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The operational safety of Automated Driving System (ADS)-Operated Vehicles (AVs) are a rising concern with the deployment of AVs as prototypes being tested and also in commercial deployment. The robustness of safety evaluation systems is essential in determining the operational safety of AVs as they interact with human-driven vehicles. Extending upon earlier works of the Institute of Automated Mobility (IAM) that have explored the Operational Safety Assessment (OSA) metrics and infrastructure-based safety monitoring systems, in this work, we compare the performance of an infrastructure-based Light Detection And Ranging (LIDAR) system to an onboard vehicle-based LIDAR system in testing at the Maricopa County Department of Transportation SMARTDrive testbed in Anthem, Arizona. The sensor modalities are located in infrastructure and onboard the test vehicles, including LIDAR, cameras, a real-time differential GPS, and a drone with a camera. Bespoke localization and tracking algorithms are created for the LIDAR and cameras. In total, there are 26 different scenarios of the test vehicles navigating the testbed intersection; for this work, we are only considering car following scenarios. The LIDAR data collected from the infrastructure-based and onboard vehicle-based sensors system are used to perform object detection and multi-target tracking to estimate the velocity and position information of the test vehicles and use these values to compute OSA metrics. The comparison of the performance of the two systems involves the localization and tracking errors in calculating the position and the velocity of the subject vehicle, with the real-time differential GPS data serving as ground truth for velocity comparison and tracking results from the drone for OSA metrics comparison.

The operational safety of Automated Driving System-equipped vehicles (AVs) is a critical issue with AVs being deployed on public roads. Methodologies for evaluating the operational safety are therefore necessary to maintain public safety. One possible approach is a safety case established by the AV developer that uses evidence to support a structured argument that the AV exhibits a given level of operational safety. One of the key components of a safety case for AVs is a set of testing results showing behavioral competency in a variety of scenarios within the AV’s operational design domain (ODD). The Institute of Automated Mobility (IAM) has previously published operational safety assessment (OSA) metrics along with a means to evaluate the severity of violations of the safety envelope-type OSA metrics for navigation of individual scenarios in the proposed OSA Methodology. The objective of the OSA Methodology is to objectively quantify the safety performance of each scenario navigated by the AV, with the aggregate quantifications to be included in the AV’s safety case. This work expands on the introduction of the OSA Methodology to (1) include more OSA metrics (beyond the safety envelope type) with clearly defined formulations for the severity of metric violation formulations, and (2) propose a methodology for interpreting the various OSA metric violation measurements to result in the desired quantification of the AV’s safety performance of a scenario navigation that is composed of evaluations of collision events, near-miss events, and non-collision events. The OSA Methodology is implemented in various simulation software and in real-world testing results at an IAM testbed to validate the concept. The results show a promising test result interpretation methodology that can be incorporated into a safety case framework to help ensure AV operational safety.

The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to answer these questions, educating the reader with the information required to make informed predictions of how and when CAVs will impact their lives. Topics covered include: History of Connected and Automated Vehicles, Localization, Connectivity, Sensor and Actuator Hardware, Computer Vision, Sensor Fusion, Path Planning and Motion Control, Verification and Validation, and Outlook for future of CAVs.

Driving safety analysis has recently experienced unprecedented improvements thanks to technological advances in precise positioning sensors, artificial intelligence (AI)-based safety features, autonomous driving systems, connected vehicles, high-throughput computing, and edge computing servers. Particularly, deep learning (DL) methods empowered volume video processing to extract safety-related features from massive videos captured by roadside units (RSU). Safety metrics are commonly used measures to investigate crashes and near-conflict events. However, these metrics provide limited insight into the overall network-level traffic management. On the other hand, some safety assessment efforts are devoted to processing crash reports and identifying spatial and temporal patterns of crashes that correlate with road geometry, traffic volume, and weather conditions. This approach relies merely on crash reports and ignores the rich information of traffic videos that can help identify the role of safety violations in crashes. To bridge these two perspectives, we define a new set of network-level safety metrics (NSM) to assess the overall safety profile of traffic flow by processing imagery taken by RSU cameras. Our analysis suggests that NSMs show significant statistical associations with crash rates. This approach is different than simply generalizing the results of individual crash analyses, since all vehicles contribute to calculating NSMs, not only the ones involved in crash incidents. This perspective considers the traffic flow as a complex dynamic system where actions of some nodes can propagate through the network and influence the crash risk for other nodes. We also provide a comprehensive review of surrogate safety metrics (SSM) in the Appendix A.

