PEM Aims to Increase Data Competence of Junior Researchers in Electric Vehicle ProductionCopyright: © Adobe Stock – Panuwat (Balls)
In the research project "NEED", the chair "Production Engineering of E-Mobility Components" (PEM) of RWTH Aachen University is concerned with sustainably increasing the data competence of young scientists in the field of electric vehicle production. To this end, the main aim is to explore whether mutual dependencies in the manufacturing processes of electric vehicles can be identified and quantified by using data-based methods, how conventional, engineering-based modeling approaches can be replaced by data-based analysis methods, and how the acquired competencies can be disseminated and sustainably anchored in both the scientific and industrial environments.
Reducing the scrap rate and increasing quality
The background to this is the challenge of being able to produce electric motors, batteries, and fuel cells for electric vehicles in a timely, economical and sustainable manner. The production of these three core components is currently still characterized by interdependencies that occur both within individual process steps and across several process steps. These interdependencies have a significant influence on the scrap rate during production as well as on the subsequent product quality and thus on the service life of the electric vehicles. In order to reduce the scrap rate and increase the quality of the vehicles, it is necessary to detect occurring defects as early as possible and to avoid corresponding errors in the future.
Current modeling approaches reach their limits
Up to now, the modeling of production processes as well as the associated interactions has mainly been carried out using conventional methods such as statistical analyses, simulations, and others. However, due to the high complexity and enormous amounts of data, these conventional modeling methods are reaching their limits, so that artificial intelligence methods are needed.
Away from experiments, towards the use of data
The expertise required for this is to come from an exchange of knowledge between Helmut Schmidt University (HSU) and the RWTH PEM chair: while the Aachen university provides HSU with know-how on electric vehicle component production, the colleagues from Hamburg bring theit expertise on artificial intelligence and machine learning to PEM. Ideally, the project is intended to bring about a paradigm shift – away from classic experiments and toward greater use of existing or easily accessible data. The associated automation in model creation should shorten development times and enable faster series start-up phases.
Further information is provided in this press release.
- "NEED": Sustainable increase of data competence of young scientists in the field of electric vehicle production
- Identification and quantification of process-side interdependencies in electric vehicle production through the use of data-based methods
- Substitution of conventional, engineering-based modeling approaches with data-based analysis methods
- Broadening and sustainable anchoring of acquired competencies in the scientific and industrial environment
Research and project partners
- 04/01/2022 through 03/31/2025