Search Results

In response to global climate change, there is a widespread push to reduce carbon emissions in the transportation sector. For the difficult to decarbonize heavy-duty (HD) vehicle sector, lower carbon intensity fuels can offer a low-cost, near-term solution for CO2 reduction. The use of natural gas can provide such an alternative for HD vehicles while the increasing availability of renewable natural gas affords the opportunity for much deeper reductions in net-CO2 emissions. With this in consideration, the US National Renewable Energy Laboratory launched the Natural Gas Vehicle Research and Development Project to stimulate advancements in technology and availability of natural gas vehicles. As part of this program, Southwest Research Institute developed a hybrid-electric medium-HD vehicle (class 6) to demonstrate a substantial CO2 reduction over the baseline diesel vehicle and ultra-low NOx emissions.

Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model.

Improving passenger safety inside vehicle cabins requires continuously monitoring vehicle seat occupancy statuses. Monitoring a vehicle seat’s occupancy status includes detecting if the seat is occupied and classifying the seat’s occupancy type. This paper introduces an innovative non-intrusive technique that employs capacitive sensing and an occupancy classifier to monitor a vehicle seat’s occupancy status. Capacitive sensing is facilitated by a meticulously constructed capacitance-sensing mat that easily integrates with any vehicle seat. When a passenger or an inanimate object occupies a vehicle seat equipped with the mat, they will induce variations in the mat’s internal capacitances. The variations are, in turn, represented pictorially as grayscale capacitance-sensing images (CSI), which yield the feature vectors the classifier requires to classify the seat’s occupancy type.

Metal cutting/machining is a widely used manufacturing process for producing high-precision parts at a low cost and with high throughput. In the automotive industry, engine components such as cylinder heads or engine blocks are all manufactured using such processes. Despite its cost benefits, manufacturers often face the problem of machining chips and cutting oil residue remaining on the finished surface or falling into the internal cavities after machining operations, and these wastes can be very difficult to clean. While part cleaning/washing equipment suppliers often claim that their washers have superior performance, determining the washing efficiency is challenging without means to visualize the water flow. In this paper, a virtual engineering methodology using particle-based CFD is developed to address the issue of metal chip cleanliness resulting from engine component machining operations. This methodology comprises two simulation methods.

Engine knock is one of the limiting factors in determining the compression ratio and engine efficiency for spark ignited engines. Using the Southwest Research Institute Knock-CoV test method, it was previously shown that the knock limited load versus combustion phasing (CA50) has a constant slope. All of the knock mitigation strategies tested provided a shift to these knock limited loads but also increased the slope. That is, for the same CA50 retard the knock limited load could be increased more. Our hypothesis was that due to fuel sensitivity, or the difference between the RON and MON, the reactions that lead to knock will behave differently as the pressure-temperature history changes with engine speeds and loads. The fuel affects on the knock and CoV limits were studied by testing fuels with various sensitivities including methanol, E85 (85% ethanol) and Iso-octane.

Off-road diesel engines remain one of the most significant contributors to the overall oxides of nitrogen (NOX) inventory and the California Air Resources Board (CARB) has indicated that reductions of up to 90% from current standards may be necessary to achieve its air quality goals. In recognition of this, CARB has funded a program aimed at demonstrating emission control technologies for off-road engines. This program builds on previous efforts to demonstrate Low NOX technologies for on-road engines. The objective was to demonstrate technologies to reduce tailpipe NOX and particulate matter (PM) emissions by 90 and 75%, respectively, from the current Tier 4 Final standards. In addition, the emission reductions were to be achieved while also demonstrating a 5 to 8.6% carbon dioxide (CO2) reduction and remaining Greenhouse Gas (GHG) neutral with respect to nitrous oxide (N2O) and methane (CH4).

This work represents an advanced engineering research project partially funded by the U.S. Department of Energy (DOE). Ford Motor Company, FEV North America, and Oak Ridge National Laboratory collaborated to develop a next generation boosted spark ignited engine concept. The project goals, specified by the DOE, were 23% improved fuel economy and 15% reduced weight relative to a 2015 or newer light-duty vehicle. The fuel economy goal was achieved by designing an engine incorporating high geometric compression ratio, high dilution tolerance, low pumping work, and low friction. The increased tendency for knock with high compression ratio was addressed using early intake valve closing (EIVC), cooled exhaust gas recirculation (EGR), an active pre-chamber ignition system, and careful management of the fresh charge temperature.

