能源电池单体层级数字孪生技术

Translated title of the contribution: Digital twin technology for energy batteries at the cell level

Jinbao Fan, Na Li, Yikun Wu, Chunwang He, Le Yang, Weili Song, Haosen Chen*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

Energy batteries with high energy density have attracted much attention as an important way to achieve China's carbon peak and carbon neutrality goals; however, the existing technologies can no longer meet the urgent need for efficient, safe, and stable operation of such energy batteries. Digital twin technology, with its characteristics of real-time sensing, efficient simulation, accurate prediction, and rapid optimization of complex systems, is expected to be an effective means of addressing these challenges. This paper analyzed the constituent elements of digital twin technology for energy batteries at the cell level. Furthermore, it describes the roles of three key technologies in the battery digital twin: implanted sensing technology, highly efficient and fidelity physical models, and machine learning algorithms. The current status of implanted sensing technology in battery temperature, strain, pressure, and gas sensing was introduced. It reviews related research on coupled models that describe the behavior of different physical fields of batteries. In addition, it discusses the application of machine learning algorithms in battery digital twin technology and recent advances in physics-based machine learning algorithms. Finally, the main challenges and development trends of battery digital twin technology are summarized, and suggestions for overcoming these challenges in future research are proposed. This research work can provide deep insights into battery digital twin technology and contribute to its further popularization and application in academic research and industrial applications.

Translated title of the contributionDigital twin technology for energy batteries at the cell level
Original languageChinese (Traditional)
Pages (from-to)3112-3133
Number of pages22
JournalEnergy Storage Science and Technology
Volume13
Issue number9
DOIs
Publication statusPublished - 2024

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Fan, J., Li, N., Wu, Y., He, C., Yang, L., Song, W., & Chen, H. (2024). 能源电池单体层级数字孪生技术. Energy Storage Science and Technology, 13(9), 3112-3133. https://doi.org/10.19799/j.cnki.2095-4239.2024.0596