TY - JOUR
T1 - 能源电池单体层级数字孪生技术
AU - Fan, Jinbao
AU - Li, Na
AU - Wu, Yikun
AU - He, Chunwang
AU - Yang, Le
AU - Song, Weili
AU - Chen, Haosen
N1 - Publisher Copyright:
© 2024 Editorial office of Energy Storage Science and Technology. All rights reserved.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - digital twin
KW - energy battery
KW - machine learning
KW - physical model
KW - sensor technology
UR - http://www.scopus.com/inward/record.url?scp=85208268480&partnerID=8YFLogxK
U2 - 10.19799/j.cnki.2095-4239.2024.0596
DO - 10.19799/j.cnki.2095-4239.2024.0596
M3 - 文献综述
AN - SCOPUS:85208268480
SN - 2095-4239
VL - 13
SP - 3112
EP - 3133
JO - Energy Storage Science and Technology
JF - Energy Storage Science and Technology
IS - 9
ER -