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A prototype 2007 ISL Cummins diesel engine equipped with a diesel oxidation catalyst (DOC), diesel particle filter (DPF), variable geometry turbocharger (VGT), and cooled exhaust gas recirculation (EGR) was tested at Southwest Research Institute (SwRI) under a high-load accelerated durability cycle for 1000 hours with B20 soy-based biodiesel blends and ultra-low sulfur diesel (ULSD) fuel to determine the impact of B20 on engine durability, performance, emissions, and fuel consumption. At the completion of the 1000-hour test, a thorough engine teardown evaluation of the overhead, power transfer, cylinder, cooling, lube, air handling, gaskets, aftertreatment, and fuel system parts was performed. The engine operated successfully with no biodiesel-related failures. Results indicate that engine performance was essentially the same when tested at 125 and 1000 hours of accumulated durability operation.

Accurately characterizing vehicle drive cycles plays a fundamental role in assessing the performance of new vehicle technologies. Repeatable, short duration representative drive cycles facilitate more informed decision making, resulting in improved test procedures and more successful vehicle designs. With continued growth in the deployment of onboard telematics systems employing global positioning systems (GPS), large scale, low cost collection of real-world vehicle drive cycle data has become a reality. As a result of these technological advances, researchers, designers, and engineers are no longer constrained by lack of operating data when developing and optimizing technology, but rather by resources available for testing and simulation. Experimental testing is expensive and time consuming, therefore the need exists for a fast and accurate means of generating representative cycles from large volumes of real-world driving data.

Accurate evaluation of vehicles' transient total power requirement helps achieving further improvements in vehicle fuel efficiency. When operated, the air-conditioning (A/C) system is the largest auxiliary load on a vehicle, therefore accurate evaluation of the load it places on the vehicle's engine and/or energy storage system is especially important. Vehicle simulation models, such as "Autonomie," have been used by OEMs to evaluate vehicles' energy performance. However, the load from the A/C system on the engine or on the energy storage system has not always been modeled in sufficient detail. A transient A/C simulation tool incorporated into vehicle simulation models would also provide a tool for developing more efficient A/C systems through a thorough consideration of the transient A/C system performance. The dynamic system simulation software MATLAB/Simulink® is frequently used by vehicle controls engineers to develop new and more efficient vehicle energy system controls.

Battery electric vehicles (BEVs) offer the potential to reduce both oil imports and greenhouse gas emissions, but high upfront costs, battery-limited vehicle range, and concern over high battery replacement costs may discourage potential buyers. A subscription model in which a service provider owns the battery and supplies access to battery swapping infrastructure could reduce upfront and battery replacement costs with a predictable monthly fee, while expanding BEV range. Assessing the costs and benefits of such a proposal are complicated by many factors, including customer drive patterns, the amount of required infrastructure, battery life, etc. The National Renewable Energy Laboratory has applied its Battery Ownership Model to compare the economics and utility of BEV battery swapping service plan options to more traditional direct ownership options.

A 2002 Cummins ISM engine was modified to be optimized for operation on gas-to-liquid (GTL) fuel and advanced emission control devices. The engine modifications included increased exhaust gas recirculation (EGR), decreased compression ratio, and reshaped piston and bowl configuration. The emission control devices included a deNOx filter and a diesel particle filter. Over the transient test, the emissions met the 2007 standards. In July 2004, the modified engine was installed into a Class 8 tractor for use by a grocery fleet. Chassis emission testing of the modified vehicle was conducted at the National Renewable Energy Laboratory's (NREL) Renewable Fuels and Lubricants (ReFUEL) facility. Testing included hot and cold replicate Urban Dynamometer Driving Schedule (UDDS) and New York Composite (NYComp) cycles and several steady-state points. The objective of the testing was to demonstrate the vehicle's with the modified engine.

