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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.

It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.

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.

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies.

The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL.

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.

Delphi and the National Renewable Energy Laboratory (NREL) collaborated to develop a simulation code to model the mechanical and electrical architectures of a series hybrid vehicle simultaneously. This co-simulation code is part of the larger ADVISOR® product created by NREL and diverse partners. Simulation of the macro power flow in a series hybrid vehicle requires both the mechanical drivetrain and the entire electrical architecture. It is desirable to solve the electrical network equations in an environment designed to comprehend such a network and solve the equations in terms of current and voltage. The electrical architecture for the series hybrid vehicle has been modeled in Saber™ to achieve these goals. This electrical architecture includes not only the high-voltage battery, generator, and traction motor, but also the normal low-voltage bus (14V) with loads common to all vehicles.

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.

Current vehicle climate control systems are dramatically overpowered because they are designed to condition the cabin air mass in a specified period of time. A more effective and energy efficient objective is to directly achieve thermal comfort of the passengers. NREL is developing numerical and experimental tools to predict human thermal comfort in non-uniform transient thermal environments. These tools include a finite element model of human thermal physiology, a psychological model that predicts both local and global thermal comfort, and a high spatial resolution sweating thermal manikin for testing in actual vehicles.

The National Renewable Energy Laboratory (NREL) has conducted a series of chassis dynamometer and road tests on the 2000 model-year Honda Insight. This paper will focus on results from the testing, how the results have been applied to NREL's Advanced Vehicle Simulator (ADVISOR), and how test results compare to the model predictions and published data. The chassis dynamometer testing included the FTP-75 emissions certification test procedure, highway fuel economy test, US06 aggressive driving cycle conducted at 0°C, 20°C, and 40°C, and the SC03 test performed at 35°C with the air conditioning on and with the air conditioning off. Data collection included bag and continuously sampled emissions (for the chassis tests), engine and vehicle operating parameters, battery cell temperatures and voltages, motor and auxiliary currents, and cabin temperatures.

The air-conditioning (A/C) system compressor load can significantly impact the fuel economy and tailpipe emissions of conventional and hybrid electric automobiles. With the increasing emphasis on fuel economy, it is clear that the A/C compressor load needs to be reduced. In order to accomplish this goal, more efficient climate control delivery systems and reduced peak soak temperatures will be necessary to reduce the impact of vehicle A/C systems on fuel economy and tailpipe emissions. Good analytical techniques are important in identifying promising concepts. The goal at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is to assess thermal comfort, fuel economy, and emissions by using an integrated modeling approach composed of CAD, computational fluid dynamics (CFD), thermal comfort, and vehicle simulation tools. This paper presents NREL's vehicle integrated modeling process.

The National Renewable Energy Laboratory’s (NREL’s) CoolSim MATLAB/Simulink modeling framework was used to explore control strategies for an electric vehicle combined loop system. Three system variants of increased complexity and efficiency were explored: a glycol-based positive temperature coefficient heater (PTC), PTC with power electronics and electric motor (PEEM) waste heat recovery, and PTC with PEEM waste heat recovery plus heat pump versions. Additionally, the benefit of electric motor preheating was considered. A two-level control strategy was developed where the mode selection and component control were treated separately. Only the parameters typically available by vehicle sensors were used to control the system. The control approach included a mode selection algorithm and controllers for the compressor speed, cabin blower flow rate, coolant flow rate, and the front-end heat exchanger coolant bypass rate.

One of the most critical elements in engineering a hydrogen fuel cell vehicle is the design of the on-board hydrogen storage system. Because the current compressed-gas hydrogen storage technology has several key challenges, including cost, volume and capacity, materials-based storage technologies are being evaluated as an alternative approach. These materials-based hydrogen storage technologies include metal hydrides, chemical hydrides, and adsorbent materials, all of which have drawbacks of their own. To optimize the engineering of storage systems based on these materials, it is critical to understand the impacts these systems will have on the overall vehicle system performance and what trade-offs between the hydrogen storage systems and the vehicle systems might exist that allow these alternative storage approaches to be viable.

The National Renewable Energy Laboratory (NREL) has developed a suite of thermal comfort tools to help develop smaller and more efficient climate control systems in automobiles. The tools consist of a thermal comfort manikin, physiological model, and psychological model that are linked together to assess comfort in a transient non-homogeneous environment. The manikin, which consists of 120 individually controlled zones, mimics the human body by heating, sweating, and breathing. The physiological model is a 40,000-node numerical simulation of the human body. The model receives heat loss data from the manikin and predicts the human physiological response and skin temperatures. Based on human subject test data, the psychological model takes the temperatures of the human and predicts thermal sensation and comfort.

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.

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. Alternative fuels such as Dimethyl Ether (DME) are gaseous at ambient conditions and must be pressurized to form a lubricating liquid. The present paper describes an apparatus purposely designed to evaluate fuel lubricity in a pressurized environment at temperatures of up to 30°C. The remaining test parameters are identical to those of the widely standardized High Frequency Reciprocating Rig (HFRR), which allows use of previously developed correlations to full-scale injection equipment. Results obtained using the High Pressure High Frequency Reciprocating Rig (HPHFRR) indicate that DME, as well as other volatile fuels evaluated, have very poor lubricity.

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.

The National Renewable Energy Laboratory’s (NREL’s) CoolSim MATLAB/Simulink modeling framework was expanded by including a newly developed coolant loop solution method aimed at reducing the simulation effort for complex thermal management systems. The new approach does not require the user to identify specific coolant loops and their flow. The user only needs to connect the fluid network elements in a manner consistent with the desired schematic. Using the new solution method, a model of NREL's advanced combined coolant loop system for electric vehicles was created that reflected the test system architecture. This system was built using components provided by MAHLE Inc. and included both air conditioning and heat pump modes. Validation with test bench data and verification with the previous solution method were performed for 10 operating points spanning a range of ambient temperatures between -2°C and 43°C.

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver.

Electric vehicles (EVs) need highly optimized thermal management systems to improve range. Climate control can reduce vehicle efficiency and range by more than 50%. Due to the relative shortage of waste heat, heating the passenger cabin in EVs is difficult. Cabin cooling can take a high portion of the energy available in the battery. Compared to internal combustion engine-driven vehicles, different heating methods and more efficient cooling methods are needed, which can make EV thermal management systems more complex. More complex systems typically allow various alternative modes of operation that can be selected based on driving and ambient conditions. A good system simulation tool can greatly reduce the time and expense for developing these complex systems. A simulation model should also be able to efficiently co-simulate with vehicle simulation programs, and should be applicable for evaluating various control algorithms.