PHYSICS-BASED DIESEL ENGINE MODEL DEVELOPMENT CALIBRATION AND VALIDATION FOR ACCURATE CYLINDER PARAMETERS AND NOX PREDICTION

Stringent regulatory requirements and modern diesel engine technologies have engaged automotive manufacturers and researchers in accurately predicting and controlling diesel engine-out emissions. As a result, engine control systems have become more complex and opaquer, increasing the development time and costs. To address this challenge, Model-based control methods are an effective way to deal with the criticality of the system study and controls. And physics-based combustion engine modeling is a key to achieve it. This thesis focuses on development and validation of a physics-based model for both engine and emissions using model-based design tools from MATLAB & Simulink. Engine model equipped with exhaust gas circulation and variable geometry turbine is adopted from the previously done work which was then integrated with the combustion and emission model that predicts the heat release rates and NO_x emission from engine. Combustion model is designed based on the mass fraction burnt from CA10 to CA90 and then NO_x predicted using the extended Zeldovich mechanism. The engine models are tuned for both steady state and dynamics test points to account for engine operating range from the performance data. Various engine and combustion parameters are estimated using parameter estimation toolbox from MATLAB and Simulink by applying least squared solver to minimize the error between measured and estimated variables. This model is validated against the virtual engine model developed in GT-power for Cummins 6.7L turbo diesel engine. To account the harmonization of the testing cycles to save engine development time globally, a world harmonized stationary cycle (WHSC) is used for the validation. Sub-systems are validated individually as well as in loop with a complete model for WHSC. Engine model validation showed promising accuracy of more than 88.4 percent in average for the desired parameters required for the NO_x prediction. NO_x estimation is accurate for the cycle except warm up and cool down phase. However, NO_x prediction during these phases is limited due to actual NO_x measured data for tuning the model for real time NO_x estimation. Results are summarized at the end to compare the trend of NO_x estimation from the developed combustion and emission model to show the accuracy of in-cylinder parameters and required for the NO_x estimation.