Speed control strategy for tractor assisted driving based on chassis dynamometer test

Realizing automation of the chassis dynamometer and the unmanned test workshop is an inevitable trend in the development of new tractor products.The accuracy of the speed control of the test tractor directly affects the accuracy of the test loading force.In order to meet the purpose of precise control of the test tractor speed on the chassis dynamometer,a fuzzy PID control strategy was developed according to the working principle of assisted driving.On the basis of traditional PID control,the parameters of fuzzy inference module were added for real-time adjustment to achieve faster response to tractor speed changes and more precise control of tractor speed.The Matlab-Cruise co-simulation platform was established for simulation,and the experiment was verified by the tractor chassis dynamometer using the NEDC working condition and tractor ploughing working condition.The results show that both PID control and fuzzy PID control can achieve tractor speed following accuracy of±0.5 km/h.Fuzzy PID control has higher tractor speed following accuracy,faster response when speed changes,less tractor speed fluctuation,and overall control effect is better than PID control.The research results can provide a reference for the realization of the chassis dynamometer unmanned test workshop.

Carbonyl emissions from heavy-duty diesel vehicle exhaust in China and the contribution to ozone formation potential

Fifteen heavy-duty diesel vehicles were tested on chassis dynamometer by using typical heavy duty driving cycle and fuel economy cycle. The air from the exhaust was sampled by 2,4- dinitrophenyhydrazine cartridge and 23 carbonyl compounds were analyzed by high performance liquid chromatography. The average emission factor of carbonyls was 97.2 mg/km, higher than that of light-duty diesel vehicles and gasoline-powered vehicles. Formaldehyde, acetaldehyde, acetone and propionaidehyde were the species with the highest emission factors. Main influencing factors for carbonyl emissions were vehicle type, average speed and regulated emission standard, and the impact of vehicle loading was not evident in this study. National emission of carbonyls from diesel vehicles exhaust was calculated for China, 2011, based on both vehicle miles traveled and fuel consumption. Carbonyl emission of diesel vehicle was estimated to be 45.8 Gg, and was comparable to gasolinepowered vehicles （58.4 Gg）. The emissions of formaldehyde, acetaldehyde and acetone were 12.6, 6.9, 3.8 Gg, respectively. The ozone formation potential of carbonyls from diesel vehicles exhaust was 537 mg O3/km, higher than 497 mg O3/km of none-methane hydrocarbons emitted from diesel vehicles.

Characterizing human driver characteristics using an artificial neural network and a theoretical model

Human drivers seem to have different characteristics,so different drivers often yield different results from the same driving mode tests with identical vehicles and same chassis dynamometer.However,drivers with different experiences often yield similar results under the same driving conditions.If the features of human drivers are known,the control inputs to each driver,including warnings,will be customized to optimize each man–machine vehicle system.Therefore,it is crucial to determine how to characterize human drivers quantitatively.This study proposes a method to estimate the parameters of a theoretical model of human drivers.The method uses an artificial neural network(ANN)model and a numerical procedure to interpret the identified ANN models theoretically.Our approach involves the following process.First,we specify each ANN driver model through chassis dynamometer tests performed by each human driver and vehicle.Subsequently,we obtain the parameters of a theoretical driver model using the ANN model for the corresponding driver.Specifically,we simulate the driver’s behaviors using the identified ANN models with controlled inputs.Finally,we estimate the theoretical driver model parameters using the numerical simulation results.A proportional-integral-differential(PID)control model is used as the theoretical model.The results of the parameter estimation indicate that the PID driver model parameter combination can characterize human drivers.Moreover,the results suggest that vehicular factors influence the parameter combinations of human drivers.