Search Results (73)

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle (PHEV) types: one Series Hybrid Electric Vehicle (S-HEV), one Parallel Hybrid Electric Vehicle (P-HEV), and one Series-Parallel Hybrid Electric Vehicle (SP-HEV), during real driving conditions. The findings show a correlation between acceleration and increased CO₂ emissions for P-HEV, while acceleration has a relatively minor impact on S-HEV and SP-HEV emissions. Under urban driving conditions, the SP-HEV displays the lowest average CO₂ emission rate. However, under suburban and highway conditions, the average CO₂ emission rates follow the order S-HEV > SP-HEV > P-HEV. An analysis of CO₂ emission factors across different road types and vehicle-specific power (VSP) ranges indicates that within low VSP intervals (VSP ≤ 0 for urban, VSP ≤ 5 for suburban, and VSP ≤ 15 for highway roads), the P-HEV exhibits the best CO₂ emission control. As VSP increases, the P-HEV’s emission factors rise under all three road conditions, with its emission control capability weakening when VSP exceeds 5 in urban, 15 in suburban, and 20 on highway roads. For the SP-HEV, CO₂ emission factors increase with VSP in urban and suburban areas but remain stable on highways. The S-HEV shows minimal changes in emission factors with varying VSP. This research provides valuable insights into the CO₂ emission patterns of PHEVs, aiding vehicle optimization and policy development. Full article

Portable emission measurement systems (PEMS) are used onboard vehicles to determine the on-road real driving emissions of the vehicles for research or regulatory purposes. The assessment of a PEMS is carried out in a laboratory comparing it with laboratory grade systems (i.e., validation test). This procedure is described in the European Commission Regulation (EU) 2017/1151 and there are limits that must be respected (permissible tolerances). A few inter-laboratory studies evaluated PEMS in the laboratories of different institutes. However, there are no on-road inter-laboratory studies of PEMS because there is no reference instrument available and the source (i.e., emissions of the vehicle) fluctuates significantly due to the variation of the trip characteristics, driver behavior, and environmental conditions, making meaningful evaluation challenging. Here, we present a concept of how such inter-laboratory studies could take place. The concept is that a ‘reference PEMS’ is evaluated first in the laboratory of one of the participating institutes. Then, the ‘reference PEMS’, with a reference vehicle (optionally) is sent to the other institutes to compare their ‘test PEMS’ with the ‘reference PEMS’ on-road. The difference (absolute or relative) of the two PEMS, corrected for any ‘bias’ of the ‘reference PEMS’, is used for the assessment of the ‘test PEMS’ (i.e., comparison with the permissible tolerances) or any statistical analysis (e.g., z-scores). Ideally, the selected reference PEMS should have negligible ‘bias’ (e.g., due to calibration uncertainties, drift), and for this reason, a thorough investigation at the beginning of the exercise is highly recommended. A statistical analysis can be made to confirm if there is bias. Using the differences (absolute or relative) of PEMS, the source (vehicle emissions) variability is cancelled out. The differences can then be compared with the permissible tolerances of the regulation, but up to 40% higher deviations should still be acceptable. We demonstrate the concept with experiments in our institute. Full article

In this study, to compensate for the constraints of high unit cost of portable emission measurement system (PEMS) and measurement environment, we developed a tractor operation-based emission prediction model. We also evaluated the developed prediction model using validation metrics. In addition to engine load data, correlation analysis was conducted on engine temperature and fuel consumption variables. The results showed a high correlation of more than 0.5 between emissions and engine temperature, and a high correlation of more than 0.5 between emissions and fuel consumption for emissions except CO and THC. The R² values of the CO, THC, NOx, and PM emission prediction models were 0.81, 0.82, 0.85, and 0.97, respectively, showing good overall predictive performance. The prediction models for CO, THC, NOx, and PM emissions developed using the third-order regression analysis all showed excellent performance with an average absolute percentage error of around 2%. Therefore, the developed emission regression model can be used to predict tractor emissions using various variables. Through the exhaust emissions prediction model developed in this study, eco-friendly technology according to the optimal engine design is expected to increase. In addition, it is expected that agricultural machinery prices will be stabilized and export competitiveness will be secured. Full article

