Real Driving Emissions Test Research Articles

Estimation of Pollutant Emissions in Real Driving Conditions Based on Data from OBD and Machine Learning.

This article proposes a methodology for the estimation of emissions in real driving conditions, based on board diagnostics data and machine learning, since it has been detected that there are no models for estimating pollutants without large measurement campaigns. For this purpose, driving data are obtained by means of a data logger and emissions through a portable emissions measurement system in a real driving emissions test. The data obtained are used to train artificial neural networks that estimate emissions, having previously estimated the relative importance of variables through random forest techniques. Then, by the application of the K-means algorithm, labels are obtained to implement a classification tree and thereby determine the selected gear by the driver. These models were loaded with a data set generated covering 1218.19 km of driving. The results generated were compared to the ones obtained by applying the international vehicle emissions model and with the results of the real driving emissions test, showing evidence of similar results. The main contribution of this article is that the generated model is stronger in different traffic conditions and presents good results at the speed interval with small differences at low average driving speeds because more than half of the vehicle’s trip occurs in urban areas, in completely random driving conditions. These results can be useful for the estimation of emission factors with potential application in vehicular homologation processes and the estimation of vehicular emission inventories.

A Study on Alternative Test Method of Real Driving Emissions for Heavy-duty Vehicle by Using Engine In the Loop Simulation

<div class="section abstract"><div class="htmlview paragraph">From the perspective of improving the atmospheric environment, evaluating the fuel efficiency and exhaust gas emissions when using actual vehicles on real roads is necessary. Passenger vehicles apply RDE (real driving emission) test. However, unlike passenger vehicles, there are many types and models of heavy-duty vehicles; thus, evaluating all of these types using real driving emission tests is difficult. Therefore, we investigated the possibility of an alternative evaluation of heavy-duty vehicle based on a simulation method that uses a vehicle model for part of the evaluation. This alternative evaluation method is a combination of the “engine HILS (hardware-in-the-loop simulation)” method, developed in previous studies and can perform tests based on vehicle speed and driving wind that is reproduced in the engine bench to appropriately cool the engine and various devices. Using this method, we obtained a possible alternative evaluation of heavy-duty vehicle RDEs, which was the objective of this study.</div></div>

Real-world emissions and fuel consumption of gasoline and hybrid light duty vehicles under local and regulatory drive cycles

In this study, driving trajectory data from private vehicles were collected in Toronto, Canada to construct representative local drive cycles. In addition, real-driving emission testing for four conventional gasoline vehicles (ICEV) and one hybrid electric vehicle (HEV) was conducted in the same region using a Portable Emissions Measurement System. Instantaneous fuel consumption and emissions of Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Particle Number (PN) were measured. The results for all vehicles indicate that the acceleration state tends to generate the highest emissions and fuel consumption with the largest variation due to higher power demand. When accelerating, the HEV was observed to generate four times more CO emissions than some ICEVs. Instantaneous fuel consumption and emissions were analyzed as a function of operating modes to estimate the fuel efficiency (FE) and emission factors (EF) associated with six representative local drive cycles and four regulatory drive cycles. With most regulatory drive cycles, vehicles can reach the labeled FE and EPA emission limits, except under the New York City Cycle with frequent stop-and-go conditions. In contrast, except for highway cycles, the FE of Toronto-specific drive cycles can hardly meet the labeled values. CO EFs of the HEV can be higher than ICEVs, while it is lower than the emission limit by 42% on average. ICEVs may exceed the CO limit by 131% under local highway cycles, while they can violate NOx and PN limits under local arterial cycles. The result of this study emphasizes the importance of local drive cycles and real driving emission tests.

Research on Exhaust Emissions in Dynamic Operating States of a Combustion Engine in a Real Driving Emissions Test

The paper describes the method of investigations of exhaust emissions from a combustion engine under operation classified in terms of its dynamic states. In this paper, the engine operating states are determined through the vehicle driving under actual traffic conditions in the RDE (Real Driving Emissions) test. Based on the recorded tracings of the vehicle velocity, the engine states were classified as static for the acceleration of the absolute value lower than the adopted classification limit. Besides, the authors analyzed the engine operating states for the positive as well as negative acceleration. For the adopted engine operating states, zero-dimensional characteristics of the emission intensity for individual exhaust components were determined (average value, coefficient of variation). The influence of the analyzed operating states on the emission of individual exhaust components was assessed. The greatest increase in the intensity of the emission of nitrogen oxides was observed for the positive vehicle acceleration model and the lowest (also for the nitrogen oxides) for the negative vehicle acceleration. On average, the greatest increase in the emission intensity of pollutants and the intensity of particle number occurred for the dynamic states of the engine corresponding to positive acceleration. The conclusions from the performed investigations entitle the authors to propose a greater allowance for the exhaust emission-related criteria in the engine control algorithms.

