Real Driving Emissions Research Articles

Impact of Shortening Real Driving Emission (RDE) Test Trips on CO, NOX, and PN10 Emissions from Different Vehicles

Impact of Shortening Real Driving Emission (RDE) Test Trips on CO, NOX, and PN10 Emissions from Different Vehicles

The analysis of the operating conditions of a heavy-duty engine of a vehicle designated for timber transport in terms of compliance with the RDE procedures

According to the data provided by Polish Central Statistical Office, the years 2000–2020 have seen a general increase in the amount of extracted timber reaching 43%. The process of obtainment and transport of timber has many stages involving multiple vehicles and machines. The prevailing type of machinery are HDVs (heavy-duty vehicles). Their application is dictated by the fact that high tonnage-rated vehicles are characterized by the greatest efficiency. The vehicles from this group are homologated under laboratory and actual traffic conditions. It is noteworthy that the transport process of timber takes place under extremely varied conditions – paved urban and rural roads a well as unpaved forest tracks. This renders the mode of operation non-compliant with the conditions set forth in the UE 582/2011 standard. Therefore, the authors decided to carry out an analysis of the operating conditions of a vehicle fitted with an HD engine used in the process of timber transport in terms of its compliance with the RDE (Real Driving Emissions) procedures. The analysis was performed based on investigations carried out under actual traffic conditions with the use of the PEMS equipment. The research object was a tractor-trailer fitted with removable stanchions and a crane used for timber handling. The paper presents, inter alia, the engine operating time share characteristics as a function of engine load and speed. The authors also compared the real-world engine operating conditions with the assumptions of the RDE procedures.

Correlation relationships of processes in the combustion engine in the RDE test

The article presents considerations on the processes taking place in the combustion engine in the in real driving operating conditions of a vehicle performing the RDE (Real Driving Emissions) test. The tests were carried out using a passenger car with a spark-ignition engine. The processes considered in the article were related to the engine operating states, exhaust emissions and fuel consumption, and the vehicle speed, which determines the engine operating conditions. The RDE test were carried out using PEMS (Portable Emissions Measurement System) equipment, and the following variables were recorded: vehicle speed, control, rotational speed, relative torque and relative engine power, emission pollutant intensity of: carbon monoxide, hydrocarbons, nitrogen oxides and carbon dioxide, the intensity of particle number and the fuel consumption intensity. The recorded signals were digitally processed, and the statistical properties of the variables and the mutual relation between the engine operating states were examined. The properties of the measured variables were investigated in the entire RDE test and in its constituent phases: the first, corresponding to vehicle movement in cities, the second – outside cities, and the third – on highways and expressways. The pollutant specific distance emission and the particle number specific distance as well as the specific distance fuel consumption were determined in relation to the average vehicle speed, and based on these results, the exhaust emissions and fuel consumption characteristics were created. Correlational studies of the considered variables were also performed. Pearson's linear correlation coefficients for the measured variables combinations were determined.

Emissions of gaseous nitrous acid (HONO) from diesel trucks: Insights from real-driving emission experiments

Nitrous acid (HONO) serves as a substantial contributor in the atmospheric chemistry of hydroxyl radicals (·OH). Despite its significance, the primary sources of atmospheric HONO, particularly diesel truck emissions, have not been thoroughly examined. This study investigated the factors influencing HONO emissions via on-road exhaust emission tests using a self-developed portable measurement system. The findings show that the HONO emissions measured during on-road testing are higher than those measured during chassis dynamometer testing, highlighting the need for on-road tests to capture HONO emissions. Emission standards and truck types greatly influence HONO emission factors (EFs), with stricter regulations leading to lower emissions. The average fuel consumption-based EFs for light-duty diesel trucks ranged from 0.93 g/kg for China III to 0.08 g/kg for China VI. For medium-duty diesel trucks, the EFs decrease from 1.43 g/kg for China III to 0.19 g/kg for China V. Moreover, the vehicle-specific power demonstrated a stronger correlation with HONO emissions. This research showed that HONO emissions were significantly higher without or before the optimal operation of the SCR device, and the device notably reduced HONO emissions. Future research should focus on the impact of various exhaust after-treatment systems and explore HONO conversion mechanisms.

