Introduction
Gains in battery performance are hard to achieve. At the same time, computational material discovery, predictive models for state estimation, and cryptographic helpers broaden opportunities to collaborate by exchanging structured data.
OpenBatt identified how experts deal with exchanging data to advance model-based battery R&D. These insights were contrasted against perceived bottlenecks of available data. As a result, four pathways are suggested that make aggregation of data feasible through multilateral trade where it isn’t today.
OpenBatt identified how experts deal with exchanging data to advance model-based battery R&D. These insights were contrasted against perceived bottlenecks of available data. As a result, four pathways are suggested that make aggregation of data feasible through multilateral trade where it isn’t today.
Method
Read more about digitalization in battery research to see how organizations use data in today's quest for better batteries.
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View the exposition to see common challenges of joint research.
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We asked battery experts about their experience and possible solutions for better data-sharing. See the interview guide for more details.
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Timeline
- Interview and surveying of experts (September - December 2016)
- Analysis and authoring (January 2017)
- Dissemination of findings (May 2017)
- Collaboration with more organizations to facilitate a transparent market of data (as see fit)
Team
The study is conducted by WU Vienna.
Alexander Hirner
Project Lead
Manufacturing engineer, speaker on open data and open processes for the industrial IoT. |
Wolfgang Sachsenhofer
Advisor
Member of the OMV and WU Energy & Strategy Think Tank and visiting fellow at UC Berkeley and Stanford University. |
