Reliability Challenges for Automotive Aftertreatment Systems: a State-of-the-art Perspective

Abstract This paper provides a critical review and discussion of major challenges with automotive aftertreatment systems from the viewpoint of the reliability of complex systems. The aim of this review is to systematically explore research efforts towards the three key issues affecting the reliability of aftertreatment systems: physical problems, control problems and fault diagnostics issues. The review covers important developments in technologies for control of the system, various methods proposed to tackle NOx sensor cross-sensitivity as well as fault detection and diagnostics methods, utilized on SCR, LNT and DPF systems. This paper discusses future challenges and research direction towards assured dependability of complex cyber-physical systems.

[1]  Ying Gao,et al.  Diesel Engine SCR Control: Current Development and Future Challenges , 2015, Emission Control Science and Technology.

[2]  Jakob Kjøbsted Huusom,et al.  Parameter estimation and analysis of an automotive heavy-duty SCR catalyst model , 2017 .

[3]  Mark A. Shost,et al.  Model Based Control of SCR Dosing and OBD Strategies with Feedback from NH 3 Sensors , 2009 .

[4]  Marco Sorrentino,et al.  Neural network models for virtual sensing of NOx emissions in automotive diesel engines with least square-based adaptation , 2017 .

[5]  Junmin Wang,et al.  Removal of NOx sensor ammonia cross sensitivity from contaminated measurements in Diesel-engine selective catalytic reduction systems , 2015 .

[6]  Junmin Wang,et al.  Estimation and adaptive nonlinear model predictive control of selective catalytic reduction systems in automotive applications , 2016 .

[7]  Junmin Wang,et al.  A novel cost-effective robust approach for selective catalytic reduction state estimations using dual nitrogen oxide sensors , 2015 .

[8]  Pierluigi Pisu,et al.  Model-based fault detection and isolation for a diesel lean NOx trap aftertreatment system , 2010 .

[9]  Christopher H. Onder,et al.  Control of an SCR catalytic converter system for a mobile heavy-duty application , 2006, IEEE Transactions on Control Systems Technology.

[10]  Kai Jiang,et al.  Adaptive unscented Kalman filter for input estimations in Diesel-engine selective catalytic reduction systems , 2016, Neurocomputing.

[11]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[12]  D. Chatterjee,et al.  Numerical Simulation of DOC+DPF+SCR Systems: DOC Influence on SCR Performance , 2008 .

[13]  Andrew G. Alleyne,et al.  Model predictive control: A unified approach for urea-based SCR systems , 2010 .

[14]  Zhen Huang,et al.  Review of the state-of-the-art of exhaust particulate filter technology in internal combustion engines. , 2015, Journal of environmental management.

[15]  M. V. Nieuwstadt,et al.  Uncertainty Analysis of Model Based Diesel Particulate Filter Diagnostics , 2008 .

[16]  Yu Sun,et al.  Model-Based Fault Detection and Fault-Tolerant Control of SCR Urea Injection Systems , 2016, IEEE Transactions on Vehicular Technology.

[17]  Lijiang Wei,et al.  NOx sensor ammonia cross-sensitivity estimation with adaptive unscented Kalman filter for Diesel-engine selective catalytic reduction systems , 2016 .

[18]  Feng Lin,et al.  Modeling and Multi-Objective Optimization of NO x Conversion Efficiency and NH 3 Slip for a Diesel Engine , 2016 .

[19]  Alan Christopher Hansen,et al.  Lean Nox Trap Storage Model for Diesel Engine Aftertreatment Control and Diagnosis , 2006 .

[20]  Rui Chen,et al.  Model-Based Fault Diagnosis of Selective Catalytic Reduction Systems for Diesel Engines , 2014 .