Analysis of pollutant emissions and fuel consumption for the use of a multi-storey carpark

In Poland, the number of vehicles owned per capita is systematically increasing. There is also a noticeable increase in traffic in large urban agglomerations. This creates a number of problems, such as difficulties in finding a parking spot. Then it becomes necessary to search for a long time to stop, which is correlated with higher emission of harmful substances and energy consumption. The aim of the work is a multi-criteria analysis of the selection of a parking spot at a shopping center focused on travel times, pollutant emissions and fuel consumption. In addition, consideration was given to the selection of the optimal parking spot. The tests were carried out in Real Driving Conditions similar to Real Driving Emissions testing. The procedure, currently used as an extension of the type-approval tests, reflects the actual exhaust emissions from vehicles more accurately than tests under laboratory conditions. Specialized PEMS (Portable Emissions Measurement System) apparatus was used for the measurements.

Assessing the effects of ethanol additive and driving behaviors on fuel economy, particle number, and gaseous emissions of a GDI vehicle under real driving conditions

Gasoline direct injection (GDI) vehicles operating on real roads produce particle number (PN) emissions. Ethanol, which is a vehicle oxygenated fuel, can meet stringent PN and gaseous emission regulations owing to its low carbon content, green sustainability, and clean combustion benefits. In this study, the fuel consumption (FC), PN, and gaseous emissions of a GDI vehicle were experimentally investigated using a chassis dynamometer following real driving emission (RDE). Two driving behaviors, the normal and aggressive driving modes, were tested with different ethanol fractions of gasoline (G100), 5% volume content in the blended fuel (G95E5), and 10% volume content in the blended fuel (G90E10). The results indicated that under the RDE test procedure, the FC, exhaust gas temperature (EGT), PN, carbon monoxide (CO), carbon dioxide (CO2), and nitrogen oxide (NOX) emissions in the aggressive driving mode were higher than those in the normal driving mode. When ethanol was added to gasoline, the FC and CO2 emissions of the blended fuels were higher than those of G100, whereas the CO and EGT of the blends decreased. The addition of small amounts of ethanol had little effect on the NOX emissions. As for PN, the PN emission of G95E5 was higher than that of G100. As the ethanol fraction further increased, the PN emission of G90E10 was the smallest among the three fuels. The GDI vehicles fueled with 10% ethanol achieved lower PN and CO emissions at a slight sacrifice in FC under real driving conditions.

Influence of the road slope on NOx emissions during start up

This paper has the purpose of estimating the NOx (Nitrous Oxides) emissions of vehicles equipped with ignition engines caused during the start-up of road slope. Studies currently carried out are scarce on driving right after start-up and its influence on NOx emissions. For the selection of the test route, the methodology proposed in the Real Driving Emission Test (RDE) was used, for which, there is a Data Logger device that acquires the data from the On Board Diagnostic port (OBD) and geolocation with the help of its Global Positioning System (GPS). Using the Random Forest technique, it is estimated that the most important variable in NOx production is engine revolutions. When the startup is carried out on road slopes ranging from 2.5% to 6.4%, there is an increase of 60.45% in NOx polluting emissions. In another situation, there is a decrease in the values of NOx polluting emissions by 87%, when the start-up occurs during the descent of road slopes in the range from −4.9% to −0.9%.

A Fuzzy Logic-Based Approach for Humanized Driver Modelling

All human drivers can be characterised by their habitual choice of driving behaviours, which results in a wide range of observed driving patterns and manoeuvres. Developing control strategies for autonomous vehicles that address this feature would increase the public acceptance of such vehicles. Therefore, this paper proposes a novel approach to developing rule-based fuzzy logic driver models that simulate different driving styles in the car-following regimes. These driver models were trained with the collected on-road driving data to capture corresponding human drivers’ characteristics. The proposed approach consists of three main components: collecting on-road driving data, developing a vehicle model, and establishing the car-following driver models. Firstly, an instrumented vehicle was used to collect driving data over the same route for three consecutive months. Car-following scenarios during these journeys were extracted, and related data were processed accordingly. Afterwards, a representative model of the instrumented vehicle was created and evaluated. Finally, a fuzzy logic driver model that uses humanized inputs was developed and calibrated with the recorded data. The developed driver model’s performance was assessed using the collected driving data and a baseline PID driver model. With the performance validated, models representing more aggressive and more defensive driving styles were derived following the same procedure. A cross-driver analysis was then implemented in a normalized car-following scenario with the established vehicle model to investigate the impacts of different driving styles further. The developed driver model can introduce driving styles into drive cycle experiments and replicate on-road real driving emission tests in the laboratory. Moreover, as the proposed method has high robustness to incomplete datasets, it can be a more cost-effective option to facilitate the development of humanized and customized vehicle control strategies for autonomous driving.