Traffic monitoring cameras are powerful tools for traffic management and essential components of intelligent road infrastructure systems. In this paper, we present a vehicle localization and traffic scene reconstruction framework using these cameras, dubbed as CAROM, i.e., "CARs On the Map". CAROM processes traffic monitoring videos and converts them to anonymous data structures of vehicle type, 3D shape, position, and velocity for traffic scene reconstruction and replay. Through collaborating with a local department of transportation in the United States, we constructed a benchmarking dataset containing GPS data, roadside camera videos, and drone videos to validate the vehicle tracking results. On average, the localization error is approximately 0.8 m and 1.7 m within the range of 50 m and 120 m from the cameras, respectively., Comment: Accepted to IEEE ICRA 2021

This paper explores the technical and economic opportunities and issues that exist to provide demand response to the electricity grid from an aggregated network of electric vehicle supply equipment. Using data from the roll out of a large scale electric vehicle charging network in the U.S., and a review of information on the current demand response programs on offer in the U.S., issues such as the recruitment of electric vehicle charging units and users, the required control and communication capabilities of electric vehicle supply equipment and back-office network, and an example of the associated revenue potential are discussed. Finally, a number of recommendations for an aggregated network of electric vehicle supply equipment to provide demand response to the electricity grid are presented.

The design of fuel cells is a challenging endeavour due to the multitude of physical phenomena that need to be simultaneously optimized in order to achieve proper fuel cell operation. Fuel cell design is a multi-objective, multi-variable problem. In order to design fuel cells by computational design, a mathematical formulation of the design problem needs to be developed. The problem can then be solved using numerical optimization algorithms and a computational fuel cell model. In the past decade, the fuel cell community has gained momentum in the area of numerical design. In this article, research aimed at using numerical optimization to design fuel cells and fuel cell systems is reviewed. The review discusses the strengths, limitations, advantages, and disadvantages of optimization formulations and numerical optimization algorithms, and insight obtained from previous studies.

A systematic method to obtain the optimal operating conditions of a fuel cell system is presented. This method is based on the coupling of a semi-empirical fuel cell stack model and an associated balance of plant (BOP) model with an optimization algorithm in order to efficiently explore the range of possible operating conditions. The approach described in the paper to obtain optimal operating conditions is applied to a fuel cell system designed to operate in two different applications: automotive and stationary. In both cases, the application of this methodology results in a set of optimal operating conditions that yields large improvements in the system performance. The optimization problem is solved for two different performance objectives: maximization of net system power and maximization of system exergetic efficiency.

The U.S. Department of Energy’s Advanced Vehicle Testing Activity Program consists of vehicle, battery, and infrastructure testing on advanced technology related to transportation. The activity includes tests on hybrid electric vehicles, including testing hybrid electric vehicle batteries when both the vehicles and batteries are new and at the conclusion of 160,000 miles of on-road fleet testing. This report documents battery testing performed for the 2011 Hyundai Sonata Hybrid (VIN KMHEC4A47BA003539). Battery testing was performed by Intertek Testing Services NA. The Idaho National Laboratory and Intertek collaborate on the Advanced Vehicle Testing Activity for the Vehicle Technologies Program of the U.S. Department of Energy.

