PEM White Paper: AI Makes Battery Production Much More Efficient


The Chair of Production Engineering of E-Mobility Components (PEM) of RWTH Aachen University has worked with three industry partners to explore the potential of artificial intelligence (AI) in battery production. In a 20-page English-language white paper, the authors address the possibilities of real-time processing of data to maximize quality, output, and efficiency in battery production. According to the partners, three promising use cases are considered: automated root cause analysis, proactive efficiency enhancement and the machine health index.

  Insight into the white paper "Maximizing Efficiency" Copyright: © PEM RWTH Aachen University

Sustainability through real-time data analysis

"Mastering the available data and analyzing them in real time is crucial for sustainable, successful battery production," says PEM Director Professor Achim Kampker. Therefore, the white paper evaluates all advantages of the examined use cases specifically for battery production and shows current solutions of the software developers "camLine" and "Elisa IndustrIQ" to increase efficiency. The authors then define an initial procedure for implementing artificial intelligence and machine-learning solutions in other companies. "Real-time data analytics, combined with remediation of deviations, can greatly improve battery production, significantly increasing profitability and quality in addition to sustainability," Kampker says.

The white paper "Maximizing Efficiency: Navigating the Gigafactory Journey with Real-Time Data Insights" is available for free download. Further publications on the topics of batteries, fuel cells, and electric motors can be found in the Electric Mobility Guides section.