能源电池单体层级数字孪生技术

doi:10.19799/j.cnki.2095-4239.2024.0596

储能科学与技术 ›› 2024, Vol. 13 ›› Issue (9): 3112-3133.doi: 10.19799/j.cnki.2095-4239.2024.0596

• AI辅助先进电池设计与应用专刊 • 上一篇下一篇

能源电池单体层级数字孪生技术

樊金保¹(), 李娜², 吴宜琨³, 贺春旺¹, 杨乐¹, 宋维力¹, 陈浩森¹()

^1.北京理工大学先进结构技术研究院，北京 100081
^2.北京科技大学绿色低碳钢铁冶金全国重点实验室，北京 100083
^3.清华大学航天航空学院，北京 100084

收稿日期:2024-06-10 修回日期:2024-07-14 出版日期:2024-09-28 发布日期:2024-09-20
通讯作者: 陈浩森 E-mail:fan_jinbao@foxmail.com;chenhs@bit.edu.cn
作者简介:樊金保（1997—），男，博士研究生，从事锂离子电池数字孪生研究，E-mail：fan_jinbao@foxmail.com；
基金资助:
国家重点研发计划项目(2023YFB2408000)

Digital twin technology for energy batteries at the cell level

Jinbao FAN¹(), Na LI², Yikun WU³, Chunwang HE¹, Le YANG¹, Weili SONG¹, Haosen CHEN¹()

^1.Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China
^2.State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
^3.Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China

Received:2024-06-10 Revised:2024-07-14 Online:2024-09-28 Published:2024-09-20
Contact: Haosen CHEN E-mail:fan_jinbao@foxmail.com;chenhs@bit.edu.cn

摘要/Abstract

摘要：

具有高能量密度的能源电池作为实现国家碳达峰和碳中和目标的重要途径备受关注，然而，现有技术已不能满足其高效安全稳定运行的迫切需求。数字孪生技术具有实时感知、高效模拟、准确预测和快速优化复杂系统的特性，有望成为解决上述挑战的有效手段。本文分析了能源电池单体层级数字孪生技术的构成要素，阐述了植入传感技术、高效保真的物理模型和机器学习算法三种关键技术在电池数字孪生中的作用，介绍了植入传感技术在电池温度、应变、气压和气体传感方面的现状，综述了描述电池不同物理场行为的耦合模型的相关研究，探讨了机器学习算法在电池数字孪生中的应用以及基于物理的机器学习算法的最新进展，最后总结了电池数字孪生技术面临的主要挑战和发展趋势，并提出了在未来研究中克服这些挑战的建议。本研究工作可为电池数字孪生技术提供更深刻的见解，并有助于其在学术研究和工业应用领域中的进一步推广与应用。

关键词: 能源电池, 数字孪生, 传感技术, 物理模型, 机器学习

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.

Key words: energy battery, digital twin, sensor technology, physical model, machine learning

中图分类号:

TM 912.9

樊金保, 李娜, 吴宜琨, 贺春旺, 杨乐, 宋维力, 陈浩森. 能源电池单体层级数字孪生技术[J]. 储能科学与技术, 2024, 13(9): 3112-3133.

Jinbao FAN, Na LI, Yikun WU, Chunwang HE, Le YANG, Weili SONG, Haosen CHEN. Digital twin technology for energy batteries at the cell level[J]. Energy Storage Science and Technology, 2024, 13(9): 3112-3133.

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能源电池单体层级数字孪生技术

Digital twin technology for energy batteries at the cell level

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