Biodiesel (i.e., mono-alkyl esters of long chain fatty acids derived from vegetable oils and animal fats) is a renewable diesel fuel providing life-cycle greenhouse gas emission reductions relative to petroleum-derived diesel. With the expectation that there would be widespread use of biodiesel as a substitute for ultra-low sulfur diesel (ULSD), there have been many studies looking into the effects of biodiesel on engine and aftertreatment, particularly its compatibility to the current aftertreatment technologies. The objective of this study was to generate experimental data to measure the effectiveness of a current technology diesel oxidation catalysts (DOC) to oxidize soy-based biodiesel at various blend levels with ULSD. Biodiesel blends from 0 to 100% were evaluated on an engine using a conventional DOC.

Hydrogen Internal Combustion Engines (H2 ICE) are gaining recognition as a nearly emission-free alternative to traditional ICE engines. However, H2 ICE systems face challenges related to thermal management, N2O emissions, and reduced SCR efficiency in high humidity conditions (15% H2O). This study assesses how hydrogen in the exhaust affects after-treatment system components for H2 ICE engines, such as Selective Catalytic Reduction (SCR), Hydrogen Oxidation Catalyst (HOC), and Ammonia Slip Catalyst (ASC). Steady-state experiments with inlet H2 inlet concentrations of 0.25% to 1% and gas stream moisture levels of up to 15% H2O were conducted to characterize the catalyst response to H2 ICE exhaust. The data was used to calibrate and validate system component models, forming the basis for a system simulation.

The process of assembling the bearing and crimp ring to the steering pinion shaft is intricate. The bearing is pressed into its position via the crimp ring, which is tipped inward and fully fitted into a groove on the pinion shaft. Only when the bearing is pressed to a low surface on the pinion shaft, the caulking force for the crimp ring is achieved. The final caulking distance for the crimp ring confirms the proper bearing position. Simulating this transient fitting process using CAE is a challenging topic. Key factors include controlling applied force, defining contact between bearing and pinion surface, and defining contact between crimp ring and bearing surface from full close to half open transition. The overall CAE process is validated through correlation with testing.

This work aims to develop a PA6 nanocomposite with glass fiber (GF) and graphene nanoplatelets (GNPs) focusing on automotive parts application. Polyamide 6 is a semi-crystalline polymer that exhibits high fatigue and flexural strength, making it viable for rigorous applications. Along with the improved electrical, mechanical, thermal, and optical performance achieved in PA6 and GF-based nanocomposites, they can fill complex geometries, have great durability, and are widely utilized due to their capacity of reducing the weight of the vehicle besides a cost reduction potential. The glass fiber is a filamentary composite, usually aggregated in polymeric matrices, which aims to amplify the mechanical properties of polymers, mainly the tensile strength in the case of PA6.

Manufacturing processes impact many factors on a product. Depending on the selected method, development time, part performance and cost are affected. In the automotive sector, there is a growing demand for weight reduction due to the advent of electrification and the greenhouse gas emission regulations. In addition, geometric complexity is a challenging factor for the feasibility of mass production of parts. In this scenario, plastic materials are a very interesting option for application in various vehicle parts, since these materials can be molded by injection, vacuum forming, among others, while maintaining good mechanical properties. Almost a third of a vehicle’s parts are polymeric, making the development of these materials strategic for car manufacturers. This article investigates the impact of the presence of fiberglass in a thermoplastic automotive body part.

Polyurethane (PU) foams are versatile in automotive applications for sound absorption, due to their superior acoustic-absorbing properties, vibration damping and robustness, and seat cushioning products due to their easiness of manufacturing process and cost-effectiveness. In recent studies, micro- and nano-particles were used to improve sound absorption efficiency, these fillers help to form interconnected pore structures in the foam matrix, and this interconnection of pores is advantageous in dissipating heat generated from wave friction with the air. Some of the micro- and nano-particles used are natural fibers (like cellulose, fir, palm), silica, clay, graphene and derivatives, zeolite, and others. This review is an overview of recent advances in the incorporation of fillers in PU foams and the influence they have on the sound absorption capacity of the foams.