The Influence of Engine Downsizing in Hybrid Powertrains on the Energy Flow Indicators under Actual Traffic Conditions

The development of internal combustion engines is currently based around the ideas of downsizing and rightsizing. These trends, however, are not very widespread in vehicles with hybrid drive systems. Nevertheless, the authors analyzed the performance indicators of hybrid drives in downsized vehicles. Two generations of a vehicle model, equipped with hybrid drive systems, were used in the analysis in which not only the design of the internal combustion engine was changed, but also other hybrid drive systems (including the transmission, electric motors and high-voltage batteries). The paper analyzes the energy flow in two hybrid vehicles of different generations during tests in real road driving conditions in accordance with the requirements of the RDE (real driving emissions) tests. The authors have confirmed that newer vehicle designs extend the vehicle range by 38% in the electric mode under the conditions of road traffic (68% in the urban conditions). The application of a combustion engine with better operating indexes did not result in its greater load, but led to limitation of the maximum pressure-volume (PV) diagram. The change of the battery to Li-ion, despite its lower electric and energy capacity, led to an increase in vehicle’s working parameters (power and regenerative braking).

Analysis of real-driving emissions from light-duty gasoline vehicles: A comparison of different evaluation methods with considering cold-start emissions

Cold-start emissions from road vehicles are an important source of air pollution. However, how to evaluate them in the real driving emission (RDE) test is controversial. Although the Commission Regulation (EU) 2017/1154 and 2018/1832 successively adopt the moving averaging window (MAW) method and the cumulative averaging (CA) method to calculate the emission factors that include the cold-start emissions in the RDE tests, both the methods have been found to be flawed when processing the RDE data including cold-starts. To demonstrate the problem, the RDE test data from three light-duty gasoline vehicles were comparatively analyzed using the MAW method and the CA method. Results reveal that the MAW method unreasonably underestimates cold-start emissions in the urban and total trip emission evaluation, and the CA method tends to increase the inconsistency of the RDE evaluation results. Considering the inherently different characteristics between the cold-start emissions and the non-cold-start emissions, this paper proposes that the entire RDE test trip should be divided into a cold-start trip and a non-cold-start trip, and the cold-start emissions are evaluated by the CA method while the non-cold-start emissions are evaluated by the MAW method. Furthermore, the pollutant emission factors in the cold-start trip are corrected referring to the CO2 emissions emitted during the cold-start period of Worldwide harmonized Light vehicles Test Cycle (WLTC). Using this proposed approach, the aforementioned RDE tests were further analyzed and evaluated again. It is verified that the consistency of the test evaluation results in the RDE tests can be improved. The results of this study could be used to improve future emission regulations for the more reasonable evaluation of cold-start emissions in RDE tests.

Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle

Euro 6 standards impose stringent nitrogen oxide (NOx) emission limits on diesel cars. NOx emissions are significantly different between Euro 6 diesel cars and the previous standards in real-world driving. In this research, the NOx concentrations of Euro 6 diesel engines during real-world driving were studied considering various factors. Real driving emission (RDE) tests were conducted using vehicles equipped with portable emissions measurement systems. Urban, rural, and motorway test routes were utilized. Road environment, atmospheric, and after-treatment performance factors were collected in each case. An artificial neural network was used for evaluation using RDE test data and various statistical parameters. It was found that the proposed method predicted the pollutant emissions effectively. Lastly, the relative importance of each predictor was derived, and the NOx concentrations were analyzed. These approaches provide accurate emission information for an environmental effect evaluation that reflects more realistic road conditions.

Plug-in Hybrid Ecological Category in Real Driving Emissions

Transportation, as one of the most growing industries, is problematic due to environmental pollution. A solution to reduce the environmental burden is stricter emission standards and homologation tests that correspond to the actual conditions of vehicle use. Another solution is the widespread introduction of hybrid vehicles—especially the plug-in type. Due to exhaust emission tests in RDE (real driving emissions) tests, it is possible to determine the real ecological aspects of these vehicles. The authors of this paper used RDE testing of the exhaust emissions of plug-in hybrid vehicles and on this basis evaluated various hybrid vehicles from an ecological point of view. An innovative solution proposed by the authors is to define classes of plug-in hybrid vehicles (classes from A to C) due to exhaust emissions. An innovative way is to determine the extreme results of exhaust gas emission within the range of acceptable scatter of the obtained results. By valuating vehicles, it will be possible in the future to determine the guidelines useful in designing more environmentally friendly power units in plug-in hybrid vehicles.

