Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach

Considering the light-weight design expectations and government requirements in the automotive industry, both structural optimisation approaches and swarm intelligence methods have been receiving gigantic attention for their high accuracy and robustness. In this research, a new hybrid salp swarm-Nelder-Mead (HSSA-NM) algorithm is developed to optimise electric vehicle components. Both Latin hypercube sampling methodology and radial basis function surrogate modelling approach are used for obtaining equations of constraints and objectives used in the shape optimisation. Initially, the performance of the HSSA-NM is tested using a coil spring problem. Finally, the HSSA-NM is used for the optimum design of a vehicle control arm. As a result, a design problem is solved using the HSSA-NM. The optimal design meets all of the problem constraints and reduces the weight by about 2056 grams compared with that of the initial model. Thus, the proposed design method is an efficient method for shape optimisation design.

• Record URL:
  https://doi.org/10.1504/IJVD.2020.114779
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14775360
• Supplemental Notes:
  • Copyright © 2020 Inderscience Enterprises Ltd.
• Authors:
  • Yıldız, Betül Sultan
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 38-53
• Serial:
  • International Journal of Vehicle Design
  • Volume: 83
  • Issue Number: 1
  • Publisher: Inderscience Enterprises Limited
  • ISSN: 1477-5360
  • Serial URL: http://www.inderscience.com/jhome.php?jcode=IJVD

Subject/Index Terms

• TRT Terms: Algorithms; Electric vehicles; Light vehicles; Optimization; Vehicle design
• Subject Areas: Design; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01778648
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 4 2021 10:13AM