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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.

The measurement of spray penetration length is one of crucial tasks for understanding the characteristics of diesel spray and combustion. For this reason, many researchers have devised various measurement techniques, including Mie scattering, schlieren photography, and laser induced exciplex fluorescence (LIEF). However, the requirements of expensive lasers, complicated optics, delicate setups, and tracers that affect fuel characteristics have been disadvantages of previous techniques. In this study, the background-oriented schlieren (BOS) technique is employed to measure the vapor penetration length of diesel spray for the first time. The BOS technique has a number of benefits over the previous techniques because of its quantitative, non-intrusive nature which does not require lasers, mirrors, optical filters, or fuel tracers.

The liquid fuel film on the cylinder liner is believed to be a major source of engine-out hydrocarbon emissions in SI engines, especially during cold start and warm-up period. Quantifying the liquid fuel film on the cylinder liner is essential to understand the engine-out hydrocarbon emissions formation in SI engines. In this research, two-dimensional visualization was carried out to quantify liquid fuel film on the quartz cylinder liner in an SI engine test rig. In addition, comparing visualization results with the trend of hydrocarbon emissions in this engine, the effect of cylinder wall-wetting during a simulated cold start and warmed-up condition was investigated with the engine experiment. The visualization was based on laser-induced fluorescence and total reflection. Using a quartz liner and a special lens, only the liquid fuel on the liner was visualized.

In recent years, Large-Eddy Simulation (LES) is spotlighted as an engineering tool and severe research efforts are carried out on its applicability to Internal Combustion Engines (ICEs). However, there is a general lack of definitive conclusions on LES quality criteria for ICE. This paper focuses on the application of LES quality criteria to ICE and to their correlation, in order to draw a solid background on future LES quality assessments for ICE. In this paper, TCC-III single-cylinder optical engine from University of Michigan is investigated and the analysis is conducted under motored condition. LES quality is mainly affected by grid size and type, sub-grid scale (SGS) model, numeric schemes. In this study, the same grid size and type are used in order to focus on the effect on LES quality of SGS models and blending factors of numeric scheme only.

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.

Currently, diesel engine-out exhaust NOx emission level prediction is a major challenge for complying with the stricter emission legislation and for control purpose of the after-treatment system. Most of the NOx prediction research is based on the Zeldovich thermal mechanism, which is reasonable from the physical point of view and for its simplicity. Nevertheless, there are some predictable range limitations, such as low temperature with high EGR rate operating conditions or high temperature with low EGR rates. In the present paper, 3 additional considerations, pilot burned gas mixing before the main injection; major NO formation area; concentration correction, were applied to the previously developed real-time NO estimation model based on in-cylinder pressure and data available from ECU. The model improvement was verified on a 1.6 liter EURO5 diesel engine in both steady and transient operation.

This paper provides an overview of spark-ignition engine unburned hydrocarbon emissions mechanisms, and then uses this framework to relate measured engine-out hydrocarbon emission levels to the processes within the engine from which they result. Typically, spark-ignition engine-out HC levels are 1.5 to 2 percent of the gasoline fuel flow into the engine; about half this amount is unburned fuel and half is partially reacted fuel components. The different mechanisms by which hydrocarbons in the gasoline escape burning during the normal engine combustion process are described and approximately quantified. The in-cylinder oxidation of these HC during the expansion and exhaust processes, the fraction which exit the cylinder, and the fraction oxidized in the exhaust port and manifold are also estimated.

The fuel injection process in the port of a firing 4-valve SI engine at part load and 25°C head temperature was observed by a high speed video camera. Fuel was injected when the valve was closed. The reverse blow-down flow when the intake valve opens has been identified as an important factor in the mixture preparation process because it not only alters the thermal environment of the intake port, but also strip-atomizes the liquid film at the vicinity of the intake valve and carries the droplets away from the engine. In a series of “fuel-on” experiments, the fuel injected in the current cycle was observed to influence the fuel delivery to the engine in the subsequent cycles.

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.