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This work presents a simple fan model that is based on the actuator disk approximation, and the blade element and vortex theory of a propeller. A set of equations are derived that require as input the rotational speed of the fan, geometric fan data, and the lift and drag coefficients of the blades. These equations are solved iteratively to obtain the body forces generated by the fan in the axial and circumferential directions. These forces are used as momentum sources in a CFD code to simulate the effect of the fan in an underhood thermal management simulation. To validate this fan model, a fan experiment was simulated. The model was incorporated into the CFD code STAR-CD and predictions were generated for axial and circumferential air velocities at different radial positions and at different planes downstream of the fan. The agreement between experimental measurements and predictions is good.

The adoption of lithium-ion batteries in vehicle electrification is fast growing due to high power and energy demand on hybrid and electric vehicles. However, the battery overall performance changes with time through the vehicle life. This paper investigates the electric vehicle battery cell aging under different usages. Battery cell experimental data including open circuit voltage and internal resistance is utilized to build a typical electric vehicle model in the AVL-Cruise platform. Four driving cycles (WLTP, UDDS, HWFET, and US06) with different ambient temperatures are simulated to acquire the battery cell terminal currents. These battery cell terminal current data are inputs to the MATLAB/Simulink battery aging model. Simulation results show that battery degrades quickly in high ambient temperatures. After 15,000 hours usage in 50 degrees Celsius ambient temperature, the usable cell capacity is reduced up to 25%.

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.

A methodology for evaluating life cycle cost of electric vehicles (EVs) to their buyers is presented. The methodology is based on an analysis of conventional vehicle costs, costs of drivetrain and auxiliary components unique to EVs, and battery costs. The conventional vehicle's costs are allocated to such subsystems as body, chassis, and powertrain. In electric vehicles, an electric drive is substituted for the conventional powertrain. The current status of the electric drive components and battery costs is evaluated. Battery costs are estimated by evaluating the material requirements and production costs at different production levels; battery costs are also collected from other sources. Costs of auxiliary components, such as those for heating and cooling the passenger compartment, are also estimated. Here, the methodology is applied to two vehicle types: subcompact car and minivan.

To address the need of increasing fuel economy requirements, automotive Original Equipment Manufacturers (OEMs) are increasing the number of turbocharged engines in their powertrain line-ups. The turbine-driven technology uses a forced induction device, which increases engine performance by increasing the density of the air charge being drawn into the cylinder. Denser air allows more fuel to be introduced into the combustion chamber, thus increasing engine performance. During the inlet air compression process, the air is heated to temperatures that can result in pre-ignition resulting and reduced engine functionality. The introduction of the charge air cooler (CAC) is therefore, necessary to extract heat created during the compression process. The present research describes the physics and develops the optimized simulation method that defines the process and gives insight into the development of CACs.

Speed and accuracy are the critical needs in software for the modeling and simulation of vehicle cooling systems. Currently, there are two approaches used in commercially available thermal analysis software packages: 1) detailed modeling using complex and sophisticated three-dimensional (3D) heat transfer and computational fluid dynamics, and 2) rough modeling using one-dimensional (1D) simplistic network solvers (flow and thermal) for quick prediction of flow and thermal fields. The first approach offers accuracy at the cost of speed, while the second approach provides the simulation speed, sacrificing accuracy and can possibly lead to oversimplification. Therefore, the analyst is often forced to make a choice between the two approaches, or find a way to link or couple the two methods. The linking between one-dimensional and three-dimensional models using separate software packages has been attempted and successfully accomplished for a number of years.

The Battery Performance and Cost Model (BatPaC), developed by Argonne National Laboratory, is a versatile tool designed for lithium-ion battery (LIB) pack engineering. It accommodates user-defined specifications, generating detailed bill-of-materials calculations and insights into cell dimensions and pack characteristics. Pre-loaded with default data sets, BatPaC aids in estimating production costs for battery packs produced at scale (5 to 50 GWh annually). Acknowledging inherent uncertainties in parameters, the tool remains accessible and valuable for designers and engineers. BatPaC plays a crucial role in National Highway Transportation Traffic Safety Administration (NHTSA) regulatory assessments, providing estimated battery pack manufacturing costs and weight metrics for electric vehicles. Integrated with Argonne's Autonomie simulations, BatPaC streamlines large-scale processes, replacing traditional models with lookup tables.

