Abstract
In order to evaluate the influence of unsprung mass on ride comfort for an in-wheel motor electric vehicle, an improved genetic algorithm based on fitness evaluation was proposed to optimize the suspension system. The simulation model was established in ADAMS software for the target sports utility vehicle with front McPherson and rear unequal length double arm suspension systems. The ride comfort of the target vehicle was analyzed by the developed simulation model. The stiffness and damping of the suspensions were optimized by the root mean square values of the vehicle weighted vertical acceleration and the pitching angle acceleration with the help of the multi-disciplinary and multi-objective optimization software, ISIGHT. The results show that, the proposed multi-objective optimization algorithm is helpful to achieve the ride comfort improvement and the computation time reduction.
Highlights
More and more people are concentrated on electric vehicles driven by in-wheel motor
Based on an improved genetic algorithm, the vehicle roll angle, yaw rate, and vehicle vibration acceleration were used as the optimization objectives; the suspension system of a light-duty bus was optimized which improved the ride comfort and handling stability performance, respectively.[1]
Linear vibration model of 1/4 vehicle was established in Ma et al.;[2] the sum of root mean square (RMS) value of the acceleration of vehicle body, suspension dynamic deflection, and wheel dynamic load was looked as the optimized target to optimize the suspension configuration by using pattern search function and improved the ride comfort
Summary
More and more people are concentrated on electric vehicles driven by in-wheel motor. This article presents a multi-dynamics model of the in-wheel motor electric vehicle with random road profile input In this model, an improved genetic algorithm based on fitness evaluation is implemented to search for the optimal parameters of the vehicle suspensions to reduce human vibration and to achieve the best comfort of the passengers. For suspension system, random road profile, dynamics simulation model of system, and optimization calculation model are needed for interactive callings of the algorithm to obtain the fitness function value of each individual. The RMS value of the vertical weighted acceleration and pitching angle of the simulation model can be seen, which presents the distribution of the objective function points in a form of three-dimensional coverage diagram. Compared to the data in original B, the results in lesser RMS values of the optimization have been reduced between 10% and 15% when the speed ranges from 50 to 70 km/h, other reduction is less than 10% when the speed is 40, 80, and 90 km/h, and the objectives were cut down below 5% when the speed
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