Discrepancies between laboratory vehicle performance and real-world traffic conditions have been reported in numerous studies. In response, emission and fuel regulatory frameworks started incorporating real-world traffic evaluations and vehicle monitoring using portable emissions measurement systems (PEMS) and on-board diagnostic (OBD) data. However, in regions with technical and economic constraints, such as Latin America, the use of PEMS is often limited, highlighting the need for low-cost methodologies to assess vehicle performance. OBD interfaces provide extensive vehicle and engine operational data in this context, offering a valuable alternative for analyzing vehicle performance in real-world conditions. This study proposes a straightforward methodology for assessing vehicle fuel efficiency and carbon dioxide (CO₂) emissions under real-world traffic conditions using OBD data. An experimental campaign was conducted with three gasoline-powered passenger vehicles representative of the Ecuadorian fleet, operating as urban taxis in Ibarra, Ecuador. This methodology employs an OBD interface paired with a mobile phone data logging application to capture vehicle kinematics, engine parameters, and fuel consumption. These data were used to develop engine maps and assess vehicle performance using the vehicle-specific power (VSP) approach based on the energy required for vehicle propulsion. Additionally, VSP analysis combined with OBD data facilitated the development of an energy-emission model to characterize fuel consumption and CO₂ emissions for the tested vehicles. The results demonstrate that OBD systems effectively monitor vehicle performance in real-world conditions, offering crucial insights for improving urban transportation sustainability. Consequently, OBD data serve as a critical resource for research supporting decarbonization efforts in Latin America. Full article

Diesel pallet trucks, a type of heavy-duty diesel trucks (HDDTs), have historically been a vital component in logistics and transport due to their high payload capacity. However, they also present significant challenges, particularly in terms of emissions which contribute substantially to urban air pollution. Traditional HDDTs emission measurement methods, such as engine bench tests and those used in laboratory settings, often fail to capture real-world emission behaviors accurately. This study specifically examines the real-world emission characteristics of diesel pallet trucks exceeding 30 t under varying loads (unloaded, half loaded, and fully loaded) and different road conditions (urban, suburban, and high-speed). Considering that data quality is the key to the accuracy of the scheme, this research utilized a portable emission measurement system (PEMS) to capture real-time emissions data of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_X), and total hydrocarbons (THC). Key findings demonstrate a direct correlation between vehicle load and emission factors, with the emission factors for CO₂, CO, and NO_X increasing by 39.5%, 105.4%, and 22.7%, respectively, from unloaded to fully loaded states under comprehensive operating conditions. Regression analyses further provide an emission factor prediction model for HDDPTs, underscoring the continuous relationship between speed, load, and emission rates. These findings provide a scientific basis for pollution control strategies for diesel trucks. Full article

The upcoming Euro 7 vehicle exhaust emissions regulation includes particle number (PN) limits for all vehicles, not only those with direct fuel injection. It also sets the lower detection particle size of the PN methodology to 10 nm from 23 nm. Recently, a commercial diffusion charger-based PEMS added the possibility of switching the lower size between 23 nm and 10 nm. In this study, we assessed the dual PEMS in the calibration laboratory using diffusion flame soot or spark discharge graphite particles following the regulated procedures. Furthermore, we compared the dual PEMS with a laboratory grade system (LABS) using soot, graphite, and vehicle exhaust particles. To put the results into perspective, we added comparisons (validations) of two additional 23 nm PEMSs with LABSs over a three-year period. The results showed that the differences of the 23 nm PEMSs remained the same (around 35% underestimation) over the years and were similar to the dual PEMS. This difference is still well within the permissible tolerance from the regulation (50%). We argued that the reason is the calibration material used by the manufacturer (spark discharge graphite). We demonstrated that calibrating with combustion soot could reduce the differences. The 10 nm PEMS gave similar results but with much smaller differences, indicating that the calibration material is of less importance for the Euro 7 step. The results showed that the measurement uncertainty has not increased but rather decreased for the specific PEMS switching from 23 nm to 10 nm. Full article

Off-road machinery is one of the significant contributors to air pollution due to its large quantity. In this study, a deep learning model was developed to predict the transient engine emissions of CO, NO, NO₂, and NO_x, which are the main pollutants emitted by off-road machinery. A portable emission measurement system (PEMS) was used to measure the exhaust emission features of four types of construction machinery. The raw PEMS data were preprocessed using data compensation, local linear regression, and normalization to ensure that the data could handle transient conditions. The proposed model utilizes the preprocessing PEMS data to estimate the CO, NO, NO₂, and NO_x emissions from off-road machinery using a recurrent neural network (RNN) based on a long short-term memory (LSTM) model. The experimental results show that the proposed method can effectively predict the emissions from off-road construction machinery under transient conditions and can be applied to controlling the emissions from off-road construction machinery. Full article

