Integrated effects of SCR, velocity, and Air-fuel Ratio on gaseous pollutants and CO2 emissions from China V and VI heavy-duty diesel vehicles

Reconstructing missing NOx emissions in heavy-duty diesel vehicle OBD data: A machine learning approach

On-Board Diagnostic (OBD) systems enable real-time monitoring of heavy-duty diesel vehicle operations and NOx emissions. However, existing OBD systems have inherent limitations, including systematic missing values. To address this issue, this study develops a data-driven approach. The method utilizes OBD-recorded upstream and downstream NOx emissions data to build machine learning models for reconstructing real-world OBD data. The modeling results indicate that machine learning models perform well in predicting upstream emissions, achieving an R² above 0.9 on the test set. However, its performance in predicting downstream emissions was highly variable, with R² ranging from 0.05 to 0.98, and showed a positive correlation with fuel-based emission factors. A case study was conducted on a selected vehicle. The total NOx emission associated with missing data for this specific vehicle was estimated at 15,741.3 g, whereas the recorded emission from available data was 6157.3 g. Missing data were then imputed for an additional 31 vehicles, revealing that normal emitters showed significantly higher emission associated with missing data. The proposed approach is highly compatible with existing big data platforms and can be easily extended to other vehicles. This will improve the platform's representation of real-world emission, enabling policymakers to implement more targeted pollution mitigation strategies.

Reducing the road freight emissions through integrated strategy in the port cities

Port cities, as crucial nodes in global supply chains, face challenges from both intense traffic emissions and vulnerability to climate-related extremes due to pollution density. Effective and near-term mitigation strategies for greener road freight are imperative to reverse the increasing trend of emissions. This study utilizes a high-resolution emission inventory from 1.2 billion global positioning system trajectories of heavy-duty trucks in Shenzhen, a global port city, to evaluate the combined effects of road network development and fleet electrification on emissions from 2016 to 2035, providing a basis for policy interventions applicable to various urban contexts. Our findings reveal that an integrated strategy decreases road freight emissions, cutting peak carbon dioxide and nitrogen oxide emissions by 34% and 43%, respectively, compared to electrification-only scenarios. While fleet electrification supported by net-zero emission grids is necessary, it is insufficient without addressing congestion and enhancing connectivity through expanded road networks. Spatial projections also assist targeted policymaking by showing how emission distributions shift with regional road network expansion.

A comprehensive OBD data analysis framework: Identification and factor analysis of high-emission heavy-duty vehicles

On-Board Diagnostic (OBD) systems enable real-time monitoring of NOx emissions from heavy-duty diesel vehicles (HDDVs). However, few studies have focused on the root cause analysis of these emissions using OBD data. To address this gap, this study proposes an integrated analysis framework for HDDV NOx emissions that combines data processing, high-emission vehicle identification, and emission cause analysis. The framework employs a fuel-based window method to identify high-emission vehicles, while binning and machine learning techniques trace the causes of NOx emissions. A case study is conducted using data from 32 vehicles sourced from Tianjin On-Board Diagnostic Platform. Of these, five vehicles were identified as high emitters. A machine learning model was trained for each vehicle, with a detailed analysis conducted on three of them. The analysis involves a preliminary investigation of vehicle emissions status, followed by bin analysis to initially identify the causes of emissions. Finally, machine learning analysis is conducted, including the generation of individual conditional expectation (ICE) plots and multivariable partial dependence plots (PDPs), serving as a supplement to bin analysis when it cannot effectively pinpoint the causes of high emissions. This approach effectively uncovers the underlying factors within OBD big data. Using the analysis framework, we discover the identified causes of high NOx emissions were uneven heating of the Selective Catalytic Reduction (SCR) system and prolonged idling and high-power operation, catalyst degradation at 200-250 °C, and SCR system failure before 425 °C. The proposed framework offers a clear approach for identifying the causes of NOx emissions, aiding policymakers in implementing effective NOx control strategies for HDDVs.

