Connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications. This interface is built upon Oak Ridge National Laboratory's (ORNL's) Real-Sim approach, can support various simulation tools and real-time X-in-the-loop (XIL) simulations, and is based on network communication protocols. The network communication delays are analyzed for simultaneous connection of up to 20 simulators. Then, two example applications are studied to demonstrate the potential usage of the Real-Sim interface: (1) a centralized merging scenario where the dynamics of two ego vehicles are emulated with detailed vehicle and powertrain dynamics models and (2) a signalcontroller-in-the-loop (SCIL) evaluation where one intersection of the simulated traffic scenario is controlled by a physical signal controller. This Real-Sim interface is a tool that allows researchers to bring real, tangible hardware and software into simulated environments to comprehensively evaluate and support a wide variety of CAV applications.

When a heavy-duty vehicle (HDV) operates at the nominal highway speed, over two-thirds of its total resistive force comes from the air drag, contributing to more than half of its fuel consumption. One effective countermeasure to reduce the fuel consumption of HDVs is platooning, which employs connectivity and automated driving technologies to link two or more HDVs in convoy. Platooning allows HDVs to drive closer together and yields improved fuel economy and less CO2 emission thanks to the reduced air drag. Maximizing the energy benefits of an HDV platoon requires quantifying the drag interaction between vehicles. In practice, modeling the drag reduction in a platoon boils down to identifying the relationship between the air drag coefficient C-d ) and the inter-vehicle distance d Existing approaches to identify C-d (d) include vehicle field tests, wind tunnel experiments, and computational fluid dynamics simulation, which can howbeit be time-consuming and cost prohibitive. In contrast, this paper proposes an algebraic approach, which relies on onboard-measurable variables, to estimate the air drag coefficient of an HDV in a platoon. Its algebraic nature avoids the classical persistence of excitation condition for parameter identification and can yield the identified parameter almost instantaneously. Simulation results demonstrate its effectiveness and the improved estimation speed over a recursive least squares identifier.

Connected and automated vehicles (CAVs) have the potential to improve many aspects of the current transportation systems such as safety, mobility, and energy efficiency. In order to evaluate the benefits and impacts of a CAV, the CAV control algorithm is typically implemented on vehicles simulated in a traffic microsimulation environment. However, traffic microsimulation usually lacks detailed vehicle and powertrain dynamics, making it challenging to fully understand how a CAV control algorithm will perform and respond on an actual vehicle. Whether the same benefits measured in the simulation will also be observed in real-world remains an open question. One potential approach to fill in this gap is to conduct a co-simulation of traffic microsimulation with detailed vehicle and powertrain dynamics models, often developed in MATLAB Simulink. However, current microsimulation tools such as VISSIM and SUMO do not have a ready-to-use interface for co-simulation with vehicle dynamics and Simulink. Also, even if such an interface exists, it will be tool-specific, making it challenging to shift from one tool to another or test CAV controls in different tools. There are needs for tool-agnostic co-simulation as different microsimulation tools have their pros and cons, and researchers often need to use different tools based on the purposes of the simulation, project needs, and applications. In this work, Flexible Interface for X-in-the-loop Simulation (FIXS) is developed that can support the co-simulation of microsimulation, CAV control algorithm, and vehicle dynamics model in Simulink. Enabled by the FIXS, the benefit and performance of a CAV control algorithm can be better understood with the consideration of vehicle responses and dynamics. The connection to VISSIM and SUMO is handled internally by the interface, and users can easily switch tools by changing a configuration file. The co-simulation capability is demonstrated for a VISSIM eco-approach and departure CAV scenario and a SUMO cooperative merging scenario for both a passenger CAV and a class 8 heavy-duty connected and automated trucks.

