Real Driving Emissions Research Articles

Emission Reduction from a Light-Duty Diesel Vehicle over Two Driving Cycles through Dynamic Correction Calibration.

The potential of a method of transient control of model-based engine charge control (MCCT) is investigated to minimize nitrogen oxide (NOx) emissions of a light-duty diesel vehicle over two driving cycles, the worldwide harmonized light-duty test cycle (WLTC) and real driving emission (RDE) cycle. The MCCT function was switched on by calibrating factor maps corrected for several parameters. In addition, the relationship between electronic control unit signals and nitrogen oxide (NOx) emissions was analyzed under the WLTC and RDE. The engine-out and tail-pipe NOx emissions in the WLTC and RDE could achieve varying degrees of reduction with MCCT.

Estimation of Fuel Consumption through PID Signals Using the Real Emissions Cycle in the City of Quito, Ecuador

In Ecuador, according to data from the Ministry of Energy, the internal combustion engine is the largest consumer of fossil fuels. For this reason, it is important to identify and develop proposals in the literature that enable the prediction of vehicle fuel consumption in both the laboratory and on the road. To accomplish this, real driving emissions (RDEs) need to be contrasted against the development of an algorithm that characterizes forces that oppose such proposals. From experimental tests, fuel consumption information was collected through a flow meter connected to the fuel line and the engine’s characteristic curves were obtained through a chassis dynamometer. Then, from the parameter identification data (PID), the most important predictors were established through an ANOVA analysis. For the acquired variables, a neural network was implemented that could predict 99% of the estimates and present a relative error lower than 5% compared to common methods. Additionally, an algorithm was developed to calculate fuel consumption as a function of the gear, inertial forces, rolling resistance, slope, and aerodynamic force.

Analysis of exhaust emission processes during the Real Driving Emissions test

The article deals with the study of exhaust emissions from a combustion engine in the Real Driving Emission (RDE) test. These tests are a simulation of real conditions of use of motor vehicles. Nowadays, RDE tests are mandatory for Light Duty Vehicle (LDV) and Heavy Duty Vehicle (HDV) vehicles and in the future, restrictive standard. Euro 7, which combines stricter limits with a comprehensive RDE test cycle, is becoming a challenge for current vehicle engineering. The paper presents the results of pollutant emission tests from a passenger car (PC). In the tests of LDV in the RDE test, the results of which are analyzed in the article, the Portable Emissions Measurement System (PEMS) mobile exhaust emission testing system was used. The processes describing the operating states of the vehicle and the combustion engine, as well as the processes of exhaust emission intensity and the intensity of the number of particulate (PN), were examined. The correlation between the considered processes was investigated. The emission of carbon monoxide, hydrocarbons, nitrogen oxides, particulate and carbon dioxide as well as the road PN were examined. The zero-dimensional statistical characteristics of the examined processes were also determined. The probability density and power spectral density of the processes were established. A great diversity was found in the properties of the process distributions, as well as in the dynamic properties of the processes. In the summary of the analysis of the results of the car speed process, the operating states of the combustion engine and the processes of exhaust emission intensity and the process of the intensity of PN in the RDE test, conclusions were formulated regarding, among others, course of the intensity of these compounds, correlation of the processes of pollution emission intensity and the intensity of the PN with the process of car speed, distribution of processes.

NOx emission deterioration in modern heavy-duty diesel vehicles based on long-term real driving measurements