Prediction of gaseous pollutant emissions from a spark-ignition direct-injection engine with gas-exchange simulation

With the further tightening of emission regulations and the introduction of real driving emission tests (RDE), the simulative prediction of emissions is becoming increasingly important for the development of future low-emission internal combustion engines. In this context, gas-exchange simulation can be used as a powerful tool for the evaluation of new design concepts. However, the simplified description of the combustion chamber can make the prediction of complex in-cylinder phenomena like emission formation quite challenging. The present work focuses on the prediction of gaseous pollutants from a spark-ignition (SI) direct injection (DI) engine with 1D–0D gas-exchange simulations. The accuracy of the simulative prediction regarding gaseous pollutant emissions is assessed based on the comparison with measurement data obtained with a research single cylinder engine (SCE). Multiple variations of engine operating parameters – for example, load, speed, air-to-fuel ratio, valve timing – are taken into account to verify the predictivity of the simulation toward changing engine operating conditions. Regarding the unburned hydrocarbon (HC) emissions, phenomenological models are used to estimate the contribution of the piston top-land crevice as well as flame wall-quenching and oil-film fuel adsorption-desorption mechanisms. Regarding CO and NO emissions, multiple approaches to describe the burned zone kinetics in combination with a two-zone 0D combustion chamber model are evaluated. In particular, calculations with reduced reaction kinetics are compared with simplified kinetic descriptions. At engine warm operation, the HC models show an accuracy mainly within 20%. The predictions for the NO emissions follow the trend of the measurements with changing engine operating parameters and all modeled results are mainly within ±20%. Regarding CO emissions, the simplified kinetic models are not capable to predict CO at stoichiometric conditions with errors below 30%. With the usage of a reduced kinetic mechanism, a better prediction capability of CO at stoichiometric air-to-fuel ratio could be achieved.

Characteristics of NOx emission of light-duty diesel vehicle with LNT and SCR system by season and RDE phase

As the regulations on vehicle emissions have become more stringent internationally and real-driving emissions (RDE) have been established, the on-road characteristics of emissions have gained importance in vehicle research and development. The results of the fuel consumption levels and emissions from on-road tests are affected by many factors, such as driving conditions, routes and environmental conditions. Therefore, more research and analysis are needed for the effects of environmental factors and driving conditions according to RDE phase on the NOx emissions. In this study, RDE tests were conducted by season to analyze the on-road NOx emission characteristics of lean NOx trap (LNT)- and selective catalytic reduction (SCR)-equipped diesel vehicles corresponding to the Euro 6b regulation prior to the application of the RDE regulation. The purpose of this study is to analyze the effects of seasonal factors and phases of the RDE routes on the NOx emission and NOx conversion efficiency of catalyst. In spring/autumn and summer, the engine-out and tail-pipe NOx emissions were higher 1.3–5.9 times for vehicle A and 1.3–28.4 times for vehicle B in the urban phase than in other phases. In the urban phase, the engine bay temperature was probable to rise owing to frequent stops and low-speed driving, leading to a high intake air temperature, which causes excessive NOx emission, particularly in summer. The average air filter temperature in urban phase was 11–15 °C higher than the environment temperature for vehicle A. The NOx conversion efficiency of the LNT was highest at 54.1% on motorway and the efficiency was dependent on the phase of the test route. The NOx conversion efficiency of the SCR, which is dependent on the catalyst temperature, was highest at 98.7% in spring motorway and the efficiency was affected by the combined factors of season and phases.

Emissions and energy consumption of a plug-in hybrid passenger car in Real Driving Emission (RDE) test

In the next years, the number of circulating plug-in hybrid electric vehicles was expected to increase worldwide. Hybrid electric/gasoline powertrain is considered a promising solution to meet the future goals of greenhouse gas emission reduction by road transport sector. This paper investigates the emissive and energetic behaviour of a plug-in hybrid electric passenger car over on road testing. Measurements of CO, CO2, NOx exhaust emissions and of main electric motor parameters were carried out by using portable emission measurement system over urban, rural and motorway routes in Naples (Italy). Routes were selected for complying with European Real Driving Emission (RDE) legislation. Moreover, an additional urban route, characterised by strong road grade and non-RDE compliant, was investigated. RDE test allowed to assess the influence of the battery state of charge, hybrid mode (charge sustaining and charge depleting), road grade and vehicle air conditioning on the exhaust emission and energy consumption. An overall advantage of charged battery and charge depleting mode was observed in terms of CO2 emissions and fuel consumption. CO and NOx emissions are strongly influenced by driving when the vehicle's internal combustion engine starts. When it occurs in motorway patterns, CO and NOx peak emissions are observed at tailpipe. Pure electric driving, with consequent zero emissions, are guaranteed over urban strong uphill when battery is full charged.