The U.S. Department of Energy’s Advanced Vehicle Testing Activity Program consists of vehicle, battery, and infrastructure testing on advanced technology related to transportation. The activity includes tests on hybrid electric vehicles, including testing traction batteries when both the vehicles and batteries are new and at the conclusion of 160,000 miles of on-road fleet testing. This report documents battery testing performed for the 2011 Honda CR-Z (VIN JHMZF1C64BS002982). Battery testing was performed by Intertek Testing Services NA. The Idaho National Laboratory and Intertek collaborate on the Advanced Vehicle Testing Activity for the Vehicle Technologies Office of the U.S. Department of Energy.

The U.S. Department of Energy’s Advanced Vehicle Testing Activity Program consists of vehicle, battery, and infrastructure testing on advanced technology related to transportation. The activity includes tests on hybrid electric vehicles, including testing traction batteries when both the vehicles and batteries are new and at the conclusion of 160,000 miles of on-road fleet testing. This report documents battery testing performed for the 2011 Honda CR-Z (VIN JHMZF1C67BS004466). Battery testing was performed by Intertek Testing Services NA. The Idaho National Laboratory and Intertek collaborate on the Advanced Vehicle Testing Activity for the Vehicle Technologies Office of the U.S. Department of Energy.

The U.S. Department of Energy Advanced Vehicle Testing Activity Program consists of vehicle, battery, and infrastructure testing on advanced technology related to transportation. The activity includes tests on hybrid electric vehicles (HEVs), including testing the HEV batteries when both the vehicles and batteries are new and at the conclusion of on-road fleet testing. This report documents battery testing performed for the 2010 Honda Civic HEV UltraBattery Conversion (VIN JHMFA3F24AS005577). Battery testing was performed by the Electric Transportation Engineering Corporation dba ECOtality North America. The Idaho National Laboratory and ECOtality North America collaborate on the AVTA for the Vehicle Technologies Program of the DOE.

The intent of the electric Ground Support Equipment (eGSE) demonstration is to evaluate the day-to-day vehicle performance of electric baggage tractors using two advanced battery technologies to demonstrate possible replacements for the flooded lead-acid (FLA) batteries utilized throughout the industry. These advanced battery technologies have the potential to resolve barriers to the widespread adoption of eGSE deployment. Validation testing had not previously been performed within fleet operations to determine if the performance of current advanced batteries is sufficient to withstand the duty cycle of electric baggage tractors. This report summarizes the work performed and data accumulated during this demonstration in an effort to validate the capabilities of advanced battery technologies. This report summarizes the work performed and data accumulated during this demonstration in an effort to validate the capabilities of advanced battery technologies. The demonstration project also grew the relationship with Southwest Airlines (SWA), our demonstration partner at Ontario International Airport (ONT), located in Ontario, California. The results of this study have encouraged a proposal for a future demonstration project with SWA.

The U.S. Department of Energy Advanced Vehicle Testing Activity Program consists of vehicle, battery, and infrastructure testing on advanced technology related to transportation. The activity includes tests on hybrid electric vehicles (HEVs), including testing the HEV batteries when both the vehicles and batteries are new and at the conclusion of 160,000 miles of on-road fleet testing. This report documents battery testing performed for the 2011 Hyundai Sonata Hybrid HEV (VIN KMHEC4A43BA004932). Battery testing was performed by the Electric Transportation Engineering Corporation dba ECOtality North America. The Idaho National Laboratory and ECOtality North America collaborate on the AVTA for the Vehicle Technologies Program of the DOE.

The U.S. Department of Energy (DOE) Advanced Vehicle Testing Activity (AVTA) program consists of vehicle, battery, and infrastructure testing on advanced technology related to transportation. The activity includes tests on plug-in hybrid electric vehicles (PHEVs), including testing the PHEV batteries when both the vehicles and batteries are new and at the conclusion of 12,000 miles of on-road fleet testing. This report documents battery testing performed for the 2011 Chevrolet Volt PHEV (VIN 1G1RD6E48BU100815). The battery testing was performed by the Electric Transportation Engineering Corporation (eTec) dba ECOtality North America. The Idaho National Laboratory and ECOtality North America collaborate on the AVTA for the Vehicle Technologies Program of the DOE.