Development of Driving Robot and Driver Model Applied Regenerative Brake Control of Electrified Vehicles

Real driving emissions (RDE) test procedures for type approval were initiated in Japan in 2022. In Japan, the test vehicle is driven on a test road (test course) by tracing the speed pattern prepared for each RDE test. The vehicle needs to trace the patterns in the same manner as in the conventional chassis dynamometer test. The use of a driving robot is effective for reproducibility in precisely tracing the target vehicle speed during these tests. In this study, we developed a driving robot for electrified vehicles (HEV, plug-in HEV, BEV, and FCV), which have regenerative brake-control systems. We applied a new logic to our robot that was developed in-house for conventional engine vehicles. We focused on braking deceleration of the regenerative brake control to change the operation timing from the accelerator pedal to the brake pedal. We verified the improvement of speed traceability of the driving robot installed with the proposed logic on the chassis dynamometer and test road.

Impact of diesel/renewable fuels blend on gaseous and particle emissions of a light-duty vehicle under real driving emissions

In this study, the environmental impact of Bio-butanol (BB) and Hydrotreated Vegetable Oil (HVO) use, each blended with diesel fuel, was investigated. To this aim, pollutant emissions were measured at the exhaust of an Euro 5 commercial light-duty vehicle, fueled with diesel, BB/diesel blend (10 %v BB) and HVO/diesel blend (15 %v HVO). The experimental activity was realized by performing Real Driving Emission (RDE) tests in Napoli (Italy), which allowed to measure total hydrocarbons (THC), carbon monoxide (CO), carbon dioxide (CO2), nitrogen oxides (NOX), and particle emissions during the real use of vehicle by using a portable emission measurement systems (PEMS). The results show that the differences between fuels are small and not statistically significant for all emission parameters. CO and THC emissions increase with the renewable fuel blends, mainly with the BB blend in the urban environment due to the increasing of cold start contribution. NOX emissions increased with BB (by approximately 4 %) and decreased with the HVO (by approximately 13 %). With both renewable fuel blends, particle emissions at engine output were reduced by 25 %–41 % and fuel consumption by 6 %–10 % compared with diesel.

Comparison of the Real-Driving Emissions (RDE) of a Gasoline Direct Injection (GDI) Vehicle at Different Routes in Europe

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 NOx, 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 NOx, 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 NOx, 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.

An Estimate of the NOX Emissions of Euro 6 Diesel Passenger Cars with Manipulated Emission Control Systems

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 (NOX) 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 NOX hot emission factor (EF) were calculated for seven different scenarios. The results showed that the NOX 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.

Analysis of the Euro 7 on-board emissions monitoring concept with real-driving data

The Euro 7 standard is expected to intensify its focus on real-driving emissions compliance thanks to the on-board monitoring (OBM) system. OBM is designed to bridge the gap between emission limits and real-world vehicle emissions by continuously monitoring their levels using on-board sensors and models. Essentially, this system aims to verify vehicles compliance, prompt timely repairs otherwise, and provide authorities with emissions data for fleet inspection. Leveraging a large real-driving emissions database from an in-service Euro 6d-Temp vehicle, this paper reviews the current OBM proposal and discusses its possible outcomes and challenges using available sensor technology.

