Principles of the Battery Data Genome

Logan Ward, Susan Babinec, Eric Dufek, David Howey, Venkat Viswanathan, Muratahan Aykol, David Beck, Benjamin Blaiszik, Bor-Rong Chen, George Crabtree, Simon Clark, Valerio De Angelis, Philipp Dechent, Matthieu Dubarry, Erica Eggleton, Donal Finegan, Ian Foster, Chirranjeevi Gopal, Patrick Herring, Victor HuNoah Paulson, Yuliya Preger, Dirk Uwe-Sauer, Kandler Smith, Seth Snyder, Shashank Sripad, Tanvir Tanim, Linnette Teo

Research output: Contribution to journalArticlepeer-review

32 Scopus Citations

Abstract

Batteries are central to modern society. They are no longer just a convenience but a critical enabler of the transition to a resilient, low-carbon economy. Battery development capabilities are provided by communities spanning materials discovery, battery chemistry and electrochemistry, cell and pack design, scale-up, manufacturing, and deployments. Despite their relative maturity, data-science practices among these diverse groups are far behind the state of the art in other fields, which have demonstrated an ability to significantly improve innovation and economic impact. The negative consequences of the present paradigm include incremental improvements but few breakthroughs, significant manufacturing uncertainties, and cascading investment risks that collectively slow deployments. The primary roadblock to a battery-data-science renaissance is the requirement for large amounts of high-quality data, which are not available in the current fragmented ecosystem. Here, we identify gaps and propose principles that enable the solution by building a robust community of data hubs with standardized practices and flexible sharing options that will seed advanced tools spanning innovation to deployment. Precedents are offered that demonstrate that both public good and immense economic gains will arise from sharing valuable battery data. The proposed Battery Data Genome looks to broadly transform innovations and revolutionize their translation from research to societal impact.

Original languageAmerican English
Pages (from-to)2253-2271
Number of pages19
JournalJoule
Volume6
Issue number10
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Inc.

NREL Publication Number

  • NREL/JA-5700-80849

Keywords

  • artificial intelligence
  • battery
  • data hub
  • data sharing
  • machine learning
  • software
  • standards

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