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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.

In an effort to characterize the dynamics typical of school bus operation, National Renewable Energy Laboratory (NREL) researchers set out to gather in-use duty cycle data from school bus fleets operating across the country. Employing a combination of Isaac Instruments GPS/CAN data loggers in conjunction with existing onboard telemetric systems resulted in the capture of operating information for more than 200 individual vehicles in three geographically unique domestic locations. In total, over 1,500 individual operational route shifts from Washington, New York, and Colorado were collected. Upon completing the collection of in-use field data using either NREL-installed data acquisition devices or existing onboard telemetry systems, large-scale duty-cycle statistical analyses were performed to examine underlying vehicle dynamics trends within the data and to explore vehicle operation variations between fleet locations.

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.

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.

The National Renewable Energy Laboratory (NREL) validated conventional diesel and diesel-hybrid, medium-duty parcel delivery vehicle models to evaluate petroleum reductions and cost implications of hybrid and plug-in hybrid diesel variants. The hybrid and plug-in hybrid variants are run on a field data-derived design matrix to analyze the effect of drive cycle, distance, engine downsizing, battery replacements, and battery energy on fuel consumption and lifetime cost. For an array of diesel fuel costs, the battery cost per kilowatt-hour at which the hybridized configuration becomes cost-effective is calculated. The results build on a previous analysis that found the fuel savings from medium-duty, plug-in hybrids more than offset vehicle incremental price for future battery and fuel cost projections; however, they seldom did so under present day cost assumptions in the absence of purchase incentives.

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.

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.

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.

The operation of air conditioning (A/C) systems is a significant contributor to the total amount of fuel used by light-and heavy-duty vehicles. Therefore, continued improvement of the efficiency of these mobile A/C systems is important. Numerical simulation has been used to reduce the system development time and to improve the electronic controls, but numerical models that include highly detailed physics run slower than desired for carrying out vehicle-focused drive cycle-based system optimization. Therefore, faster models are needed even if some accuracy is sacrificed. In this study, a validated model with highly detailed physics, the “Fully-Detailed” model, and two models with different levels of simplification, the “Quasi-Transient” and the “Mapped-Component” models, are compared. The Quasi-Transient model applies some simplifications compared to the Fully-Detailed model to allow faster model execution speeds.

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.

A simple cost analysis framework compares hydrogen and electricity as energy carriers delivering wind energy to light-duty vehicles (LDVs). We compare four wind energy pathways within a 2040-2050 timeframe and at large scale: a dedicated electricity transmission pathway and three distinct wind-hydrogen delivery pathways. Our results suggest that wind-hydrogen pathways will tend to be more costly than pure electricity transmission pathways on a per-mile driven cost basis ($/mile), but to a greater or lesser degree depending upon the pathway. The additional cost could be warranted to the degree that the hydrogen pathway adds value to consumers through full performance fuel cell electric vehicles (FCEV) compared to plug-in electric vehicles (PEVs), or through reduced variability in wind energy supply. If these benefits add value beyond the incremental costs suggested by our simple cost framework, some shift toward co-production or even dedicated hydrogen wind farms may be warranted.

Currently, connected and autonomous vehicle (CAV) technology is being developed for Class 8 tractor trucks aimed at improved safety and fuel economy and reduced CO2 emissions. Despite extensive efforts conducted across the world, the reported efficiency gains were varied from different research groups, raising concerns about the fidelity of models, the performance of control, and the effectiveness of the experimental validation. One root cause for this variation stems from the fact that the efficiency gain obtained from the CAV is sensitive to real-world conditions, including surrounding traffic and road grade. This study presents an approach aimed at identifying representative public road sections and facilitating CAV research from this perspective. By employing the decision tree regression (DTR) method to the Fleet DNA database, the most representative road sections can be identified.

This paper describes the need for dynamic (transient) simulation of automotive air conditioning systems, the reasons why such simulations are challenging, and the applicability of a general purpose off-the-shelf thermohydraulic analyzer to answer such challenges. An overview of modeling methods for the basic components are presented, along with relevant approximations and their effect on speed and accuracy of the results.

This work presents a methodology to determine the off-cycle fuel economy benefit of a 2-Layer HVAC system which reduces ventilation and heat rejection losses of the heater core versus a vehicle using a standard system. Experimental dynamometer tests using EPA drive cycles over a broad range of ambient temperatures were conducted on a highly instrumented 2016 Lexus RX350 (3.5L, 8 speed automatic). These tests were conducted to measure differences in engine efficiency caused by changes in engine warmup due to the 2-Layer HVAC technology in use versus the technology being disabled (disabled equals fresh air-considered as the standard technology baseline). These experimental datasets were used to develop simplified response surface and lumped capacitance vehicle thermal models predictive of vehicle efficiency as a function of thermal state.