Connected and automated vehicle (CAV) technologies need comprehensive testing and evaluation before actual implementation in the real world. However, many inherent technical challenges exist due to the complexity of CAVs. An integrated evaluation platform is needed with vehicle and traffic simulation tools from different domains and X-in-the-loop (XIL) components to fully evaluate all aspects of CAV technologies. In this work, a multi-resolution XIL simulation framework named Real-Sim is presented to support inclusive testing and evaluation of CAVs. Real-Sim approach is simply defined as nearly any part of a system can be "in the loop", either physically or virtually. Oak Ridge National Laboratory (ORNL) has adapted Real-Sim into all its XIL capable laboratories, as well as much of its simulation and model-based design. The foundation of this approach is the system-of-systems concept which has become more relevant as much of transportation research has expanded beyond the vehicle into the traffic networks and traffic control devices. Real-Sim allows researchers to bring real, tangible hardware and software into simulated environments. It applies to a wide variety of applications, including Real controller – Simulated vehicle, Real vehicle – Simulated virtual environment, Real signal controllers – Simulated traffic, etc.

Hybrid electric powertrains are a growing market in medium- and heavy-duty applications. There is a lack of available information to understand the challenges in the integration of engine platforms into electrified powertrains, such as cold-start, restart, and load-reduction effects on emissions and emission control devices. Results from the Heavy Heavy-Duty Diesel Truck (HHDDT) cycle using a conventional medium-duty diesel engine were compared with those of a parallel hybrid architecture. Oak Ridge National Laboratory in collaboration with the US Department of Energy and Odyne Systems, LLC developed a powertrain in a hardware-in-the-loop environment, integrating the Odyne Systems, LLC medium-duty parallel hybrid system, which was used for the hybrid portion of this study. Experiments under the HHDDT cycle showed increasing improvements in fuel consumption and engine-out emissions with the integration of stop/start, hybrid, and hybrid with stop/start. However, the effects of load reduction and exhaust temperature on the thermal management strategy have shown an increase in fueling in the second part of the HHDDT cycle. Four configurations of medium-duty electrification were studied and contributed to building a unique data set containing combustion, emissions, and system integration data. Each electrification level was compared with the conventional baseline. The calibration of the conventional engine was not altered for this study. Opportunities to tailor the combustion process were identified with the stop/start strategy.

The increased market penetration of hybrid electric powertrains in medium heavy- duty (MHD) applications has provided a novel platform for vehicle research. One example of such a platform is the MHD parallel hybrid truck developed by Odyne Systems, LLC. In collaboration with Odyne Systems, LLC and the Department of Energy (DOE), Oak Ridge National Laboratory (ORNL) developed a validated vehicle plant model for this truck and tested the Odyne powertrain in a hardwarein-the-loop (HIL) environment. While testing in the HIL environment, the effects of reduced engine load, and thus catalyst heating, on the selective catalytic reduction (SCR) catalyst produced diminished hybrid improvement as the level of energy storage usage increased. This article will discuss these results and the potentially unforeseen interactions with modern aftertreatment systems when hybridizing conventional powertrains. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC0500OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

The rapid evolution of autonomous vehicles (AVs) has exposed the need for fast-paced development and testing processes of a variety of perception, planning, and control algorithms. To expedite development, the AV industry and researchers leverage virtual vehicle environments to simulate a range of test scenarios that may otherwise be costly or difficult to conduct on a real test track. However, the various virtual environments may have different results depending on the fidelity of various simulation features, such as vehicle dynamics, sensor simulation, and environment recreation. This tutorial article examines a proposed framework for constructing, parameterizing, and validating a virtual vehicle environment using an existing AV data set. First, an overview of several open source and commercially available simulation tools, including their associated workflows, for scene and scenario creation is presented. Next, various open AV data sets are examined to inform the data set selection for the validation framework. Then, an example workflow of recreating a real-world scene from the selected data set in a simulation tool with various emulated sensors parameterized to match the data set is demonstrated. Finally, an example AV-perception algorithm is subjected to data streams from virtual and real-world environments and suggested metrics for analyzing the results are discussed.

