Seattle, Washington
Project Term:
01/01/2013 - 12/31/2018

Technology Description:

University of Washington (UW) is developing a predictive battery management system that uses innovative modeling software to manage how batteries are charged and discharged, helping to optimize battery use. A significant problem with today's battery packs is their lack of internal monitoring capabilities, which interferes with our ability to identify and manage performance issues as they arise. UW's system would predict the physical states internal to batteries quickly and accurately enough for the data to be used in making decisions about how to control the battery to optimize its output and efficiency in real time. UW's models could be able to predict temperature, remaining energy capacity, and progress of unwanted reactions that reduce the battery lifetime.

Potential Impact:

If successful, UW's predictive battery management system would improve the safety, charging rate, useful capacity, and lifetime of electric vehicle batteries.


Advances in energy storage management could reduce the cost and increase the adoption of electric vehicles and renewable energy storage technologies, which in turn would reduce our nation's dependence on foreign sources of energy.


Improving the reliability and safety of electric vehicles and renewable energy storage facilities would enable more widespread use of these technologies, resulting in a substantial reduction in carbon dioxide emissions.


Enabling alternatives to conventional sources of energy could insulate consumers, businesses, and utilities from unexpected price swings.


ARPA-E Program Director:
Dr. Scott Litzelman
Project Contact:
Venkat Subramanian
Press and General Inquiries Email:
Project Contact Email:


National Renewable Energy Laboratory

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