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Fuel chemistry plays a crucial role in the continued reduction of particulate emissions (PE) and cleaner air quality from vehicles and equipment powered by internal combustion engines (ICE). Over the past ten years, there have been great improvements in predictive particulate emissions indices (correlative mathematical models) based on the fuel’s composition. Examples of these particulate indices (PI) are the Honda Particulate Matter Index (PMI) and the General Motors Particulate Evaluation Index (PEI). However, the analytical chemistry lab methods used to generate data for these two PI indices are very time-consuming. Because gasoline can be mixtures of hundreds of hydrocarbon compounds, these lab methods typically include the use of the high resolution chromatographic separation techniques such as detailed hydrocarbon analysis (DHA), with 100m chromatography columns and long (3 - 4 hours) analysis times per sample.

As the proliferation of downsized boosted engines continues, it becomes increasingly important to understand how engine and vehicle hardware impact vehicle transient response. Several different methodologies can be used to understand hardware impacts, such as vehicle testing, 0-D vehicle models, and constant engine speed load steps. The next evolution of predicting vehicle transient response is to transition to a system level vehicle analysis by coupling a detailed engine model, utilizing crank angle resolved calculations, with a simple vehicle model. This allows for the evaluation of engine and vehicle hardware effects on vehicle acceleration and the rate of change of vehicle acceleration, or jerk, and the tradeoffs that can be made between the hardware in early program development. By comparing this system level vehicle model to the different methodologies, it can be shown that a system level vehicle analysis allows for higher fidelity evaluations of vehicle transient response.

To achieve high robustness and quality, automotive ECUs must initialize from low-power states as quickly as possible. However, microprocessor and memory advances have failed to keep pace with software image size growth in complex ECUs such as in Infotainment and Telematics. Loading the boot image from non-volatile storage to RAM and initializing the software can take a very long time to show the first screen and result in sluggish performance for a significant time thereafter which both degrade customer perceived quality. Designers of mobile devices such as portable phones, laptops, and tablets address this problem using Suspend mode whereby the main processor and peripheral devices are powered down during periods of inactivity, but memory contents are preserved by a small “self-refresh” current. When the device is turned back “on”, fully initialized memory content allows the system to initialize nearly instantaneously.

The impact of gasoline composition on vehicle particulate emissions response has been widely investigated and documented. Correlation equations between fuel composition and particulate emissions have also been documented, e.g. Particulate Matter Index (PMI) and Particulate Evaluation Index (PEI). Vehicle PM/PN emissions correlate very well with these indices. In a previous paper, global assessment with PEI on fuel sooting tendency was presented [1]. This paper will continue the previous theme by the authors, and cover China gasoline in more detail. With air pollution an increasing concern, along with more stringent emission requirements in China, both OEMs and oil industries are facing new challenges. Emissions controls require a systematic approach on both fuels and vehicles. Chinese production vehicle particulate emissions for a range of PEI fuels are also presented.

Over the past several years a task group within the SAE Automotive Corrosion and Protection (ACAP) Committee has conducted extensive on-vehicle field testing and numerous accelerated lab tests with the goal of establishing a standard accelerated test method for cosmetic corrosion evaluations of finished aluminum auto body panels. This project has been a cooperative effort with OEM, supplier, and consultant participation and was also supported in part by DOE through USAMP (AMD 309). The focus of this project has been the identification of a standardized accelerated cosmetic corrosion test that exhibits the same appearance, severity, and type of corrosion products that are exhibited on identical painted aluminum panels exposed to service relevant environments. Multi-year service relevant exposures were conducted by mounting panels on-vehicles in multiple locations in the US and Canada.

A gasoline’s chemical composition impacts a vehicle’s sooting tendency and therefore has been the subject of numerous emissions studies. From these studies, several mathematical correlation equations have been developed to predict a gasoline’s sooting tendency in modern spark-ignited internal combustion engine vehicles. This paper reviews the recently developed predictive tool methods and summarizes five years of global market fuel survey data to characterize gasoline sooting tendency trends around the world. Additionally, the paper will evaluate and suggest changes to the predictive methods to improve emissions correlations.

Electric vehicles have a strong potential to reduce a continued dependence on fossil fuels and help the environment by reducing pollution. Despite the desirable advantage, the introduction of electrified vehicles into the market place continues to be a challenge due to cost, safety, and life of the batteries. General Motors continues to bring vehicles to market with varying level of hybrid functionality. Since the introduction of Li-ion batteries by Sony Corporation in 1991 for the consumer market, significant progress has been made over the past 25 years. Due to market pull for consumer electronic products, power and energy densities have significantly increased, while costs have dropped. As a result, Li-ion batteries have become the technology of choice for automotive applications considering space and mass is very critical for the vehicles.

