In this study, ammonia emissions characteristics of typical light-duty gasoline vehicles were obtained through laboratory vehicle bench test and combined with New European Driving Cycle (NEDC) condition and Worldwide Harmonized Light Vehicles Test Cycle (WLTC) condition. The influence of ambient temperature on ammonia emissions is mainly concentrated in the cold start stage. The influence of ambient temperature on ammonia emission is shown that the ammonia emissions of light-duty gasoline vehicles under ambient temperature conditions (14 and 23°C) are lower than those under low ambient temperature conditions (-7°C) and high ambient temperature conditions (35 and 40°C). The influence of TWC on ammonia emission is shown that ammonia is a by-product of the catalytic reduction reaction of conventional gas pollutants in the exhaust gas in the TWC. Under NEDC operating conditions and WLTC operating conditions, ammonia emissions after the catalyst are 45 times and 72 times that before the catalyst, respectively. In terms of ammonia emissions control strategy research, Pd/Rh combination can reduce NH3 formation more effectively than catalyst with a single Pd formula. Precise control of the engine's air-fuel ratio and combination with the optimized matched precious metal ratio TWC can effectively reduce ammonia emissions.

The steady state VOC concentration in automobile cabin is taken as a good indicator to characterize the material emission behaviors and evaluate the vehicular air quality. Most studies in this field focus on experimental investigation while theoretical analysis is lacking. In this paper we firstly develop a simplified physical model to describe the VOC emission from automobile materials, and then derive a theoretical correlation between the steady state cabin VOC concentration (C_a) and temperature (T), which indicates that the logarithm of C_a/T^0.75 is in a linear relationship with 1/T. Experiments of chemical emissions in three car cabins at different temperatures (24°C, 29°C, 35°C) were conducted. Eight VOCs specified in the Chinese National Standard GB/T 27630–2011 were taken for analysis. The good agreement between the correlation and experimental results from our tests, as well as the data taken from literature demonstrates the effectiveness of the derived correlation. Further study indicates that the slope and intercept of the correlation follows linear association. With the derived correlation, the steady state cabin VOC concentration different from the test conditions can be conveniently obtained. This study should be helpful for analyzing temperature-dependent emission phenomena in automobiles and predicting associated health risks.

There is increasing demand for the on-board diagnosis of lubricating oils. In this research, we consider various sensor principles for on-board diagnosis of the thermal aging of engine oils. One of the parameters investigated is the viscosity of the lubricating oil, which can be efficiently measured using a microacoustic sensor. Compared with conventional viscometers, these sensors probe a different rheological domain, which needs to be considered in the interpretation of measurement results. This specific behavior is examined by systematically investigating engine oils, with and without additive packages, that were subjected to a defined artificial aging process. This paper presents design strategies for the algorithm developed and applied for direct on-board diagnosis of engine oil conditions with a fluid property sensor; this enables prediction of remaining oil life and optimization of oil change intervals, thereby minimizing the likelihood of dramatic engine failure and reducing maintenance costs. After a general description of the principles of sensor measurement, different engine oil contaminants, aging phenomena, and associated sensor detection and measurement capabilities are discussed.

In the China VI regulations for light-duty vehicles, the (RDE) test is introduced as a supplementary test procedure. In the actual test process, the RDE results are more significantly influenced by driving behavior and vehicle type. To reduce the test cost, the NOx and PN prediction models are established based on the GA-BP method. The results showed that the coefficients of determination of the GA-BP model for NOx and PN predictions are all greater than 0.9 and are linearly highly correlated at the instantaneous emission level. At the overall emission level, the overall error of the GA-BP model is less than 7% for NOx prediction and less than 6% for PN prediction. The model has high accuracy for both instantaneous and overall emissions of light-duty vehicles. This provides practical engineering value for guiding the RDE test.