The rise of greenhouse gas emissions has reached historic levels, with 37 billion tons of CO2 released into the atmosphere in 2018 alone. In the European Union, 32% of these emissions come from transportation, with 73.3% of that percentage coming from vehicles. To address this problem, solutions such as cleaner fuels and more efficient engines are necessary. Artificial Intelligence can also play a crucial role in climate analysis and verification to move towards a more sustainable future. By utilizing connected vehicle data, automakers can analyze real-time vehicle performance data to identify opportunities for improvement and reduce carbon emissions. This approach benefits the environment, improves vehicle quality, and reduces engineering work time, making it a win-win solution. Connected vehicle data offers a wealth of information on vehicle performance, such as fuel consumption and carbon emissions.

1Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure.

Conventional diesel combustion (CDC) is known to provide high efficiency and reliable engine performance, but often associated with high particulate matter (PM) and nitrogen oxides (NOX) emissions. Combustion of fossil diesel fuel also produces carbon dioxide (CO2), which acts as a harmful greenhouse gas (GHG). Renewable and low-carbon fuels such as renewable diesel (RD) and methanol can play an important role in reducing harmful criteria and CO2 emissions into the atmosphere. This paper details an experimental study using a single-cylinder research engine operated under dual-fuel combustion using methanol and RD. Various engine operating strategies were used to achieve diesel-like fuel efficiency. Measurements of engine-out emissions and in-cylinder pressure were taken at test conditions including low-load and high-load operating points.

In recent years, the automotive industry has seen an exponential increase in the replacement of mechanical components with electronic-controlled components or systems. engine, transmission, brake, exhaust gas recirculation (EGR), lighting, driver-assist technologies, etc. are all monitored and/or controlled electronically. Connected vehicles are increasingly being used by Original Equipment Manufacturers (OEMs) to collect and transmit vehicle data in real-time via the use of various sensors, actuators, and communication technologies. Vehicle telematics devices can collect and transmit data about the vehicle location, speed, fuel efficiency, State Of Charge (SOC), auxiliary battery voltage, emissions, performance, and more. This data is sent over to the cloud via cellular networks, where it can be processed and analyzed to improve their products and services by automotive companies and/or fleet management.

Low carbon emissions policies for the transportation sector have recently driven more interest in using low net-carbon fuels, including biodiesel. An internal combustion engine (ICE) can operate effectively using biodiesel while achieving lower engine-out emissions, such as soot, mostly thanks to oxygenate content in biodiesel. This study selected a heavy-duty (HD) single-cylinder engine (SCE) platform to test biodiesel fuel blends with 20% and 100% biodiesel content by volume, referred to as B20, and B100. Test conditions include a parametric study of exhaust gas recirculating (EGR), and the start of injection (SOI) performed at low and high engine load operating points. In-cylinder pressure and engine-out emissions (NOX and soot) measurements were collected to compare diesel and biodiesel fuels.

The capacity of a lithium-ion battery decreases during cycling. This capacity loss or fade occurs due to several different mechanisms associated with unwanted side reactions that occur in these batteries. The same reactions occur during overcharge and cause electrolyte decomposition, passive film formation, active material dissolution, and other phenomena. As the battery ages the accuracy of state of charge prediction decreases and vulnerability to persistent overcharge increases. Moreover, as the battery ages, its tolerance to such unintended overcharge changes. This tolerance depends on the nature of the history of cycle and calendar aging. A map of this tolerance in the BMS can provide awareness of the factor of safety due to overcharge as battery ages. Signatures of early warning signs of incipient thermal runaway due to overcharge can also be very useful features in a BMS.

Alternative fuels such as methanol can significantly reduce greenhouse gas (GHG) emissions when used in internal combustion engines (ICEs). This study characterized the combustion of methanol, methanol/diesel, and methanol/renewable diesel numerically. Numerical findings were also compared with engine experiments using a single-cylinder engine (SCE). The engine was operated under a dual-fuel combustion mode: methanol was fumigated at the intake port, and diesel was injected inside the cylinder. The characteristic of ignition delay trend as methanol concentration increased is being described at low temperature (low engine load) and high temperature (high engine load) conditions.