The need for robust verification and validation of automated vehicles (AVs) to ensure driving safety grows more urgent as increasing numbers of AVs are allowed to operate on open roads. To address this need, AV developers can present a safety case to regulators and the public that provides an evidence-based justification of their assertion that an AV is safe to operate on open roads. This work aims to describe the development of a scenario-based testing methodology that contributes to this safety case. A high-level definition of this test selection and scoring methodology (TSSM) is first presented, along with an outline of its scope and key ideas. This is followed by a literature review that details the current state of the art in AV testing, including the driving performance metrics and equations that provide a basis for the TSSM. A chart-based method for quantifying an AV’s operational design domain (ODD) and behavioral competency portfolio is then described that provides the foundation for a scenario generation and filtration process. After outlining a method for the AV to progress through increasingly robust test methods based on its current technology readiness level (TRL), the generation and filtration of two sets of scenarios by the TSSM is outlined: a standardized set that can be used to compare the performance of vehicles with identical ODD and behavioral competency portfolios, and a set containing high-relevance scenarios that is partially randomized to ensure test integrity. A related framework for incorporating testing on open roads is subsequently specified. An equation for an overall AV driving performance score is then defined that quantifies the aggregate performance of the AV across all generated scenarios. The TSSM continues according to an iterative process, which includes a method for exploring edge and corner scenarios, until a stopping condition is achieved. Two proofs of concept are provided: a demonstration of the ability of the TSSM to pare scenarios from a preexisting database, and an example ODD and behavioral competency portfolio specification form. Finally, this work concludes by evaluating the TSSM and its proofs of concept and outlining possible future work on the methodology.

To date, there is not a standardized method for consistently quantifying the performance of an automated driving system (ADS)-equipped vehicle (AV). The purpose of this dissertation is to contribute to a framework for such an approach referred to throughout as the operational safety assessment (OSA) methodology. Through this research, safety metrics are identified, researched, and analyzed to capture aspects of the operational safety of AVs, interacting with other salient objects. This dissertation outlines the approach for developing this methodology through a series of key steps including: (1) comprehensive literature review; (2) research and refinement of OSA metrics; (3) generation of MATLAB script for metric calculations; (4) generation of simulated events for analysis; (5) collection of real-world data for analysis; (6) review of OSA methodology results; and (7) discussion of future work to expand complexity, fidelity, and relevance aspects of the OSA methodology. The detailed literature review includes the identification of metrics historically used in both traditional and more recent evaluations of vehicle performance. Subsequently, the metric formulations are refined, and robust severity evaluations are proposed. A MATLAB script is then presented which was generated to calculate the metrics from any given source assuming proper formatting of the data. To further refine the formulations and the MATLAB script, a variety of simulated scenarios are discussed including car-following, intersection, and lane change situations. Additionally, a data collection activity is presented, leveraging the SMARTDRIVE testbed operated by Maricopa County Department of Transportation in Anthem, AZ to collect real-world data from an active intersection. Lastly, the efficacy of the OSA methodology with respect to the evaluation of vehicle performance for a set of scenarios is evaluated utilizing both simulated and real-world data. This assessment provides a demonstration of the ability and robustness of this methodology to evaluate vehicle performance for a given scenario. At the conclusion of this dissertation, additional factors including fidelity, complexity, and relevance are explored to contribute to a more comprehensive evaluation.

All around the automotive industry, the chassis dynamometer exists in a variety of configurations but all function to provide one common goal. The underlying goal is to measure a vehicle’s performance by measuring torque output and taking that measurement to calculate horsepower. This data is crucial in situations of testing development vehicles or for tuning heavily modified vehicles. While the current models in the industry serve their purposes for what they were intended to do, in theory, an additional system can be introduced to the dyno to render the system into an electric generator.
The hardware will consist of electric motors functioning as a generator by reversing the rotation of the motor (regenerative braking). Using the dynamometer with the additional motor system paired with a local battery, the entire system can be run off by their tuning service. When considering the Dynojet and Dynapack dynamometer, it was calculated that an estimated return of 81.5% of electricity used can be generated. Different factors such as how frequent the dyno is used and for how long affect the savings. With a generous estimate of 6 hours dyno run time a day for 250 business days and the cost of electricity being 13.19 cents/kwh the Dynapack came out to $326.45 a year and $1424.52 for the Dynojet. With the return of electricity, the amount saved comes out to $266.18 for the Dynapack and $1161.50 for the Dynojet. This will alleviate electrical costs dramatically in the long term allowing for performance shops to invest their saved money into more tools and equipment.

Research studies on improving Automated Driving Systems (ADS) have focused mainly on enhancing safety, through the development of more sophisticated sensors that have the ability to detect objects promptly. Safety is indeed a priority especially when the public has raised concerns regarding unmanned vehicles failing to make informed decisions in unforeseen situations, for example, the Uber Automated Vehicle (AV) crash that happened in Arizona, in 2018 (Griggs & Wakabayashi, 2018). However, one question still remains suppositious: How will the continuous development of AVs impact carbon emissions and energy consumption? Since many automakers claim that automated driving is part of the future of mobility, there is a possibility that automated driving could promote the use of alternative clean fuels like electric batteries and support further travels with the least amount of energy. Therefore, this paper discusses how new ADS technologies with energy-saving benefits, will enable multiple levels of vehicle autonomy to perform efficiently and cause less environmental impacts. In addition, this paper discusses prospective developments in other industries, that could emerge to compliment the next generation ADS technologies and also help decrease the global energy demand that is projected to increase by some 28 percent between now and the year 2040 (“EIA projects 28% increase in world energy use by 2040 - Today in Energy - U.S. Energy Information Administration (EIA),” n.d.)

With the growing popularity and advancements in automation technology, Connected and Automated Vehicles (CAVs) have become the pinnacle of ground-vehicle transportation. Connectivity has the potential to allow all vehicles—new or old, automated or non-automated—to communicate with each other at all times and greatly reduce the possibility of a multi-vehicle collision. This project sought to achieve a better understanding of CAV communication technologies by attempting to design, integrate, test, and validate a vehicular ad-hoc network (VANET) amongst three automated ground-vehicle prototypes. The end goal was to determine what current technology best satisfies Vehicle-to-Vehicle (V2V) communication with a real-time physical demonstration. Although different technologies, such as dedicated short-range communication (DSRC) and cellular vehicle to everything (C-V2X) were initially investigated, due to time and budget constraints, a FreeWave ZumLink Z9-PE DEVKIT (900 MHz radio) was used to create a wireless network amongst the ground-vehicle prototypes. The initial testing to create a wireless network was successful and demonstrated but creating a true VANET was unsuccessful as the radios communicate strictly peer to peer. Future work needed to complete the simulated VANET includes programming the ZumLink radios to send and receive data using message queuing telemetry transport (MQTT) protocol to share data amongst multiple vehicles, as well as programming the vehicle controller to send and receive data utilizing terminal control protocol (TCP) to ensure no data loss and all data is communicated in correct sequence.

Commercial Li-ion cells (18650: Li4Ti5O12 anodes and LiCoO2 cathodes) were subjected to simulated Electric Vehicle (EV) conditions using various driving patterns such as aggressive driving, highway driving, air conditioning load, and normal city driving. The particular drive schedules originated from the Environment Protection Agency (EPA), including the SC-03, UDDS, HWFET, US-06 drive schedules, respectively. These drive schedules have been combined into a custom drive cycle, named the AZ-01 drive schedule, designed to simulate a typical commute in the state of Arizona. The battery cell cycling is conducted at various temperature settings (0, 25, 40, and 50 °C). At 50 °C, under the AZ-01 drive schedule, a severe inflammation was observed in the cells that led to cell failure. Capacity fading under AZ-01 drive schedule at 0 °C per 100 cycles is found to be 2%. At 40 °C, 3% capacity fading is observed per 100 cycles under the AZ-01 drive schedule. Modeling and prediction of discharge rate capability of batteries is done using Electrochemical Impedance Spectroscopy (EIS). High-frequency resistance values (HFR) increased with cycling under the AZ-01 drive schedule at 40 °C and 0 °C. The research goal for this thesis is to provide performance analysis and life cycle data for Li4Ti5O12 (Lithium Titanite) battery cells in simulated Arizona conditions. Future work involves an evaluation of second-life opportunities for cells that have met end-of-life criteria in EV applications.