NOx emissions from diesel vehicles generally deteriorate with increased durability mileage owing to the wear and deterioration of engines and after-treatment systems. Three China-VI heavy-duty diesel vehicles (HDDVs) were selected for four-phase long-term real driving emission (RDE) tests using the portable emission measurement system (PEMS). After 200,000 km of on-road driving, the maximum NOx emission factor of the test vehicles (387.06 mg/kWh) was found to be significantly lower than the NOx limit of 690 mg/kWh. Under all driving conditions, the NOx conversion efficiency of selected catalytic reduction (SCR) decreased almost linearly as the durability mileage increased. Importantly, the deterioration rate of the NOx conversion efficiency in low-temperature intervals was discernibly higher than that in high-temperature intervals. The NOx conversion efficiency at 200 °C dropped by 16.67–19.82% with higher durability mileage; however, the highest values at 275–400 °C only decreased by 4.11%. Interestingly, the SCR catalyst at 250 °C showed strong NOx conversion efficiency and durability (maximum decline of 2.11%). Overall, the poor de-NOx performance of SCR catalysts at low temperatures significantly challenges the long-term effective control of NOx emissions from HDDVs. Thus, improving the NOx conversion efficiency and durability at low-temperature intervals is the top priority for SCR catalyst optimization; NOx emissions from HDDVs at low velocities and loads should also be monitored by environmental authorities. The linear fitting coefficient for the NOx emission factors of the four-phase RDE tests was 0.90–0.92, indicating that NOx emissions deteriorated linearly with an increase in mileage. Based on the linear fitting results, the NOx emission control of the test vehicles during 700,000 km of on-road driving was highly likely to be qualified. These results can be used by environmental authorities to supervise the NOx emission conformity of in-use HDDVs after validation using other types of vehicles.

Comparison of the Energy Consumption and Exhaust Emissions between Hybrid and Conventional Vehicles, as Well as Electric Vehicles Fitted with a Range Extender

The introduction of new Euro exhaust emission standards and CO2 limits has forced carmakers to implement alternative hybrid and electric powertrains. We are observing a dynamic advancement of this sector. The authors’ primary motivation was to perform a series of measurements of the exhaust emissions and fuel mileages from vehicles fitted with hybrid, conventional and electric (range extender) powertrains. Three vehicles were used in the research project. The first one was a passenger car with a full hybrid powertrain. The vehicle was fitted with a 1.6 dm3 spark ignition engine. The second one was fitted with a 2.2 dm3 diesel engine. The third one was fitted with a 125 kW electric motor and a 28 kW combustion engine used as a range extender. The investigations were carried out according to the RDE (Real Driving Emission) methodology on a test route composed of urban, rural and highway portions. The test route was set in the Poznan agglomeration, and its distance was approx. 80 km. For the measurements, the authors used SEMTECH-DS from the PEMS (Portable Emissions Measurement System) equipment group. Based on the obtained results, the authors validated the test route in terms of the RDE compliance and determined the exhaust emissions and fuel mileages. The authors also analyzed the influence of the conditions of the measurements on the powertrain characteristics of each of the tested vehicles.

Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario

In a context in which the connectivity level of last-generation vehicles is constantly on the rise, the combined use of Vehicle-To-Everything (V2X) connectivity and autonomous driving can provide remarkable benefits through the synergistic optimization of the route and the speed trajectory. In this framework, this paper focuses on vehicle ecodriving optimization in a connected environment: the virtual test rig of a premium segment passenger car was used for generating the simulation scenarios and to assess the benefits, in terms of energy and time savings, that the introduction of V2X communication, integrated with cloud computing, can have in a real-world scenario. The Reference Scenario is a predefined Real Driving Emissions (RDE) compliant route, while the simulation scenarios were generated by assuming two different penetration levels of V2X technologies. The associated energy minimization problem was formulated and solved by means of a Variable Grid Dynamic Programming (VGDP), that modifying the variable state search grid on the basis of the V2X information allows to drastically reduce the DP computation burden by more than 95%. The simulations show that introducing a smart infrastructure along with optimizing the vehicle speed in a real-world urban route can potentially reduce the required energy by 54% while shortening the travel time by 38%. Finally, a sensitivity analysis was performed on the biobjective optimization cost function to find a set of Pareto optimal solutions, between energy and travel time minimization.

Measuring the route topography impact on real driving emissions based on neural network models

Route topography is an important test boundary of real driving emission (RDE) tests. However, the RDE test boundaries, such as atmospheric environment, driver behavior, route topography, and traffic congestion, are random, uncertain, and completely coupled. It is difficult to know to what extent route topography can determine on-road emissions, especially in a region with hilly topography. In this regard, the neural network predictor importance algorithms were proposed to measure the importance of the route topography test boundary. Based on tens of thousands of data window samples from the RDE tests in Chongqing, factor analysis was performed to reduce the data dimensionality and eliminate information overlap, and neural network models were established to predict pollutant emissions and calculate the relative importance of input variables. The results show that route topography is comparable to trip dynamics for on-road emissions but the importance of the route topography test boundary is not fully appreciated in the existing RDE regulation, making mountain cities suffer from severe vehicle emissions that are not effectively controlled.

Evaluating the Measurement Uncertainty of On-Road NOx Using a Portable Emission Measurement System (PEMS) Based on Real Testing Data in China

An evaluation of the measurement uncertainty of on-road NOx emissions using portable emission measurement system (PEMS) based on real local testing data collected in China was carried out as per the type B method defined in the EN 17507 standard. The aim of this work was to quantify the “absolute” measurement uncertainty of PEMSs, which excluded “PEMS relative to laboratory constant volume sampler (CVS)” uncertainty from the calculation of on-road NOx measurement uncertainty using PEMSs. PEMS instruments from three mainstream manufacturers were employed. The zero drift of the NOx analyzers was evaluated periodically during the real driving emissions (RDE) test, and it was noticed that there was neither a linear nor step model of zero drift, with no correlation with the boundary conditions or measurement principle. Additionally, from the 256 valid RDE tests, the zero drift always ranged from 3.8 ppm to −3.8 ppm, and more than 95% of the span drifts were within a range of 1.5%. Based on the laboratory testing of ten vehicles using the worldwide harmonized light-duty vehicle test cycle (WLTC), the type B uncertainty of PEMS NOx measurements corresponding to China-6a and China-6b limits was assessed. An uncertainty of 26.5% for China-6a was found (NOx limit = 60 mg/km over the WLTC), which is very close to the 22.5% from the EU evaluation results (NOx limit = 80 mg/km over the WLTC); the uncertainty with respect to China-6b was found to be 42.8% because the type-I limit was tuned down to 35 mg/km. This result indicates that, with the ever-tightening regulatory limits of vehicle NOx emissions, big challenges will be posed in terms of the reliability of PEMS measurements, which requires PEMS manufacturers to improve the performance of the instruments and policymakers to refine the test procedures and/or result calculation method to minimize the impacts.

RDE Calibration—Evaluating Fundamentals of Clustering Approaches to Support the Calibration Process

The topics of climate change and pollutant emission reduction are dominating societal discussions in many areas. In automotive development, with the introduction of real driving emissions (RDE) testing and the upcoming EU7 legislation, there are endless boundary conditions and potential scenarios that need to be evaluated. In terms of vehicle calibration, this is leading to a strong focus on alternative approaches such as virtual calibration. Due to the flexibility of virtual test environments and the variety of RDE scenarios, the amount of data collected is rapidly increasing. Supporting the calibration engineers in using the available data and identifying relevant information and test scenarios requires efficient approaches to data analysis. This paper therefore discusses the potential of data clustering to support this process. Using a previously developed approach for event detection in emission calibration, a methodology for the automatic categorization of events is presented. Approaches to clustering algorithms (hierarchical, partitioning, and density-based) are discussed and applied to data of interest. Their suitability for different signals is investigated exemplarily, and the relevant inputs are analyzed for their usability in calibration procedures. It is shown which clustering approaches have the potential to be implemented in the vehicle calibration process to provide added value to data evaluation by calibration engineers.

Route selection for road vehicle real-driving emissions test using GIS

The European Commission has developed a Real-Driving Emissions (RDE) test for road vehicles that complies with the urban, rural and motorway criteria to ensure the exhaust emission produced could be accurately evaluated. Therefore, careful measures are taken in selecting the best routes to ensure the testing fulfils the targeted criteria. As there is no common method exist to select the testing route, analysis on the testing route selection was implemented using Geographic Information System (GIS) platform that is capable to deal with large vector data. Thus, the proposed framework facilitates the testing route selection process which is difficult to determine without a proper spatial analysis of network data. Specific route information comprises of road categories, route speed range and route classes were collected from the Open StreetMap (OSM) database. The generated route density and the topological route network structure were used to further analyse the suitability route as the requirement of Real-Driving Emissions test. As a result, five candidates of Real-Driving Emissions test route have been selected in Malaysia as a case study using GIS platform.

Assessment of two condensation particle counters (CPCs) in photometric mode for high concentration exhaust emission measurements

Condensation particle counters (CPCs) use light scattering to count particles after they have grown to micron size in a supersaturated environment. In single counting mode each particle is counted depending on whether the scattered light exceeds a threshold value or not. In photometric mode the total scattered light is converted in particle number concentration. While for laboratory grade particle number systems, CPCs are allowed to operate only in single counting mode, there is no such requirements for portable emissions measurements systems (PEMS) for real-driving emissions (RDE) testing or for instruments for periodic technical inspection (PTI) of vehicles. In this study two CPCs of the same model were assessed in single counting and photometric modes with silver and graphite particles with sizes ranging from 10 nm to 100 nm. The results showed that the concentration was measured accurately enough for particles in the 25 nm to 50 nm size range, but was underestimated and overestimated for smaller and larger particles, respectively. The key message is that the photometric mode should be avoided or calibrated in function of concentration and particle size.

Monitoring the real-driving emissions considering the fleet and the driving behavior by geographical area

Air quality has been become a major public health issue and the road traffic emissions is one of the most important sources. The WHO (2013) classified the air pollution as carcinogenic and even if there is an improvement over time, according to The EEA (2015), the air pollution in Europe constitutes the first risk on the environmental health, and they emphasized that air quality monitoring is not enough to estimate the contribution of real-driving emissions with confidence.In this context, the proposed approach aims to monitor and understand the contributions of real-driving emissions by coupling mobile crowdsensing, on-road measurements and a microscopic vehicle model. This approach quantifies the variability of real-driving emissions related to the fleet and the driving behavior considering the real conditions on the analyzed geographic area.

Real Driving Emission from Vehicle Fuelled by Petrol and Liquefied Petroleum Gas (LPG)

In recent years, in addition to laboratory tests to determine emissions from road motor vehicles, tests in real driving conditions RDE (Real Driving Emissions) with the use of portable measuring equipment PEMS (Portable Emissions Measurement System) have been conducted. The paper presents the results of the emission research conducted on a vehicle Dacia Duster 1.0 TCe 100 ECO-G fuelled by petrol Eurosuper 95 and liquefied petroleum gas (LPG). The aim of the research was to determine the emissions of harmful substances and carbon dioxide, i.e. fuel consumption, in accordance with the prescribed RDE procedure and their comparison for the two types of motor vehicle fuel. Results clearly showed that the method used can differentiate between fuel types. The results correspond to the RDE emissions public data, which secures that the methodology used is in accordance with the procedure for measuring emissions in real driving conditions.

RDE cycle simulation by 0D/1D models to investigate IC engine performance and cylinder-out emissions

In this work, the development and application of advanced predictive 0D/1D methodologies to simulate Real Driving Emission (RDE) cycles are described. Firstly, the 1D simulation model is validated on a map of steady state operating points, which allows to use successively the very same model, with its calibration, during an RDE cycle simulation, considering the sequence of varying loads, and rotational speeds. In particular, the validated 1D model is used to simulate a typical RDE transient cycle of approximately 1 h and 45 min. The test case investigated is a modern plug-in hybrid passenger car engine, in which the thermal power unit consists of a 1 L three-cylinder, turbocharged gasoline engine. The experimental and simulated RDE cycle is characterized by a sensibly varying Internal Combustion Engine (ICE) operation, allowing to evaluate engine performance and cylinder out emissions. To speed up the calculation and significantly lower the Central Processing Unit (CPU)/real time ratio a dedicated numerical solver for fast simulation has been implemented and tested, while keeping the fidelity of the results. A predictive 0D, multi-zone model for Spark Ignition (SI) combustion has been applied, together with emission sub-models for the calculation of the main pollutants. Both instantaneous and cumulative emissions have been evaluated. The results of the simulations have been compared to the experimental data of RDE cycles, showing a good predictiveness of the models and the high potential of 0D/1D simulation codes as design tools, in the new scenario of demanding testing procedures. This approach can be applied for any engine configuration operating under any transient condition.

Numerical assessment of integrated thermal management systems in electrified powertrains

Temperatures in internal combustion engines (ICE) impact fuel consumption and pollutant emissions, especially under transient operating conditions. In hybrid powertrains, where the reciprocating internal combustion engine has intermittent operating conditions, a optimum control of these temperatures is critical. In this work, a detailed methodology for studying integrated thermal management systems for hybrid propulsion system was presented. Both experimental measurements and 0D/1D models were implemented and validated for the different components of the hybrid vehicle powertrain. The novelty of this work consists in the extensive experimental measurements involved for the development of the different models, specially the ICE, in order to study the integration of the different thermal flows of a hybrid powertrain. Furthermore, the simulation methodology used in this work integrates different modelling tools and takes advantage of their strengths when compared to using a single modelling tool. Two different thermal management systems have been evaluated under different Real Driving Emission (RDE) cycles at two different temperatures (at 20 °C and -20 °C). Results have shown that during the ICE warming up, the integrated thermal management system improved energy consumption by 1.74% and 3% for warm and cold conditions, respectively. This was because, the integrated TMS allows to avoid the temperature drop of the ICE when the propulsive system is in pure electric mode.

Development of a neural network-based energy management system for a plug-in hybrid electric vehicle

The high potential of Artificial Intelligence (AI) techniques for effectively solving complex parameterization tasks also makes them extremely attractive for the design of the Energy Management Systems (EMS) of Hybrid Electric Vehicles (HEVs). In this framework, this paper aims to design an EMS through the exploitation of deep learning techniques, which allow high non-linear relationships among the data characterizing the problem to be described. In particular, the deep learning model was designed employing two different Recurrent Neural Networks (RNNs). First, a previously developed digital twin of a state-of-the-art plug-in HEV was used to generate a wide portfolio of Real Driving Emissions (RDE) compliant vehicle missions and traffic scenarios. Then, the AI models were trained off-line to achieve CO2 emissions minimization providing the optimal solutions given by a global optimization control algorithm, namely Dynamic Programming (DP). The proposed methodology has been tested on a virtual test rig and it has been proven capable of achieving significant improvements in terms of fuel economy for both charge-sustaining and charge-depleting strategies, with reductions of about 4% and 5% respectively if compared to the baseline Rule-Based (RB) strategy.

Characterizing and assessing the fuel economy, particle number and gaseous emissions performance of hybrid electric and conventional vehicles under different driving modes

Hybrid electric vehicle (HEV) is considered to be more energy efficient and less emissions than conventional gasoline vehicle (CV). A growing number of studies suggested that real-road emissions may be much higher than laboratory measurements. To compare the fuel economy and emissions performance of HEV and CV on real roads, this study conducted real driving emission (RDE) tests on HEV and CV of the same configuration under real driving conditions with aggressive and normal driving styles. The results indicated that the HEV had lower fuel consumption (L/100 km), nitrogen oxides (NOX) and carbon dioxide (CO2) emissions compared to the CV. Fuel saving of HEV compared to CV was up to 41.14% under urban conditions. Compared to emissions of other trip conditions, The CO2 and NOX reductions of HEV under urban conditions were the largest, which were more than 33.65% and 61.98% respectively. However, the particle number (PN) and carbon monoxide (CO) emissions of HEV were larger than those of CV. This was due to the frequent stopping and restarting of the internal combustion engine of the HEV. In order to comprehensively compare the fuel economy and emissions performance of HEV and CV, the evaluation index of real conformity factor was proposed. The analysis showed that to reduce the high PN and CO emissions of HEV, a combination of fuel economy-focused and emission control-focused strategies should be used.

Comparison of Gasoline Engine Exhaust Emissions of a Passenger Car through the WLTC and RDE Type Approval Tests

The article presents a comparison of exhaust emission test results from a passenger car with a spark-ignition engine examined with the WLTC (Worldwide Harmonized Light-Duty Vehicles Test Cycle) test, which was carried out on a chassis dynamometer, and examined with a RDE (Real Driving Emissions) test, which was conducted in real vehicle operating conditions. The exhaust emissions and the emitted particle number in the individual phases of both tests were determined. Large disparities were found in the results of the two tests. The cold start-up had a particularly significant impact on the test results in the case of the WLTC test. This impact is much greater than in the RDE test, mainly due to the fact that the RDE test is much longer than the WLTC test. Moreover, the engine load in the RDE test was greater than in the WLTC test. As a result of the conducted analyses, it was postulated that the research should be continued in stochastic conditions for the vehicle speed function, e.g., in the implementation of the speed function determined for the real conditions of the vehicle operation.

Comparative Evaluation of Data-Driven Approaches to Develop an Engine Surrogate Model for NOx Engine-Out Emissions under Steady-State and Transient Conditions

In this paper, a methodology based on data-driven models is developed to predict the NOx emissions of an internal combustion engine using, as inputs, a set of ECU channels representing the main engine actuations. Several regressors derived from the machine learning and deep learning algorithms are tested and compared in terms of prediction accuracy and computational efficiency to assess the most suitable for the aim of this work. Six Real Driving Emission (RDE) cycles performed at the roll bench were used for the model training, while another two RDE cycles and a steady-state map of NOx emissions were used to test the model under dynamic and stationary conditions, respectively. The models considered include Polynomial Regressor (PR), Support Vector Regressor (SVR), Random Forest Regressor (RF), Light Gradient Boosting Regressor (LightGBR) and Feed-Forward Neural Network (ANN). Ensemble methods such as Random Forest and LightGBR proved to have similar performances in terms of prediction accuracy, with LightGBR requiring a much lower training time. Afterwards, LightGBR predictions are compared with experimental NOx measurements in steady-state conditions and during two RDE cycles. Coefficient of determination (R2), normalized root mean squared error (nRMSE) and mean average percentage error (MAPE) are the main metrics used. The NOx emissions predicted by the LightGBR show good coherence with the experimental test set, both with the steady-state NOx map (R2 = 0.91 and MAPE = 6.42%) and with the RDE cycles (R2 = 0.95 and nRMSE = 0.04).

Prediction of Real Driving Emission of Light Vehicles in China VI Based on GA-BP Algorithm

In the China VI regulations for light-duty vehicles, the (RDE) test is introduced as a supplementary test procedure. In the actual test process, the RDE results are more significantly influenced by driving behavior and vehicle type. To reduce the test cost, the NOx and PN prediction models are established based on the GA-BP method. The results showed that the coefficients of determination of the GA-BP model for NOx and PN predictions are all greater than 0.9 and are linearly highly correlated at the instantaneous emission level. At the overall emission level, the overall error of the GA-BP model is less than 7% for NOx prediction and less than 6% for PN prediction. The model has high accuracy for both instantaneous and overall emissions of light-duty vehicles. This provides practical engineering value for guiding the RDE test.