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Diesel engines are still widely used in heavy-duty engine industry because of their high energy conversion efficiency. In recent decades, governmental institutions limit the maximum acceptable hazardous emissions of diesel engines by stringent international regulations, which enforces engine manufacturers to find a solution for reducing the emissions while keeping the power requirements.A reliable model of the diesel engine combustion process can be quite useful to search for the best engine operating conditions. In this study, nonlinear modeling of a heavy-duty diesel engine NOx emission formation is presented. As a new experiment design, air-path and fuel-path input channels were excited by chirp signals where the frequency profile of each channel is different in terms of the number and the direction of the sweeps. This method is proposed as an alternative to the steadystate experiment design based modeling approach to substantially reduce testing time and improve modeling accuracy in transient operating conditions. Sigmoid based nonlinear autoregressive with exogenous input (NARX) model is employed to predict NOx emissions with given input set under both steady-state and transient cycles. Models for different values of parameters are generated to analyze the sensitivity to parameter changes and a parameter selection method using an easy-tointerpret map is proposed to find the best modeling parameters. Experimental results show that the steady-state and the transient validation accuracies for the majority of the obtained models are higher than 80% and 70%, respectively.

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Researchers in the automotive industry aim to enhance the performance, safety and energy management of intelligent vehicles with driver assistance systems. The performance of such systems can be improved with a better understanding of driving behaviors. In this paper, a driving behavior recognition algorithm is developed with a Long Short Term Memory (LSTM) Network using driver models of IPG's TruckMaker. Six driver models are designed based on longitudinal and lateral acceleration limits. The proposed algorithm is trained with driving signals of those drivers controlling a realistic truck model with five different trailer loads on an artificial training road. This training road is designed to cover possible road curves that can be seen in freeways and rural highways. Finally, the algorithm is tested with driving signals that are collected with the same method on a realistic road. Results show that the LSTM structure has a substantial capability to recognize dynamic relations between driving signals even in small time periods.