Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches

Authors:
Open Access
Publisher website
Google Scholar
Abstract
With an exponentially growing demand for rechargeable batteries, the development of new ultra‐performant, fully scalable, and sustainable battery technologies and materials must be accelerated. Creating a holistic, closed‐loop infrastructure for materials discovery, manufacturing, and battery testing that utilizes a common data infrastructure and autonomous workflows to bridge big data from all domains of the battery value chain, can pave the way for a transformative reduction in the required time to discovery. By embedding multisensory and self‐healing capabilities in future battery technologies and integrating these with AI and physics‐aware machine learning models capable of predicting the spatio‐temporal evolution of battery materials and interfaces, it will, in time, be possible to identify, predict and prevent potential degradation and failure modes. This will facilitate enhanced battery quality, reliability, and life, for example, by preemptively changing the battery charging conditions or releasing self‐healing additives from the separator membrane, akin to preemptive medicine, and form the basis for inverse design of new battery materials, interfaces, and additives. The large‐scale and long‐term European research initiative BATTERY 2030+ seeks to make this longer‐than ten‐year vision a reality through the development of a versatile and chemistry neutral “Battery Interface Genome—Materials Acceleration Platform” infrastructure (BIG‐MAP).
Funding Information
  • Knut och Alice Wallenbergs Stiftelse (INTELL 139501042)
  • Novo Nordisk Fonden (NNF19OC0057822)
  • H2020 Future and Emerging Technologies (957213)

This publication has 104 references indexed in Scilit: