BMW
What does a Battery Production Data Scientist do?
Patrick Zimmermann is a Battery Production Data Scientist at BMW.
BMW
The high-voltage battery is considered the most important differentiating feature in electric cars. BMW uses highly intelligent data-driven systems in battery production. To ensure these become more sophisticated, there are people like Patrick Zimmermann.
With the Neue Klasse, the BMW Group is soon entering a new era of purely electric driving. A crucial component in the electric vehicle is the high-voltage battery. For the production of the sixth generation, the company is establishing five assembly sites on three continents: Irlbach-Straßkirchen (Bavaria), Debrecen (Hungary), Shenyang (China), San Luis Potosí (Mexico), and Woodruff (USA).
However, before large-scale series production begins, the production processes are developed and pre-series batteries are tested thoroughly. This takes place in the BMW pilot plants for high-voltage batteries in Parsdorf, Hallbergmoos, and the Munich Research and Innovation Centre (FIZ). Patrick Zimmermann is responsible as IT project manager for the implementation of the Industrial Internet of Things (IIoT) and Data Analytics in high-voltage battery production. He is the one where everything comes together.
What does a Battery Production Data Scientist do?
“I coordinate an interdisciplinary team that looks at the entire data processing chain: from data collection in the production facilities through edge applications to the transfer of data to the cloud and analytics applications,” explains Zimmermann. “The job profile initially requires a good technical understanding of the production technology of our new high-voltage batteries.” Especially since BMW is relying on innovative, sometimes completely new manufacturing processes in the production of their new high-voltage batteries.
“Of course, IT skills are required - with a focus on software architectures for implementing data analytics and artificial intelligence,” says the 35-year-old, “Since the role as a project manager primarily involves coordinating various teams, understanding the individual software components and interfaces is particularly important.”
The topics of data analytics and AI in production are currently developing at an enormous pace. Therefore, it is important to keep an eye on new approaches and assess whether they are suitable for BMW; or whether they can replace existing solutions (for example, due to lower costs or better features). “One of the most important tasks in my job is to choose the right option for our production from the variety of technologies and use cases,” the expert emphasizes, “Not every analytics or AI solution that has achieved excellent results in a particular industry or use case can be directly transferred to battery production.” What is suitable for production, what is not? “I have to work with my team to develop a strategy for how the necessary IT architectures can be rolled out in a company like the BMW Group,” reports Zimmermann.
Analytics Architecture for New Class Batteries
His biggest project so far was implementing the new analytics architecture in the production of sixth-generation high-voltage batteries. "What is special about this project is the comprehensive responsibility from data provision, data transfer to the cloud, to specific or even permanent analyses," explains Zimmermann. "Both numerical data from the systems and recordings from the cameras installed in production are considered."
For system connectivity, BMW relies on the interface technology OPC UA. "This allows us to model standardised digital twins directly in our systems and avoid additional data preparation," emphasises the BMW man. The data is transferred to the group's cloud and follows the same data structure at all production sites. "This allows us to roll out standardised analysis dashboards worldwide and, for example, carry out process optimisations even faster," says Zimmermann.
In his professional everyday life, he essentially acts as a link between management and the project team. The aim is to refine the specifications discussed with management for implementation and derive corresponding tasks and goals for the team. "I personally find it particularly exciting to be able to help shape and realise IIoT & Data Analytics on such a scale from the very beginning," says the data specialist. In his previous positions, integrating data provision into existing production systems was one of the biggest challenges. Because several plants for the Gen6 high-voltage battery were built on a "greenfield" site, Zimmermann was offered a completely new scope for design: "Here, much bigger leaps are possible in IT than would be the case with integration into existing plants."
These qualifications should be brought along
But what skills should one have for this job? Solid knowledge in the field of production technology as well as an understanding of IT architectures are advantageous. In the role of project manager, professional experience is also important, Zimmermann emphasises: "You can attend as many project management training courses as you like, but ultimately there is no universal recipe for success here." Soft skills are often more important than actual technical expertise. Without which, of course, it doesn't work either.
What Zimmermann's career path shows: After studying automotive engineering, he focused on digitalisation, particularly on the concept of Industry 4.0 in automation technology. Subsequently, he researched in the field of data analytics and artificial intelligence, with his focus on data models and the ability to connect systems in production, particularly during his work at the Fraunhofer Institute. Finally, he began working at the BMW Group as an IT project manager for the Industrial Internet of Things and data analytics.
A job with a future?
One thing is certain: data analytics and AI in production will become increasingly important in the coming years. Historically, the renewal cycles of machines and systems in production are rather long, which has led to an advantage of IT possibilities compared to operational technology (OT), i.e., the shop floor. With each newly introduced system, each new production facility, the prerequisites for the application of data analytics can be increased many times over, says Zimmermann: "The full potential will probably only be realised in the coming decades." And this will not succeed without people like him.
This article was first published at automotiveit.eu