The next challenge in emissions control for heavy-duty diesel vehicles: From NOx to N2O

Vehicle emissions are a major source of greenhouse gases globally. Dual selective catalytic reduction (SCR), an advanced version of single SCR, is crucial under stricter nitrogen oxide (NOx) emission standards for heavy-duty diesel vehicles (HDDVs). However, the emission characteristics of nitrous oxide (N2O), a byproduct of SCR and a potent greenhouse gas, remain unclear. This study investigates the N2O emissions from HDDVs equipped with single or dual SCR systems using heavy-duty chassis dynamometers under various ambient temperatures, altitudes, and loading masses. The results showed that the brake-specific emissions (EFb) of N2O from HDDVs with single and dual SCRs were 76.28-269.65 mg/kWh and 147.50-170.22 mg/kWh, respectively. Notably, the dual SCR-equipped HDDV emitted 6-22 times more N2O than NOx under all tested conditions. As ambient temperature increased from -10 °C to 25 °C and from 25 °C to 40 °C, the average distance-based emission factors (EFd) of N2O for the single SCR-equipped HDDV increased by 87.73% and 48.26%, respectively. However, the variation was not significant for the dual SCR-equipped HDDV. Under half- and full-load conditions, the average EFd of N2O for the single SCR-equipped HDDV increased by 47.57% and 110.92%, respectively, compared to those without loading. Similarly, N2O emissions for dual SCR-equipped HDDV increased by 41.40% and 65.37% under the same loading variations. As altitude increased from 0 m to 3000 m, the average EFd of N2O for the single SCR-equipped HDDV decreased by 64.31%. Additionally, N2O emissions were significantly affected by SCR temperature, engine power, and nitric oxide (NO)/nitrogen dioxide (NO2) ratio. These findings are crucial for setting future greenhouse gas limits of HDDVs and informing carbon reduction strategies.

Exploring high-emission driving behaviors of heavy-duty diesel vehicles based on engine principles under different road grade levels

To reveal the outstanding high-emission problems that occur when heavy-duty diesel vehicles (HDDV) pass uphill and downhill, this study proposes a method to depict the nitrogen oxides (NOx) and carbon dioxide (CO2) high-emission driving behaviors caused by slopes from the perspective of engine principles. By calculating emission and grade data of HDDV based on on-board diagnostic (OBD) data and digital elevation model (DEM) data, the 262 short trips including uphill, flat-road and downhill are firstly obtained through the rule-based short trip segmentation method, and the significant correlation between the road grade and emissions of the short trips is verified by Kendall's Tau and K-means clustering. Secondly, by comparing the distribution changes of three speed categories (acceleration state, constant speed state and deceleration state), the differences in HDDV operating states under different grade levels are discussed. Finally, the machine learning models (Random Forest, XGBoost and Elastic Net), are used to develop the NOx and CO2 emission estimation model, identifying high-emission driving behaviors, particularly during uphill driving, which showed the highest proportion of high-emission. Explained by the feature importance and SHapley Additive exPlanations (SHAP) model that large accelerator pedal opening, frequent aggressive acceleration, and high engine load have positive effects both on NOx and CO2 emissions. The difference is in the air-fuel ratio that the engine in the rich or slightly lean burning state will increase CO2 emissions and the lean burning state will increase NOx emissions. In addition, due to the uncertainty of the actual uphill, drivers often undergo a rapid "deceleration-uniform-acceleration" process, which significantly contributes to high NOx and CO2 emissions from the engine perspective. The findings provide insights for designing driving strategies in slope scenarios and offer a novel perspective on depicting driving behaviors.

On-road evaluation and regulatory recommendations for NOx and particle number emissions of China VI heavy-duty diesel trucks: A case study in Shenzhen

