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SEMS for Individual Trip Reports and Long-Time Measurement
Technical Paper
2019-01-0752
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
There is an increasing need to understand the discrepancy between the real-world driving emissions and the emissions determined from type approval in a laboratory. Currently all modern passenger cars and trucks in Europe must undergo an on-road driving emissions test with portable emissions measurement systems (PEMS). Due to the large volume and big operational effort the currently used PEMS has however the constraint for monitoring the emissions of a vehicle within a limited time. To cover as many real-world driving conditions as possible, an online measurement system with automatic functions in data taking, data transferring and data analysis is needed.
A Smart Emissions Measurement System (SEMS) was developed by TNO to derive robust and reliable emission factors, this system provides a simple and easy-to-use emissions monitoring solution. Since 2012, this automotive sensor-based system and toolbox has been continuously developed and validated, through numerous measurements of emissions from light duty vehicles (LDV), heavy duty vehicles (HDV) and non-road mobile machinery (NRMM).
HORIBA and TNO are collaborating on the industrialization and large-scale deployment of SEMS, enabling new possibilities for measuring real-world driving emissions, accelerating monitoring and evaluation of actual emissions performance and providing valuable insights for improving sustainability of vehicles and machinery.
Such system is a precondition for collecting real-world mass emission data from an enormous number of vehicles under real-driving conditions, even over their life-time. Measurement data such as concentrations of NOx and NH3 in the exhaust gas, GPS signals, etc. is stored in a cloud-based database, which enables multiple services to be offered.
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Citation
Heepen, F. and Yu, W., "SEMS for Individual Trip Reports and Long-Time Measurement," SAE Technical Paper 2019-01-0752, 2019, https://doi.org/10.4271/2019-01-0752.Data Sets - Support Documents
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References
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