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On-road driving tests are performed to determine the emission of harmful exhaust compounds from vehicles. These primarily include carbon dioxide, nitrogen oxides, and particle number. However, there is a lack of indicators that combine the first three substances that are the most important in assessing the environmental aspects of vehicles. The purpose of this article is to indicate the possibility of assessing emissions in real driving conditions from light-duty and heavy-duty vehicles of different categories. In order to do so, a portable emissions measurement system (PEMS) and an instrument for measuring the particle number were used. The tests were carried out on routes designed to comply with the requirements and regulations laid down in the European Union legislation. On-road emissions of carbon dioxide, nitrogen oxides and particle number have been determined. Factors have been determined as the multiplication of these compounds for each vehicle category in three phases of the test: urban, rural, and motorway. A new way of assessing emissions from vehicles using new factors has been proposed.

Polyoxymethylene dimethyl ethers (PODE), as the most potential oxygenated alternative fuel for diesel engines, is widely investigated. Considering the importance of research on real driving emissions (RDE) and the few studies focus on the emission characteristics of the PODE/diesel blended fuels under real driving conditions, a portable emission measurement system (PEMS) was applied to measure the RDE of a heavy-duty tractor fueled with diesel or PODE/diesel blends. The tests were carried out in accordance with the relevant regulations of the CHINA VI emission standards. The second-by-second data from PEMS and the OBD system were utilized to construct engine transient operating maps. The results indicated that the addition of PODE can still decrease CO and PN emissions significantly under real driving conditions, although the low load conditions are still the areas of high brake specific CO and brake specific PN emissions. The NOx emissions, however, were not reduced as the results of the steady-state experiment of the same model of the engine. Fuel mass consumption raised when PODE was added, while the overall brake thermal efficiency improved, especially for the blending ratio of 30%, up to 40.3%, which is higher than 38.4% of pure diesel operation.

The manufacturers of China VI heavy-duty vehicles were required to conduct in-service conformity (ISC) tests by using a portable emissions measurement system (PEMS). The moving averaging window (MAW) method was used to evaluate the NOx emission required by the China VI emission standard. This paper presented the results of four PEMS tests of a China VI (step B) N3 category vehicle. Our analyses revealed that the real NOx emission of the test route was much higher than the result evaluated by the MAW method. We also found the data produced during the urban section of a PEMS test was completely excluded from the evaluation based on the current required boundary conditions. Therefore, in order to ensure the objectivity of the evaluation, this paper proposed three different evaluation methods. Method 1 merely set the power threshold as 10% for valid MAWs; Method 2 reclassified the MAWs into “Urban MAWs”, “Rural MAWs” and “Motorway MAWs” according to the vehicle speed. Method 3 reclassified the MAWs into “Hot MAWs” and “Cold MAWs” according to engine coolant temperature. The NOx emission evaluation results for Method 1 were not satisfactory, but those for Method 2 and Method 3 were close to the real NOx emission, the errors were all within ±10%.

NOx is one of the main sources of pollutants for motor vehicles. Nowadays, many diesel vehicle manufacturers may use emission-cheating equipment to make the vehicles meet compliance standards during emission tests, but the emissions will exceed the standards during actual driving. In order to strengthen the supervision of diesel vehicles for emission monitoring, this article intends to establish a model that can predict the transient emission characteristics of heavy-duty diesel vehicles and provide a solution for remote online monitoring of diesel vehicles. This paper refers to the heavy-duty vehicle National VI emission regulations and uses vehicle-mounted portable emission testing equipment (PEMS) to conduct actual road emission tests on a certain country’s VI heavy-duty diesel vehicles. Then, it proposes a new feature engineering processing method that uses gray correlation analysis and principal component analysis to eliminate invalid data and reduce the dimensionality of the aligned data, which facilitates the rapid convergence of the model during the training process. Then, a double-hidden-layer BP (Back propagation) neural network was established, and the improved gray wolf algorithm was used to optimize the threshold and weight of the neural network, and a heavy-duty diesel vehicle NOx emission prediction model was obtained. Through the training of the network, the root mean square error (RMSE) of the improved model on the test set between the predicted value and the true value is 1.9144 (mg/s), and the coefficient of determination (R2) is 0.87024. Compared with single-hidden-layer network and double-hidden-layer BP neural network models, the accuracy of the model has been improved. The model can well predict the actual road NOx emissions of heavy-duty diesel vehicles.