Compressed natural gas (CNG) taxis represent the most ubiquitous and dynamically active passenger vehicles in urban settings. The pollutant emission characteristics of in-use CNG taxis driving on a typical mountain city before and after three-way catalyst (TWC) replacement was examined using a modular on-board portable emissions measurement system (PEMS), the OBS-ONE developed by Horiba. The results showed that the exhaust NO of CNG taxis equipped with deactivation TWC exceeded the emission limits, even higher than gasoline vehicles. The high emission rate of CNG taxis is mainly concentrated on road slopes between a 2% and 6% gradient and a deceleration rate in the interval of [0.5, 4], respectively, which results in higher emissions from CNG taxis traveling in the mountain city of Chongqing than other cities and vehicles. Moreover, the pollutant emission rates of the in-use CNG taxis were highly correlated with the velocity and the vehicle specific power (VSP). After a new TWC replacement, the emission factors of carbon monoxide (CO), total hydrocarbons (THC), nitrogen oxides (NOx), and particle number (PN) decreased by 85.21–89.11%, 68.71–85.49%, 60.91–81.11%, and 62.26–68.39%, respectively. Our results will provide guidance for urban environments to carry out the comprehensive management of in-use vehicles and emphasize the importance of TWC replacement for CNG taxis. Full article

In response to increasingly stringent global environmental policies, this study addresses the pressing need for accurate prediction models of CO₂ emissions from vehicles powered by alternative fuels, such as compressed natural gas (CNG). Through experimentation and modelling, one of the pioneering CO₂ emission models specifically designed for CNG-powered vehicles is presented. Using data from chassis dynamometer tests and road assessments conducted with a portable emission measurement system (PEMS), the study employs the XGBoost technique within the Optuna Python programming language framework. The validation of the models produced impressive results, with R² values of 0.9 and 0.7 and RMSE values of 0.49 and 0.71 for chassis dynamometer and road test data, respectively. The robustness and precision of these models offer invaluable information to transportation decision-makers engaged in environmental analyses and policymaking for urban areas, facilitating informed strategies to mitigate vehicular emissions and foster sustainable transportation practices. Full article

On-road real-driving emissions (RDE) tests with portable emissions measurement systems (PEMS) are part of the vehicle emissions regulations in the European Union (EU). For a given vehicle, the final emission results depend on the influence of the ambient conditions and the trip characteristics (including the driver’s behaviour) on the vehicle performance and the instrument measurement uncertainty. However, there are not many studies that have examined the emissions variability of a single vehicle following different routes. In this study, a 1.2 L gasoline direct injection (GDI) Euro 5b passenger car without a particulate filter and a PEMS was circulated in seven European laboratories. At their premises, the laboratories performed two to five repetitions of on-road trips compliant with the EU RDE regulation. The ambient temperature ranged between 7 °C and 23 °C. The average emission levels of the vehicle were 135 g/km for CO₂, 77 mg/km for CO, 55 mg/km for NO_x, and 9.2 × 10¹¹ #/km for particle number. The coefficient of variance in the emissions following the same route was 2.9% for CO₂, 23.8% for CO, 23.0% for NO_x, and 5.8% for particle number. The coefficient of variance in the emissions following different routes in Europe was 6.9% for CO₂, 9.1% for CO, 0.0% for NO_x, and 9.1% for particle number. The previous values include the specific vehicle emissions variability under the narrow test conditions of this study, but only partly the PEMS measurement uncertainty because the same instrument was used in all the trips. The results of this study can be used by laboratories conducting RDE tests to assess their uncertainty budget when testing or comparing vehicles of similar technology. Full article

The motivation for conducting this research stems from the increasingly applied manipulations of emission control systems (ECSs), especially those in diesel passenger cars (PCs). The study aimed to investigate the influence of manipulations of exhaust gas recirculation (EGR) valves and a diesel exhaust fluid (DEF)-dosing system on the nitrogen oxide (NO_X) emissions of a Euro 6 diesel vehicle and, through the quantification of vehicles with manipulated ECSs, estimate the emissions of Euro 6 diesel PCs. Portable emissions measurement system (PEMS) measurements were performed on a Euro 6 diesel vehicle at a constant speed and on real driving emission (RDE) routes. The speed-dependent functions of the NO_X hot emission factor (EF) were calculated for seven different scenarios. The results showed that the NO_X EFs for the worst-case scenarios were more than two orders of magnitude higher than those where all ECSs were active. Applying the calculated EFs and the survey answers on the percentage of manipulated PCs to the Croatian Euro 6 diesel PC fleet, the results showed that the emission levels were up to 46.3% higher than the emissions calculated by the official computer program COPERT v5.6.5, with a tendency towards significantly higher values. The main conclusion is that vehicle manufacturers, policymakers, and the general public need to be informed about the enormous damage that in-use vehicles with manipulated ECSs cause to the environment and human health, in order to prevent such actions. Full article

