Comparison of NOx and PN emissions between Euro 6 petrol and diesel passenger cars under real-world driving conditions

Comparative analysis of real-world vehicular emissions from BS-IV and BS-VI cars in India

This study investigates real-world carbon dioxides (CO2) and nitrogen oxides (NOx) emissions from diesel (Bharat Stage-IV (BS-IV)) and petrol/gasoline (BS-IV and BS-VI) cars in Indian driving conditions using a portable emission measurement system (PEMS). The paired sample t-test revealed a significant difference ( p < 0.05) in NOx and CO2 emissions among the three types of cars, except for CO2 emissions ( p > 0.05) between BS-IV petrol and BS-VI petrol cars. The highest NOx emission rates were observed in all car types during acceleration (> 1 m/s2) and deceleration (- 2 m/s2). CO2 emission rates were also high during acceleration (> 1 m/s2) for all car types. At low speeds (around 20 kmph), all car types had low emissions of CO2 and NOx, with acceleration and deceleration rates ranging from - 0.5 to 0.5 m/s2. BS-IV diesel cars emit significantly higher NOx emissions compared to petrol cars, especially at vehicle-specific power (VSP) bin 0 (deceleration to idling mode) and during VSP bin 7 (acceleration mode). BS-IV diesel cars emit 228% and 530% higher NOx emissions than BS-IV and BS-VI petrol cars at VSP bins 0 and 7, respectively. CO2 emissions from BS-VI petrol cars were 10% lower than those from BS-IV petrol cars across all VSP bins, indicating moderate reductions. Furthermore, diesel cars emit 140% less CO2 emissions than petrol cars across various VSP bins. The findings highlight the need for cleaner technologies and responsible driving practices to address vehicular emission concerns.

A review of regeneration mechanism and methods for reducing soot emissions from diesel particulate filter in diesel engine

With the global emphasis on environmental protection and the proposal of the climate goal of "carbon neutrality," countries around the world are calling for reductions in carbon dioxide, nitrogen oxide, and particulate matter pollution. These pollutants have severe impacts on human lives and should be effectively controlled. Engine exhaust is the most serious pollution source, and diesel engine is an important contributor to particulate matter. Diesel particulate filter (DPF) technology has proven to be an effective technology for soot control at the present and in the future. Firstly, the exacerbating effect of particulate matter on human infectious disease viruses is discussed. Then, the latest developments in the influence of key factors on DPF performance are reviewed at different observation scales (wall, channel, and entire filter). In addition, current soot catalytic oxidant schemes are presented in the review, and the significance of catalyst activity and soot oxidation kinetic models are highlighted. Finally, the areas that need further research are determined, which has important guiding significance for future research. Current catalytic technologies are focused on stable materials with high mobility of oxidizing substances and low cost. The challenge of DPF optimization design is to accurately calculate the balance between soot and ash load, DPF regeneration control strategy, and exhaust heat management strategy.

Impacts on real-world extra cold start emissions: Fuel injection, powertrain, aftertreatment and ambient temperature

Vehicles emit substantial amounts of pollutants during start periods. Engine starts mainly occur in urban areas, causing serious harm to humans. To investigate the impacts on extra cold start emissions (ECSEs), eleven China 6 vehicles with various control technologies (fuel injection, powertrain, and aftertreatment) were monitored with a portable emission measurement system (PEMS) at different temperatures. For conventional internal combustion engine vehicles (ICEVs), the average ECSEs of CO₂ increased by 24%, while the average ECSEs of NOx and particle number (PN) decreased by 38% and 39%, respectively, with air conditioning (AC) on. Gasoline direct injection (GDI) vehicles had 5% lower CO₂ ECSEs, but 261% higher NOx ECSEs and 318% higher PN ECSEs than port fuel injection (PFI) vehicles at 23 °C. The average PN ECSEs were significantly reduced by gasoline particle filters (GPFs). The GPF filtration efficiency was higher in GDI than PFI vehicles due to particle size distribution. Hybrid electric vehicles (HEVs) generated excessive PN extra start emissions (ESEs), resulting in a 518% increase compared to ICEVs. The start times of the GDI-engine HEV accounted for 11% of the whole test time, but the proportion of PN ESEs relative to total emissions were 23%. Linear simulation based on the decrease in ECSEs with increasing temperature underestimated the PN ECSEs from PFI and GDI vehicles by 39% and 21%, respectively. For ICEVs, CO ECSEs varied with temperature in a U shape with a minimum at 27 °C; NOx ECSEs decreased as ambient temperature increased; PFI vehicles generated more PN ECSEs at 32 °C than GDI vehicles, stressing the significance of ECSEs at high temperature. These results are useful for improving emission models and assessing air pollution exposure in urban aeras.