Electric drives, whether in battery electric vehicles (BEVs) or various other applications, are an important part of modern transportation. Traditionally, physics-based models based on steady-state mapping of electric drives have been used to evaluate their behavior under transient conditions. Hardware-in-the-Loop (HIL) testing seeks to provide a more accurate representation of a component's behavior under transient load conditions that are more representative of real world conditions it will operate under, without requiring a full vehicle installation. Oak Ridge National Laboratory (ORNL) developed such a HIL test platform capable of subjecting electric drives to both conventional steady-state test procedures as well as transient experiments such as vehicle drive cycles. This facility was used to compare the behavior of an electric drive installed in a BEV with the two methods: offline simulation built from the experimental steady state efficiency map, and HIL experimentation of the same electric drive simulating the same BEV. The aim of this study is to evaluate the accuracy of steady state map based simulation against experimental HIL results in the case of an electric drive. This paper first outlines HIL test procedures as well as the key aspects of utilizing steady-state maps to develop a model of the drive. Then both quantitative and qualitative differences in the experimental results obtained from the two processes are presented. Differences in specific transient behaviors between the two methods are discussed. Although both methods agree well in most transient situations, direct comparison of the offline simulation against the HIL results demonstrates that transient behaviors are not captured entirely by simulation alone.

Manufacturers of medium- and heavy-duty engines and transmissions continually try to improve the efficiency of their components; however, it is becoming more difficult and costly to improve those technologies, and a new approach is needed to find efficiency gains in other vehicle systems. For example, most ancillary loads on the engines in medium- and heavy-duty trucks, both at idle and under driving conditions, are generated by accessories that are designed to address worst-case scenarios or to meet the minimum requirements at one operating point. Improvements to those systems would improve overall powertrain efficiency. The objectives of this cooperative research and development agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and Cummins Inc. (CRADA No. NFE-13-04402) are (1) to analytically verify novel accessory electrification /hybridization approaches for medium- and heavy-duty trucks and (2) to experimentally validate prototype hardware utilizing the ORNL Vehicle Systems Integration Laboratory and the Cummins T112 test vehicle. Hybrid powertrains can provide significant reductions in fuel consumption, criteria pollutants, and greenhouse gas emissions. They provide these benefits by meeting vehicle power requirements in more flexible and optimal ways than a conventional powertrain (e.g., capture and use of braking energy, optimization of engine and component operation). Realizing the full potential of hybridization requires management of all vehicle power demands, including accessory loads. One of the major petroleum-reduction opportunities of hybridization is to shut the engine down when power demand is low. However, accessory loads, such as power steering, braking, climate control, and other electric auxiliaries, must still be managed when the engine is off.

Cooperative Research and Development Agreement between UT-Battelle, LLC (herein referred to as the “Contractor”) and ArvinMeritor, Inc., (herein referred to as the “Participant”) is to develop control strategies and models to optimize the operation of the dual mode hybrid powertrain for Class 8, heavy duty (HD) trucks. This includes intelligent power and energy apportionment from the engine and the battery pack. Hybrid powertrains are of considerable interest because of potential reductions in fuel consumption, criteria pollutants and green house gas (GHG) emissions. Parallel hybrids have been applied to light and medium duty trucks, where urban driving cycles are prevalent, while series hybrids have been successfully used for other applications like transit and school buses. Unfortunately, hybridization of the Class 8, heavy-duty (HD) powertrain is inherently challenging due the expected long-haul driving requirements and limited opportunities for regenerative braking. The Participant has conceived and demonstrated a transformational Dual Mode Hybrid Powertrain (DMHP) technology developed specifically for the needs and function of Class 8 line haul trucks. The DMHP system enables a new paradigm in powertrain operational efficiency in the Class 8 truck segment. It decouples the connection between the engine operating point and the truck road load demands over a broad operating range through an innovative hybrid design. The DMHP operation choices include running in full series, full parallel and engine-off modes. The DMHP offers the opportunity for an engine to operate in a narrow range, thus providing a strategy for maximized fuel economy and minimized emissions. Further, it is expected that transient torque and power wheel demands are handled in whole or part by the electric system, thus reducing the frequency and intensity of engine transients and further improving the fuel economy and emissions. Fuel consumption and emissions have been further reduced through the elimination of overnight hoteling and idling at stops. Finally, based on the unique operating profile of an engine integrated into our hybrid powertrain, a transformational HD truck engine design concept next can emerge. Recent research activities by Oak Ridge National Laboratory (ORNL) have yielded significant data in real-life speed and load profiles of Class 8, long haul trucks. In addition, preliminary simulations of the DMHP carried out by ORNL reveal significant optimization opportunities of the DMHP by applying systematic simulation and controls approaches. An improved understanding of the complex interactions offered by the on-board engine, energy storing system, and electric machines is necessary for the development of control methodologies and practical implementation.

