Robot driver for vehicle durability emission test on chassis dynamometer

A method for a vehicle durability emission test using a robot driver insteadof human drivers on the chassis dynamometer is presented. The system architecture of vehicledurability emission test cell, the road load simulation strategy and the tele-monitoring systembased on Browser/Client structure are described. Furthermore, the construction of the robot driver,vehicle performance self-learning algorithm, multi-mode vehicle control model and vehicle speedtracking strategy based on fuzzy logic arealso discussed. Besides, the capability of controlparameters self-compensation on-line makes it possible to compensate the wear of vehicle componentsand the variety of clutch true bite point during the long term test. Experimental results show thattherobot driver can be applicable to a wide variety of vehicles and the obtained results stay withina tolerance band of ± 2 km/h. Moreover the robot driver is able to control tested vehicles withgood repeatability and consistency; therefore, this methodpresents a solution to eliminate theuncertainty of emission test results by human drivers and to ensure the accuracy and reliability ofemission test results.

Investigation of performance and emission characteristics using ethanol-blended gasoline fuel as a flex-fuel in two-wheeler vehicle mounted on a chassis dynamometer

This study investigated the performance and emissions of flex fuels in a 110-cc BS6-compliant fuel-injected two-wheeler without ethanol adaptation adjustments.The tests were carried out under controlled conditions on a chassis dynamometer at 1000,2000 and 3000 r.p.m.using ethanol blends from 10%ethanol(E10)to 85%ethanol(E85).Parameters examined included brake power(BP)output,brake-specific fuel consumption(BSFC),peak in-cylinder pressure and exhaust temperature.Emissions,including carbon monoxide(CO),hydrocarbons(HC),nitrogen oxide(NOx)and unregulated emissions,were also assessed.As the percentages of the ethanol blend increased from E10 to E85,there was a noticeable improvement in power output.At 1000 r.p.m.,the BP ranged from 2.4 to 4.6 kW for different blends.The BSFC and the peak in-cylinder pressure followed a similar pattern,indicating enhanced performance and fuel efficiency with higher ethanol concentrations.Interestingly,using E85 at 1000 r.p.m.resulted in a significant 41.08%reduction in exhaust temperature compared with E10,although this difference decreased with higher blend percentages.Furthermore,replacing E10 with E85 at 1000 r.p.m.reduced CO and HC emissions by 9.17%and 38.34%,respectively.In contrast,NOx emissions increased at all r.p.m.levels with higher-ethanol blends,peaking at a 415 parts per million increase at 3000 r.p.m.However,unregulated emissions decreased significantly with increased r.p.m.and ethanol content.In summary,the use of flex-fuel blends in a two-wheeler resulted in a modest increase in BP output,improved fuel efficiency and lower CO and HC emissions.These findings are vital for optimizing ethanol blend utilization in two-wheeler engines under low-load conditions,considering both performance and environmental aspects.

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.