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For (benchmark) tests it is not only useful to study the acoustic performance of the whole vehicle, but also to assess separate components such as the engine. Reflections inside the engine bay bias the acoustic radiation estimated with sound pressure based solutions. Consequently, most current methods require dismounting the engine from the car and installing it in an anechoic room to measure the sound emitted. However, this process is laborious and hard to perform. In this paper, two particle velocity based methods are proposed to characterize the sound radiated from an engine while it is still installed in the car. Particle velocity sensors are much less affected by reflections than sound pressure microphones when the measurements are performed near a radiating surface due to the particle velocity's vector nature, intrinsic dependency upon surface displacement and directivity of the sensor. Therefore, the engine does not have to be disassembled, which saves time and money.

The Particulate Matter Index (PMI) is a tool that provides an indication of a fuel’s tendency to produce Particulate Matter (PM) emissions. Currently, the index is being used by various fuel laboratories and the Automotive OEMs as a tool to understand the gasoline fuel’s impact on both PM from engine hardware and vehicle-out emissions. In addition, a newer index that could be used to give an indication of the PM tendency of the gasoline range fuels, called the Particulate Evaluation Index (PEI), is shown to have a good correlation to PMI. The data used in those indices are collected from chemical analytical methods. This paper will compare gas chromatography (GC) methods used by three laboratories and discuss how the different techniques may affect the PMI and PEI calculation.

CO2 emission is more serious in recent years and automobile manufacturers are interested in developing technologies to reduce CO2 emissions. Among various environmental-technologies, the use of solar roof as an electric energy source has been studied extensively. For example, in order to reduce the cabin ambient temperature, automotive manufacturers offer the option of mounting a solar cell on the roof of the vehicle [1]. In this paper, we introduce the semi-transparent solar cell mounted on a curved roof glass and we propose a solar energy management system to efficiently integrate the electricity generated from the solar roof into internal combustion engine (ICE) vehicles. In order to achieve a high efficiency solar system in different driving, we improve the usable power other than peak power of solar roof. Peak power or rated power is measured power (W) in standard test condition (@ 25°C, light intensity of 1000W/m2(=1Sun)).

Fuel lean combustion and exhaust gas dilution are known to increase the thermal efficiency and reduce NOx emissions. In this study, experiments are performed to understand the effect of equivalence ratio on flame kernel formation and flame propagation around the spark plug for different low turbulent velocities. A series of experiments are carried out for propane-air mixtures to simulate engine-like conditions. For these experiments, equivalence ratios of 0.7 and 0.9 are tested with 20 percent mass-based exhaust gas recirculation (EGR). Turbulence is generated by a shrouded fan design in the vicinity of J-spark plug. A closed loop feedback control system is used for the fan to generate a consistent flow field. The flow profile is characterized by using Particle Image Velocimetry (PIV) technique. High-speed Schlieren visualization is used for the spark formation and flame propagation.

Pressure variation during engine combustion generates torque fluctuation that is delivered through the driveline. Torque fluctuation delivered to the tire shakes the vehicle body and causes the body components to vibrate, resulting in booming noise. HKMC (Hyundai Kia Motor Company)’s TMED (Transmission Mounted Electric Device) type generates booming noises due to increased weight from the addition of customized hybrid parts and the absence of a torque converter. Some of the improvements needed to overcome this weakness include reducing the torsion-damper stiffness, adding dynamic dampers, and moving the operation point of the engine from the optimized point. These modifications have some potential negative impacts such as increased cost and sacrificed fuel economy. Here, we introduce a method of reducing lock-up booming noise in an HEV at low engine speed.

The purge pump is used to pull evaporative gases from canister and send to engine for combustion in Turbocharged engines. The purge pump with impeller at one end and electric motor at the other end is supported by the ball bearing assembly. A bearing kinematic model to predict forcing function due to defect in ball bearing arrangement, coupled with bearing dynamic model of rotor because of rotating component, is proposed in this paper to get accumulated effect on transmitted force to the purge pump housing. Rotor dynamic of purge pump rotor components only produces certain order forcing responses which can be simulated into the multibody software environment, knowing the ball bearing geometry parameters hence providing stiffness parameter for rotor system.

