Battery Cell Assembly Twin

电池组双体

• 批准号: 10091190
• 金额: $ 25.09万
• 依托单位: GOLDBECK CONSULTING LIMITED
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10091190
• 关键词: Battery; Cell; Assembly; Twin

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 multicriteriaoptimization. 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 formaldeductive 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.

BatCAT 是一个实现 BATTERY 2030+ 路线图可制造性计划的项目，为电池制造创建了一个集成了数据驱动和基于物理方法的数字孪生。它为两种技术开发了跨化学数据空间，(1) 锂离子和钠离子纽扣电池和 (2) 氧化还原液流电池，解决了数字制造中的三重挑战：(i) 设计，(ii) 操作， (iii) 信任。 (i) 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，使人类决策者能够解决复杂的决策问题。 BatCAT 的数字孪生技术提供了基于多标准优化的可解释工业决策支持系统 (IIDSS)。代理建模将高级分析与真实数据牢固地联系起来。 (ii) 通过实时采集和分析传感和操作数据来改进流程操作和控制，促进工业 5.0 实时环境中的实时干预。 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：机器学习始终保留与事实真相的明确特征联系，并且归纳推理的任何决策支持或决策都受到形式演绎推理的约束的保障。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且可解释的人工智能就绪（XAIR）。数字孪生在 (1) 纽扣电池和 (2) 氧化还原液流电池的试点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 计划、BIG-MAP 和 BATTERY 2030+、BEPA、DigiPass CSA、EOSC、EMMC 和知识图联盟密切相关，确保社区和行业对成果的吸收。