DigiBatMat
Development of a data-driven platform for linking and analyzing battery material data from different sources in order to optimize production processes, enable quality forecasts, and promote sustainable battery cell production.
The goal of the DigiBatMat project is to develop a data-driven platform that consolidates, analyzes, and prepares battery material data from various sources in a user-friendly manner to support the sustainable production of high-performance battery cells. By intelligently linking data and knowledge, DigiBatMat enables the early detection of production defects and the prediction of key battery metrics based on selected process parameters. Users receive on-demand recommendations and information that help them specifically optimize manufacturing processes.
At its core is a digital platform that acts as an interface between the MaterialDigital innovation platform and the ProZell competence cluster. The analysis of the collected material data allows both quality prediction and a deeper understanding of the complex interrelationships in battery production. This allows critical parameters to be identified, processes to be specifically adapted, and unnecessary waste of resources to be avoided. With this holistic approach, DigiBatMat makes an important contribution to transparency, efficiency, and innovation in battery research and production – while simultaneously strengthening the competitiveness of European industry in the future-oriented field of energy storage.
Our role in the project
Implementation and realization of a holistic platform. For this purpose, the AWS Institute is developing an application for structured data collection. Data from different sources are brought together and linked.
Predictable Quality Assurance. The AWS Institute uses machine learning and artificial intelligence methods to enable data analysis and battery quality prediction.
The initial situation
Overview of the need for action
Powerful and durable batteries are important building blocks of the energy transition and crucial for competitive electric vehicles. Production problems result in low quality batteries that cannot be used. The result is unnecessary environmental pollution. This is to be avoided by predicting quality and possible defects before production.
During the development and analysis of lithium-ion batteries, the industry is faced with specific questions, e.g. about the durability or the charging capacity of the batteries. So far, these can only be answered with great effort in series tests. This is where the DigiBatMat research project comes in. With a digital platform for battery material data, knowledge and their linking, an important cornerstone for future, sustainable battery production is laid.
The market potential for automotive batteries in Europe could reach €250 billion by the mid-2020s
The battery contributes 40% to the added value of an electric car
DigiBatMat
Digital platform for battery material data and knowledge and their linking
Our solution approach in focus
A digital platform for data management of battery materials is being developed. The collected data is described and linked in such a way that it can be used for quality predictions using machine learning and correlation analysis to answer precisely asked questions. In this way, critical parameters in battery production are identified. The connection to ProZell, the German competence cluster for battery cell production, helps us to consider the needs of battery research when developing the platform and to provide tools that can be used widely.
Funding notice
The DigiBatMat project is funded by the Federal Ministry of Education and Research.
Funding code: 03XP0367D
Running time: 01.03.2021-29.02.2024