As energy storage system (ESS) technology advances, vehicle testing in both laboratory and on-road settings is needed to characterize the performance of state-of-the-art technology and also identify areas for future improvement. The Idaho National Laboratory (INL), through its support of the U.S. Department of Energy’s (DOE) Advanced Vehicle Testing Activity (AVTA), is collaborating with ECOtality North America and Oak Ridge National Laboratory (ORNL) to conduct on-road testing of advanced ESSs for the Electric Drive Advanced Battery (EDAB) project. The project objective is to test a variety of advanced ESSs that are close to commercialization in a controlled environment that simulates usage within the intended application with the variability of on-road driving to quantify the ESS capabilities, limitations, and performance fade over cycling of the ESS. To accommodate on-road testing of a wide range of ESS size, mass, and intended applications, the EDAB testbed was constructed on a mid-sized pickup truck chassis. This truck was converted into a Series Plug-In Hybrid Electric Vehicle (PHEV) which enables vehicle operation consistent with any electrified vehicle. Sophisticated software algorithms were prepared and integrated into the testbed to emulate the physical characteristics and ESS demands of the intended application during on-road operation. This emulation is vital for proper ESS operation since the testbed is larger and heavier than the vehicle for which the ESS is typically designed. On-road testing is conducted over a range of ambient temperatures and driving route types ranging from ‘stop-and-go’ city driving to constant-speed highway driving. Battery laboratory cycling with standard test procedures has been conducted throughout all phases of testing to corroborate the on-road data and accurately measure the ESS degradation. The first ESS to be tested is the Type I EV Pack manufactured by EnerDel, Inc. The ESS has a Li-ion chemistry, with a mixed-oxide cathode and amorphous hard carbon anode and a rated capacity of 70 Ah (at a C/3 rate). Due to the sealed enclosure, there is no internal thermal management system (TMS). The intended application for this ESS is for a small EV. This paper will report on current results of energy consumption, city vs. highway proportions, battery throughput, and laboratory testing results. The results illustrate the performance of the unit under test and the degradation throughout. The end-of-test criteria are 100,000 miles, three years of operation, or a 23% decrease in battery capacity, whichever occurs first.

Vehicles equipped with idle-stop (IS) systems are capable of engine shut down when the vehicle is stopped and rapid engine re-start for the vehicle launch. This capability reduces fuel consumption and emissions during periods when the engine is not being utilized to provide propulsion or to power accessories. IS systems are a low-cost and fast-growing technology in the industry-wide pursuit of increased vehicle efficiency, possibly becoming standard features in European vehicles in the near future. In contrast, currently there are only three non-hybrid vehicle models for sale in North America with IS systems and these models are distinctly low-volume models. As part of the United States Department of Energy’s Advanced Vehicle Testing Activity, ECOtality North America has tested the real-world effect of IS systems on fuel consumption in three vehicle models imported from Europe. These vehicles were chosen to represent three types of systems: (1) spark ignition with 12-V belt alternator starter; (2) compression ignition with 12-V belt alternator starter; and (3) direct-injection spark ignition, with 12-V belt alternator starter/combustion restart. The vehicles have undergone both dynamometer and on-road testing; the test results show somewhat conflicting data. The laboratory data and the portion of the on-road data in which driving is conducted on a prescribed route with trained drivers produced significant fuel economy improvement. However, the fleet data do not corroborate improvement, even though the data show significant engine-off time. It is possible that the effects of the varying driving styles and routes in the fleet testing overshadowed the fuel economy improvements. More testing with the same driver over routes that are similar with the IS system-enabled and disabled is recommended. There is anecdotal evidence that current Environmental Protection Agency fuel economy test procedures do not capture the fuel economy gains that IS systems produce in real-world driving. The program test results provide information on the veracity of these claims.

