The Potential of Data-Driven Engineering Models: An Analysis Across Domains in the Automotive Development Process 2023-01-0087
Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains. In consequence, we show the necessities and benefits of adopting such models by analyzing the current methods used in industry. We derive commonalities in approaches and applications across domains to subsequently provide detailed case studies along the development cycle. Here, we highlight essential data acquisition concepts and suggest promising approaches for four different engineering use-cases, while pointing out limitations and pitfalls in application. Conclusively, we present our perspective on further challenges and opportunities in the evolution of the automotive industry in terms of data-driven system models for technical use-cases.
Citation: Knödler, J., Könen, C., Muhl, P., Rudolf, T. et al., "The Potential of Data-Driven Engineering Models: An Analysis Across Domains in the Automotive Development Process," SAE Technical Paper 2023-01-0087, 2023, https://doi.org/10.4271/2023-01-0087. Download Citation
Author(s):
Julian Knödler, Christian Könen, Philip Muhl, Thomas Rudolf, Eric Sax, Hans-Christian Reuss, Lutz Eckstein, Sören Hohmann
Affiliated:
Porsche AG, Porsche Engineering Services GmbH, FZI Research Center for Information Technologies, University of Stuttgart, RWTH Aachen University, KIT Karlsruhe Institute of Technology
Pages: 19
Event:
WCX SAE World Congress Experience
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Analysis methodologies
Automated Vehicles
Product development
Data acquisition and handling
Artificial intelligence (AI)
Manifolds
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