Fraunhofer Research Institution for Battery Cell Production FFB 
AIMACC
Research project, »AIMACC - AI-Managed Adaptive Closed-Loop Formation Control«
In battery cell manufacturing, formation is one of the biggest drivers of production costs due to long process times and capital-intensive equipment. In the industry, formation is typically carried out at low current rates because there is limited understanding of the process, especially for new cell materials, regarding the maximum current for formation. As a result, the formation process can take up to a day.
To reduce the high manufacturing costs and energy consumption associated with formation, the research project “AI-Managed Adaptive Closed-Loop Formation Control (AIMACC)” focuses on optimizing the formation process. This is achieved through real-time control of the formation current using simulation and AI models based on 2D pressure and temperature sensors. This has not yet been implemented in current state-of-the-art technology, but offers enormous advantages in terms of:
- faster and more detailed predictions of cell quality;
- acceleration of the forming process design for new cell material systems;
- shortening of the forming time to reduce energy consumption and costs;
- improvement of cell quality.
Work Plan
The project is divided into six work packages:
WP1 begins with the execution of benchmark cell formation tests, which will serve as a reference for subsequent optimization. In addition, a requirements analysis for the project’s innovations will be conducted.
WP2 covers cell manufacturing, testing, and analysis of the produced battery cells.
In WP3, the concept for the new 2D sensor technology and the closed-loop interface is developed, and the hardware is integrated into the formation systems.
In WP4, models and AI methods are developed to optimize the formation process.
In P5, optimization approaches, including closed-loop control, are applied to various cell formats.
P6 covers project management activities throughout the project duration.
Utilization of results
The pre-competitive research results developed in the project will be made available to the broader battery industry through presentations at conferences and publications in professional journals. This will help strengthen Germany’s position as a center of research. Furthermore, the results will be shared within research networks, thereby ensuring their wider dissemination. Fraunhofer FFB is a member of the Allianz Batterie, SRIA, ELLB, LiPLANET, IPCEI, and the Big Data AI Alliance.
Building on these results, research in battery production technology will continue to further expand our expertise. The AI models developed, insights into formation, and the combination of sensor infrastructure and AI data processing will be of great benefit for future follow-up projects.
Furthermore, within the framework of “AIMACC,” targeted support for young professionals is being promoted, for example through research-oriented master’s programs and doctoral studies, as well as university-internal projects and theses made possible by addressing the project’s research questions. Furthermore, the scientific results can be utilized at various levels in the teaching of electrical and mechanical engineering programs, particularly in the fields of “Electrochemical Energy Storage Systems” and “Production Engineering.”
The results will be incorporated into the “Battery Production” course taught by FFB faculty at Münster University of Applied Sciences. The ISEA university institute will ensure that the project’s scientific and technical findings are utilized in the following areas:
(1) Publication in scientific journals, particularly in articles and dissertations;
(2) Integration into academic teaching, particularly through incorporation into seminars and internships.
For German battery cell manufacturers such as Cellforce, research projects like “AIMACC” represent an important tool for differentiating themselves from international competitors through innovative, data-driven solutions. The results of this project are intended to yield both cost (OPEX) and quality benefits in production. These efficiency gains are highly relevant in the first instance for the battery cell manufacturing facility currently under construction in Baden-Württemberg and will be incorporated into further scaling scenarios.