An investigation of high speed direct injection (DI) compression ignition (CI) engine combustion fueled with gasoline (termed GDICI for Gasoline Direct-Injection Compression Ignition) in the low temperature combustion (LTC) regime is presented. As an aid to plan engine experiments at full load (16 bar IMEP, 2500 rev/min), exploration of operating conditions was first performed numerically employing a multi-dimensional CFD code, KIVA-ERC-Chemkin, that features improved sub-models and the Chemkin library. The oxidation chemistry of the fuel was calculated using a reduced mechanism for primary reference fuel combustion. Operation ranges of a light-duty diesel engine operating with GDICI combustion with constraints of combustion efficiency, noise level (pressure rise rate) and emissions were identified as functions of injection timings, exhaust gas recirculation rate and the fuel split ratio of double-pulse injections.

The diesel particulate filters (DPF) are considered the most robust technologies for particle emission reduction both in terms of mass and number. On the other hand, the increase of the backpressure in the exhaust system due to the accumulation of the particles in the filter walls leads to an increase of the engine fuel consumption and engine power reduction. To limit the filter loading, and the backpressure, a periodical regeneration is needed. Because of the growing interest about particle emission both in terms of mass, number and size, it appears important to monitor the evolution of the particle mass and number concentrations and size distribution during the regeneration of the DPFs. For this matter, in the presented work the regeneration of a catalyzed filter was fully analyzed. Particular attention was dedicated to the dynamic evolution both of the thermodynamic parameters and particle emissions.

The present paper describes the results of a cooperative research project between GM Powertrain Europe and Istituto Motori - CNR aimed at studying the capability of GM Combustion Closed-Loop Control (CLCC) in enabling seamless operation with high biodiesel blending levels in a modern diesel engine for passenger car applications. As a matter of fact, fuelling modern electronically-controlled diesel engines with high blends of biodiesel leads to a performance reduction of about 12-15% at rated power and up to 30% in the low-end torque, while increasing significantly the engine-out NOx emissions. These effects are both due to the interaction of the biodiesel properties with the control logic of the electronic control unit, which is calibrated for diesel operation. However, as the authors previously demonstrated, if engine calibration is re-tuned for biodiesel fuelling, the above mentioned drawbacks can be compensated and the biodiesel environmental inner qualities can be fully deployed.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

In light-duty direct-injection (DI) diesel engines, combustion chamber geometry influences the complex interactions between swirl and squish flows, spray-wall interactions, as well as late-cycle mixing. Because of these interactions, piston bowl geometry significantly affects fuel efficiency and emissions behavior. However, due to lack of reliable in-cylinder measurements, the mechanisms responsible for piston-induced changes in engine behavior are not well understood. Non-intrusive, in situ optical measurement techniques are necessary to provide a deeper understanding of the piston geometry effect on in-cylinder processes and to assist in the development of predictive engine simulation models. This study compares two substantially different piston bowls with geometries representative of existing technology: a conventional re-entrant bowl and a stepped-lip bowl. Both pistons are tested in a single-cylinder optical diesel engine under identical boundary conditions.

More stringent emissions regulations are continually being proposed to mitigate adverse human health and environmental impacts of internal combustion engines. With that in mind, it has been proposed that vehicular particulate matter (PM) emissions should be regulated based on particle number in addition to particle mass. One aspect of this project is to study different sample handling methods for number-based aerosol measurements, specifically, two different methods for removing volatile organic compounds (VOCs). One method is a thermodenuder (TD) and the other is an evaporative chamber/diluter (EvCh). These sample-handling methods have been implemented in an engine test cell with a spark-ignited direct injection (SIDI) engine. The engine was designed for stoichiometric, homogeneous combustion.

There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential “off-cycle” impacts, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing.

Adopting the Spray-guided Gasoline Direct Injection (SGDI) concept, a new multi-cylinder engine has designed. The engine has piezo injectors at the central position of its combustion chamber, while sparkplugs are also at the center. The sparkplug location is designed so that the spark location is at the outer boundary of the fuel spray where the appropriate air-fuel mixture is formed. A few important operating parameters are chosen to investigate their effects on the combustion stability and fuel consumption. The final experimental results show a good potential of the SGDI engine; the fuel consumption rate was much less than that of the base Multi Port Injection (MPI) engine at various engine operating conditions.

