Digital Twin Battery Modeling and Simulations: A New Analysis and Design Tool for Rechargeable Batteries

The intricate correlation between microstructural properties and performance in lithium rechargeable batteries necessitates advanced methods to elucidate their mechanisms. In this regard, digital twin simulations have been utilized by creating virtual replicas that simulate battery behaviors and performances under various conditions. However, the relationship between microstructural parameters and battery performances is still not fully understood. This focus review aims to revisit the state of digital twin simulations, with a particular focus on its effectiveness for analyzing microstructures and unraveling the hidden parameters. For this purpose, we explore microstructure formation and validation methods as top-down and bottom-up simulation techniques and provide a comprehensive view of multiphysics approaches for understanding the electrochemical, mechanical, and thermal behaviors. Finally, we discuss the potential of artificial intelligence (AI)-driven multiscale modeling strategies and dynamic simulations, offering insights into how digital twin technology can advance battery design and optimization for enhanced performance and safety.

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Publication Details

Journal
ACS Energy Letters
Published
2024-10-03
DOI
https://doi.org/10.1021/acsenergylett.4c01931
Citations
23
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
4.68

Funders

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Digital Twin Battery Modeling and Simulations: A New Analysis and Design Tool for Rechargeable Batteries

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Digital Twin Battery Modeling and Simulations: A New Analysis and Design Tool for Rechargeable Batteries

Hyobin Lee, Suhwan Kim, Jaejin Lim, Yong Min Lee, Joonam Park
article en
23 citations

Abstract

The intricate correlation between microstructural properties and performance in lithium rechargeable batteries necessitates advanced methods to elucidate their mechanisms. In this regard, digital twin simulations have been utilized by creating virtual replicas that simulate battery behaviors and performances under various conditions. However, the relationship between microstructural parameters and battery performances is still not fully understood. This focus review aims to revisit the state of digital twin simulations, with a particular focus on its effectiveness for analyzing microstructures and unraveling the hidden parameters. For this purpose, we explore microstructure formation and validation methods as top-down and bottom-up simulation techniques and provide a comprehensive view of multiphysics approaches for understanding the electrochemical, mechanical, and thermal behaviors. Finally, we discuss the potential of artificial intelligence (AI)-driven multiscale modeling strategies and dynamic simulations, offering insights into how digital twin technology can advance battery design and optimization for enhanced performance and safety.

ACS Energy LettersVol. 9(10)
Daegu Gyeongbuk Institute of Science and Technology (KR), Yonsei University (KR)
Ministry of Science and ICT, South Korea
Affordable and clean energy
Openalex Percentile: Top 4%
Advanced Battery Technologies Research
4.68
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