The automotive industry is committed to moving towards sustainable modes of transportation through electrified vehicles to improve the fuel economy with a reduced carbon footprint. In this context, battery-operated hybrid, plug-in hybrid and all-electric vehicles (EVs) are becoming commercially viable throughout the world. Lithium-ion (Li-ion) batteries with various active materials, electrolytes, and separators are currently being used for electric vehicle applications. Specifically, lithium-ion batteries with Lithium Iron Phosphate (LiFePO4 - LFP) and Lithium Nickel Manganese Cobalt Oxide (Li(NiMnCo)O2 - NMC) cathodes are being studied mainly due to higher cycle life and higher energy density values, respectively. In the present work, 26650 Li-ion batteries with LFP and NMC cathodes were evaluated for Plug-in Hybrid Electric Vehicle (PHEV) applications, using the Federal Urban Driving Schedule (FUDS) to discharge the batteries with 20 A current in simulated Arizona, USA weather conditions (50 ⁰C & <10% RH). In addition, 18650 lithium-ion batteries (LFP cathode material) were evaluated under PHEV mode with 30 A current to accelerate the ageing process, and to monitor the capacity values and material degradation. To offset the high initial cost of the batteries used in electric vehicles, second-use of these retired batteries is gaining importance, and the possibility of second-life use of these tested batteries was also examined under constant current charge/discharge cycling at 50 ⁰C.

The capacity degradation rate under the PHEV test protocol for batteries with NMC-based cathode (16% over 800 cycles) was twice the degradation compared to batteries with LFP-based cathode (8% over 800 cycles), reiterating the fact that batteries with LFP cathodes have a higher cycle life compared to other lithium battery chemistries. Also, the high frequency resistance measured by electrochemical impedance spectroscopy (EIS) was found to increase significantly with cycling, leading to power fading for both the NMC- as well as LFP-based batteries. The active materials analyzed using X-ray diffraction (XRD) showed no significant phase change in the materials after 800 PHEV cycles. For second-life tests, these batteries were subjected to a constant charge-discharge cycling procedure to analyze the capacity degradation and materials characteristics.

This research effort focuses on thermal management system (TMS) design for a high-performance, Plug-in Hybrid Electric Vehicle (PHEV). The thermal performance for various components in an electrified powertrain is investigated using a 3D finite difference model for a complete vehicle system, including inherently temperature-sensitive components. The components include the electric motor (EM), power electronics, Energy Storage System (ESS), and Internal Combustion Engine (ICE).

A model-based design approach is utilized, where a combination of experimental work and simulation are integrated. After defining heat sources and heat sinks within the power train system, temporal and spatial boundary conditions were extracted experimentally to facilitate the 3D simulation under different road-load scenarios. Material properties, surface conditions, and environmental factors were defined for the geometrical surface mesh representation of the system. Meanwhile the finite differencing code handles the heat transfer phenomena via conduction and radiation, all convective heat transfer mode within the powertrain are defined using fluid nodes and fluid streams within the powertrain.

Conclusions are drawn through correlating experimental results to the outcome from the thermal model. The outcome from this research effort is a 3D thermal performance predictive tool that can be utilized in order to evaluate the design of advanced thermal management systems (TMSs) for alternative powertrains in early design/concept stages of the development process.

For future work, it is recommended that a full validation of the 3D thermal model be completed. Subsequently, design improvements can be made to the TMS. Some possible improvements include analysis and evaluation of shielding of the catalytic converter, exhaust manifold, and power electronics, as well as substituting for material with better thermal performance in other temperature-sensitive components, where applicable. The result of this improvement in design would be achieving an effective TMS for a high-performance PHEV.