Plug-in electric vehicles (PEVs), including battery electric, plug-in hybrid electric, and extended range electric vehicles, are under evaluation by the U.S. Department of Energy's Advanced Vehicle Testing Activity (AVTA) and other various stakeholders to better understand their capability and potential petroleum reduction benefits. PEVs could allow users to significantly improve fuel economy over a standard hybrid electric vehicles, and in some cases, depending on daily driving requirements and vehicle design, PEVs may have the ability to eliminate petroleum consumption entirely for daily vehicle trips. The AVTA is working jointly with the Society of Automotive Engineers (SAE) to assist in the further development of standards necessary for the advancement of PEVs. This report analyzes different methods and available hardware for advanced communications between the electric vehicle supply equipment (EVSE) and the PEV; particularly Power Line Devices and their physical layer. Results of this study are not conclusive, but add to the collective knowledge base in this area to help define further testing that will be necessary for the development of the final recommended SAE communications standard. The Idaho National Laboratory and the Electric Transportation Applications conduct the AVTA for the United States Department of Energy's Vehicle Technologies Program.

Hybrid vehicle technology is beginning to make a significant mark in the automotive industry, most notably by the Toyota Prius THS-II and its one-mode technology, but also by two-mode architectures recently introduced. GM-Allison, Renault, and the Timken Company have attempted to capitalize on the advantages over simpler series and parallel architectures that the series-parallel configuration confers on the Prius while also improving the design by allowing the powertrain configuration to physically shift and operate in two different modes depending on the driving load. This work provides an overview of the state-of-the-art in two-mode hybrid vehicle architectures, and demonstrates the performance of this technology in comparison to the market-leading Toyota Prius one-mode hybrid vehicle technology and conventional ICE technology. Simulations in the NREL ADVISOR® software compare the performances of the one- and two-mode architectures against a parallel-full design and the ICE baseline for four different drive cycles and a vehicle with varying weight that simulates a commercial vehicle application. A configuration that is a variation of those designed by GM-Allison was chosen as the representative of the two-mode architectures. The performance metric was fuel economy. The fuel economy was measured over the course of the drive cycles: (1) Urban Dynamometer Driving Schedule for Heavy Duty Vehicles (UDDSHDV); (2) New York City Truck (NYCT); (3) City-Suburban Heavy Vehicle Route (CSHVR); and (4) Highway Fuel Economy Test (HWFET). The vehicle model uses a module developed in-house for a Kenworth T400 truck with a payload that varies from empty to completely full. The results demonstrate that the two-mode architecture provides significantly improved performance to that of the conventional non-hybrid design and comparable performance to that of the parallel-full hybrid design. Furthermore, the one-mode design is shown to be sub-optimal for this vehicle type. Development and optimization of the control strategy, which is the direction of the current research, should allow for additional improvement in fuel economy; optimization of vehicular components could result in improvements in acceleration ability, gradeability, and top speed performance, which lags behind the performance capabilities of the conventional powertrain vehicle in these metrics. The study confirms that two-mode architecture presents unique advantages for constantly changing driving cycles and vehicle payloads and represents the future of hybrid vehicle technology.Copyright © 2007 by ASME

Design optimization is performed by presenting a systematic method to obtain the optimal operating conditions of a Proton Exchange Membrane (PEM) fuel cell system targeted towards a vehicular application. The fuel cell stack model is a modified version of the semi-empirical model introduced by researchers at the Royal Military College of Canada and one that is widely used by industry. Empirical data obtained from tests of PEM fuel cell stacks are used to determine the empirical parameters of the fuel cell performance model. Based on this stack model, a fuel cell system model is built in MATLAB. Included in the system model are heat transfer and gas flow considerations and the associated Balance of Plant (BOP) components. The modified ADVISOR vehicle simulation tool is used to integrate the New York City Cycle (NYCC) drive cycle and vehicle model to determine the power requirements and hence the load cycle of the fuel cell system for a low-speed fuel cell hybrid electric vehicle (LSFCHEV). The optimization of the powerplant of this vehicle type is unique. The vehicle model has been developed in the work to describe the characteristics and performance of an electric scooter, a simple low-speed vehicle (LSV). The net output power and system exergetic efficiency of the system are maximized for various system operating conditions using the weighted objective function based on the load cycle requirement. The method is based on the coupling of the fuel cell system model with three optimization algorithms (a) sequential quadratic programming (SQP); (b) simulated annealing (SA); and (c) genetic algorithm (GA). The results of the optimization provide useful information that will be used in future study on control algorithms for LSFCHEVs. This study facilitates research on more complex fuel cell system modeling and optimization, and provides a basis for experimentation to verify the fuel cell system model.Copyright © 2006 by ASME

