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Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

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.

The study of spray-wall interaction is of great importance to understand the dynamics that occur during fuel impingement onto the chamber wall or piston surfaces in internal combustion engines. It is found that the maximum spreading length of an impinged droplet can provide a quantitative estimation of heat transfer and energy transformation for spray-wall interaction. Furthermore, it influences the air-fuel mixing and hydrocarbon and particle emissions at combusting conditions. In this paper, an analytical model of a single diesel droplet impinging on the wall with different inclined angles (α) is developed in terms of βm (dimensionless maximum spreading length, the ratio of maximum spreading length to initial droplet diameter) to understand the detailed impinging dynamic process.

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.

Annual fuel use for sleeper cab truck rest period idling is estimated at 667 million gallons in the United States, or 6.8% of long-haul truck fuel use. Truck idling during a rest period represents zero freight efficiency and is largely done to supply accessory power for climate conditioning of the cab. The National Renewable Energy Laboratory’s CoolCab project aims to reduce heating, ventilating, and air conditioning (HVAC) loads and resulting fuel use from rest period idling by working closely with industry to design efficient long-haul truck thermal management systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In addition, if the fuel savings provide a one- to three-year payback period, fleet owners will be economically motivated to incorporate them.

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.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

A 2-D CFD model was developed to describe the heat transfer, and reaction kinetics in a honeycomb structured ceramic diesel particulate trap. This model describes the steady state as well as the transient behavior of the flow and heat transfer during the trap regeneration processes. The trap temperature profile was determined by numerically solving the 2-D unsteady energy equation including the convective, heat conduction and viscous dissipation terms. The convective terms were based on a 2-D analytical flow field solution derived from the conservation of mass and momentum equations (Opris, 1997). The reaction kinetics were described using a discretized first order Arrhenius function. The 2-D term describing the reaction kinetics and particulate matter conservation of mass was added to the energy equation as a source term in order to represent the particulate matter oxidation. The filtration model describes the particulate matter accumulation in the trap.

This study focuses on how to predict hot spots of one of the cylinders of a V8 5.4 L FORD engine running at full load. The KIVA code with conjugate heat transfer capability to simulate the fast transient heat transfer process between the gas and the solid phases has been developed at the Michigan Technological University and will be used in this study. Liquid coolant flow was simulated using FLUENT and will be used as a boundary condition to account for the heat loss to the cooling fluid. In the first step of calculation, the coupling between the gas and the solid phases will be solved using the KIVA code. A 3D transient wall heat flux at the gas-solid interface is then compiled and used along with the heat loss information from the FLUENT data to obtain the temperature distribution for the engine metal components, such as cylinder wall, cylinder head, etc.

One-dimensional single-zone and two-zone analyses have been exercised to calculate the mass fraction burned in an engine operating on ethanol/gasoline-blended fuels using the cylinder pressure and volume data. The analyses include heat transfer and crevice volume effects on the calculated mass fraction burned. A comparison between the two methods is performed starting from the derivation of conservation of energy and the method to solve the mass fraction burned rates through the results including detailed explanation of the observed differences and trends. The apparent heat release method is used as a point of reference in the comparison process. Both models are solved using the LU matrix factorization and first-order Euler integration.

As the incentive to produce cleaner and more efficient engines increases, diesel engines will become a primary, worldwide solution. Producing diesel engines with higher efficiency and lower emissions requires a fundamental understanding of the interaction of the injected fuel with air as well as with the surfaces inside the combustion chamber. One aspect of this interaction is spray impingement on the piston surface. Impingement on the piston can lead to decreased combustion efficiency, higher emissions, and piston damage due to thermal loading. Modern high-speed diesel engines utilize high pressure common-rail direct-injection systems to primarily improve efficiency and reduce emissions. However, the high injection pressures of these systems increase the likelihood that the injected fuel will impinge on the surface of the piston.

