Author:
BAUER Lukas,STÜTZ Leon,KLEY Markus
Abstract
The increasing electrification of powertrains leads to increased demands for the test technology to ensure the required functions. For conventional test rigs in particular, it is necessary to have knowledge of the test technology's capabilities that can be applied in practical testing. Modelling enables early knowledge of the test rigs dynamic capabilities and the feasibility of planned testing scenarios. This paper describes the modelling of complex subsystems by experimental modelling with artificial neural networks taking transmission efficiency as an example. For data generation, the experimental design and execution is described. The generated data is pre-processed with suitable methods and optimized for the neural networks. Modelling is executed with different variants of the inputs as well as different algorithms. The variants compare and compete with each other. The most suitable variant is validated using statistical methods and other adequate techniques. The result represents reality well and enables the performance investigation of the test systems in a realistic manner.
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Computer Science Applications,Economics, Econometrics and Finance (miscellaneous),Mechanical Engineering,Biomedical Engineering,Information Systems,Control and Systems Engineering
Reference23 articles.
1. Albers, A., Behrendt, M., Klingler, S., & Matros, K. (2016). Verifikation und Validierung im Produktentstehungsprozess [E-Book]. In M. Behrendt, S. Klingler & K. Matros (Eds.), Handbuch Produktentwicklung (pp. 541–557). Carl Hanser Verlag. https://doi.org/10.3139/9783446445819.019
2. Bauer, L., Bauer, M., & Kley, M. (2021). Modelbasierte Validierung der Prüfstandsdynamik zur Erprobung von Komponenten elektrifizierter Antriebsstränge mithilfe eines digitalen Zwillings. Stuttgarter Symposium für Produktentwicklung, SSP 2021, 105–116. https://doi.org/10.18419/opus-11478
3. Bauer, L., Beck, P., Stütz, L., & Kley, M. (2021). Enhanced efficiency prediction of an electrified off-highway vehicle transmission utilizing machine learning methods. Procedia Computer Science, 192, 417–426. https://doi.org/10.1016/j.procs.2021.08.043
4. Beine, M., & Rasche, R. (2018). Datenmanagement für das szenariobasierte Testen. ATZextra, 23(S4), 20–25. https://doi.org/10.1007/s35778-018-0024-9
5. ÇElik, E., Gör, H., ÖZtürk, N., & Kurt, E. (2017). Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator. International Journal of Hydrogen Energy, 42(28), 17692–17699. https://doi.org/10.1016/j.ijhydene.2017.01.168
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献