Real-time NOx estimation in light duty diesel engine with in-cylinder pressure prediction

2021 ◽  
pp. 146808742110157
Author(s):  
Youngbok Lee ◽  
Seungha Lee ◽  
Kyoungdoug Min

Recently, there have been numerous efforts to cope with automotive emission regulations. Various strategies to reduce engine-out NOx emissions and proper after-treatment systems, such as selective catalytic reduction (SCR) and lean NOx trap (LNT), have been taken into account in the engine research field. In this study, real-time engine-out NOx prediction model was established where zero-dimensional NO and NO2 models were combined with in-cylinder pressure model. During the procedure for estimating NO and NO2 (NOx), a real-time prediction model of in-cylinder pressure was applied so that the inputs to the NOx prediction model could be provided only by the data acquired from the engine control unit (ECU). This implies that an in-cylinder pressure sensor is not necessarily required to properly predict the engine-out NOx in real time. The real-time NOx estimation model was validated through the worldwide harmonized light-duty vehicle test cycle (WLTC) without a pressure sensor, and the total NOx error during the mode was comparable with the total NOx error of the portable NOx sensor. This real-time NOx estimation model can ultimately contribute to minimizing tail-pipe NOx emissions by influencing both emission calibration at the engine design stage and the management of NOx after-treatment systems where NOx conversion efficiency is heavily affected by the NO2/NO ratio.

2015 ◽  
Vol 22 (10) ◽  
pp. 7450-7460 ◽  
Author(s):  
Ricardo Suarez-Bertoa ◽  
Alessandro A. Zardini ◽  
Velizara Lilova ◽  
Daniel Meyer ◽  
Shigeru Nakatani ◽  
...  

2009 ◽  
Author(s):  
Seungsuk Oh ◽  
Daekyung Kim ◽  
Junsoo Kim ◽  
Byounggul Oh ◽  
Kangyoon Lee ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3122
Author(s):  
Qiming Wang ◽  
Tao Sun ◽  
Zhichao Lyu ◽  
Dawei Gao

As a crucial and critical factor in monitoring the internal state of an engine, cylinder pressure is mainly used to monitor the burning efficiency, to detect engine faults, and to compute engine dynamics. Although the intrusive type cylinder pressure sensor has been greatly improved, it has been criticized by researchers for high cost, low reliability and short life due to severe working environments. Therefore, aimed at low-cost, real-time, non-invasive, and high-accuracy, this paper presents the cylinder pressure identification method also called a virtual cylinder pressure sensor, involving Frequency-Amplitude Modulated Fourier Series (FAMFS) and Extended-Kalman-Filter-optimized (EKF) engine model. This paper establishes an iterative speed model based on burning theory and Law of energy Conservation. Efficiency coefficient is used to represent operating state of engine from fuel to motion. The iterative speed model associated with the throttle opening value and the crankshaft load. The EKF is used to estimate the optimal output of this iteration model. The optimal output of the speed iteration model is utilized to separately compute the frequency and amplitude of the cylinder pressure cycle-to-cycle. A standard engine’s working cycle, identified by the 24th order Fourier series, is determined. Using frequency and amplitude obtained from the iteration model to modulate the Fourier series yields a complete pressure model. A commercial engine (EA211) provided by the China FAW Group corporate R&D center is used to verify the method. Test results show that this novel method possesses high accuracy and real-time capability, with an error percentage for speed below 9.6% and the cumulative error percentage of cylinder pressure less than 1.8% when A/F Ratio coefficient is setup at 0.85. Error percentage for speed below 1.7% and the cumulative error percentage of cylinder pressure no more than 1.4% when A/F Ratio coefficient is setup at 0.95. Thus, the novel method’s accuracy and feasibility are verified.


2017 ◽  
Vol 19 (3) ◽  
pp. 293-307 ◽  
Author(s):  
Hoon Cho ◽  
Brien Fulton ◽  
Devesh Upadhyay ◽  
Thomas Brewbaker ◽  
Michiel van Nieuwstadt

A real-time implementable, zero-dimensional model for predicting engine-out emissions of nitrogen oxides using in-cylinder pressure measurements is developed. The model is an extension of existing works in open literature that align well with the objectives of real-time implementation. The proposed model uses a simplified Zeldovich NOx mechanism that uses combustion-related parameters derived from simplified thermodynamic and combustion sub-models. The performance of the model is discussed for both a heavy-duty and a light-duty diesel engines. The model behavior is evaluated under input uncertainty so as to provide realistic performance bounds.


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