Lithium-ion battery (LIB) performance is significantly influenced by its manufacturing process. Manufacturing of an optimized electrode can incur high production costs such as high energy consumption, high scrap rates and emissions. This is due to the process that consists of a series of manufacturing steps presenting a complex interrelationship, thus limiting the understanding of performance as a function of manufacturing parameters. While several empirical and computational methods are employed for optimization, they are demanding in terms of resources such as materials or computational effort. By leveraging Deep Learning (DL), we can enhance our understanding of the complex manufacturing processes and accelerate its optimization. We propose a data-driven supervised DL methodology to complement physics-based LIB cathode manufacturing simulations. The trained DL-based predictive model integrates well into the manufacturing simulation framework to forecast cathode slurry microstructures. The DL model demonstrates robust predictive performance for LIB NMC-111 and LiFePO₄–based slurries and slurries for a solid-state battery NMC-622/argyrodite composite electrode preparation. While the current work is focused on the cathode slurry process, the proposed methodology has potential for application to drying and calendering steps. This approach will be helpful in streamlining lab-scale electrode manufacturing, and reducing errors, waste and resource consumption.

中文翻译：

---

一种耦合基于物理的仿真和深度学习的混合建模方法，用于电池电极制造仿真


锂离子电池 （LIB） 的性能受其制造工艺的显著影响。制造优化的电极可能会产生高生产成本，例如高能耗、高废品率和排放。这是因为该工艺由一系列制造步骤组成，呈现出复杂的相互关系，从而限制了对性能作为制造参数函数的理解。虽然采用了几种经验和计算方法来进行优化，但它们对材料或计算工作量等资源的要求很高。通过利用深度学习 （DL），我们可以增强对复杂制造流程的理解并加速其优化。我们提出了一种数据驱动的监督 DL 方法，以补充基于物理的 LIB 阴极制造仿真。经过训练的基于 DL 的预测模型可以很好地集成到制造仿真框架中，以预测阴极浆料的微观结构。DL 模型证明了 LIB NMC-111 和 LiFePO₄ 基浆料以及用于固态电池 NMC-622/argyrodite 复合电极制备的浆料的稳健预测性能。虽然目前的工作集中在阴极浆料工艺上，但所提出的方法有可能应用于干燥和压延步骤。这种方法将有助于简化实验室规模的电极制造，并减少错误、浪费和资源消耗。