This research analyzed the real-world NOx and particle number (PN) emissions of 21 China VI heavy-duty diesel trucks (HDDTs). On-road emission conformity was first evaluated with portable emission measurement system (PEMS). Only 76.19 %, 71.43 % and 61.90 % of the vehicles passed the NOx test, PN test and both tests, respectively. The impacts of vehicle features including exhaust gas recirculation (EGR) equipment, mileage and tractive tonnage were then assessed. Results demonstrated that EGR helped reducing NOx emission factors (EFs) while increased PN EFs. Larger mileages and tractive tonnages corresponded to higher NOx and PN EFs, respectively. In-depth analyses regarding the influences of operating conditions on emissions were conducted with both numerical comparisons and statistical tests. Results proved that HDDTs generated higher NOx EFs under low speeds or large vehicle specific powers (VSPs), and higher PN EFs under high speeds or small VSPs in general. In addition, unqualified vehicles generated significantly higher NOx EFs than qualified vehicles on freeways or under speed≥40 km/h, while significant higher PN EFs were generated on suburban roads, freeways or under operating modes with positive VSPs by unqualified vehicles. The reliability and accuracy of on-board diagnostic (OBD) NOx data were finally investigated. Results revealed that 43 % of the test vehicles did not report reliable OBD data. Correlation analyses between OBD NOx and PEMS measurements further demonstrated that the consistency of instantaneous concentrations were generally low. However, sliding window averaged concentrations show better correlations, e.g., the Pearson correlation coefficients on 20s-window averaged concentrations exceeded 0.85 for most vehicles. The research results provide valuable insights into emission regulation, e.g., focusing more on medium- to high-speed operations to identify unqualified vehicles, setting higher standards to improve the quality of OBD data, and adopting window averaged OBD NOx concentrations in evaluating vehicle emission performance.

A fuel-consumption based window method for PEMS NOx emission calculation of heavy-duty diesel vehicles: Method description and case demonstration

Due to the lack of engine reference torque and the accumulated work of reference transient cycle, the work based window (WBW) method for portable emission measurement system test data processing cannot be used for vehicle emission assessment in the current on-board diagnostics (OBD) system in China. In this work, a fuel-consumption based window (FBW) method was proposed to imitate a WBW method procedure by using fuel consumption rate as an alternative parameter to scale the window so the entire procedure can be based on the attainable data items in the OBD system. Some key issues regarding converting WBW method to FBW method, including window separation, window average power ratio calculation and specific NOx emission conversion from mg/kg. fuel to mg/kW.h, were solved by linking the 100-km fuel consumption and the average vehicle specific power of China World Transient Vehicle Cycle test. The comparison between the FBW and WBW methods on the NOx emission calculation results shows that the number of all windows, the number of valid windows, and the thresholds for >50% valid windows are quite similar for WBW and FBW methods. The estimation accuracy of average power ratio for the FBW method depends on the value of transmission efficiency of vehicle driveline. The deviations of 90% specific NOx emission in mg/kW.h between the two methods are smaller than 6% for the cases investigated in the present work.

Measurement of Cyclic Variation of the Air-to-Fuel Ratio of Exhaust Gas in an SI Engine by Laser-Induced Breakdown Spectroscopy

Temporally and spatially resolved laser-induced breakdown spectroscopy (LIBS) was applied to a four-stroke, single-cylinder test engine’s cyclic exhaust gas to demonstrate engine performance. The LIBS technique provided quantitative air-to-fuel ratio (A/F) measurements by generating localized breakdown plasma during the compression and exhaust strokes. The results showed that the timing and duration settings of the emission energy ionization and molecular spectra affect the intensity peaks. Optimum measurements performed between 200 ns and 10 ms after breakdown resulted in observed atomic spectra of CI (248 nm), Hβ (485 nm), Hα (656 nm), NI (745, 824 nm), and OI (777, 844 nm). The intensities of CI (248 nm) and Hα (656 nm) decreased with increasing A/F, whereas the intensity ratios of NI and OI remained constant. A decrease in the intensity ratio of C/O and Hα/O was observed as the A/F increased. This study is a major step toward defining a means of using LIBS to control the A/F ratio in gasoline engines by focusing on the exhaust gas rather than the flame.

CO2 Emission Factors and Carbon Losses for Off-Road Mining Trucks

There are myriad questions that remain to be answered in greenhouse gas (GHG) emissions trading. This article addresses carbon dioxide (CO2) emission factors and carbon losses from heavy equipment that is used to transport ores. Differences occurred between the Intergovernmental Panel for Climate Change (IPCC) emission factor and those that were obtained by considering incomplete combustion and on-site exhaust concentration measurements. Emissions from four off-road vehicles were analyzed. They operated at idle (loading, unloading, and queuing) and in motion (front and rear, loaded and unloaded). The results show that the average CO2 emission factors can be as low as 64.8% of the IPCC standard value for diesel fuel. On the other hand, carbon losses can be up to 33.5% and energy losses up to 25.5%. To the best of the authors’ knowledge, the method that was developed here is innovative, simple, useful, and easily applicable in determining CO2 emission factors and fuel losses for heavy machinery.