Diesel engines are widely used in automotive sector due to their high fuel efficiency, distinguished durability and great reliability. However, NOx and particulate matters (PM) are main concerns of the Diesel engines due to their lean burn conditions. To reduce these emissions, Diesel engines are usually coupled with state-of-the-art Diesel aftertreatment systems including a Diesel Oxidation Catalyst (DOC), a Diesel Particulate Filter (DPF), and a Selective Catalytic Reduction system (SCR). With increasingly stringent regulations, the estimation and control strategies of Diesel after-treatment systems for NOx and PM reduction are becoming more and more critical and challenging, especially under transient conditions with unknown system dynamics including disturbances and model uncertainties. To address these problems, this thesis focuses on advanced strategies based on disturbance estimation and compensation for improving the performance of Diesel after-treatment systems. Urea injection and ammonia storage ratio are critical for the SCR system to achieve high NOx reduction efficiency and low NH3 slip. Nevertheless, unknown system dynamics including input (urea injection) disturbances and model uncertainties of SCR system make it challenging to achieve high NOx reduction efficiency and low NH3 slip. To deal with these obstacles, Paper 1, Paper 2 and Paper 3 (Chapter 2, 3, and 4 respectively) proposed active disturbance estimation and compensation methods for enhancing the SCR performance. Paper 1 (Chapter 2) introduces two different methods to accurately detect urea injection and correct for urea dosing control. Paper 2 (Chapter 3) depicts a robust Nonlinear Disturbance Observer (robust NDO) to effectively estimate the ammonia storage ratio in a cost-effective way. Paper 3 (Chapter 4) presents a compound control strategies based on active disturbance rejection control (ADRC) to precisely keep NH3 slip low and achieve high NOx reduction efficiency. DOC thermal management is critical to effectively burn the soot during DPF regeneration (PM reduction). But unknown system dynamics including DOC inlet emissions and model uncertainties make it difficult for DOC mean temperature estimation and DOC outlet temperature control during DPF regeneration. To deal with these challenges, Paper 4 and Paper 5 (Chapter 5 and 6 respectively) developed active disturbance estimation and compensation strategies for improving DOC thermal management during DPF regeneration. Paper 4 (Chapter 5) introduces a robust filter based on Smooth Variable Structure Filter (SVSF) with augmented disturbance states to estimate the mean temperature of DOC. Paper 5 (Chapter 6) presents a composite controller combining a feedforward controller and an modified Active Disturbance Rejection Controller (mADRC) with time delay compensation for the DOC outlet temperature control. The proposed methods in the 5 papers are either validated by the calibrated GT-power model or experiments with Diesel after-treatment systems. / Thesis / Doctor of Philosophy (PhD)

<p>The NOx conversion efficiency of a combined Selective Catalytic Reduction and</p> <p>Ammonia Slip Catalyst (SCR-ASC) in a Diesel Aftertreatment (AT) system degrades with</p> <p>time. A novel model-informed data-driven On-Board Diagnostic (OBD) binary classification</p> <p>strategy is proposed in this paper to distinguish an End of Useful Life (EUL) SCR-ASC catalyst</p> <p>from Degreened (DG) ones. An optimized supervised machine learning model was used for the</p> <p>classification with a calibrated single-cell 3-state Continuous Stirred Tank Reactor (CSTR)</p> <p>observer used for state estimation. The method resulted in 87.5% classification accuracy when</p> <p>tested on 8 day-files from 4 trucks (2 day-files per truck; 1 DG and 1 EUL) operating in realworld on-road conditions.</p>