Increasing fuel costs and the desire for reduced dependence on foreign oil has brought the diesel engine to the forefront of future medium-duty vehicle applications in the United States due to its higher thermal efficiency and superior durability. The main obstacle to the increased use of diesel engines in this platform is the upcoming extremely stringent, Tier 2 emission standard. In order to succeed, diesel vehicles must comply with emissions standards while maintaining their excellent fuel economy. The availability of technologies such as common rail fuel injection systems, low sulfur diesel fuel, NOX adsorber catalysts (NAC), and diesel particle filters (DPFs) allow the development of powertrain systems that have the potential to comply with these future requirements. In meeting the Tier 2 emissions standards, the heavy light-duty trucks (HLDTs) and medium-duty passenger vehicles (MDPVs) will face the greatest technological challenges. In support of this, the U.S.

Increasing fuel costs and the desire for reduced dependence on foreign oil has brought the diesel engine to the forefront of future medium-duty vehicle applications in the United States due to its higher thermal efficiency and superior durability. The main obstacle to the increased use of diesel engines in this platform is the upcoming extremely stringent, Tier 2 emission standard. In order to succeed, diesel vehicles must comply with emissions standards while maintaining their excellent fuel economy. The availability of technologies such as common rail fuel injection systems, low sulfur diesel fuel, NOX adsorber catalysts (NAC), and diesel particle filters (DPFs) allow the development of powertrain systems that have the potential to comply with these future requirements. In meeting the Tier 2 emissions standards, the heavy light-duty trucks (HLDTs) and medium-duty passenger vehicles (MDPVs) will face the greatest technological challenges. In support of this, the U.S.

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS).

Fleet managers need a tool to assist them in assessing their need to comply with EPAct and to provide them with the ability to obtain information that will allow them to make alternative fuel vehicle purchasing decisions. This paper will describe the Web-based tool that will inform a fleet manager, based on their geographic location, the type of fleet they own or operate, and the number and types of vehicles in their fleet, whether or not they need to meet the requirements of EPAct, and, if so, the percentage of new vehicle purchases needed to comply with the law. The tool provides detailed specifications on available OEM alternative fuel vehicles, including the purchase cost of the vehicles, fuel and fuel system characteristics, and incentives and rebates surrounding the purchase of each vehicle. The full set of federal, state, and local incentives is made available through the tool, as well as detailed access to refueling site and dealership locations.

Automobile manufacturers and suppliers are under pressure to develop more efficient thermal management systems as fuel consumption and emission regulations become stricter and buyers demand greater comfort and safety. Additionally, engines must be very efficient and windows must deice and defog quickly. These requirements are often in conflict. Moreover, package styling and cost constraints severely limit the design of coolant and air conditioning systems. Simulation-based design and virtual prototyping can ensure greater product performance and quality at reduced development time and cost. The representation of the vehicle thermal management needs a scalable approach with 0-D, 1-D, and 3-D fluid dynamics, multi-body dynamics, 3-D structural analysis, and control unit simulation capabilities. Different combinations and complexities of the simulation tools are required for various phases of the product development process.

Some CO2-reducing technologies have real-world benefits not captured by regulatory testing methods. This paper documents a two-layer heating, ventilation, and air-conditioning (HVAC) system that facilitates faster engine warmup through strategic increased air recirculation. The performance of this technology was assessed on a 2020 Hyundai Sonata. Empirical performance of the technology was obtained through dynamometer tests at Argonne National Laboratory. Performance of the vehicle across multiple cycles and cell ambient temperatures with the two-layer technology active and inactive indicated fuel consumption reduction in nearly all cases. A thermally sensitive powertrain model, the National Renewable Energy Laboratory’s FASTSim Hot, was calibrated and validated against vehicle testing data. The developed model included the engine, cabin, and HVAC system controls.

Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses a Monte Carlo method to generate parameter sets that are fed to a variant of the road load equation. The modeled road load is then compared to the measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the current state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters.

Raising interest in Diesel powered passenger cars in the United States in combination with the government mandated policy to reduce dependency of foreign oil, leads to the desire of operating Diesel vehicles with Biodiesel fuel blends. There is only limited information related to the impact of Biodiesel fuels on the performance of advanced emission control systems. In this project the implementation of a NOx storage and a SCR emission control system and the development for optimal performance are evaluated. The main focus remains on the discussion of the differences between the fuels which is done for the development as well as useful life aged components. From emission control standpoint only marginal effects could be observed as a result of the Biodiesel operation. The NOx storage catalyst results showed lower tailpipe emissions which were attributed to the lower exhaust temperature profile during the test cycle. The SCR catalyst tailpipe results were fuel neutral.