Sector mesh modeling is the dominant computational approach for combustion system design optimization. The aim of this work is to quantify the errors descending from the sector mesh approach through three geometric modeling approaches to an optical diesel engine. A full engine geometry mesh is created, including valves and intake and exhaust ports and runners, and a full-cycle flow simulation is performed until fired TDC. Next, an axisymmetric sector cylinder mesh is initialized with homogeneous bulk in-cylinder initial conditions initialized from the full-cycle simulation. Finally, a 360-degree azimuthal mesh of the cylinder is initialized with flow and thermodynamics fields at IVC mapped from the full engine geometry using a conservative interpolation approach. A study of the in-cylinder flow features until TDC showed that the geometric features on the cylinder head (valve tilt and protrusion into the combustion chamber, valve recesses) have a large impact on flow complexity.

A primary goal of large eddy simulation, LES, is to capture in-cylinder cycle-to-cycle variability, CCV. This is a first step to assess the efficacy of 35 consecutive computed motored cycles to capture the kinetic energy in the TCC-III engine. This includes both the intra-cycle production and dissipation as well as the kinetic energy CCV. The approach is to sample and compare the simulated three-dimensional velocity equivalently to the available two-component two-dimensional PIV velocity measurements. The volume-averaged scale-resolved kinetic energy from the LES is sampled in three slabs, which are volumes equal to the two axial and one azimuthal PIV fields-of-view and laser sheet thickness. Prior to the comparison, the effects of sampling a cutting plane versus a slab and slabs of different thicknesses are assessed. The effects of sampling only two components and three discrete planar regions is assessed.

Cast Al-Si alloys are widely used in automotive industry to produce structural components, such as engine block and cylinder head, because of the increasing demands in reducing mass for improved fuel efficiency. The fatigue performance of the castings is critical in their application. Porosity is highly detrimental to the fatigue behavior of cast Al-Si alloys. Therefore, accurate measurement of pore sizes is important in order to develop the correlations between porosity and fatigue strength. However, quantification of porosity is challenging and shows large variation depending on the measurement methods, particularly for micro-shrinkage porosity due to the torturous and complex morphology. The conventional metallographic image analysis method in the 2D polished surface often underestimates the actual pore size particularly when the porosity morphology is complex.

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.

Prior to making rotational vibration measurements with a laser vibrometer, it is good practice to establish that the instrument is operating properly. This can be accomplished by comparative measurement of a rotational vibration source with known amplitude and frequency. This paper describes the design and development of a rotational vibration apparatus with known amplitude and frequency to be used as a reference for comparison to concurrent and co-located measurements made by a rotational laser vibrometer (RLV). The comparative measurements acquired with the apparatus are helpful to verify proper laser vibrometer operation in between regular calibration intervals, and/or whenever the functionality of the vibrometer is suspect. In the subject apparatus, a Cardan shaft with variable input speed and angle is used to provide output torsional vibration with variable frequency and amplitude.

GM's R oad-to- L ab-to- M ath (RLM) initiative is a fundamental engineering strategy leading to higher quality design, reduced structural cost, and improved product development time. GM started the RLM initiative several years ago and the RLM initiative has already provided successful results. The purpose of this paper is to detail the specific RLM efforts at GM related to powertrain controls development and calibration. This paper will focus on the current state of the art but will also examine the history and the future of these related activities. This paper will present a controls development environment and methodology for providing powertrain controls developers with virtual (in the absence of ECU and vehicle hardware) calibration capabilities within their current desktop controls development environment.

The front camera module is a fundamental component of a modern vehicle’s active safety architecture. The module supports many active safety features. Perception of the road environment, requests for driver notification or alert, and requests for vehicle actuation are among the camera software’s key functions. This paper presents a novel method of testing these functions virtually. First, the front camera module software is compiled and packaged in a Docker container capable of running on a standard Linux computer as a software in the loop (SiL). This container is then integrated with the active safety simulation tool that represents the vehicle plant model and allows modeling of test scenarios. Then the following simulation components form a closed loop: First, the active safety simulation tool generates a video data stream (VDS). Using an internet protocol, the tool sends the VDS to the camera SiL and other vehicle channels.