I am delighted to have been part of a team writing a forward-looking Perspective on the future of data-driven Li-ion battery research with focus on improved safety. The Perspective was a collaboration with co-authors from NREL, MIT, Tsinghua, and Imperial College and is now available in the academic journal Joule. The full text can be accessed at the Joule journal website: The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety, with direct link to download PDF.

One of the most intriguing sections, Practical Implementation for Commercial Applications, takes inspiration from the field of robotics and automation research where a neural network is used to combine data from simulation and real-world experiments to achieve high accuracy with comparatively small datasets. Further resources for a deeper dive into this approach1 is available through the KTH twitter account.

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  1. Martin Hwasser, Danica Kragic, Rika Antonova “Variational Auto-regularized Alignment for Sim-to-Real Control” ICRA2020 ↩