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

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.