Despite the strengthening of vehicle emissions standards and test methods, nitrogen oxide (NOx) emissions from on-road mobile sources are not being notably reduced. The introduction of real driving emission (RDE) regulations is expected to reduce the discrepancy between emission regulations and actual air pollution. To analyze the effects of RDE regulations on heavy-duty diesel vehicles, pollutants emitted while driving were measured using a portable emission measurement system (PEMS) for Euro 5 and Euro 6 vehicles, which were produced before and after RDE regulations, respectively. NOx emissions were compared as a function of emissions allowance standards, gross vehicle weight (GVW), average vehicle speed, and ambient temperature. NOx emissions from Euro 6 vehicles were found to be low, regardless of GVW; emissions from both vehicular categories increased with a decline in the average speed. To reflect real road driving characteristics more broadly in the RDE test method for heavy-duty vehicles, it is necessary to consider engine power, which is a criterion for classifying effective sections, in the moving average window (MAW) analysis method, as well as including cold start conditions.

With the continuous promotion of urbanization in China, the economic level of small and medium-sized cities has been further improved. The transportation industry is crucial in promoting urban–rural integration and construction. Still, motor vehicle emissions also bring air pollution problems to cities, with heavy-duty diesel vehicle emissions severely impacting the urban environment. This study used a bottom-up approach to analyze the spatial emission characteristics of heavy-duty diesel vehicles under different road types in Kunming, a typical medium-sized city in China. A high-resolution emission inventory (1 km × 1 km) of heavy-duty diesel vehicles was developed using the vehicle emission inventory model (VEIN) and ArcGIS, and the vehicle emission standards were determined by the Weibull survival rate curve. The VEIN emission model was optimized using a velocity correction curve. The results showed that heavy-duty vehicles had a more significant impact on the emissions during the morning and evening peak hours, with low emission levels during the day and high emission levels at night and early morning. The total daily emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and particulate matter (PM 10 and PM 2.5 ) from heavy-duty diesel vehicles in Motorway, Trunk, Primary, Secondary, and Tertiary were 14.44 tons, 5.26 tons, 4.78 tons, 7.02 tons, and 3.83 tons, respectively. China III heavy-duty diesel vehicles mainly contributed to CO, HC, NOx, and PM emissions. This study can be used as an essential reference for controlling the exhaust emissions of HDDVs in Kunming.

Motor vehicles emit a large amount of air pollutants. Inspection and Maintenance (I/M) systems serve as a pivotal strategy for mitigating emissions from operational diesel trucks. However, the prevalent issue of blind repairs persists due to insufficient diagnostic capabilities at maintenance stations (M stations). To address this challenge, a multi-source information fusion methodology is proposed, integrating load deceleration testing from inspection stations (I stations), on-board diagnostics (OBD) data, and manual measurements at M stations. Critical diagnostic parameters—including nitrogen oxides (NOx) and particulate matter (PM) emissions, the ratio of measured wheel-side power to rated power, intake volume, common rail pressure, and exhaust back pressure—are systematically selected through statistical analysis and expert evaluations. An adaptive membership function is developed to resolve ambiguities in emission thresholds, enabling the construction of a robust fault diagnosis framework. Validation using 800 National V diesel truck maintenance records from a provincial automotive electronic health platform (2022 data) demonstrates a diagnostic accuracy of 92.8% for 153 emission-exceeding vehicles, surpassing traditional machine learning approaches by over 20%. By minimizing unnecessary repairs and optimizing maintenance efficiency, this approach significantly reduces resource waste and the lifecycle environmental footprints of diesel fleets. The proposed fuzzy-logic-based model effectively detects latent faults during routine maintenance, directly contributing to sustainable transportation through reductions in NOx and PM emissions—critical for improving air quality and advancing global climate objectives. This establishes a scalable technical framework for the effective implementation of I/M systems in alignment with sustainable urban mobility policies.

