Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-26T03:04:16.588Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust Design Optimization of Mechatronics Systems: Parallel Electric Drivetrain Application

Published online by Cambridge University Press:  26 May 2022

A. Rosich ,
C. López ,
P. Dewangan  and
G. Abedrabbo
A. Rosich*
Affiliation:
Flanders Make, Belgium
C. López
Affiliation:
Flanders Make, Belgium
P. Dewangan
Affiliation:
Flanders Make, Belgium
G. Abedrabbo
Affiliation:
Flanders Make, Belgium
*
[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.

This paper addresses the problem of finding a robust optimal design when uncertain parameters in the form of crisp or interval sets are present in the optimization. Furthermore, in order to make the approach as general as possible, direct search methods with the help of sensitivity analysis techniques are employed to optimize the design. Consequently, the presented approach is suitable for black box models for which no, or very little, information of the equations governing the model is available. The design of an electric drivetrain is used to illustrate the benefits of the proposed method.

Keywords

robust designdesign optimisationuncertaintymechatronics
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1727 - 1736
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), 2022.

References

Abramson, M.A., Audet, C., Chrissis, J.W. and Walston, J.G. (2009), “Mesh adaptive direct search algorithms for mixed variable optimization”, Optimization Letters, Vol. 3 No. 1, pp. 35–47.Google Scholar
Abramson, M.A., Audet, C., Couture, G., Dennis, J.E., Digabel, J. Le and Digabel, S. Le. (n.d.) “The NOMAD project”, available at: http://www.gerad.ca/nomad.Google Scholar
Bayrak, A.E., Kang, N. and Papalambros, P.Y. (2015), “Decomposition-Based Design Optimization of Hybrid Electric Powertrain Architectures: Simultaneous Configuration and Sizing Design”, Proceedings of the ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2015, Vol. 138 No. July, pp. 1–10.Google Scholar
Ben-Tal, A., Ghaoui, L. and Nemirovski, A. (2009), Robust Optimization, University, Princeton, available at: 10.1515.Google Scholar
Beyer, H.G. and Sendhoff, B. (2007), “Robust optimization - A comprehensive survey”, Computer Methods in Applied Mechanics and Engineering, Vol. 196 No. 33–34, pp. 3190–3218.CrossRefGoogle Scholar
Cotter, S.C. (1979) "A screening design for factorial experiments with interactions" Biometrika, Vol. 66, pp. 317–320.Google Scholar
Digabel, S. Le. (2011), “NoAlgorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm”, ACM Transactions on Mathematical Software, Vol. 37(4):44:1.Google Scholar
Glynn, P.W. and Iglehart, D.L. (1989) “Importance Sampling for Stochastic Simulations”. Management Science Vol. 35, No. 11, pp. 1367–1392.CrossRefGoogle Scholar
Goerigk, M. and Schöbel, A. (2016), “Algorithm engineering in robust optimization”, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9220 LNCS No. 246647, pp. 245–279.Google Scholar
Gorissen, B.L., Yanikoğlu, I. and den Hertog, D. (2015), “A practical guide to robust optimization”, Omega (United Kingdom), Vol. 53, pp. 124–137.Google Scholar
Hamel, J. (2010), “Design Improvement by Sensitivity Analysis Under Interval Uncertainty Using Multi-Objective Optimization”, Most, Vol. 132 No. August, pp. 1–10.Google Scholar
Hamzaçebi, C., 2020, 'Taguchi Method as a Robust Design Tool', in Li, P., Pereira, P. A. R., Navas, H. (eds.), Quality Control - Intelligent Manufacturing, Robust Design and Charts, IntechOpen, London. 10.5772/intechopen.94908.Google Scholar
Iooss, B., Lema^, P., Iooss, B. and Lema^, P. (2015), “A review on global sensitivity analysis methods To cite this version”, Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications, Springer US, available at:10.1007/978-1-4899-7547-8_5.Google Scholar
Klir, G. and Filger, T. (1998), Fuzzy Sets, Uncertainty, and Information, edited by Prentice-Hall, Englewood Cliffs, NJ.Google Scholar
Kucherenko, S., Tarantola, S. and Annoni, P. (2012) “Estimation of global sensitivity indices for models with dependent variables”, Computer Physics Communications, Vol. 183, No. 4, pp. 937–946Google Scholar
Martins, J.R.R.A. and Ning, A. (2021), Engineering Design Optimization, Cambridge University Press.CrossRefGoogle Scholar
McKay, M.D., Beckman, R.J. and Conover, W.J. (1979), “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code”, Technometrics, Vol. 21 No. 2, pp. 239–245.Google Scholar
Metropolis, N. and Ulam, S. (1949) "The Monte Carlo Method" Journal of the American Statistical Association, Vol. 44, No. 247, pp. 335–341Google Scholar
Morris, M.D. (1991), “Factorial Sampling Plans for Preliminary Computational Experiments”, Technometrics, Vol. 33 No. 2, pp. 161–174.CrossRefGoogle Scholar
Sahinidis, N. V. (2004), “Optimization under uncertainty: State-of-the-art and opportunities”, Computers and Chemical Engineering, Vol. 28 No. 6–7, pp. 971–983.CrossRefGoogle Scholar
Saltelli, A. (2008). Global sensitivity analysis: the primer. Chichester, England, John Wiley.Google Scholar
Schuëller, G.I. and Jensen, H.A. (2008), “Computational methods in optimization considering uncertainties - An overview”, Computer Methods in Applied Mechanics and Engineering, available at:10.1016/j.cma.2008.05.004.Google Scholar
Silvas, E., Hofman, T., Murgovski, N., Etman, P. and Steinbuch, M. (2016), “Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles”, IEEE Transactions on Vehicular Technology, pp.1–1.Google Scholar
Sobol, I.M. (1993), “Sensitivity estimates for nonlinear mathematical models”, Mathematical Modelling and Computational Experiments, Vol. 1, pp. 407–414.Google Scholar
Son, H., Park, K., Hwang, S. and Kim, H. (2017), “Design methodology of a power split type plug-in hybrid electric vehicle considering drivetrain losses”, Energies, Vol. 10 No. 4, available at:10.3390/en10040437.CrossRefGoogle Scholar
Vandenhove, A. A. -E. Abdallh, F. Verbelen, M. V. Cavey and J. Stuyts, “Electrical Variable Transmission for Hybrid Off-highway Vehicles," 2020 International Conference on Electrical Machines (ICEM), 2020, pp. 2176-2182, doi: 10.1109/ICEM49940.2020.9270723.Google Scholar
Zang, C., Friswell, M.I. and Mottershead, J.E. (2005), “A review of robust optimal design and its application in dynamics”, Computers and Structures, Vol. 83 No. 4–5, pp. 315–326.CrossRefGoogle Scholar