Digital twins are cyber-physical systems that fuse real-time sensor data with models to make accurate, asset-specific predictions and optimal decisions. For batteries, this concept has been applied across length scales, from materials to systems. However, a holistic approach with a strong conceptual and mathematical framework is needed for battery digital twins to achieve their full potential at the industrial scale. Developing a standardized and transparent approach for data sharing between stakeholders that respects confidentiality is essential. Industrial battery digital twins also need principled methods to quantify and propagate uncertainty from sensors and models to predictions. Ensuring retention of physical understanding is important for the identification of “stiff” parameters, which require careful measurement. Combined with uncertainty analysis, this can unlock optimal data-driven sensor selection and placement and improved root-cause analysis. However, better physical modeling and sensing approaches for battery manufacturing and thermal runaway are needed. Furthermore, immutability of data is also necessary for industrial uptake, with digital ledger technology providing new avenues of research. We believe that digital twins could be transformative for the current lithium-ion battery technologies and also as an enabler for emerging new battery technologies, optimizing lifetime and value through asset-specific control.

数字孪生是网络物理系统，它将实时传感器数据与模型融合在一起，以做出准确的、特定于资产的预测和最佳决策。对于电池，这一概念已应用于从材料到系统的各个长度尺度。然而，电池数字孪生需要一种具有强大概念和数学框架的整体方法，才能在工业规模上发挥其全部潜力。为利益相关者之间的数据共享制定一种尊重机密性的标准化和透明方法至关重要。工业电池数字孪生还需要有原则的方法来量化不确定性并将其从传感器和模型传播到预测。确保保留物理理解对于识别“刚性”参数很重要，这需要仔细测量。结合不确定性分析，这可以解锁最佳的数据驱动传感器选择和放置，并改进根本原因分析。然而，需要针对电池制造和热失控的更好的物理建模和传感方法。此外，数据的不变性对于工业应用也是必要的，数字分类账技术提供了新的研究途径。我们相信，数字孪生可以为当前的锂离子电池技术带来变革，也可以作为新兴电池技术的推动者，通过资产特定控制优化寿命和价值。数据的不变性对于工业应用也是必要的，数字分类账技术提供了新的研究途径。我们相信，数字孪生可以为当前的锂离子电池技术带来变革，也可以作为新兴电池技术的推动者，通过资产特定控制优化寿命和价值。数据的不变性对于工业应用也是必要的，数字分类账技术提供了新的研究途径。我们相信，数字孪生可以为当前的锂离子电池技术带来变革，也可以作为新兴电池技术的推动者，通过资产特定控制优化寿命和价值。