It is unreasonable to exclude all or part of the cold starting stage data in the current PEMS test data processing methods of heavy-duty diesel vehicles domestic and international. Therefore, this paper studies the PEMS data evaluation method including cold start stage data. In this study, PEMS tests were carried out on three different types of heavy-duty diesel vehicles. After that, the common data processing methods of the PEMS experiment were summarized and analyzed. In order to study the validity of different methods in processing the different stages’ experimental data of PEMS, the moving average window method (MAW) and cumulative averaging method (CA) were used to process the PEMS experiment data of cold stage and non-cold stage, respectively. Finally, a novel weighted evaluation method has been established creatively. The rationality of weight distribution was discussed, and 5 experiments were designed to verify the validity of the method. The results show that the MAW method only evaluates the effective window accounting for about 40%. The CA method can effectively evaluate the emission characteristics of cold and non-cold stages. The weighting method established based on the WHTC test can well reflect the contribution rate of pollutant emission in the cold start-up stage of the PEMS test of heavy diesel vehicles. Furthermore, for CO, NOx, and PN, the variation correlation coefficients between the emission in the cold stage and the weighted results are all above 0.8. The novel evaluation method can reasonably reflect the emission characteristics of the PEMS test of heavy-duty diesel vehicles during the cold stage and non-cold stage.

A dump truck with a maximum designed total mass of 25000 kg was selected to measure the real driving emission characteristics of pollutants and carbon dioxide (CO 2 ) under different altitudes of 0 meters, 800 meters and 1600 meters, with the same simulated road, driver and vehicle model but different vehicle loading of 0% and 40% by using the engine-in-the-loop (EIL) methodology. The results indicate that the EIL methodology can qualitatively and quantitatively analyze the impact of altitude on real driving emissions. The emissions of carbon monoxide (CO) and total hydrocarbons (THC) show a trend of first decreasing and then increasing with increasing altitude, while the emissions of particulate number (PN) show a trend of first sharply increasing and then slightly decreasing with increasing altitude. The variation pattern of nitrogen oxide (NOx) emissions with altitude is not significant. Under different altitude conditions, the specific emissions of CO 2 and THC decrease with increasing vehicle loading, while the specific emissions of PN and CO increase with increasing vehicle loading. However, the variation pattern of NOx emissions with vehicle loading is not obvious. In addition to being strongly correlated with exhaust temperature, the peak value of NOx emissions is highly correlated with road slope and vehicle acceleration. Cold start increases the specific emissions of CO 2 and pollutants, especially for PN and CO.

Over the last decade, Nitrogen Oxide (NOx) emissions have garnered significantly greater attention due to the worldwide emphasis on sustainable development strategies. In response to the issues of dynamic measurement delay and low measurement accuracy in the NOx sensors of heavy-duty diesel vehicles, a novel Multilayer Perceptron (MLP)–Random Forest Regression (RFR) fusion algorithm was proposed and explored in this research. The algorithm could help perform post-correction processing on the measurement results of diesel vehicle NOx sensors, thereby improving the reliability of the measurement results. The results show that the measurement errors of the On-board Nitrogen oxide Sensors (OBNS) were reduced significantly after the MLP-RFR fusion algorithm was corrected. Within the concentration range of 0–90 ppm, the absolute measurement error of the sensor was reduced to ±4 ppm, representing a decrease of 73.3%. Within the 91–1000 ppm concentration range, the relative measurement error was optimised from 35% to 17%, providing a reliable solution to improve the accuracy of the OBNS. The findings of this research make a substantial contribution towards enhancing the efficacy of the remote monitoring of emissions from heavy-duty diesel vehicles.