Search Results

Electric drive vehicles (EDVs) have complex thermal management requirements not present in conventional vehicles. In addition to cabin conditioning, the energy storage system (ESS) and power electronics and electric motor (PEEM) subsystems also require thermal management. Many current-generation EDVs utilize separate cooling systems, adding both weight and volume, and lack abundant waste heat from an engine for cabin heating. Some use battery energy to heat the cabin via electrical resistance heating, which can result in vehicle range reductions of 50% under cold ambient conditions. These thermal challenges present an opportunity for integrated vehicle thermal management technologies that reduce weight and volume and increase cabin heating efficiency. Bench testing was conducted to evaluate a combined fluid loop technology that unifies the cabin air-conditioning and heating, ESS thermal management, and PEEM cooling into a single liquid coolant-based system.

Adoption of high-pressure common-rail (HPCR) fuel systems, which subject diesel fuels to higher temperatures and pressures, has brought into question the veracity of ASTM International specifications for biodiesel and biodiesel blend oxidation stability, as well as the lack of any stability parameter for diesel fuel. A controlled experiment was developed to investigate the impact of a light-duty diesel HPCR fuel system on the stability of 20% biodiesel (B20) blends under conditions of intermittent use and long-term storage in a relatively hot and dry climate. B20 samples with Rancimat induction periods (IPs) near the current 6.0-hour minimum specification (6.5 hr) and roughly double the ASTM specification (13.5 hr) were prepared from a conventional diesel and a highly unsaturated biodiesel. Four 2011 model year Volkswagen Passats equipped with HPCR fuel injection systems were utilized: one on B0, two on B20-6.5 hr, and one on B20-13.5 hr.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

It is estimated that operating continuously on a B20 fuel containing the current allowable ASTM specification limits for metal impurities in biodiesel could result in a doubling of ash exposure relative to lube-oil-derived ash. The purpose of this study was to determine if a fuel containing metals at the ASTM limits could cause adverse impacts on the performance and durability of diesel emission control systems. An accelerated durability test method was developed to determine the potential impact of these biodiesel impurities. The test program included engine testing with multiple DPF substrate types as well as DOC and SCR catalysts. The results showed no significant degradation in the thermo-mechanical properties of cordierite, aluminum titanate, or silicon carbide DPFs after exposure to 150,000 mile equivalent biodiesel ash and thermal aging. However, exposure of a cordierite DPF to 435,000 mile equivalent aging resulted in a 69% decrease in the thermal shock resistance parameter.

For renewable fuels to displace petroleum, they must be compatible with emissions control devices. Pure biodiesel contains up to 5 ppm Na + K and 5 ppm Ca + Mg metals, which have the potential to degrade diesel emissions control systems. This study aims to address these concerns, identify deactivation mechanisms, and determine if a lower limit is needed. Accelerated aging of a production exhaust system was conducted on an engine test stand over 1001 h using 20% biodiesel blended into ultra-low sulfur diesel (B20) doped with 14 ppm Na. This Na level is equivalent to exposure to Na at the uppermost expected B100 value in a B20 blend for the system full-useful life. During the study, NOx emissions exceeded the engine certification limit of 0.33 g/bhp-hr before the 435,000-mile requirement.

A new type of solar-reflective glass that improves reflection of the near-infrared (NIR) portion of the solar spectrum has been developed. Also developed was a prototype solar-reflective paint that increases the NIR reflection of opaque vehicle surfaces while maintaining desired colors in the visible portion of the spectrum. Both of these technologies, as well as solar-powered parked car ventilation, were tested on a Cadillac STS as part of the Improved Mobile Air Conditioning Cooperative Research Program (I-MAC). Significant reductions in interior and vehicle skin temperatures were measured. The National Renewable Energy Laboratory (NREL) performed an analysis to determine the impact of reducing the thermal load on the vehicle. A simplified cabin thermal/fluid model was run to predict the potential reduction in A/C system capacity. The potential reduction in fuel use was calculated using a vehicle simulation tool developed by the U.S. Department of Energy (DOE).

Researchers at the National Renewable Energy Laboratory conducted outdoor vehicle thermal soak tests in Golden, Colorado, in September 2002. The same environmental conditions and vehicle were then tested indoors in two DaimlerChrysler test cells, one with metal halide lamps and one with infrared lamps. Results show that the vehicle's shaded interior temperatures correlated well with the outdoor data, while temperatures in the direct sun did not. The large lamp array situated over the vehicle caused the roof to be significantly hotter indoors. Yet, inside the vehicle, the instrument panel was cooler due to the geometry of the lamp array and the spectral difference between the lamps and sun. Results indicate that solar lamps effectively heat the cabin interior in indoor vehicle soak tests for climate control evaluation and SCO3 emissions tests. However, such lamps do not effectively assess vehicle skin temperatures and glazing temperatures.

