Enabling battery digital twins at the industrial scale

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Title: Enabling battery digital twins at the industrial scale
Authors: Dubarry, M
Howey, D
Wu, B
Item Type: Journal Article
Abstract: Digital twins are cyber-physical systems that fuse real-time sensor data with models to make accurate, asset-specific predictions and optimal decisions. For batteries, this concept has been applied across length scales, from materials to systems. However, a holistic approach with a strong conceptual and mathematical framework is needed for battery digital twins to achieve their full potential at the industrial scale. Developing a standardized and transparent approach for data sharing between stakeholders that respects confidentiality is essential. Industrial battery digital twins also need principled methods to quantify and propagate uncertainty from sensors and models to predictions. Ensuring retention of physical understanding is important for the identification of “stiff” parameters, which require careful measurement. Combined with uncertainty analysis, this can unlock optimal data-driven sensor selection and placement and improved root-cause analysis. However, better physical modeling and sensing approaches for battery manufacturing and thermal runaway are needed. Furthermore, immutability of data is also necessary for industrial uptake, with digital ledger technology providing new avenues of research. We believe that digital twins could be transformative for the current lithium-ion battery technologies and also as an enabler for emerging new battery technologies, optimizing lifetime and value through asset-specific control.
Issue Date: Jun-2023
Date of Acceptance: 1-May-2023
DOI: 10.1016/j.joule.2023.05.005
ISSN: 2542-4351
Publisher: Elsevier
Start Page: 1134
End Page: 1144
Journal / Book Title: Joule
Volume: 7
Issue: 6
Copyright Statement: Copyright © 2023 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publication Status: Published
Embargo Date: 2024-05-25
Online Publication Date: 2023-05-26
Appears in Collections:Dyson School of Design Engineering

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