Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-25T02:37:21.287Z Has data issue: false hasContentIssue false
  • English
  • Français

SYSTEMATIC OPTIMISATION PROCESS FOR AN EBIKE DRIVE UNIT IN A HIGHLY VARIABLE ENVIRONMENT

Published online by Cambridge University Press:  19 June 2023

Marco Steck  and
Stephan Husung
Marco Steck*
Affiliation:
Robert Bosch GmbH; TU Ilmenau
Stephan Husung
Affiliation:
TU Ilmenau
*
Steck, Marco, Robert Bosch GmbH, Germany, [email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Drive units of eBikes are used in every type of bicycle and for different riding scenarios and riders. Due to the different riders and bike types, an enormous variety of influencing parameters and load spectra must be considered during the design process. Therefore, in this paper, a systematic approach for the optimization of the drive unit is presented, which adopts and combines several approaches from design theory. The focus is on efficient modeling and simulation of the relevant parameters and load spectra to minimize uncertainties in the design process.

Based on a system analysis, dimension-reduced parameter spaces are formed for the simulation of the system, meta-models are integrated into the simulation model and the results of the simulation are transferred into a data-based surrogate model to cover the parameter space in an efficient way with a minimum number of time consuming FE simulations. Furthermore, a coordinate-based evaluation method is presented for the FE model in order to form the input for the surrogate model, reduces the amount of data, and to allows a geometry- and mesh-independent evaluation to compare different models.

Keywords

Systems Engineering (SE)SimulationComputational design methodsLightweight design
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3305 - 3314
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

de Wit, Albert and Fred van Keulen, F. de Wit, A. (2010) “Overview of Methods for Multi-Level and/or Multi-Disciplinary Optimization”, 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, https://doi.org/10.2514/6.2010-2914CrossRefGoogle Scholar
Albers, A. and Wintergerst, E. (2014) “The Contact and Channel Approach (C&C2-A): Relating a System's Physical Structure to Its Functionality”, in: An anthology of theories and models of design: Philosophy, approaches and empirical explorations, London, Springer, pp. 151–171.Google Scholar
DIN EN 15194, (2017) “Fahrräder – Fahrräder –Elektromotorisch unterstützte Räder –E-PAC” (15194:2017)Google Scholar
Hoffer, J.G.; Geiger, B.C.; Ofner, P.; Kern, R. (2021), “Mesh-Free Surrogate Models for Structural Mechanic FEM Simulation: A Comparative Study of Approaches” Applied Sciences. 2021; 11(20):9411. https://doi.org/10.3390/app11209411Google Scholar
Husung, S., Weber, C., & Mahboob, A. (2022). “Integrating Model-Based Design of Mechatronic Systems with Domain-Specific Design Approaches” Proceedings of the Design Society, 2, 1895-1904. https://doi.org/10.1017/pds.2022.192CrossRefGoogle Scholar
Jiang, J., Ding, G., Zhang, J., Zou, Y., Qin, S., (2018), “A Systematic Optimization Design Method for Complex Mechatronic Products Design and Development”, Mathematical Problems in Engineering. https://doi.org/10.1155/2018/3159637CrossRefGoogle Scholar
Koeppe, A. (2021) Deep learning in the finite element method, Dissertation RWTH Aachen University, Aachen https://doi.org/10.18154/RWTH-2021-04990CrossRefGoogle Scholar
Kudela, J., Matousek, R. (2022), “Recent advances and applications of surrogate models for finite element method computations: a review”, Soft Computing 26, 13709–13733, https://doi.org/10.1007/s00500-022-07362-8CrossRefGoogle Scholar
Most, T. and Will, J. (2008), “Sensitivity analysis using the Meta-model of Optimal Prognosis – An automatic approach for variable reduction and optimal meta-model selection”, Weimarer Optimierungs-und Stochastiktage, Weimar.Google Scholar
Stangl, T., Walter, M., Wartzack, S. Schneyer, T. (2013) “Robust design proposal by the use of structural topology optimization considering uncertainties of input parameters and boundary conditions”, 19th International Conference on Engineering Design (ICED13)Google Scholar
Steck, M., Husung, S., Hassler, J., (2022)“Determination and systematization of load situations for eBike drive units as basis for their design and optimization”, 8. IFToMM-D-A-CH Konferenz https://doi.org/10.17185/duepublico/75445CrossRefGoogle Scholar
Steck, M., Husung, S., Hassler, J., (2023)“Determination and characterization of the influences of the bike frame on eBike drive units as the basis for their design and optimization”, 9. IFToMM-D-A-CH Konferenz https://doi.org/10.17185/duepublico/77395CrossRefGoogle Scholar
Weber, C. (2005) “CPM/PDD - An Extended Theoretical Approach to Modelling Products and Product Development Processes”, 2nd German-Israeli Symposium on Advances in Methods and Systems for Development of Products and Processes, Fraunhofer-IRB-Verlag, pp. 159–179.Google Scholar
Weber, C., Husung, S. (2016). “Solution patterns- their Role in innovation, practice and education” 14th International Design ConferenceGoogle Scholar
Wolniak, P., Cramer, J., & Lachmayer, R. (2021). “Robust multi-objective optimization in product development with respect to user-scenario and manufacturing uncertainties”, 23rd International Conference on Engineering Design (ICED21), https://doi.org/10.1017/pds.2021.520CrossRefGoogle Scholar
Yildirim, U., Campean, F. and Williams, H. (2017) “Function modeling using the system state flow diagram”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, vol. 31, no. 4, pp. 413–435.CrossRefGoogle Scholar
Zerwas, Thilo, Jacobs, Georg, Kowalski, Julia, Husung, Stephan, Gerhard, Detlef, Rumpe, Bernhard, Zeman, Klaus, Vafaei, Seyedmohammad, König, Florian, and Höpfner, Gregor. (2022). “Model Signatures for the Integration of Simulation Models into System Models” Systems 10, https://doi.org/10.3390/systems10060199CrossRefGoogle Scholar