The overall program objectives were three fold: assess the benefits and limitations of oxygenated diesel fuels on engine performance and emissions identify oxygenates most suitable for potential use in future diesel formulations based on physico-chemical properties (e.g. flash point), toxicity, biodegradability and estimated cost of production perform limited emissions and performance testing of the oxygenated diesel blends select at least two oxygenated compounds for advanced engine testing In Part 1 of this program which is described in this paper, an extensive literature review was conducted to identify potential oxygenates for blending into diesel fuels. As many as 71 oxygenates were identified for the initial screening process. Based on a set of physical and chemical properties, a screening methodology was developed to select the 8 oxygenates that will be eligible for engine testing.

While standardized laboratory-scale wear tests are available to predict the lubricity of liquid fuels under ambient conditions, the reality is that many injection systems operate at elevated temperatures where fuel vaporization is too excessive to perform the measure satisfactorily. The present paper describes a High Pressure High Frequency Reciprocating Rig (HPHFRR) purposely designed to evaluate fuel lubricity in a pressurized environment at temperatures of up to 300°C. The remaining test parameters are identical to those of the widely standardized High Frequency Reciprocating Rig (HFRR). Results obtained using the HPHFRR indicate that wear rate with poor lubricity fuels is strongly sensitive to both temperature and oxygen partial pressure and may be orders of magnitude higher than at ambient conditions. Surprisingly however, wear rate was found to decrease dramatically at temperatures above 100°C, possibly due to evaporation of dissolved moisture.

An ultra-low sulfur diesel (ULSD) fuel was blended with three different biodiesel samples at 5 and 20 volume percent. The biodiesel fuels were derived from rapeseed and soybean oils, and in addition, a highly oxidized biodiesel was prepared from the soy biodiesel by oxidation under controlled conditions. A set of five elastomers commonly used in automotive fuel systems were examined before and after immersion in the six test blends and base fuel at 60°C for 1000 hours. The elastomers were evaluated for hardness, tensile strength, volume change and compression. Injector wear tests were also conducted on the base petrodiesel fuel and the biodiesel blends using a 500-hour test method developed for this study. Bosch VE (in-line) rotary pumps were evaluated for wear after testing for 500 hours on the base fuel, B5 and B20 test fuels. Additionally, a test procedure was developed to accelerate wear on common rail pumps over 500 hours.

This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other three as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method.

When developing and designing new technology for integrated vehicle systems deployment, standard cycles have long existed for chassis dynamometer testing and tuning of the powertrain. However, to this day with recent developments and advancements in plug-in hybrid and battery electric vehicle technology, no true “work day” cycles exist with which to tune and measure energy storage control and thermal management systems. To address these issues and in support of development of a range-extended pickup and delivery Class 6 commercial vehicle, researchers at the National Renewable Energy Laboratory in collaboration with Cummins analyzed 78,000 days of operational data captured from more than 260 vehicles operating across the United States to characterize the typical daily performance requirements associated with Class 6 commercial pickup and delivery operation.

Cab climate conditioning is one of the primary reasons for operating the main engine in a long-haul truck during driver rest periods. In the United States, sleeper cab trucks use approximately 667 million gallons of fuel annually for rest period idling. The U.S. Department of Energy's National Renewable Energy Laboratory's (NREL) CoolCab Project works closely with industry to design efficient thermal management systems for long-haul trucks that minimize engine idling and fuel use while maintaining occupant comfort. Heat transfer to the vehicle interior from opaque exterior surfaces is one of the major heat pathways that contribute to air conditioning loads during long-haul truck daytime rest period idling. To quantify the impact of paint color and the opportunity for advanced paints, NREL collaborated with Volvo Group North America, PPG Industries, and Dometic Environmental Corporation.

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts.

Mixing controlled compression ignition, i.e., diesel engines are efficient and are likely to continue to be the primary means for movement of goods for many years. Low-net-carbon biofuels have the potential to significantly reduce the carbon footprint of diesel combustion and could have advantageous properties for combustion, such as high cetane number and reduced engine-out particle and NOx emissions. We developed a list of over 400 potential biomass-derived diesel blendstocks and populated a database with the properties and characteristics of these materials. Fuel properties were determined by measurement, model prediction, or literature review. Screening criteria were developed to determine if a blendstock met the basic requirements for handling in the diesel distribution system and use as a blend with conventional diesel. Criteria included cetane number ≥40, flashpoint ≥52°C, and boiling point or T90 ≤338°C.

Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling.