Monitoring the filtration efficiency of Diesel Particulate Filters (DPF), is a legislative requirement for minimizing PM emissions from diesel engines of passenger cars and heavy-duty vehicles. To reach this target On Board Diagnostics (OBD) implementation in real-time operation is necessary. These systems in passenger cars are often utilizing a PM sensor, models for PM emissions simulation and algorithms for diagnosis. Their performance is associated with a series of challenges related with the accuracy and effectiveness of involved models, algorithms and hardware. This paper analyzes the main influencing factors and their impact on the effectiveness of the OBD system. Error propagation analysis is being performed to quantify the error of detection. The comparison results in conclusions on the performance of the sensor based OBD model and its ability to fulfil legislative requirements.
Monitoring the filtration efficiency of the diesel particulate filter (DPF), is a legislative requirement for minimizing particulate matter (PM) emissions from diesel engines of passenger cars and heavy-duty vehicles. To reach this target, on-board diagnostics (OBD) in real-time operation are required. Such systems in passenger cars are often utilizing a soot sensor, models for PM emissions simulation and algorithms for diagnosis. Their performance is associated with a series of challenges related to the accuracy and effectiveness of involved models, algorithms and hardware. This paper analyzes the main influencing factors and their impact on the effectiveness of the OBD system. The followed method comprised an error propagation analysis to quantify the error of detection during a New European Driving Cycle (NEDC). The results of the study regarding the performance of the OBD model showed that the total error of diagnosis is ± ; 28%. This performance can be improved by increasing the sensor accuracy and the soot model, which can make the model appropriate for even tighter legislation limits and other approaches such as on-board monitoring (OBM).
Monitoring the filtration efficiency of the diesel particulate filter (DPF), is a legislative requirement for minimizing particulate matter (PM) emissions from diesel engines of passenger cars and heavy-duty vehicles. To reach this target, on-board diagnostics (OBD) in real-time operation are required. Such systems in passenger cars are often utilizing a soot sensor, models for PM emissions simulation and algorithms for diagnosis. Their performance is associated with a series of challenges related to the accuracy and effectiveness of involved models, algorithms and hardware. This paper analyzes the main influencing factors and their impact on the effectiveness of the OBD system. The followed method comprised an error propagation analysis to quantify the error of detection during a New European Driving Cycle (NEDC). The results of the study regarding the performance of the OBD model showed that the total error of diagnosis is ±28%. This performance can be improved by increasing the sensor accuracy and the soot model, which can make the model appropriate for even tighter legislation limits and other approaches such as on-board monitoring (OBM).
The current experimental study presents particulate emissions from 30 Euro 1-4 L-category vehicles (i.e. 2-, 3- and 4-wheelers such as mopeds, motorcycles, quads and minicars, registered in Europe between 2009 and 2016) tested on a chassis dynamometer. The objectives were to identify those sub-categories with high emissions, to assess whether the measures prescribed in the Euro 5 legislation will effectively control particulate emissions and finally to investigate the need for additional measures. The results showed that 2-stroke (2S) mopeds and diesel minicars comprised the vehicles with the highest particulate mass (PM) and solid particle number above 23 nm (SPN23) emissions (up to 64 mg/km and 4.5 × 10(13) km(−1), respectively). It is uncertain whether the installation of diesel particulate filters (DPF) is a cost-effective measure for diesel mini-cars in order to comply with Euro 5 standard, while advanced emission controls will be required for 2S mopeds, if such vehicles remain competitive for Euro 5. Regarding 4-stroke mopeds, motorcycles and quads, PM emissions were one order of magnitude lower than 2S ones and already below the Euro 5 limit. Nevertheless, SPN23 emissions from these sub-categories were up to 5 times higher than the Euro 6 passenger cars limit (6 × 10(11) km(−1)). Even recent Euro 4 motorcycles exceeded this limit by up to 3 times. These results indicate that L-category vehicles are a significant contributor to vehicular particulate emissions and should be further monitored during and after the introduction of the Euro 5 step. Moreover, including SPN in the range 10–23 nm increases emission levels by up to 2.4 times compared to SPN23, while volatile and semi-volatile particle numbers were even higher. Finally, cold engine operation was found to be a significant contributor on SPN23 emissions, especially for vehicles with lower overall emission levels. These results indicate that a specific particle number limit may be required for L-category to align emissions with passenger cars.