A heavy-duty engine is homologated in a test cell. However, starting with Euro VI regulation, the in-service conformity is controlled with the engine installed in the vehicle using portable emission measurement systems (PEMS). In Europe, the application of solid particle number (SPN) PEMS started in 2021 for compression ignition (diesel) vehicles and in 2023 for positive ignition vehicles, thus including those operating with compressed natural gas (CNG). Even though today only particles with sizes > 23 nm are regulated, the Euro 7 proposal includes particles > 10 nm. There are not many studies on the accuracy of the SPN PEMS, especially for heavy-duty applications. In this study, PEMS measuring > 23 and >10 nm from two instrument manufacturers were compared with laboratory-grade instruments. The particle detector of one PEMS was a condensation particle counter (CPC), and of the other a the diffusion charger (DC). The results showed the robustness and good accuracy (40% or 1 × 10¹¹ #/kWh) of the PEMS for ambient temperatures from −7 °C to 35 °C, active regeneration events, different fuels (Diesel B7, HVO, and CNG), different test cycles, cold start or hot engine operations, and high exhaust gas humidity content. Nevertheless, for the DC-based PEMS, sensitivity to pre-charged urea particles was identified, and for the CPC-based PEMS, sensitivity to pressure changes with one vehicle was nnoticed. Nevertheless, the results of this study confirm that the PEMS are accurate enough to measure even the stricter Euro 7 limits. Full article

In the field of smart mobility, Artificial Intelligence (AI) approaches are influential and can make a highly beneficial contribution. Our project aims to develop a real-time ecological map of road traffic. This map will allow electric vehicles (EVs) and thermal vehicles (TVs) to display the cost of energy consumption and CO${}_2$ emissions on different road sections. In urban environments, road traffic emissions are a significant contributor to environmental pollution, with vehicle emissions being a major component. Addressing these impacts requires a thorough understanding of the operational behavior of vehicles on different road infrastructures within the region. This paper presents a novel, comprehensive dataset, the Vehicle Activity Dataset (VAD), designed to assess the emissions and fuel consumption characteristics of vehicles about their actual operating environment. Constructed from a large number of real-world driving scenarios, VAD incorporates emission data collected by an industrial Portable Emission Measurement System (PEMS), road scenes captured by an RGB camera, and the detection of different object classes within these images. The primary objective of VAD is to provide a comprehensive understanding of the relationship between vehicle emissions and the diverse range of objects present on the road. Experimental results in real road traffic environments through different studies demonstrate the robustness of the developed dataset. Full article

Owing to society’s growing ecological awareness, researchers and car manufacturers have increasingly been focusing on the adverse impact of transport on the environment. Many scientific publications have been published addressing the influence of a variety of factors on the exhaust emissions generated by vehicles and machinery. In this paper, the authors present an analysis of the exhaust emissions of components such as CO, THC, and NO_x in relation to the type and condition of the road surface. The analysis was performed on a heavy-duty truck designed for carriage of timber. The investigations were carried out with the use of the PEMS equipment (portable emission measurement system) on bitumen-paved roads and unpaved forest access roads. The portable measurement system allowed for an accurate determination of the influence of the road conditions on the operating parameters of the vehicle powertrain and its exhaust emissions. Additionally, the authors present the influence of the type of road surface on the vehicle fuel consumption calculated based on the carbon balance method. Full article

Enhancing gasoline detergency is pivotal for enhancing fuel efficiency and mitigating exhaust emissions in gasoline vehicles. This study investigated gasoline vehicle emission characteristics with different gasoline detergency, explored synergistic emission reduction potentials, and developed versatile emission prediction models. The results indicate that improved fuel detergency leads to a reduction of 5.1% in fuel consumption, along with decreases of 3.2% in total CO₂, 55.4% in CO, and 15.4% in HC emissions. However, during low-speed driving, CO₂ and CO emissions reductions are limited, and HC emissions worsen. A synergistic emission reduction was observed, particularly with CO exhibiting a pronounced reduction compared to HC. The developed deep-learning-based vehicle emission model for different gasoline detergency (DPVEM-DGD) enables accurate emission predictions under various fuel detergency conditions. The Pearson correlation coefficients (Pearson’s r) between predicted and measured values of CO₂, CO, and HC emissions before and after adding detergency agents are 0.913 and 0.934, 0.895 and 0.915, and 0.931 and 0.969, respectively. The predictive performance improves due to reduced peak emissions resulting from improved fuel detergency. Elevated gasoline detergency not only reduces exhaust emissions but also facilitates more refined emission management to a certain extent. Full article