The purpose of this paper is to investigate the air/fuel mixture formation and combustion characteristics in a spray-guided GDI engine using a commercial code, STAR-CD. This engine adopted the outwardly opening injector located in the center of cylinder head, which forms a hollow cone spray. The spray injection was modeled arranging multiple points using random function along the ring-shaped nozzle exit. To predict the breakup of spray, Reitz-Diwakar's breakup model was used, and the model constants were calibrated against published experimental data in a constant volume chamber. The validated spray models were applied to the analysis of spray behavior and mixture formation process inside the engine combustion chamber under operating condition of ultra-lean mixture (λ ≈ 4). To predict the combustion process, the modified eddy breakup combustion model was applied.

CAI (Controlled Auto Ignition) combustion is already well known to be advantageous over conventional cycles in that it facilitates higher engine efficiency and has low emission characteristics. The CAI combustion process is mainly governed by in-cylinder RGF (Residual Gas Fraction), therefore achieving good control of in-cylinder RGF is essential in the development of CAI combustion engine. Usually, in-cylinder RGF controlled via low lift cam, short valve duration and negative valve overlap. More importantly on the other hand, accurate and instantaneous prediction of RGF must be done as a prerequisite to control. However, on-line prediction of RGF is not always practical due to the requirement of expensive fast response exhaust gas analyzers in the empirical case or otherwise due to theoretical models which are just too slow for application by means of simulation solving. In this paper, a newly enhanced theoretical model for predicting on-line in-cylinder RGF is introduced.

In this study a new model is proposed for turbulent premixed combustion in a spark-ignition engine. An independent transport equation is solved for the mean reaction progress variable in a propagation form in KIVA-3V. An expression for turbulent burning velocity was previously given as a product of turbulent diffusivity in unburned gas, laminar flame speed and maximum flame surface density. The model has similarity with the G equation approach, but originates from zone conditionally averaged formulation for unburned gas. A spark kernel grows initially as a laminar flame and becomes a fully developed turbulent flame brush according to a transition criterion in terms of the kernel size and the integral length scale. Simulation of a homogeneous charge pancake chamber engine showed good agreement with measured flame propagation and pressure trace. The model was also applied against experimental data of Hyundai θ-2.0L SI engine.

The development of a new method to evaluate the NVH quality of diesel combustion noise bases upon following questions by regarding typical driving modes: Driving behavior with diesel vehicles Which driving situation causes an annoying diesel combustion noise Judgment of diesel combustion noise as good or bad A suitable test course was determined to regard typical driving situations as well as the European driving behavior. Vehicles of different segments were tested on that course. The recorded driving style and the simultaneously given comments on the diesel combustion noise results to a typical driving mode linked to acoustics sensation of diesel combustion noise. The next step was to simulate this driving mode on the chassis dynamometer for acoustical measurements. The recordings of several vehicles were evaluated in listening test to identify a metric. The base of metric was objective analyses evaluating diesel combustion noise in relevant driving situations.

For a complete automotive HVAC system, it is desirable to keep good air quality control for the interior vehicle cabin. This experimental study for evaluating the CO2 concentration levels in a vehicle cabin was done on the roads in South Korea. Increasing levels of CO2 can cause a passenger to become tired, sleepy and cause headaches or discomfort. The study results shows that CO2 and fine dust concentration is a result of the number of passengers,_driving condition and HVAC user settings. The result from this investigation can be used to establish a development guide for air quality in a vehicle cabin.

The combustion characteristics of a HSDI diesel engine were analyzed numerically using a reduced chemical kinetics. The reaction mechanism consisting of 26 steps and 17 species including the Zel'dovich NOx mechanism for the higher hydrocarbon fuel was implemented in the KIVA-3V. The characteristic time scale model was adopted to account for the effects of turbulent mixing on the reaction rates. The soot formation and oxidation processes are represented by Hiroyasu's model and NSC's model. The validation cases include the homogenous fuel/air mixture and the spray combustion in a constant volume chamber. After the validation, the present approach was applied to the analysis of the spray combustion processes in a HSDI diesel engine. The present approach reasonably well predicts the ignition delay, combustion processes, and emission characteristics in the high-pressure turbulent spray flame-field encountered in the practical HSDI diesel engines.