The importance of radiative heat transfer on the combustion and soot formation characteristics under nominal ECN Spray A conditions has been studied numerically. The liquid n-dodecane fuel is injected with 1500 bar fuel pressure into the constant volume chamber at different ambient conditions. Radiation from both gas-phase as well as soot particles has been included and assumed as gray. Three different solvers for the radiative transfer equation have been employed: the discrete ordinate method, the spherical-harmonics method and the optically thin assumption. The radiation models have been coupled with the transported probability density function method for turbulent reactive flows and soot, where unresolved turbulent fluctuations in temperature and composition are included and therefore capturing turbulence-chemistry-soot-radiation interactions. Results show that the gas-phase (mostly CO2 ad H2O species) has a higher contribution to the net radiation heat transfer compared to soot.

To reduce development time and introduce technologies faster to the market, many companies have been turning more and more to Model Based Design. In Model Based Design, the development process centers around a system model, from requirements capture and design to implementation and test. Engineers can skip over a generation of system design processes on the basis of hand coding and use graphical models to design, analyze, and implement the software that determines machine performance and behavior. This paper describes the process implemented in Autonomie, a Plug-and-Play Software Environment, to design and evaluate component hardware in an emulated environment. We will discuss best practices and provide an example through evaluation of advanced high-energy battery pack within an emulated Plug-in Hybrid Electric Vehicle.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

At the current stage of engine technology, diesel engines typically require diesel particulate filter (DPF) systems to meet recent particulate emissions standards. To assure the performance and reliability of DPF systems, profound understanding of filtration and regeneration mechanisms is required. Among extensive efforts for developing advanced DPF systems, the development of effective thermal management strategies, which control the thermal runaway taking place in oxidation of an excess amount of soot deposit in DPF, is quite challenging. This difficulty stems mainly from lack of sufficient knowledge and understanding about DPF regeneration mechanisms, which need detailed information about oxidation of diesel particulate matter (PM). Therefore, this work carried out a series of oxidation experiments of diesel particulates collected from a DPF on a diesel engine, and evaluated the oxidation rates of the samples using a thermo-gravimetric analyzer (TGA).

With the increasing demand for Battery Electric Vehicles (BEVs) capable of extended mileage, optimizing their efficiency has become paramount for manufacturers. However, the challenge lies in balancing the need for climate control within the cabin and precise thermal regulation of the battery, which can significantly reduce a vehicle's driving range, often leading to energy consumption exceeding 50% under severe weather conditions. To address these critical concerns, this study embarks on a comprehensive exploration of the impact of weather conditions on energy consumption and range for the 2019 Nissan Leaf Plus. The primary objective of this research is to enhance the understanding of thermal management for BEVs by introducing a sophisticated thermal management system model, along with detailed thermal models for both the battery and the cabin.

It is well known that thermal management is a key factor in design and performance analysis of Lithium-ion (Li-ion) battery, which is widely adopted for hybrid and electric vehicles. In this paper, an air cooled battery thermal management system design has been proposed and analyzed for mild hybrid vehicle application. Computational Fluid Dynamics (CFD) analysis was performed using CD-adapco's STAR-CCM+ solver and Battery Simulation Module (BMS) application to predict the temperature distribution within a module comprised of twelve 40Ah Superior Lithium Polymer Battery (SLPB) cells connected in series. The cells are cooled by air through aluminum cooling plate sandwiched in-between every pair of cells. The cooling plate has extended the cooling surface area exposed to cooling air flow. Cell level electrical and thermal simulation results were validated against experimental measurements.

Battery packs for plug-in hybrid electrical vehicle (PHEV) applications can be characterized as high-capacity and high-power packs. For PHEV battery packs, their power and electrical-energy capacities are determined by the range of the electrical-energy-driven operation and the required vehicle drive power. PHEV packs often employ high-power lithium-ion (Li-ion) pouch cells with large cell capacity in order to achieve high packing efficiency. Lithium-ion battery packs for PHEV applications generally have a 96SnP configuration, where S is for cells in series, P is for cells in parallel, and n = 1, 2 or 3. Two PHEV battery packs with 355V nominal voltage and 25-kWh nominal energy capacity are studied. The first pack is assembled with 96 70Ah high-power Li-ion pouch cells in 96S1P configuration. The second pack is assembled with 192 35Ah high-power Li-ion pouch cells in 96S2P configuration.

