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The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

A model for calculating the trap pressure drop, various particulate properties, filtration characteristics and trap temperatures was developed during the steady-state and transient cycles using the theory originated by Opris and Johnson, 1998. This model was validated with the data obtained from the steady-state cycles run with an IBIDEN SiC diesel particulate filter. To evaluate the trap experimental filtration efficiency, raw exhaust samples were taken upstream and downstream of the trap. A trap scaling and equivalent comparison model was developed for comparing different traps at the same volume and same filtration area. Using the model, the trap pressure drop data obtained from different traps were compared equivalently at the same trap volume and same filtration area. The pressure drop performance of the IBIDEN SiC trap compared favorably to the previously tested NoTox SiC and the Cordierite traps.

A computer simulation of the turbocharged direct- injection diesel engine was developed to enhance the capabilities of the Vehicle Engine Cooling System Simulation (VECSS) developed at Michigan Technological University. The engine model was extensively validated against Detroit Diesel Corporation's (DDC) Series 60 engine data. In addition to the new engine model a charge-air-cooler model was developed and incorporated into the VECSS. A Freightliner truck with a Detroit Diesel's Series 60 engine, Behr McCord radiator, AlliedSignal/Garrett Automotive charge air cooler, Kysor DST variable speed fan clutch and other cooling system components was used for the study. The data were collected using the Detroit Diesel Electronic Controls (DDEC)-Electronic Control Module (ECM) and Hewlett Packard data acquisition system. The enhanced model's results were compared to the steady state TTD (top tank differential) data.

This paper details the computer algorithm which was developed to determine the A/C refrigeration circuit balance point under the system transient operating conditions. The A/C circuit model consisting of major component submodels, such as the evaporator, compressor, condenser, orifice, air handling system, and connecting hoses, are included in the study. Pressure drop and thermal capacity for the evaporator, condenser, and connecting ducts/hoses are also considered in the simulation. The results obtained from the simulation model are in good agreement with the experimental data. Users can take advantage of this CAE tool to optimize the A/C system design and to minimize the development process with time-saving and cost-effective perspectives.

A Computer-Aided Engineering (CAE) model for automobile climate control system is presented to provide engineers with an cost effective analysis tool for designing, developing, and optimizing the vehicle interior climate. It is the objective of this paper to develop a mathematical model which predicts the lumped temperature and lumped humidity variations inside the passenger compartment under design and operating conditions. The transient nature of the passenger cabin temperature, average interior mass temperature, and humidity are modeled using three coupled non-linear ordinary differential equations based on mass and energy balances. These equations are then solved by a fourth-order Runge-Kutta method with adaptive step size control.

Striving for sustainable transportation solutions, hydrogen is often identified as a promising energy carrier and internal combustion engines are seen as a cost effective consumer of hydrogen to facilitate the development of a large-scale hydrogen infrastructure. Driven by efficiency and emissions targets defined by the U.S. Department of Energy, a research team at Argonne National Laboratory has worked on optimizing a spark-ignited direct injection engine for hydrogen. Using direct injection improves volumetric efficiency and provides the opportunity to properly stratify the fuel-air mixture in-cylinder. Collaborative 3D-CFD and experimental efforts have focused on optimizing the mixture stratification and have demonstrated the potential for high engine efficiency with low NOx emissions. Performance of the hydrogen engine is evaluated in this paper over a speed range from 1000 to 3000 RPM and a load range from 1.7 to 14.3 bar BMEP.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

Too often many heat management problems are not solved with thermal analysis because of excessive complexity, time, and cost. A method for quickly solving a sophisticated thermal/fluid system with minimal user interaction and with common desktop computer resources is presented. A desktop (Microsoft Windows™) thermal analysis package, WinTherm, consists of the Generic Processor (pre-processing software), the 3-D Thermal Model (a finite difference nodal network solver), and an Image Viewer (wireframe and animated thermal display). The theoretical basis for this thermal analysis toolkit will be discussed as well as examples of its implementation.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

Traditional Lagrangian spray modeling approaches for internal combustion engines are highly grid-dependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was recently developed and implemented by Senecal et al., (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against non-evaporating, evaporating, and reacting data from the literature.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

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.

A set of control functions have been investigated for a computer controlled diesel cooling system, using the vehicle engine cooling system code. Various engine operating conditions such as the engine load, engine speed, and ambient temperature are considered as the controlling variables in the control loops. The truck simulated in the study was an International Harvester COF-9670 cab over chassis heavy-duty vehicle equipped with a standard cab heater, a Cummins NTC-350 diesel engine with a McCord radiator and standard cooling system components and after-cooler. The vehicle also had a Kysor fan-clutch and shutter system. Comparison simulation tests between the conventional cooling system and the computer controlled cooling system using the Vehicle-Engine-Cooling Computer System model under different ambient and route conditions show that the computer controlled cooling system would offer the following benefits: 1.

Spray behavior is regarded as one of main factors which influence engine performance, fuel consumption and emissions for diesel engine. In practice, spray characteristics from each orifice from a multi-hole nozzle are normally arranged symmetrically, while the hole-to-hole spray variation is unavoidable. This variation will cause spatial uneven distribution of spray and combustion degrade, which will be no longer inconsiderable in face of the more and more stringent emission rules. In this paper, two methods including spray macro-characteristics experiment and separated fuel mass measurement are employed to test the hole-to-hole spray variation of two six-hole symmetric VCO injectors of different brands, and experiments are operated under different conditions including different injection pressures, back pressures and injection durations.

The purpose of this study was to investigate the pressure drop and regeneration characteristics of a silicon carbide (SiC) wall-flow diesel particulate filter. The performance of a 25 μm mean pore size SiC dual trap system (DTS) consisting of two 12 liter traps connected in parallel in conjunction with a copper (Cu) fuel additive was evaluated. A comparison between the 25 μm DTS and a 15 μm DTS was performed, in order to show the effect of trap material mean pore size on trap loading and regeneration behavior. A 1988 Cummins LTA 10-300 diesel engine was used to evaluate the performance of the 15 and 25 μm DTS. A mathematical model was developed to better understand the thermal and catalytic oxidation of the particulate matter. For all the trap steady-state loading tests, the engine was run at EPA mode 11 for 10 hours. Raw exhaust samples were taken upstream and downstream of the trap system in order to determine the DTS filtration efficiency.

Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice.

For several years there has been a great deal of effort made in researching ways to run a compression ignition engine with simultaneously high efficiency and low emissions. Recently much of this focus has been dedicated to using gasoline-like fuels that are more volatile and less reactive than conventional diesel fuel to allow the combustion to be more premixed. One of the key challenges to using fuels with such properties in a compression ignition engine is stable engine operation at low loads. This paper provides an analysis of how stable gasoline compression ignition (GCI) engine operation was achieved down to idle speed and load on a multi-cylinder compression ignition engine using only 87 anti-knock index (AKI) gasoline. The variables explored to extend stable engine operation to idle included: uncooled exhaust gas recirculation (EGR), injection timing, injection pressure, and injector nozzle geometry.