A critical element to the success of new propulsion technologies that enable reductions in fuel use is the integration of component thermal management technologies within a viable vehicle package. Vehicle operation requires vehicle thermal management systems capable of balancing the needs of multiple vehicle systems that may require heat for operation, require cooling to reject heat, or require operation within specified temperature ranges. As vehicle propulsion transitions away from a single form of vehicle propulsion based solely on conventional internal combustion engines (ICEs) toward a wider array of choices including more electrically dominant systems such as plug-in hybrid electric vehicles (PHEVs), new challenges arise associated with vehicle thermal management. As the number of components that require active thermal management increase, so do the costs in terms of dollars, weight, and size.

As part of a U.S. Department of Energy supported study, the National Renewable Energy Laboratory has benchmarked a Toyota Prius hybrid electric vehicle from three aspects: system analysis, auxiliary loads, and battery pack thermal performance. This paper focuses on the testing of the battery back out of the vehicle. More recent in-vehicle dynamometer tests have confirmed these out-of-vehicle tests. Our purpose was to understand how the batteries were packaged and performed from a thermal perspective. The Prius NiMH battery pack was tested at various temperatures (0°C, 25°C, and 40°C) and under driving cycles (HWFET, FTP, and US06). The airflow through the pack was also analyzed. Overall, we found that the U.S. Prius battery pack thermal management system incorporates interesting features and performs well under tested conditions.

Proper electric and thermal management of an HEV battery pack, consisting of many modules of cells, is imperative. During operation, voltage and temperature differences in the modules/cells can lead to electrical imbalances from module to module and decrease pack performance by as much as 25%. An active battery management system (BMS) is a must to monitor, control, and balance the pack. The University of Toledo, with funding from the U.S. Department of Energy and in collaboration with DaimlerChrysler and the National Renewable Energy Laboratory has developed a modular battery management system for HEVs. This modular unit is a 2nd generation system, as compared to a previous 1st generation centralized system. This 2nd generation prototype can balance a battery pack based on cell-to-cell measurements and active equalization. The system was designed to work with several battery types, including lithium ion, NiMH, or lead acid.

A 2-dimensional numerical model (MTU-FILTER) for a single channel of a honeycomb ceramic diesel particulate trap has been developed. The mathematical modeling of the filtration, flow, heat transfer and regeneration behavior of the particulate trap is described. Numerical results for the pressure drop and particulate mass were compared with existing experimental results. Parametric studies of the diesel particulate trap were carried out. The effects of trap size and inlet temperature on the trap performance are studied using the trap model. An approximate 2-dimensional analytical solution to the simplified Navier-Stokes equations was used to calculate the velocity field of the exhaust flow in the inlet and outlet channels. Assuming a similarity velocity profile in the channels, the 2-dimensional Navier-Stokes equations are approximated by 1-dimenisonal conservation equations, which is similar to those first developed by Bissett.

An analysis of vehicle engine cooling airflow by means of a one-dimensional, transient, compressible flow model was carried out and revealed that similarity theory could be applied to investigate the variation of the airflow with ambient and operating conditions. It was recognized that for a given vehicle engine cooling system, the cooling airflow behavior could be explained using several dimensionless parameters that involve the vehicle speed, fan speed, heat transfer rate through the radiator, ambient temperature and pressure, and the system characteristic dimension. Using the flow resistance and fan characteristics measured from a prototype cooling system and the computer simulation for the one-dimensional compressible flow model, a quantitative correlation of non-dimensional mass flow rate to three dimensionless parameters for a prototype heavy-duty truck was established. The results are presented in charts, tables, and formulas.

A one-dimensional incompressible flow model covering the mechanisms involved in the airflow through an automotive radiator-shroud-fan system with no heat transfer was developed. An analytical expression to approximate the experimentally determined fan performance characteristics was used in conjunction with an analytical approach for this simplified cooling airflow model, and the solution is discussed with illustrations. A major result of this model is a closed form equation relating the transient velocity of the air to the vehicle speed, pressure rise characteristics and speed of the fan, as well as the dimensions and resistance of the radiator. This provides a basis for calculating cooling airflow rate under various conditions. The results of the incompressible flow analysis were further compared with the computational results obtained with a previously developed one-dimensional, transient, compressible flow model.

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.

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.