ENABLING THE BATTERY INDUSTRY WITH BETTER DIAGNOSTICS
Feasible is hiring: learn more and apply.
Andrew Hsieh, Barry Van Tassell, and Shaurjo Biswas met as postdoctoral researchers in Prof. Daniel Steingart’s lab at Princeton University, where Feasible's core technology was first developed. After securing an ARPA-E IDEAS seedling grant, the four of them founded Feasible in late 2015 and participated in UC Berkeley’s LAUNCH Accelerator Program as part of the 2016 class.
Hsieh holds a Ph.D. in Chemical and Materials Engineering from Princeton University and a B.S. in chemical and biomolecular engineering from UCLA. Van Tassell holds a Ph.D. from The City College of New York and a B.S. from the University at Buffalo, both in chemical engineering. Biswas holds a Ph.D. in materials science from Michigan and a B.Tech. in mechanical engineering from IIT Madras.
Critical need: There is a major effort to develop cheaper, safer, longer-lasting batteries, and to improve the efficiency of battery usage. However, the battery industry is limited by the diagnostic tools and sensors at its disposal as there is no accurate, scalable, non-invasive way to measure the physical processes within a battery during operation.
Technology vision: Our vision is to improve the way batteries are made, tested, and managed by providing the industry with low-cost, non-destructive, physical diagnostics in real time and at scale.
Current state-of-the-art: Physical analysis of batteries can be carried out with x-ray, electron, or neutron sources, but these methods are expensive and are not deployable in commercial installations. On-line testing of state of charge and state of health is done mainly with electrical techniques, which may interrupt operation, deplete charge, and only provide cell-averaged, abstract information of internal features.
Key innovation: The behavior of sound in a battery is sensitive to any changes in the physical properties along the path of acoustic waves. Leveraging know-how from seismology, acoustic analysis of batteries offers direct and detailed information about their internal components and physical structure, which electrical, thermal, and strain-based methods cannot.
Manufacturing challenges: As we decrease the geometric footprint and cost of our system, the biggest challenge will be to maintain the repeatability and quality of measured signals.
Competing technology: There is a growing effort in developing new, real-time physical diagnostic methods for batteries, but most have been limited to experimental systems. Truly in operando diagnostic methods have been limited to neutron or x-ray methods, which are not scalable, and to strain or thermal techniques, which are indirect and cell-averaged.
First market hypothesis: Currently, our initial target is the battery manufacturing market to improve yield and quality control.
Potential for impact: The need for better batteries is keeping many promising technologies from widespread adoption. Our goal is to develop tools that will enable the development of next-generation batteries.
We're looking for:
- Technical collaborators
- Technoeconomic analysis
- Team members - scientists, engineers
- Team members - business
- Joint development partners
andrew [at] feasible [dot] io, barry [at] feasible [dot] io, shaurjo [at] feasible [dot] io