September 2014 OFFICIAL USE ONLY May be exempt from public release under the Freedom of Information Act (5 U.S.C. 552), exemption number and category: 4, Commercial/Proprietary Information. Department of Energy Review required before public release. Name/Org: Leesa Laymance/ORNL Date: 1/23/2015 PROTECTED CRADA INFORMATION This report contains protected CRADA information which was produced on September 5, 2014, under CRADA No. NFE-11-03310 and is not to be further disclosed for a period of five years from the date it was produced except as expressly provided for in the CRADA. PROTECTED CRADA INFORMATION This report contains protected CRADA information which was produced on September 5, 2014, under CRADA No. NFE-11-03310 and is not to be further disclosed for a period of five years from the date it was produced except as expressly provided for in the CRADA.

Plug-in hybrid electric vehicles (PHEV) operate predominantly as electric vehicles (EV) with intermittent assist from the engine. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current conventional vehicles, the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.ORNL, in collaboration with the University of Tennessee, developed an Engine-In-the-Loop (EIL) test platform to investigate cold start emissions on a 2.0l Gasoline Turbocharged Direct Injection (GTDI) Ecotec engine coupled to a virtual series hybrid electric vehicle. The end-goal of this project is to demonstrate the benefits of coordinating engine and powertrain supervisory control strategies to minimize cold start emissions.First, this paper provides a summary of the results obtained by optimizing engine cold start strategies on their own within the context of a PHEV application where the engine can be motored up to speed and supplemented with the electric machine. These specific operating modes open up new engine calibration opportunities. This study investigated the effect of different cranking injection, post-start load, idle speed and spark timing strategies.The paper then reports on the second phase of the project which focuses on the coordination of engine control strategies and hybrid energy management strategies. Stand-alone optimization of each component's algorithms does not guarantee that the resulting hybrid powertrain will operate efficiently. Therefore cold start strategies have to be controlled and optimized as a system to minimize tailpipe emissions. Comparison results of different coordination algorithms are presented to demonstrate the benefit of system coordination and optimization.

This project involves the design and installation of a compressed hydrogen fueling system in a 2009 Chevrolet Colorado pickup truck. The design tasks consisted of developing the high‐ pressure tank mounting system, designing the hydrogen delivery system, and specifying the engine fueling system. All systems had to be in compliance with federal motor vehicle standards. These systems were incorporated with the goal of minimizing changes to the vehicles exterior appearance. Introduction: As the world burns more and more crude oil and coal our supplies slowly deplete and the pollution generated also increases. It is because of these problems that we as engineers must now, more than ever, explore new types of alternative fuels. . These new fuels may not be cost effective now. But without the new technologies generated from research, economic viability is an impossible goal. This is true of hydrogen. So the purpose of this senior design project was to show that a standard car can be fairly easily converted to hydrogen with some simple vehicular modifications. Our only limitations for this project were more or less time constraints. Literature Review: As far as our research goes the majority of what we found came from SAE papers, the Challenge X rules, and the Future Truck rules. Through our readings and findings we were able to develop a plan of attack. Methodology: The first objective was mounting the three 5000psi hydrogen tanks. The tanks are aluminum pressure vessels with either a fiber glass or carbon fiber wrap to improve strength and also protect from debris. They came with circular mounting loops that we then mounted to a 3/16” and 1/4” thick steel ladder style frame. The frame is held together with both MIG welds and bolts to insure strength. After that a common rail bar or steel bolts to the top as seen in figures 1, 2, and 3.