Applying Density-Based Clustering for the Analysis of Emission Events in Real Driving Emissions Calibration

Further reducing greenhouse gas and pollutant emissions from road vehicles is a major task for the automotive industry. Stricter regulations regarding emissions and fleet fuel consumption require the continuous development of new powertrains and methods. In particular, the combination of hybrid powertrains on the technical side and the focus on real driving emissions (RDE) on the legislative side pose significant challenges to the vehicle calibration process. Against this background, new test methods and environments are being investigated to counteract the high number of interactions between hybrid drive systems and quasi-infinite test conditions due to RDE. Complementary to new test environments, innovative methods for data analysis are needed that allow the exploitation of the complete potential of measurement data. The application of such a method in the field of emission calibration is presented in this paper. For this purpose, a clustering method (HDBSCAN) is applied to critical sequences from emission tests. Within this presentation, the clustering process is based on a single signal only. This paper shows how signals of various characteristics can be processed with dynamic time warping and generically structured with the clustering method used. Here, 959 single events are automatically categorized into 24 clusters. This provides a new basis for system evaluation, enabling the automatic identification, categorization, and prioritization of calibration weaknesses. Using twelve signals of different characteristics, the generic usability of the clustering method is demonstrated.

Analysis of vehicle specific power and road grade in a high-altitude city

Vehicles are a major source of atmospheric pollutants and greenhouse gases. Real Driving Emissions (RDE) testing is used to study the real-world effects of parameters that are not considered in laboratory testing but that can influence fuel consumption and vehicle emissions. This paper analyzes the vehicle specific power (VSP) and the effects of positive and negative road slopes on the fuel consumption of a sport utility vehicle (SUV). The vehicle was tested on a route at an altitude of 2750 meters in Riobamba, Ecuador. The circuit design included urban, rural, and highway driving that met the requirements of European Union (EU) Regulation 2018/1832. Low-cost devices were used to record data from the road tests to determine fuel consumption as a function of road slope. VSP+ analysis revealed that there is a good correlation with fuel consumption, with an R2 of 0.86. For road slopes of -6% to +6%, the percentage variation in fuel consumption is linearly correlated (R2 = 0.85) with the slope variations.

Developing Fuel Efficiency and CO2 Emission Maps of a Vehicle Engine Based on the On-Board Diagnostic (OBD) Approach

A vehicle interacts with the road, other vehicles, and traffic control devices in real traffic conditions. The level of traffic influences driving patterns and, consequently, this can affect the vehicle´s fuel efficiency and emissions. This study aims to develop engine maps of fuel consumption and CO2 emissions for a light vehicle operating under real traffic conditions. A representative passenger vehicle of the Ecuadorian vehicle fleet, powered by gasoline, was selected for the experimental campaign that was developed on a test route designed according to real driving emission (RDE) regulation. An on-board diagnostic (OBD) device was used for recording in real-time engine and vehicle operating parameters. Moreover, CO2 emissions were estimated using the fuel rate registered from the OBD system of the vehicle This study proposed a novel methodology for developing two-dimensional contour engine maps based on OBD data. The result showed that the vehicle engine operated in real traffic conditions with a brake thermal efficiency (BTE) of 27%, a brake-specific fuel consumption (BSFC) of 275 g/kWh, and a carbon dioxide (CO2) energy-emission factor of 716 g/kWh. In terms of distance, the CO2 emission factor for the tested vehicle was approximately 190 g/km. Overall, this study demonstrates that the OBD approach is a potential method to be used to assess the fuel consumption and emissions of a vehicle operating under real-world traffic conditions, especially in Latin American countries, where portable emission measurement systems (PEMS) are not readily available.

Study on Control Strategies on NOX Emissions to Meet Real Driving Emissions.

The aim of this study was to fulfill the NOx emissions standards for a light-duty diesel vehicle under real driving emissions (RDE) testing conditions by implementing various control strategies. In this study, RDE tests were performed by adjusting the air mass quantity and postinjection quantity in order to analyze engine-out and tail-pipe nitrogen oxides (NOx) emissions for different phases of RDE. The results showed that reducing in air mass quantity enabled the engine to operate in higher exhaust gas recirculation (EGR) rate regions, resulting in a 32.5% reduction in engine-out NOx emissions and an 80.4% decrease in tail-pipe NOx emissions. Increasing the postinjection quantity primarily enhanced the NOx conversion efficiency for the urban phase by 7.5%, leading to a 22.6% reduction in tail-pipe NOx emissions. By employing both strategies, vehicles can comfortably meet the CN6b emission regulations by a substantial margin.