The CFR engine is the widely accepted platform to test standard Research Octane Number (RON) and Motored Octane Number (MON) for determining anti-knock characteristics of motor fuels. With increasing interest in engine downsizing, up-torquing, and alternative fuels for modern spark ignition (SI) engines, there is a need to better understand the conditions that fuels are subjected to in the CFR engine during octane rating. To take into account fuel properties, such as fuel heat of vaporization, laminar flame speed and auto-ignition chemistry; and understand their impacts on combustion knock, it is essential to estimate accurate cylinder conditions. In this study, the CFR F1/F2 engine was modeled using GT-Power with the Three Pressure Analysis (TPA) and the model was validated for different fuels and engine conditions.

This paper presents the vehicle model development and validation process for the Ford Focus battery electric vehicles (BEVs) using Autonomie and test results from Advanced Powertrain Research Facility in Argonne National Laboratory. The parameters or characteristic values for the important components such as the electric machine and battery pack system are estimated through analyzing the test data of the multi cycle test (MCT) procedure under the standard ambient condition. A novel process was used to import vehicle test data into Autonomie. Through this process, a complete vehicle model of the Ford Focus BEV is developed and validated under ambient temperature for different drive cycles (UDDS, HWFET, US06 and Steady-State). The simulation results of the developed vehicle model show coincident results with the test data within 0.5% ∼ 4% discrepancies for electrical consumption.

Response Surface Methodology (RSM) techniques are applied to develop brake specific fuel consumption (BSFC) maps of a test vehicle over standard drive cycles under various ambient conditions. This technique allows for modeling and predicting fuel consumption of an engine as a function of engine operating conditions. Results will be shown from Federal Test Procedure engine starts of 20°C, and colder conditions of -7°C. Fueling rates under a broad range of engine temperatures are presented. Analysis comparing oil and engine coolant as an input factor of the model is conducted. Analysis comparing the model to experimental datasets, as well as some details into the modeling development, will be presented. Although the methodology was applied to data collected from a vehicle, the same technique could be applied to engines run on dynamometers.

EcoCAR 2: Plugging in to the Future (EcoCAR) is North America's premier collegiate automotive engineering competition, challenging students with systems-level advanced powertrain design and integration. The three-year Advanced Vehicle Technology Competition (AVTC) series is organized by Argonne National Laboratory, headline sponsored by the U. S. Department of Energy (DOE) and General Motors (GM), and sponsored by more than 30 industry and government leaders. Fifteen university teams from across North America are challenged to reduce the environmental impact of a 2013 Chevrolet Malibu by redesigning the vehicle powertrain without compromising performance, safety, or consumer acceptability. During the three-year program, EcoCAR teams follow a real-world Vehicle Development Process (VDP) modeled after GM's own VDP. The EcoCAR 2 VDP serves as a roadmap for the engineering process of designing, building and refining advanced technology vehicles.

The cooling system of a Class 8 truck engine was modeled using the Flowmaster computer code. Numerical simulations were performed replacing the standard coolant, 50/50 mixture of ethylene-glycol and water, with nanofluids comprised of CuO nanoparticles suspended in a base fluid of a 50/50 mixture of ethylene-glycol and water. By using engine and cooling system parameters from the standard coolant case, the higher heat transfer coefficients of the nanofluids resulted in lower engine and coolant temperatures. These temperature reductions introduced flexibility in system parameters - three of which were investigated for performance improvement: engine power, coolant pump speed and power, and radiator air-side area.

Hybrid electric vehicle (HEV) is one of the most highly pursued technologies for improving energy efficiency while reducing harmful emissions. Thermal modeling and control play an ever increasing role with HEV design and development for achieving the objective of improving efficiency, and as a result of additional thermal loading from electric powertrain components such as electric motor, motor controller and battery pack. Furthermore, the inherent dual powertrains require the design and analysis of not only the optimal operating temperatures but also control and energy management strategies to optimize the dynamic interactions among various components. This paper presents a complete development process and simulation results for an efficient modeling approach with integrated control strategy for the thermal management of plug-in HEV in parallel-through-the road (PTTR) architecture using a flexible-fuel engine running E85 and a battery pack as the energy storage system (ESS).