Optimization of electric vehicle suspension parameters using improved artificial fish swarm algorithm

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Authors

  • Nguyen Tien Dung School of Mechanical Engineering, Hanoi University of Science and Technology
  • Bui Van Cuong Faculty of Vehicle and Energy Engineering, Thai Nguyen University of Technology
  • Le Van Quynh (Corresponding Author) Faculty of Vehicle and Energy Engineering, Thai Nguyen University of Technology
  • Hoang Anh Tan Faculty of Vehicle and Energy Engineering, Thai Nguyen University of Technology

DOI:

https://doi.org/10.54939/1859-1043.j.mst.FEE.2024.191-197

Keywords:

Electric vehicle; Suspension parameters; Optimization; IAFSA.

Abstract

This study proposes a solution to reduce vertical vibrations and body pitching in response to random road surface excitations. To achieve these objectives, a half-vehicle model of an electric vehicle (EV) is developed to determine optimal parameters for both the EV suspension system and the driver's seat suspension system. An Improved Artificial Fish Swarm Algorithm (IAFSA) is implemented using MATLAB software to optimize these suspension parameters. The optimization aims to minimize the root mean square (RMS) values of three objective functions: vertical driver's seat acceleration (aws), vertical vehicle body acceleration (awb), and pitching vehicle body acceleration (awphi). The optimization results reveal that the values of these three objective functions decrease when using the optimized suspension parameters compared to the original suspension settings. Specifically, the aws, awb and awphi values are reduced by 15.44%, 11.46%, and 8.65%, respectively, when the vehicle travels on an ISO road class B at a speed of 20 m/s with a full load. Furthermore, the peak amplitude values of as, ab, and aphi in the frequency domain are also reduced with the optimized suspension parameters compared to the original settings under the specified conditions.

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Published

06-12-2024

How to Cite

Nguyen Tien Dung, Bui Van Cuong, Le Van Quynh, and Hoang Anh Tan. “Optimization of Electric Vehicle Suspension Parameters Using Improved Artificial Fish Swarm Algorithm”. Journal of Military Science and Technology, no. FEE, Dec. 2024, pp. 191-7, doi:10.54939/1859-1043.j.mst.FEE.2024.191-197.

Issue

No. FEE (2024)

Section

Mechanics & Mechanical engineering