Effect of ambient high-temperature on real driving emission tests of a light-duty gasoline vehicle

In real driving emission (RDE) test procedures, the extended high-temperature conditions of 30–35 °C are set to correct for the effect of ambient high-temperature on RDE tests. However, direct test data supporting or explaining the rationality of this provision remains lacking. To understand the impact of high-temperature environmental conditions on the RDE tests, ten RDE tests of a light-duty gasoline vehicle were conducted in an ambient temperature range of approximately 10–40 °C with the same driver and driving route. From the sample data of the ten RDE tests, the RDE tests were more affected by the non-temperature test conditions of trip dynamic, route topography, etc., than by the ambient temperature test conditions. The CO2, CO, NOx, and PN emissions were not sensitive to the variations in ambient temperature except for very few cases in urban CO2 and CO emissions. The ambient high-temperature conditions did not necessarily lead to severe pollutant emissions in the ten RDE tests. Extending the upper limit of the moderate temperature conditions upwards to a higher ambient temperature in the RDE test procedures should be considered.

Prediction of instantaneous particle number for light-duty gasoline vehicles under real driving conditions based on ensemble learning

With increasingly stringent emission regulations for particle number (PN) from real driving emissions (RDE), accurate prediction and evaluation of instantaneous PN emissions from RDE test is becoming increasingly important. However, there is still a lack of reliable PN emission prediction models for RDE test due to the complex nonlinear relationship between various affecting factors and PN emissions. Therefore, this study conducted RDE tests on a light-duty gasoline vehicle to acquire 10,646 sets of data, and four ensemble learning algorithms (randomforest, xgboost, catboost, and lightGBM) were introduced to construct prediction models of instantaneous PN emissions. Based on these models, the importance of 9 features affecting PN emissions was discussed using the Spearman correlation analysis and Shapley Additive Explanation (SHAP) algorithm. Among them, carbon dioxide share of exhaust (CO2,ratio), acceleration, vehicle specific power (VSP) and engine speed were identified as the critical input features. The catboost model has the optimal performance in prediction accuracy among the four models, with an average R2 and RMSE of 0.832 and 0.00253 respectively for the 10-fold cross-validated. Further analysis of the PN prediction results on different types of roads showed that the catboost model has high accuracy for instantaneous PN emissions on urban, rural and motorway trips. Moreover, the SHAP summary plot was employed to reveal the prediction mechanism of catboost algorithm through quantifying the feature contribution. The prediction model for instantaneous PN emissions provided a methodological reference for constructing vehicular emission models for RDE test.

Effect of driving characteristics and ambient temperature on the particle emissions during engine restart of spark ignition hybrid electric vehicle

In this study, we analyzed particle emission characteristics in the engine restart (ER) phase of a hybrid electric vehicle (HEV) based on driving characteristics and ambient temperature. The ambient temperature was set at intervals of 10 °C from − 10 °C to 20 °C. ES-582.1, PPS-M, EEPS, and temperature sensors were installed to acquire hybrid control unit (HCU), particle number (PN), PN size distribution, and exhaust temperature data. The on board test route was conducted in the South Korean real driving emissions (RDE) certification route, consisting of urban, rural, and motorway phases. The test HEV was controlled by dividing the engine operation during driving into ER and normal phases. Within 5 s immediately after ER, it emitted PN equivalent to 90% of the total test emissions. The count of ER was higher in urban phases compared to rural and motorway phases. As the ambient temperature decreased, PN emissions increased regardless of the driving mode, but the ER PN percent decreased. Immediately after ER, PN emissions increased rapidly, peaked at around 2–3 s, and then decreased thereafter. The average engine-off time before ER was the longest in the urban phase, and the average ER exhaust temperature was the highest in the motorway phase. The size fraction of large particles increased as the ambient temperature decreased.