Assessing on-road emissions from urban buses in different traffic congestion scenarios by integrating real-world driving, traffic, and emissions data

In recent years, the integration of traffic simulators and emission models has become the most preferred option for evaluating vehicle emissions in different traffic states. However, the definition of a 'traffic condition' is often subjective, as driving patterns can vary significantly with the spatial domain of study. Alternatively, the implementation of 'Cooperative Intelligent Transport Systems' has led to a growing variety of devices being installed, both on the road and in public transport vehicles for monitoring traffic-flow conditions and vehicle speeds in cities. This study purposed an original approach for integrating real-world emissions (as an micro-emission model), real-world driving profiles, and city traffic sensor data to assess the effects of traffic congestion at the route level on emissions from urban buses in Madrid (Spain). The definition of the traffic scenarios was based on a K-means clustering analysis by linking stationary (from city sensors) and dynamic (from bus driving profiles) congestion indicators. In parallel, a micro-emissions model based on vehicle-specific power (VSP) methodology was used to model second-by-second CO₂ and NO_x emissions from individual trips of the diesel and compressed natural gas (CNG) buses. Finally, the clustering and modelled emissions data were combined. A comparison of the free flow and the severe congestion scenarios showed that the average speed of the route decreased by approximately 50 %, and the number of stops per kilometre increased by a multiple of 1.5; furthermore, the CO₂ and NO_x emissions from buses increased by approximately 50 % and 85 %, respectively. The diesel bus showed a lower sensitivity to variations in the congestion level at the route level, although the low-NO_x emissions from the CNG buses were evident for all traffic scenarios. The results of this study, based on extensive real-world data, can be used to develop high-resolution vehicle emissions inventories.

Variations of significant contribution regions of NOx and PN emissions for passenger cars in the real-world driving

Nitrogen oxides (NO_x) and particulate number (PN) emissions are the main concerns of the passenger cars in the real-world driving. NO_x and PN emissions are greatly dependent on the driving behaviors which differ significantly between standard driving cycles and real-world driving. However, the significant contribution regions (short durations corresponding to high proportions of total emissions) of NO_x and PN emissions regarding different driving behaviors (e.g. vehicle speed and acceleration) are still uncovered. NO_x20% and NO_x50% refer to instantaneous NO_x emission rates when NO_x emission rates are ranked from high to low level where the sums of NO_x emission rates being higher than NO_x20% and NO_x50% correspond to 20% and 50% of total NO_x emissions, respectively. t_20% and t_50% are corresponding durations where NO_x emission rates are higher than NO_x20% and NO_x50%. In this paper, three Euro-6 compliant direct injection gasoline passenger cars and a diesel passenger car are tested in a real-world driving trial in which nineteen drivers are involved. Novel key performance indicators with reference to the regimes of specific NO_x and PN contributions to total emissions are defined. Instantaneous NO_x and PN emissions are monitored using a portable emission measurement system (PEMS) in the test. The results indicate that the maximum and minimum average speed over the four cars being approximately 32.3 km/h s and 42.6 km/h, respectively. Average PN emission factor of the diesel car is the lowest among the four given cars. Average t_20% and t_50% corresponding to NO_x20% and NO_x50% are lower than 3% and 12%, respectively, for all the passenger cars; additionally, these two parameters show the same pattern. The corresponding t_20% and t_50% variations of the Euro-6a gasoline car and the diesel car are much lower than the other two. Average acceleration corresponding to 20% and 50% of total NO_x emissions for the given diesel car is approximately 1.25 m/s² and 0.6 m/s², respectively, being much higher than that of the other three gasoline cars (lower than 1 m/s² and 0.4 m/s² respectively) over the specific driving route and drivers. The average PN_20% and PN_50% of the given diesel car are approximately 7 × 10⁷#/s and 3 × 10⁷#/s respectively, being much lower than the three given gasoline cars (higher than 8 ×10⁹#/s and 2 ×10⁹#/s respectively) under the given test conditions; the corresponding t_20% and t_50% are lower than 4% and 17% respectively for all the three gasoline cars.

Development of a cold-start emission model for diesel vehicles using an artificial neural network trained with real-world driving data

During the cold start and warm-up phase, modern vehicles emit considerable amounts of pollutants due to the incomplete combustion and deteriorated performance of aftertreatment devices. In terms of emission modeling, there have been many attempts to estimate cold start emission such as cold-hot conversion factor, regression model, and physis-based model. However, as the emission characteristic become complicated due to the adoption of aftertreatment devices and various emission control strategies for the strengthened emission regulations, the conventional cold start emission models do not always show satisfactory performances. In this study, artificial neural networks were used to predict the cold start emissions of carbon dioxide, nitrogen oxides, carbon monoxide, and total hydrocarbon of diesel passenger vehicles. We used real-world driving data to train neural networks as an emission prediction tool. Through machine leaning, numerous trainable variables of neural networks were properly adjusted to predict cold start emissions. For input variables of the ANN model, the velocity, vehicle specific power, engine speed, engine torque, and engine coolant temperature were used. The proposed ANN models accurately predicted sharp increases in carbon monoxide, hydrocarbon, and nitrogen oxides during the cold start phase. In addition to the quantitative estimations, the correlations between the operating variables and exhaust gas emissions were visually described in the form of emission maps. The emission map graphically showed the emission levels according to the vehicle and engine operating parameters.