The purposes of this paper are to improve on the nomenclature of Hybrid Vehicles (HVs), and to present an enhanced methodology to report the Fuel Economy (FE) of emerging Plug-in Hybrid Vehicles (PHVs). New definitions for HVs and PHVs are presented that include some powertrain configurations that were excluded by the previous definitions, such as the parallel micro-hybrid configuration. The methodology presented to calculate the FE for PHVs involves independently reporting fuel and electrical consumption and eliminates the usage of US driving and owner-charging statistics used in previous methods.

In this paper, we present a review of the recent advances in hybrid vehicle powertrain architectural design. The paper begins with a discussion and categorisation of the various types of hybrid vehicles. The current leading technology is the one-mode design of the Toyota Hybrid System, and the intent of the state-of-the-art architectures is to improve upon the perceived weaknesses of the one-mode design. The proposed designs employ multi-regime architectures that allow for the transmission to operate in any one of series, parallel, or power-split configurations, depending upon the road load, driver demand, and control strategy. These new designs found in the literature are discussed and compared. The work allows further study and detailed modelling of the promising multi-regime architectures to be carried out.

In this work, the dynamic model of a multi-regime hybrid vehicle powertrain architecture is presented. The study focuses on the formulae governing the operation of the planetary gear systems in the powertrain and on the performance of a more complex heavy-duty vehicle with varying loading conditions. The model is compared with models of the Toyota Hybrid System, a generic full-parallel design, and a conventional powertrain, all implemented for a commercial delivery vehicle in the ADVISOR simulation software. Computer simulations in ADVISOR compare the performance of the various designs, using fuel consumption as the performance metric, for four different drive cycles common for this vehicular application. The results demonstrate that the multi regime architecture provides significantly improved performance to that of the conventional and THS design and comparable performance to that of the full parallel hybrid design. The study confirms that the multi-regime architecture presents unique advantages for wide-ranging road loads and vehicle payloads and that multi-regime designs likely represent the future of hybrid vehicle technology.

Further improvements of hybrid electric vehicle (HEV) with advanced, next generation powertrain design require design optimization using advanced computer powertrain modeling tools. This work explores new hybrid powertrain architectures using advanced multi-physics modeling tools, and vehicle performance simulation based design optimization. The optimization problem is formulated using fuel consumption minimization as design objective, and battery SOC and driving cycle as functional constraints. The multi-physics and hybrid vehicle modeling tools from Dymola are used to model and simulate the vehicle performance and fuel consumption of a template commercial hybrid electric vehicle with similar powertrain structure as the 2008 GM Chevrolet Tahoe Hybrid SUV. The optimization tool box in Dymola is used to carry out the preliminary control strategy optimization. This study provides guidelines for introducing more advanced hybrid powertrain architectures and control algorithms for HEVs.

This document reports the work performed under Task 1.2.1.1: 'The development of a charge protocol to take advantage of off- and on-peak demand economics at facilities'. The work involved in this task included understanding the experimental results of the other tasks of SOW-5799 in order to take advantage of the economics of electricity pricing differences between on- and off-peak hours and the demonstrated charging and facility energy demand profiles. To undertake this task and to demonstrate the feasibility of plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) bi-directional electricity exchange potential, BEA has subcontracted Electric Transportation Applications (now known as ECOtality North America and hereafter ECOtality NA) to use the data from the demand and energy study to focus on reducing the electrical power demand of the charging facility. The use of delayed charging as well as vehicle-to-grid (V2G) and vehicle-to-building (V2B) operations were to be considered.