Passive Suspension Optimization Using Teaching Learning Based Optimization and Genetic Algorithm Considering Variable Speed Over a Bump

Abstract

The main objectives of a vehicle suspension system are to isolate the road excitations to reach the sprung mass of the vehicle and proper road holding. This paper proposes a solution to optimize a quarter car linear passive suspension parameters while passing over a bump with variable speeds to improve the ride comfort and road holding. The Teaching-learning based optimization algorithm (TLBO) is used to solve the problem and results are compared to those obtained by Genetic algorithm (GA) technique. The quarter car model presented is simulated in time domain subjected to a Cosine speed bump considering the variable speeds of the vehicle over it. Results show sprung mass acceleration, and tire displacement are reduced by 26.03%, and 23.7% respectively by using TLBO and 22.3%, and 18.52% respectively by using GA, conforming the capabilities of the optimization techniques.