Modelling of a Selective Catalytic Reduction (SCR) System for a Heavy-Duty Diesel Engine and Development of an Optimal Dosing Policy

Uwe Schimatzek

Research output: ThesisMaster's / Diploma thesis

Abstract

Increasing traffic density, especially in urban areas, and the associated stricter exhaust gas standards are important reasons for car manufacturers to put more effort in the development of exhaust gas aftertreatment systems. However, quantities such as engine power or fuel consumption must not be affected. Nitrogen oxide emissions represent an essential component of the harmful exhaust products of internal combustion engines. These lead on the one hand to negative effects in the human respiratory system and contribute on the other hand significantly to the depletion of ozone. For this reason, so-called SCR (Selective Catalytic Reduction) systems have been developed, which aim at minimizing these emissions. The aim of the present work is to determine and evaluate the optimization potential of nitrogen oxide emissions for the fulfilment of current and future emission standards. For this purpose, a model is first developed which simulates the real behaviour of the SCR system as accurately as possible, on the condition that the model is not too complex in order to keep the computational effort for simulation and control design low. This model is subsequently identified and validated with the aid of measurement data acquired on the real system. Based on this model, an optimal urea solution dosing strategy is then calculated, which leads to reduced nitrogen oxide emissions compared to the ECU (engine control unit) strategy. This optimal policy is calculated for the test cycle of the Euro VI standard, the World Harmonized Transient Cycle (WHTC), using dynamic programming.
Original languageEnglish
Publication statusPublished - 2019

Fields of science

  • 206002 Electro-medical engineering
  • 207109 Pollutant emission
  • 202 Electrical Engineering, Electronics, Information Engineering
  • 202027 Mechatronics
  • 202034 Control engineering
  • 203027 Internal combustion engines
  • 206001 Biomedical engineering

JKU Focus areas

  • Digital Transformation
  • Sustainable Development: Responsible Technologies and Management

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