fuel bills, maintenance costs, and healthier community environments. It is not well understood how electric vehicle (EV) battery packs in cars on the road today degrade over time and how they should be valued. This uncertainty in future battery performance directly impacts confidence in buying a used EV. The project proposes the development of a battery report that is able to remotely and rapidly diagnose and predict the state of health of EV battery packs through machine learning models validated by physical inspection and evaluation of a sample of EVs. The projectâs scale, scope, and commercial result would accelerate the adoption and accessibility of EVs. This Small Business Innovation Research Phase I project addresses the need for scalable, accessible and non-invasive state of health prediction on a wide variety of plug-in electric vehicles (PEVs) by using a set of machine learning models. This will be accomplished by collecting daily observation data from an unprecedented number of PEVs on the road from a diverse set of makes, models, years, and climates. This dataset will be used to train machine learning algorithms on a key set of remotely accessible features to predict range and battery health metrics. The machine learning approach builds on a combination of tree-based ensemble models and linear models informed by physics-based experiments, and will eventually expand to other more advanced algorithms as appropriate. The goals of this Phase I project are to (1) recruit 25,000+ more PEV drivers over 6 months, (2) demonstrate a set of tools that are accurate enough to meet consumer demand for near-term degradation models for the used PEVs on the road, (3) validate machine learning determined state of health estimates with hands-on experimentation with a subset of vehicles, and (4) use these hands-on experiments to better understand the relationships between remotely accessible proxy data and state of health.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