Research on the CO2 Emission Characteristics of a Light-Vehicle Real Driving Emission Experiment Based on Vehicle-Specific Power Distribution

China implemented the China VI emission standard in 2020. The China VI emission standard has added requirements for the RDE (real-world driving emission) test. To evaluate vehicle CO2 emission for different vehicles, 10 conventional gasoline vehicles were tested under the RDE procedure using the PEMS (portable emission testing system) method. All vehicles tested meet the sixth emission regulation with a displacement of 1.4 L~2.0 L. Among the vehicles tested, the highest CO2 emission factor was 281 g/km and the lowest was 189 g/km, while the acceleration of RDE gets a wider distribution, varying from −2.5 m/s2 to 2.5 m/s2. The instantaneous mass emission rate could reach around 16 g/s. The amounts of total CO2 emission in the positive region and the negative region make up 82~89% and 11~18% of the overall CO2 emission during the entire RDE driving period, respectively. The same vehicle has a wide range of CO2 emission factors at different VSP (vehicle specific power) intervals. Different RDE test conditions can lead to large differences in CO2 emissions.

Real-driving CO2, NOx and fuel consumption estimation using machine learning approaches

Real driving emissions (RDE) testing are gaining attention for monitoring and regulatory purposes because of providing more realistic emission and fuel consumption measurements compared to laboratory tests. This study aims to develop machine learning (ML) based emission and fuel consumption estimation models using real-driving measurement data. A light-duty diesel vehicle equipped with a portable emissions measurement system (PEMS) was driven in an urban test route by 30 participant drivers of disparate backgrounds to obtain a wide variety of data in terms of driving behaviour and traffic conditions. The Pearson correlation coefficient was used to select the input variables among 36 driving behaviours and 6 engine parameters. The CO2, NOx and fuel consumption prediction models were developed using linear regression (LR), support vector machine (SVM) and Gaussian process regression (GPR). The results showed that all three models could predict CO2 with an absolute relative error (ARE) of less than 9%. The GPR model showed the best performance in CO2 prediction with an R2 of 0.74 and ARE of 3.30%. LR model showed the best prediction accuracy for NOx with an R2 of 0.80 and ARE of 8.91%. All three models worked well for fuel consumption prediction, however, GPR showed the best accuracy with an R2 of 0.81 and ARE of 3.52%. This method lays a foundation for developing route/region specific emission and fuel consumption models that will help to monitor and reduce the environmental impact and the amount of burned fuel. Moreover, developing models from different driver classes will provide valuable insights into emission-optimal driving behaviour which could be used to train new drivers.

Traffic Flow Pattern Based Approach to Predict Real Driving Emission Test Routes

Due to the poor representation of NEDC emission test, the European Commission put into the force a new procedure, which contains both a laboratory test and a real word driving test. In this new test procedure, even though WLTC replaces NEDC, it is required to test the vehicle under real traffic conditions, which contains urban, rural and highway segments. Due to the new emission regulation, considerable number of academic work focus on the creation of RDE (Real Driving Emission) routes and RDE tests. In this study, it is introduced a new methodology for creating potential RDE routes. The routes, created in MATLAB code by using Istanbul road data containing a half an hour average trip records were used to predict a probable RDE test road. This model creates a number of RDE routes starting from defined coordinates and then analyses all alternative routes with respect to traffic flow rate and RDE boundary conditions identified by the European Commission. The routes obtained using the methodology developed are tested in real life conditions and evaluated according to actual existing traffic conditions. The agreement between the results of the method and the results of the actual driving test is limited in urban road section. However, for rural and highway road sections, there is a significant agreement between the predictions and actual test results.

Integrated MOVES model and machine learning method for prediction of CO2 and NO from light-duty gasoline vehicle

With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management. Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R2 values. On average, the improvement for CO2 was 0.132, and for NO, it was 0.261. This work will provide references for MOVES improvements in practical scenarios and better predict pollutant emissions for LDGVs using limited resources of field tests in cities outside the US.