The present paper shows the development of a strategy for the calculation of the air brake forces of European freight trains. The model is built to upgrade the existing Politecnico di Torino longitudinal train dynamic...The present paper shows the development of a strategy for the calculation of the air brake forces of European freight trains. The model is built to upgrade the existing Politecnico di Torino longitudinal train dynamics(LTD) code LTDPoliTo, which was originally unable to account for air brake forces. The proposed model uses an empirical exponential function to calculate the air brake forces during the simulation, while the maximum normal force on the brake friction elements is calculated according to the indication of the vehicle braked weight percentage.Hence, the model does not require to simulate in detail the fluid dynamics in the brake pipe nor to precisely know the main parameters of the braking system mounted on each vehicle. The model parameters are tuned to minimize the difference between the braking distance computed by the LTDPoliTo code and the value prescribed by the UIC544-1 leaflet in emergency braking operations. Simulations are run for different configurations of freight train compositions including a variable number of Shimmns wagons trailed by an E402B locomotive at the head of the train, as suggested in a reference literature paper. The results of the proposed method are in good agreement with the target braking distances calculated according to the international rules.展开更多
To evaluate the software behavior of the electronic control unit （ECU） of automotive electrical parking brake （EPB）, a software- in-the-loop （SiL） simulation system is built. The EPB is simulated by ARX （auto-r...To evaluate the software behavior of the electronic control unit （ECU） of automotive electrical parking brake （EPB）, a software- in-the-loop （SiL） simulation system is built. The EPB is simulated by ARX （auto-regressive with auxiliary input） model, ARMAX （auto-regressive moving average with auxiliary input） model, and NNARMAX （neural network ARMAX） model. By system identification, the ARX（3,4,2）, ARX（4,4,2）, ARMAX（3,3,1,1）, and ARMAX（4,4,3,2） models are derived. Validation results show that the four-order ARMAX model and the NNARMAX model better simulate the actuator of the EPB.展开更多
文摘The present paper shows the development of a strategy for the calculation of the air brake forces of European freight trains. The model is built to upgrade the existing Politecnico di Torino longitudinal train dynamics(LTD) code LTDPoliTo, which was originally unable to account for air brake forces. The proposed model uses an empirical exponential function to calculate the air brake forces during the simulation, while the maximum normal force on the brake friction elements is calculated according to the indication of the vehicle braked weight percentage.Hence, the model does not require to simulate in detail the fluid dynamics in the brake pipe nor to precisely know the main parameters of the braking system mounted on each vehicle. The model parameters are tuned to minimize the difference between the braking distance computed by the LTDPoliTo code and the value prescribed by the UIC544-1 leaflet in emergency braking operations. Simulations are run for different configurations of freight train compositions including a variable number of Shimmns wagons trailed by an E402B locomotive at the head of the train, as suggested in a reference literature paper. The results of the proposed method are in good agreement with the target braking distances calculated according to the international rules.
基金Sichuan Province Key Discipline Con-struction for Automotive Engineering ( No.SZD0410 )Research Foundation of Xihua University (No.R0620301)
文摘To evaluate the software behavior of the electronic control unit （ECU） of automotive electrical parking brake （EPB）, a software- in-the-loop （SiL） simulation system is built. The EPB is simulated by ARX （auto-regressive with auxiliary input） model, ARMAX （auto-regressive moving average with auxiliary input） model, and NNARMAX （neural network ARMAX） model. By system identification, the ARX（3,4,2）, ARX（4,4,2）, ARMAX（3,3,1,1）, and ARMAX（4,4,3,2） models are derived. Validation results show that the four-order ARMAX model and the NNARMAX model better simulate the actuator of the EPB.
