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The recent development pace of the automotive technology is so rapid worldwide. Especially in a green car, hybrid electric vehicles (HEVs) have been studied a lot due to their significant effects on the urban driving. In the vehicle energy management strategy study, the driving speed is assumed to be known in advance, however the speed is not given in a real world. Accordingly, the prediction of vehicle speed is very important. In this study, we study the prediction methodology for the speed prediction using deep learning. Based on the vehicle driving speed data, the supervised deep learning has been used and the speed prediction accuracy using deep learning shows accurate results comparing to the actual speed. The supervised deep learning is used which is suitable for driving cycle database. As a result, the speed prediction after few seconds is feasible.

Much research has been devoted to reducing NOx and soot emissions simultaneously in diesel engines. The low temperature combustion (LTC) concept has the potential to reduce these emissions at the same time, but it has limitations to its commercialization. In-cylinder EGR stratification is another combustion concept meant to reduce both types of emissions simultaneously using non-uniform in-cylinder EGR gas distribution. The EGR stratification concept uses a locally high EGR region of the in-cylinder so that the emissions can be reduced without increasing the overall EGR rate. In this study, the EGR stratification concept was improved with a CFD-based analysis. First, a two-step piston was developed to maximize the stratified EGR effect. Then, the feasibility of combustion and emission control by stratified EGR was evaluated under cases of artificially distributed EGR stratification and conventional diesel engine conditions.

Regulations regarding vehicles’ CO2 emissions are continuing to become stricter due to global warming. The CO2 regulations urge automobile manufacturers to develop gasoline engines with improved efficiency; however, the main obstacle to the improvement is the knock phenomenon in spark-ignition engines. If knock is predicted, the efficiency potential can be maximized in an engine by applying modest spark timing. Several research regarding knock prediction modeling have been conducted, and typically Livengood-Wu integral model is used to predict the knock occurrence. For the prediction, knock onset should be determined on a given pressure signal of given knock cycles for establishing the 0D ignition delay model. Several methodologies for knock onset determination have been developed because checking all the knock onset position by hand is impossible considering the breadth of data sets.