In-use, light-duty vehicles were recruited in Cary, North Carolina for emissions testing on a transportable dynamometer in 1999. Two hundred forty-eight vehicles were tested in as received condition using the IM240 driving cycle. The study was conducted in two phases, a summer and winter phase, with half of the vehicles recruited during each phase. Regulated emissions, PM10, carbonaceous PM, aldehydes and ketones were measured for every test. PM2.5, individual volatile hydrocarbons, polycyclic aromatic hydrocarbons, sterane and hopane emissions were measured from a subset of the vehicles. Average light-duty gasoline PM10 emission rates increased from 6.5 mg/mi for 1993-97 vehicles to 53.8 mg/mi for the pre-1985 vehicles. The recruited fleet average, hot-stabilized IM240 PM10 emission rate for gasoline vehicles was 19.0 mg/mi.

When operated, the cabin climate control system is the largest auxiliary load on a vehicle. This load has significant impact on fuel economy for conventional and hybrid vehicles, and it drastically reduces the driving range of all-electric vehicles (EVs). Heating is even more detrimental to EV range than cooling because no engine waste heat is available. Reducing the thermal loads on the vehicle climate control system will extend driving range and increase the market penetration of EVs. Researchers at the National Renewable Energy Laboratory have evaluated strategies for vehicle climate control load reduction with special attention toward grid-connected electric vehicles. Outdoor vehicle thermal testing and computational modeling were used to assess potential strategies for improved thermal management and to evaluate the effectiveness of thermal load reduction technologies. A human physiology model was also used to evaluate the impact on occupant thermal comfort.

Passenger compartment climate control is one of the largest auxiliary loads on a vehicle. Like conventional vehicles, electric vehicles (EVs) require climate control to maintain occupant comfort and safety, but cabin heating and air conditioning have a negative impact on driving range for all-electric vehicles. Range reduction caused by climate control and other factors is a barrier to widespread adoption of EVs. Reducing the thermal loads on the climate control system will extend driving range, thereby reducing consumer range anxiety and increasing the market penetration of EVs. Researchers at the National Renewable Energy Laboratory have investigated strategies for vehicle climate control load reduction, with special attention toward EVs. Outdoor vehicle thermal testing was conducted on two 2012 Ford Focus Electric vehicles to evaluate thermal management strategies for warm weather, including solar load reduction and cabin pre-ventilation.

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.

Understanding the real-world power demand of modern automobiles is of critical importance to engineers using modeling and simulation in the design of increasingly efficient powertrains. Increased use of global positioning system (GPS) devices has made large-scale data collection of vehicle speed (and associated power demand) a reality. While the availability of real-world GPS data has improved the industry's understanding of in-use vehicle power demand, relatively little attention has been paid to the incremental power requirements imposed by road grade. This analysis quantifies the incremental efficiency impacts of real-world road grade by appending high-fidelity elevation profiles to GPS speed traces and performing a large simulation study. Employing a large, real-world dataset from the National Renewable Energy Laboratory's Transportation Secure Data Center, vehicle powertrain simulations are performed with and without road grade under five vehicle models.

In the United States, intercity long-haul trucks idle approximately 1,800 hrs per year primarily for sleeper cab hotel loads, consuming 838 million gallons of diesel fuel [1]. The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is working on solutions to this challenge through the CoolCab project. The objective of the CoolCab project is to work closely with industry to design efficient thermal management systems for long-haul trucks that keep the cab comfortable with minimized engine idling. Truck engine idling is primarily done to heat or cool the cab/sleeper, keep the fuel warm in cold weather, and keep the engine warm for cold temperature startup. Reducing the thermal load on the cab/sleeper will decrease air conditioning system requirements, improve efficiency, and help reduce fuel use. To help assess and improve idle reduction solutions, the CoolCalc software tool was developed.

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.