Novel approach for connecting real driving emissions to the European vehicle laboratorial certification test procedure.

Vehicle emission certification is evaluated under laboratorial conditions, where vehicles perform a standard driving cycle in controlled conditions leading to several critics, which have resulted in the implementation of the Worldwide harmonized Light Vehicle Test Procedure (WLTP) and the Real Driving Emissions (RDE) testing procedure, as a complementary certification procedure. RDE is still under debate since boundary conditions; evaluation and trip selection methods are still being studied to allow test reproducibility. Currently, the official data analysis method uses the moving average window (MAW_EC), based on the WLTP CO2 emissions for trip validity evaluation (RDE package 4) and emissions (RDE package 3). However, this does not consider the impact of vehicle dynamics. Consequently, this work focuses on developing a novel method to relate certification driving cycle dynamics and on-road test vehicle dynamics, to evaluate RDE tests fuel use and exhaust emissions in a comparable way to certification driving cycles, indicating how close, or far, real-world driving is from the laboratorial certification test. For this, a new method was developed called road vehicle evaluation method (ROVET), which relies on the cycle vehicle dynamic and on-road trip dynamics for assessing if both tests are comparable. Results from 5 measured vehicles with a portable emissions measurement system (PEMS) through reproducibility tests and 2 case studies, show that the ROVET provides results closer to the certification calculated reference than the most commonly used method in Europe (1% avg. difference for ROVET while 8% avg. difference for MAW_EC, regarding CO2 emission, for example). The use of vehicle dynamics on construction and references of a method could be used to incentivize the regulators to review the references used by the current used methods, which suffers several criticisms since their release. As the regulated methods are in constant update, this study could be useful for helping to improve or to be used as additional method for future vehicle certification procedures. Graphical abstract.

Assessing the influence of boundary conditions, driving behavior and data analysis methods on real driving CO2 and NOx emissions.

The need to verify vehicle emissions in real world operation led to the implementation of Real Driving Emissions (RDE) test procedures, effective since September 2017 for new Euro 6 cars following the Commission Regulation (EU) 2016/427, which defines the RDE test conditions and data analysis methods to allow representative results. Main factors addressed by the regulation include the share of driving operation, ambient temperature range, altitude and elevation difference. However, RDE is still debatable since not only boundary conditions but also the evaluation methods and trip selection are being discussed together with a carbon dioxide (CO2) regulation, which is planned to be implemented in the short term. Thus, this work focuses on analyzing the effect of different data measurement and analysis methods (i.e. cold-operation, road grade, trip selection and driving style) on CO2 and nitrogen oxides (NOx) emissions based on 13 RDE tests performed in the Lisbon Metropolitan Area, Portugal. The tests were conducted by 2 drivers using 5 vehicles. Each driver performed 2 trips per vehicle, one in normal driving and other in aggressive driving. A Portable Emissions Measurement System (PEMS) was used to collect 1 Hz data, which was compared and analyzed using the European Commission (EC) proposed method for RDE tests. Results show the effects of each parameter such as average difference between drivers (7% in CO2 and 55% in NOx emissions) and between aggressive and normal driving. For road grade, big oscillations happen during the slope profile, which impacts emissions during all trips. Considering cold-operation, CO2 and NOx emissions are, on average, ~25% and 55% higher, respectively, than in hot-operation. These results highlight the need for deeper studies on these factors to assure that RDE tests evolve to a more established certification procedure than laboratorial certifications.

Engine cold start analysis using naturalistic driving data: City level impacts on local pollutants emissions and energy consumption.

The analysis of vehicle cold start emissions has become an issue of utmost importance since the cold phase occurs mainly in urban context, where most of the population lives. In this sense, this research work analyzes and quantifies the impacts of cold start in urban context using naturalistic driving data. Furthermore, an assessment of the influence of ambient temperature on the percentage of time spent on cold start was also performed. Regarding the impacts of ambient temperature on cold start duration, a higher percentage of time spent on cold start was found for lower ambient temperatures (80% of the time for 0°C and ~50% for 29°C). Results showed that, during cold start, energy consumption is >110% higher than during hot conditions while emissions are up to 910% higher. Moreover, a higher increase on both energy consumption and emissions was found for gasoline vehicles than for diesel vehicles. When assessing the impacts on a city perspective, results revealed that the impacts of cold start increase for more local streets. The main finding of this study is to provide evidence that a higher increase on emissions occurs on more local streets, where most of the population lives. This kind of knowledge is of particular relevance to urban planners in order to perform an informed definition of public policies and regulations to be implemented in the future, to achieve a cleaner and healthier urban environment.

Scenario-based analysis of traffic-related PM2.5 concentration: Lisbon case study.

Air quality in urban centers constitutes a challenge ahead for most cities. For this reason, the objective of this research work was to evaluate the impacts of changing traffic-related parameters on particulate matter (PM) concentration for several hierarchical street levels using real-world data for the city of Lisbon, Portugal. For that purpose, 2014 data regarding meteorological conditions, traffic volumes, typical vehicle speed, and a vehicle representative of the fleet was used in an air quality dispersion model (CALINE4). The available data allowed building a baseline case for several streets that are representative of the traffic circulation conditions in Lisbon, which was compared with different scenarios: hypothetical introduction of a cordon toll (S1.1 to S1.4) and the implementation of a low emission zone (S2.1 and 2.2). The results indicate that reductions on PM2.5 concentrations from 5 to 42 % may be obtained from the implementation of the scenarios. Overall, this study demonstrates that modeling tools based on real-world data can provide a good approach to study urban policies regarding traffic-related PM exposure. Additionally, implementation of such measures requires an integrated strategy that enables proper enforcement and monitoring, as well as an adequate management of traffic flows between the implementation boundaries.