Virtual design of structured battery electrodes

结构化电池电极的虚拟设计

• 批准号: 507026050
• 负责人: Professor Dr.-Ing. Ulrich Nieken
• 金额: --
• 依托单位: Max-Planck-Institut für medizinische Forschung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/507026050?language=en
• 关键词: Virtual; design; structured; battery; electrodes

To improve the performance of Li-ion batteries, a metallic network of very thin fibers was produced at the Max Planck Institute for Intelligent Systems (AK Spatz). These were successfully introduced into the active material layers of cells. First measurements on prototype cells with a metallic, fiber-like 3D network could show that the performance of the electrodes could be significantly improved. The capacity of the electrode with fiber material was improved by 25% (in the 50th charge/discharge cycle) compared to a reference electrode without fibers with the same active material loading. A micro-model was developed to simulate the battery cells. The spatially resolved geometry of the porous electrode structure and the embedded fibers required for this can be measured or virtually generated. Due to the high computational cost, the micro-model is not suitable for a virtual cell design and for extensive parameter studies. For this reason, it is imperative to derive a dimensionally reduced model that takes into account the influence of the microstructure on the process with less computing time compared to the detailed model. In the AK Nieken there is a lot of experience with the method of asymptotic homogenization (AH). This makes it possible to derive effective rates of transport processes from a detailed geometry model. Thus, it is possible to build a macroscopic process model for structured porous materials that intrinsically accounts for the microstructure in a rigorous manner. The challenge for the macroscopic process model is to extend the range of validity to an operation with high C-rates. At high C rates, large concentration gradients form on the microscopic scale, so that (sufficient) scale separation is not possible. This is a well-known problem in the application of AH. First approaches to solve this problem are formulated in the proposal. Based on these approaches, it should be possible to predict the electrochemical behavior of the batteries developed by AK Spatz at technically relevant C rates to a good approximation. The aim of the project is therefore to derive an effective, macroscale process model that is suitable for carrying out parameter studies and structure designs. For this purpose, the existing process models are to be extended by the influence of the metal fiber network and methodological improvements are to be developed for large local gradients.

为了提高锂离子电池的性能，马克斯·普朗克智能系统研究所(AK Spatz)生产了一种由非常细的纤维组成的金属网络。这些材料被成功地引入细胞的活性材料层。首次对具有金属、纤维状3D网络的原型电池进行的测量表明，电极的性能可以显著提高。在第50次充放电循环中，与相同活性物质负载量的不含纤维的参比电极相比，使用纤维材料的电极容量提高了25%。开发了一个微观模型来模拟电池单元。可以测量或虚拟产生多孔电极结构和为此所需的嵌入纤维的空间分辨几何形状。由于计算成本高，微观模型不适合于虚拟单元设计和广泛的参数研究。因此，与详细模型相比，推导出一种考虑微结构对过程影响的降维模型势在必行。在AK Nieken中，使用渐近齐次化(AH)的方法有很多经验。这使得可以从详细的几何模型中推导出传输过程的有效速率。因此，有可能建立结构多孔材料的宏观过程模型，该模型以严格的方式内在地解释微结构。宏观过程模型的挑战是将有效性范围扩展到具有高C率的操作。在高C率下，在微观尺度上形成大的浓度梯度，因此(充分的)尺度分离是不可能的。这是AH应用中的一个众所周知的问题。首先，提案中提出了解决这一问题的办法。基于这些方法，应该有可能预测AK Spatz开发的电池在技术上相关的C速率下的电化学行为，达到一个很好的近似值。因此，该项目的目的是得出一个有效的、宏观的、适合进行参数研究和结构设计的过程模型。为此，现有的过程模型将被金属纤维网络的影响扩展，并将针对大的局部梯度开发方法改进。