Uncertainties in Model-Based Diesel Particulate Filter Diagnostics Using a Soot Sensor.

Dimitrios Kontses,Savas Geivanidis,Pavlos Fragkiadoulakis,Zissis Samaras

doi:10.3390/s19143141

Dimitrios Kontses, Savas Geivanidis + Show 2 more

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https://doi.org/10.3390/s19143141

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Abstract

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).

Highlights

Air pollution is recognized as a serious and worldwide concern for human beings and the natural environment [1,2]
This study focuses on the European Union (EU) legislation and requirements to detect any malfunction of the system before the Particulate matter (PM) emissions exceed the mass limit of 12 mg/km during a specific legislative driving cycle and the malfunction indication light (MIL) illuminates [9,10]
Considering the low availability of publicized results for the performance of the overall on-board diagnostics (OBD) model, this paper presents and describes in details the involved models and algorithms, compares the different approaches in every step and calculates the propagated errors starting from the sensor signal and getting through the final diesel particulate filter (DPF) diagnosis based on an example of a New European Driving Cycle (NEDC)

Summary

Introduction

Air pollution is recognized as a serious and worldwide concern for human beings and the natural environment [1,2]. Particulate matter (PM) emissions, notably, are considered one of the most harmful pollutants [4,5]. Transportation sector and road vehicles are historically one of the primary sources of pollutants, including PM emissions [6]. One of the most significant current discussions for countries and authorities is the measures that need to be adopted to reduce the PM emissions from internal combustion engines of road vehicles [7]. The current stringent legislation in the European Union (EU) and the United States (US) for PM emissions from a diesel engine has led to installation of a diesel particulate filter (DPF) in the exhaust aftertreatment (EAT) system of modern diesel vehicles [8,9].

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