Investigations of Exhaust Emissions from a Combustion Engine under Simulated Actual Operating Conditions in Real Driving Emissions Test

The paper describes the methodology of research of exhaust emissions from a combustion engine under engine states determined by the vehicle actual operation in the RDE test. The processes of quantities determining the vehicle motion and engine states have been recorded, along with the exhaust emission intensity. Based on the developed research methodology, zero-dimensional characteristics of the processes of the emission intensity have been determined under the conditions of urban, rural and motorway traffic, as well as in the entire test. The authors also determined the average specific distance exhaust emissions under the conditions of urban, rural and motorway traffic, as well as in the entire test. Based on the above results, the unique characteristics of the relation of the average specific distance emissions and the average vehicle speed have been obtained. The obtained characteristics may be used in the modeling of exhaust emissions from motor vehicles under actual traffic conditions. The authors also explored the sensitivity of the average specific distance emissions to the vehicle driving style.

Research on cold start and new data evaluation method influence on RDE test for light-duty vehicles

The Real Driving Emission test (RDE) required in China 6 regulation (GB 18352.6-2016) followed the EU 6 regulation (EC 2016/646), where cold start is excluded in emission calculation. Since cold start is an important stage of pollutant formation, the European Union had developed further requirements for cold start in EU 6 regulation (EC 2017/1154 and EC 2018/1832), which created generation gaps between China 6 and EU 6 RDE test requirements. This work is based on real RDE tests on public road and repetition tests on Chassis dynamometer with relevant cold start requirements of EC 2017/1154 described and demonstrated, the data evaluation method difference between China 6 regulation including cold start and new requirements in EC 2018/1832 are compared, and the following cold start requirements of China 6 RDE regulation is discussed and proposed.

Testing and evaluation of cold-start emissions from a gasoline engine in RDE test at two different ambient temperatures

Abstract In order to better reflect the actual ecological performance of vehicles in traffic conditions, both the emission standards and the applied emission tests are being developed, for example by considering exhaust emissions for a cold engine start. This article presents the research results on the impact of ambient temperature during the cold start of a gasoline engine in road emission tests. The Real Driving Emissions (RDE) tests apply to passenger cars that meet the Euro 6 emissions norm and they are complementary to their type approval tests. A portable emissions measurement system was used to record the engine and vehicle operating parameters, as well as to measure the exhaust emissions during tests. This allowed for parameters such as engine load, engine speed and vehicle speed to be monitored. The cold start conditions for two different temperatures (8°C and 25°C) were compared in detail. Moreover, the engine operating parameters, exhaust concentration values and road emissions for the 300 s time interval, were compared. The summary of the article presents the share of a passenger car’s cold start phase for each exhaust compound in the urban part of the test and in the entire Real Driving Emissions test depending on the ambient temperature.

The evaluation of NOx emissions in RDE tests including dynamic driving conditions

Nowadays, one of the most critical roles of both legislators and society itself is to look after the future of our environment. There are many reasons for the deterioration of air quality. Unfortunately, pollution from the automotive sector is still widely regarded as one of them. For this reason, the European Commission has been improving testing procedures for years, including more and more advanced measurement capabilities and extending testing to include further exhaust gas compounds. The object of the article is to assess the actual emission of nitrogen oxides (NOx) from two passenger cars equipped with gasoline engines. The authors carried out the tests according to the guidelines of the Real Driving Emissions (RDE) test procedure with the use of PEMS (Portable Emissions Measurements System) mobile measuring equipment. The influence of dynamic conditions (such as vehicle speed, acceleration and product of vehicle speed and acceleration) during the tests on NOx emissions was determined and the areas in which this relation was the most visible were estimated.

Research on real-driving emissions of heavy tractor under different loads

The actual road diesel emissions of heavy-duty vehicle is very high, which has been a concern around the world. In 2018, Chinese government promulgated the "China VI" regulations containing real-driving emission test requirements and limits, requiring vehicle manufacturers to effectively control vehicle emissions to meet the requirements of China VI. This article takes a heavy-duty tractor equipped with a "China VI" engine as the research object, and performs actual road emission tests after loading 10%, 25%, 50%, 75%, and 100% respectively. The results show that NOx emissions are higher at low loads and PN emissions are higher at high loads, and CO emissions are not sensitive to load changes.