Flexible and scalable digital-twin platform for enhanced production efficiency and yield in battery cell production lines - BATTwin
Flexible and scalable digital-twin platform for enhanced production efficiency and yield in battery cell production lines - BATTwin
批准号:
10118186
负责人:
金额:
$22.06万
依托单位国家:
英国
项目类别:
EU-Funded
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
点击翻译按钮获取中文摘要
英文摘要
Li-ion batteries are fundamental components for the energy transition of the European eco-system. Currently Europe lags behind Asia in terms of Li-ion battery cell manufacturing and more than 90% of the world's production takes place in China, Korea and Japan. To overcome this situation, there is an ambitious ramp-up plan of 25 new gigafactories in Europe with an expected value of €35 billion by 2030. However, in the ramp-up phase of these Gigafactories, a massive production of defects is expected, between 15% - 30%. The new European Gigafactories will also bring demand for €150 bn of battery manufacturing equipment. To support this demand, the EU production equipment industry needs to fill the current knowledge gap and gain competitiveness towards Asian providers, grounding on its world-wide leadership in high-tech, green technologies, enhanced by industry 4.0 digital solutions, exploiting the European Zero Defect Manufacturing paradigm. The objective of BATTwin is to support this scenario by developing a novel Multi-level Digital Twin platform towards Zero-Defect Manufacturing in battery production, that will reduce defect rates in battery production lines. The solution integrates four pillars, namely (i) a multi-sensor data acquisition and management layer, supported by data semantics through a Digital Battery Passport data model, (ii) process-level digital twins, modeling the critical stages of electrode manufacturing, cell assembly and conditioning through multi-physics, data-driven and hybrid approaches, (iii) system-level digital twins, based on simulation and analytical modeling, (iv) user-centric, goal-driven digital twin workflows, increasing the explainability of digital twins and driving the user in system design and control. The approach will be tested in two industrial pilots producing different battery chemistries and geometries, validating the flexibility and scaleability of the approach towards Zero Defect European Gigafactories.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
-
批准号:--
-
项目类别:合作创新研究团队
-
资助金额:--
-
批准年份:2024
-
负责人:姚韬
-
依托单位: