Ride Comfort Analysis And Optimization Of Electric Sightseeing Vehicle Oriented To Self-driving Comfort

Car ride comfort is to keep the impact of vibration and shock on occupant comfort within a certain limit.Good ride comfort ensures sufficient comfort for occupants and guarantees cargo integrity for trucks.At present,there has been a lot of research on the ride comfort of traditional cars,however,there is still a lack of research on the ride comfort of autonomous driving.Based on this background,this paper carried out research.Firstly,it took an electric sightseeing car as the object to construct its ride comfort model.Then an objective function that can characterize the performance and ride comfort of autonomous driving was constructed,and the multi-objective optimization algorithm was used to obtain the optimal vehicle speed sets.The comfort threshold was set in combination with specific working conditions,and finally the optimal vehicle speed for autonomous driving was obtained,which can provide a reference for the speed control of autonomous driving.Firstly,an eight-degree-of-freedom ride comfort model of an electric sightseeing vehicle was built based on the centralized mass method.In order to lay the foundation for the research of the model from the random road surface and pulse input conditions,a four-wheel correlation time-domain random road surface was constructed according to the filtered white noise method,and the pulse input model was constructed by using the actual measurement of the deceleration belt size.Secondly,a test plan for the parameters required by the ride comfort model was formulated,and the ride comfort performance parameters of the entire vehicle were obtained through multiple repeated tests and reasonable estimates.According to the measured parameters,the ride comfort model was used to study the ride comfort response characteristics from the perspective of frequency domain and time domain,and analyze the influence of different factors on the ride comfort of the whole vehicle.Then,the ride comfort tests were carried out at the speed of 10～30km/h.First of all,the characteristics of the test data were analyzed,and then the ride comfort of the car was evaluated with reference to the ride comfort test standard.The test results were compared with the simulation results to verify the accuracy of the built model and the measured parameters.Finally,on the one hand,from the perspective of ride comfort,various response values at different speeds were obtained through simulation,and the functions of each response value changing with speed were fitted as one of the objective functions.On the other hand,from the perspective of autonomous driving performance,the deviation formula between actual vehicle speed and expected vehicle speed was constructed as another objective function.Then by setting specific working conditions,Pareto optimal multi-objective genetic algorithm was used to solve the optimization model,and the optimal vehicle speed solution sets were obtained.Finally,in order to ensure the comfort of the position of the center of mass,the threshold constraints of the root mean square value of acceleration and the maximum value of acceleration were set reasonably for the random road and over-speed bump conditions,and the optimal vehicle speed under this condition was obtained.