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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.

As a result of stringent global regulations on fuel economy and CO2 emissions, the development of high-efficiency SI engines is more urgent now than ever before. Along with advanced techniques in friction reduction, many researchers endeavor to decrease the B/S (bore-to-stroke) ratio from 1.0 (square) to a certain value, which is expected to reduce the heat loss and enhance the burning rate of SI engines. In this study, the effects of B/S ratios were investigated in aspects of efficiency and knock characteristics using a single-cylinder LIVC (late intake valve closing) GDI (gasoline direct injection) engine. Three B/S ratios (0.68, 0.83 and 1.00) were tested under the same mechanical compression ratio of 12:1 and the same displacement volume of 0.5 L. The head tumble ratio was maintained at the same level to solely investigate the effects of geometrical changes caused by variations in the B/S ratio.

Improving fuel efficiency and emission characteristics are significant issues in engine research. Because the engine has complex systems and various operating parameters, the experimental research is limited by cost and time. One-dimensional (1D) simulation has attracted the attention of researchers because of its effectiveness and relatively high accuracy. In a 1D simulation, the applied model must be accurate for the reliability of the simulation results. Because in-cylinder turbulence mainly determines the combustion characteristics, and mean flow velocity affects the in-cylinder heat transfer and efficiency in a spark-ignited (SI) engine, a number of sophisticated models have been developed to predict in-cylinder turbulence and mean flow velocity. In particular, tumble is a significant factor of in-cylinder turbulence in SI engine.

The increasing interest in the application of Large Eddy Simulation (LES) to Internal Combustion Engines (hereafter ICEs) flows is motivated by its capability to capture spatial and temporal evolution of turbulent flow structures. Furthermore, LES is universally recognized as capable of simulating highly unsteady and random phenomena driving cycle-to-cycle variability (CCV) and cycle-resolved events such as knock and misfire. Several quality criteria were proposed in the recent past to estimate LES uncertainty: however, definitive conclusions on LES quality criteria for ICEs are still far to be found. This paper describes the application of LES quality criteria to the TCC-III single-cylinder optical engine from University of Michigan and GM Global R&D; the analyses are carried out under motored condition.

The role of 1D simulation tool is growing as the engine system is becoming more complex with the adoption of a variety of new technologies. For the reliability of the 1D simulation results, it is necessary to improve the accuracy and applicability of the combustion model implemented in the 1D simulation tool. Since the combustion process in SI engine is mainly determined by the turbulence, many models have been concentrating on the prediction of the evolution of in-cylinder turbulence intensity. In this study, two turbulence models which can resemble the turbulence intensity close to that of 3D CFD tool were utilized. The first model is dedicated to predicting the evolution of turbulence intensity during intake and compression strokes so that the turbulence intensity at the spark timing can be estimated properly. The second model is responsible for predicting the turbulence intensity of burned and unburned zone during the combustion process.

Diesel engine noise is classified into mechanical noise, flow dynamic noise and combustion noise. Among these, combustion noise level is higher than the others due to the high compression ratio of diesel combustion and auto ignition. The injected fuel is mixed with air in the ignition delay process, followed by simultaneous ignition of the premixed mixture. This process results in a rapid pressure rise, which is the main source of combustion noise. The amount of fuel burned during premixed combustion is mainly affected by the ignition delay. The exhaust gas recirculation (EGR) rate has an impact on ignition delay, and thus, it influences the combustion noise characteristics. Therefore, during the transient state, the combustion noise characteristics change as the EGR rate deviates from the target value. In this study, the effect of the EGR rate deviation during the transient state of the combustion noise is examined. A 1.6 liter diesel engine with a VGT was used for the experiment.

A waste heat recovery system is applied to a heavy-duty series hybrid electric vehicle. The engine in a series hybrid electric vehicle can operate at steady state for most of the time because the engine and drivetrain are decoupled, providing the waste heat recovery system with a steady state heat source. Thus, it is possible to optimize the waste heat recovery system design while maximizing the amount of useful energy converted in the system. To realize such a waste heat recovery system, the Rankine steam cycle is selected for the bottoming cycle. The heat exchanger is implemented as a quasi-1D simulation model to calculate the accurate quantity of recovered energy and to determine the working fluid state. The optimal geometric characteristics of the heat exchanger and the efficiency are considered according to the working fluid. The Rankine steam cycle model is constructed, and the output power is calculated.

A gasoline homogeneous charged compression ignition (HCCI) called the controlled auto ignition (CAI) engine is an alternative to conventional gasoline engines with higher efficiency and lower emission levels. However, noise and vibration are currently major problems in the CAI engine. The problems result from fast burning speeds during combustion, because in the CAI engine combustion is controlled by auto-ignition rather than the flame. Thus, the ignition delay of the local mixture has to vary according to the location in the combustion chamber to avoid noise and vibration. For making different ignition delays, stratification of temperature or mixing ratio was tested in this study. In charge stratification, which determines the difference between the start of combustion among charges with different properties, two kinds of mixtures with different properties flow into two intake ports.

Ignition delay of the second injection of HSDI diesel engines is generally much shorter than that of the first injection because of the interaction between the radicals generated during the combustion process and the mixed gas of the second injection. Although previous Diesel combustion models could not explain this reaction, Hasse and Peters described the mass and heat transfer of the second injection and estimated the ignition delay of the second injection using two-dimensional flamelet equations. But a simulation of the two-dimensional flamelet equations requires enormous computational time. Thus, to analyze the combustion phenomena of the multiple injection mode in HSDI diesel engines effectively, the two-dimensional flamelet combustion model was modified in this study. To reduce the calculation time, two-dimensional flamelet equations were only applied near the stoichiometric region.