OPERA - DEVELOPMENT OF OPERANDO TECHNIQUES AND MULTISCALE MODELLING TO FACE THE ZERO-EXCESS SOLID-STATE BATTERY CHALLENGE

基于丰富材料的绿色、高性能和安全的电池是过渡到碳中性未来的关键因素。然而，为了加速它们的发展，需要对电池内部复杂的电化学-机械过程有深入的了解，这只有通过先进的实验和计算方法才能实现。负极就地形成的零过剩固态电池是一种很有前途的新一代环保电池，具有能量密度高、安全性好、成本效益高等优点，但必须解决负极形成不均匀的问题。在OPERA方面，七家领先的研究机构、两个同步辐射设施、一家中小型企业和一家大型科技公司都来自互补的研究领域，如电池、表面和材料科学以及多尺度建模，他们提出了一项独特的战略，以应对这项技术目前的挑战。OPERA依赖于在ESRF、ALBA和DESY同步加速器以及实验室规模上开发新的OPERANDO实验技术，提供关于多轴应力场、化学成分、成核和生长动力学、结构缺陷形成和定义明确的模型细胞的降解的补充信息，分辨率可达原子尺度。新的见解和收集的多参数数据将被纳入由机器学习算法支持的新的多尺度建模方法中。这最终将导致对原位阳极形成的概念性理解，并在此基础上提出创新的改进方法，以实现这种类型的储能技术，这将是提高欧盟全球竞争力、弹性和独立性的重要一步。

Green, high-performing and safe batteries based on abundant materials are a key element in the transition to a carbon-neutral future. However, to accelerate their development, a deep understanding of the complex electro-chemo-mechanical processes within the battery is required, which is only accessible through advanced experimental and computational methods. Zero-excess solid-state batteries, where the anode is formed in situ, have emerged as a promising new generation of environmentally friendly batteries with high energy density, improved safety and higher cost-efficiency, but only after solutions for non-uniform anode formation were found. In OPERA, seven leading research institutions, two synchrotron radiation facilities, a small-medium sized enterprise and a large technological company, all from complementary research fields such as batteries, surface and material science, and multiscale modelling, propose a unique strategy to face the current challenges of this technology. OPERA relies on the development of novel operando experimental techniques at the ESRF, ALBA and DESY synchrotrons and at the lab-scale, providing complementary information on multiaxial stress fields, chemical composition, nucleation and growth kinetics, structural defect formation and degradation of well-defined model cells with a resolution down to the atomic scale. The new insights and collected multiparameter data will be incorporated into a novel multiscale modelling approach supported by machine learning algorithms. This will ultimately lead to a conceptual understanding of the in-situ anode formation and, based on this, innovative improvement approaches to enable this type of energy storage technology, which will be an important step towards increasing the global competitiveness, resilience and independence of the EU.