A novel method of matching stiffness and continuous variable damping of an ECAS(electronically controlled air suspension) based on LQG(linear quadratic Gaussian) control was proposed to simultaneously improve the road-friendliness and ride comfort of a two-axle school bus.Taking account of the suspension nonlinearities and target-height-dependent variation in suspension characteristics,a stiffness model of the ECAS mounted on the drive axle of the bus was developed based on thermodynamics and the key parameters were obtained through field tests.By determining the proper range of the target height for the ECAS of the fully-loaded bus based on the design requirements of vehicle body bounce frequency,the control algorithm of the target suspension height(i.e.,stiffness) was derived according to driving speed and road roughness.Taking account of the nonlinearities of a continuous variable semi-active damper,the damping force was obtained through the subtraction of the air spring force from the optimum integrated suspension force,which was calculated based on LQG control.Finally,a GA(genetic algorithm)-based matching method between stepped variable damping and stiffness was employed as a benchmark to evaluate the effectiveness of the LQG-based matching method.Simulation results indicate that compared with the GA-based matching method,both dynamic tire force and vehicle body vertical acceleration responses are markedly reduced around the vehicle body bounce frequency employing the LQG-based matching method,with peak values of the dynamic tire force PSD(power spectral density) decreased by 73.6%,60.8% and 71.9% in the three cases,and corresponding reduction are 71.3%,59.4% and 68.2% for the vehicle body vertical acceleration.A strong robustness to variation of driving speed and road roughness is also observed for the LQG-based matching method.
Referring to the 1 248 survey data of rural population in 14 provinces of China, the influencing factors of trip time choice were analyzed. Based on the basic theory of disaggregate model and its modelling method, nine grades were selected as the alternatives of trip time, the variables affecting time choice and the method getting their values were determined, and a multinomial logit (MNL) model was developed. Another 1 200 trip data of rural population were selected to testify the model's validity. The result shows that the maximum absolute error of each period between calculated value and statistic is 3.6%, so MNL model has high calculation accuracy.
