Data driven nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions

Official URL: http://risc01.sabanciuniv.edu/record=b2313612_ (Table of contents)

Diesel engines' reliable and durable structures, high torque generation capabilities at low speeds, and fuel consumption efficiencies make them irreplaceable for heavy-duty vehicles in the market. However, ine ciencies in the combustion process result in the release of emissions to the environment. In addition to the restrictive international regulations for emissions, the competitive demands for more powerful engines and increasing fuel prices obligate heavy-duty engine and vehicle manufacturers to seek for solutions to reduce the emissions while meeting the performance requirements. In line with these objectives, remarkable progress has been made in modern diesel engine systems such as air handling, fuel injection, combustion, and after-treatment. However, such systems utilize quite sophisticated equipment with a large number of calibratable parameters that increases the experimentation time and effort to find the optimal operating points. Therefore, a dynamic model-based transient calibration is required for an e cient combustion optimization which obeys the emission limits, and meets the desired power and efficiency requirements. This thesis is about developing optimizationoriented high delity nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions. Contributions of the thesis are: (i) A new design of experiments is proposed where air-path and fuel-path input channels are excited by chirp signals with varying frequency pro les in terms of the number and directions of the sweeps. The proposed approach is a strong alternative to the steady-state experiment based approaches to reduce the testing time considerably and improve the modeling accuracy in both steady-state and transient conditions. (ii) A nonlinear nite impulse response (NFIR) model is developed to predict indicated torque by including the estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer. (iii) Two different nonlinear autoregressive with exogenous input (NARX) models are proposed to predict NOx emissions. In the first structure, input regressor set for the nonlinear part of the model is reduced by an orthogonal least square (OLS) algorithm to increase the robustness and decrease the sensitivity to parameter changes, and linear output feedback is employed. In the second structure, only the previous output is used as the output regressor in the model due to the stability considerations. (iv) An analysis of model sensitivities to parameter changes is conducted and an easy-tointerpret map is introduced to select the best modeling parameters with limited testing time in powertrain development. (v) Soot (particulated matter) emission is predicted using LSTM type networks which provide more accurate and smoother predictions than NARX models. Experimental results obtained from the engine dynamometer tests show the e ectiveness of the proposed models in terms of prediction accuracies in both NEDC (New European Driving Cycle) and WHTC (World Harmonized Transient Cycle) cycles