能源电池单体层级数字孪生技术

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.