BatCAT - Battery Cell Assembly Twin

BatCAT是实现电池2030+路线图可制造性计划的项目，为电池制造创建了一个数字孪生，集成了数据驱动和基于物理的方法。它为两种技术（1)锂离子和钠离子硬币电池和(2)氧化还原液流电池）开发了一个交叉化学数据空间，解决了数字化制造中的三重挑战：(i)设计，（ii）操作和（iii）信任。(1)通过改进产品和工艺设计和优化，提高产品质量和工艺效率。这需要决策支持，使人类决策者能够访问复杂的决策问题。BatCAT的数字孪生技术提供了一个基于多准则优化的可解释工业决策支持系统（IIDSS）。代理模型将高层分析与真实数据紧密地联系在一起。（ii）通过实时获取和分析感官和操作数据，改进了过程操作和控制，促进了工业5.0实时环境中的现场干预。BatCAT遵循严格的可操作建模方法，将数据驱动方法与基于本体和形式化方法（答案集编程和基于bpmn的模型检查）的演绎推理相结合，以保证可靠的行为。（iii） BatCAT的方法产生了值得信赖的模型：机器学习始终保持与基本事实的明确特征连接，任何来自归纳推理的决策支持或决策制定都受到形式演绎推理的约束的保护。我们所有的模型和方法都是可解释的，我们所有的数据都是公平和可解释的- ai -ready （XAIR）。数字孪生在(1)纽扣电池和(2)氧化还原液流电池的中试生产线上进行了验证，证明了其跨化学物质的可转移性。该项目与先进材料2030倡议、BIG-MAP和BATTERY 2030+、BEPA、DigiPass CSA、EOSC、EMMC和知识图谱联盟密切相关，确保社区和行业吸收成果。

BatCAT is the project that realizes the manufacturability programme from the BATTERY 2030+ Roadmap, creating a digital twin for battery manufacturing that integrates data-driven and physics-based methods. It develops a cross-chemistry data space for two technologies, (1) Li-ion and Na-ion coin cells and (2) redox flow batteries, addressing a triple challenge in digital manufacturing: (i) Design, (ii) operation, and (iii) trust. (i) By improved product and process design and optimization, product quality and process efficiency increase. This requires decision support that makes complex decision problems accessible to human decision makers. The digital twin technology from BatCAT provides an interpretable industrial decision support system (IIDSS) based on multicriteria optimization. Surrogate modelling connects the high-level analysis firmly to ground-truth data. (ii) Process operation and control is improved by acquiring and analysing sensory and operando data at real time, facilitating live interventions within an Industry 5.0 real-time environment. BatCAT follows a rigorous approach to actionable modelling, combining data-driven methods with deductive reasoning based on ontologies and formal methods (answer set programming and BPMN-based model checking) to guarantee a reliable behaviour. (iii) The approach from BatCAT produces trustworthy models: Machine learning always retains a clearly characterized connection to the ground truth, and any decision support or decision making from inductive reasoning is safeguarded by constraints through formal deductive reasoning. All our models and methods are explainable, and all our data are FAIR and explainable-AI-ready (XAIR). The digital twin is validated in pilot production lines for (1) coin cells and (2) redox flow batteries, proving its transferability across chemistries. The project is closely connected to the Advanced Materials 2030 Initiative, BIG-MAP and BATTERY 2030+, BEPA, DigiPass CSA, EOSC, EMMC, and the Knowledge Graph Alliance, ensuring a community and industry uptake of the results.