The latest generation of internal combustion engines may emit significant levels of sub-23 nm particles. The main objective of the Horizon 2020 "DownToTen"project was to develop a robust methodology and provide policy recommendations towards the particle number (PN) emissions measurements in the sub-23 nm region. In order to achieve this target, a new portable exhaust particle sampling system (PEPS) was developed, being capable of measuring exhaust particles down to at least 10 nm under real-world conditions. The main design target was to build a system that is compatible with current PMP requirements and is characterized by minimized losses in the sub-23 nm region, high robustness against artefacts and high flexibility in terms of different PN modes investigation, i.e. non-volatile, volatile and secondary particles. This measurement setup was used for the evaluation of particle emissions from the latest technology engine and powertrain technologies (including vehicles from other Horizon 2020 projects), different fuel types, and a wide range of exhaust aftertreatment systems. Results revealed that in most cases (non-volatile), PN emissions down to 10 nm (SPN10) do not exceed the current SPN23 limit of 6×1011 p/km. However, there are some cases where SPN10 emissions exceeded the limit, although SPN23 were below that. An interesting finding was that even in the latter cases, the installation of a particle filter could significantly reduce PN emissions across a wide particle size range, fuels, and combustion technology. DownToTen results are being used to scientifically underpin the Euro 7/VII emission standard development in the EU. The method developed and the results obtained may be used to bring in the market clean and efficient vehicle technologies, improve engine and emission control performance with different fuels, and characterize size-fractionated particle chemistry to identify the formation mechanisms and control those in a targeted, cost-effective fashion. ; acceptedVersion ; Peer reviewed
Current legislations typically characterize systems of aerosols, such as from vehicle exhaust, primarily by number concentration and size distributions. While potential health threats have a dependence on the particle size, the chemical composition of particles, including the volatile and semi-volatile components adsorbed onto nonvolatile particle cores present at roadside and urban settings, is important in understanding the impact of exhaust particles on health. To date, the only tools suitable for an online in-depth chemical aerosol characterization are aerosol mass spectrometers, which are typically composed of complex and cost intensive instrumentation. We present a new analytical system, which combines a novel inexpensive infrared-radiation-based evaporation system (HELIOS) with a commercially available highly efficient atmospheric ionization source (SICRIT) connected to a rather low-price ion-trap mass spectrometer. Our inexpensive, robust and mobile aerosol characterization HELIOS/SICRIT/Mass Spectrometry system enables highly sensitive chemical analysis of particle-associated volatile substances. We validate the HELIOS/SICRIT/Mass Spectrometry system in laboratory experiments with coated particles generated under controlled conditions, and show that the system is capable of identification of combustion-generated polycyclic aromatic hydrocarbons and relative quantification of individual chemical species adsorbed on particle surfaces. We then employ our system to analyze real-world vehicle engine exhaust aerosol and show through time-resolved measurements with high time resolution (<10 s) that the chemical composition of the particles changes during different parts of an engine test cycle. ; acceptedVersion ; Peer reviewed
Concerns regarding noxious emissions from internal combustion engines have increased over the years. There is a strong need to understand the nature of sub-23 nm particles and to develop measurement techniques to evaluate the feasibility of new regulations for particle number emissions in the sub-23 nm region (down to at least 10 nm). This paper presents the results of three EU-funded projects (DownToTen, PEMs4Nano and SUREAL-23) which supported the understanding, measurement and regulation of particle emissions below 23 nm and have successfully developed sub-23 nm particle measurement devices, specifically laboratory systems and mobile devices for RDE tests. The new technology was validated in chassis dyno tests and on the real road. The results show that sub-23 nm particles are mainly generated at the engine start and during acceleration phases. The innovations show that the technology is mature and robust enough to serve as a basis for regulating sub-23 nm particles. ; The DownToTen project has received funding from the European Union's Horizon 2020 research and innovation programme under agreement No 724085. The PEMs4Nano project has received funding from the European Union's Horizon 2020 research and innovation programme under agreement No 724145. The SUREAL-23 project has received funding from the European Union's Horizon 2020 research and innovation programme under agreement No 724136.
