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In order to determine the overall fuel economy of a plug-in hybrid electric vehicle (PHEV), the amount of operation in charge depleting (CD) versus charge sustaining modes must be determined. Mode of operation is predominantly dependent on customer usage of the vehicle and is therefore highly variable. The utility factor (UF) concept was developed to quantify the distance a group of vehicles has traveled or may travel in CD mode. SAE J2841 presents a UF calculation method based on data collected from travel surveys of conventional vehicles. UF estimates have been used in a variety of areas, including the calculation of window sticker fuel economy, policy decisions, and vehicle design determination. The EV Project, a plug-in electric vehicle charging infrastructure demonstration being conducted across the United States, provides the opportunity to determine the real-world UF of a large group of privately owned Chevrolet Volt extended range electric vehicles.

To understand the real world conditions of use of plug-in hybrid electric vehicles, the electrical energy consumption and gasoline energy consumption must be weighted according to how often a consumer will drive fueled by each energy source. To perform this weighting The Society of Automotive Engineers Hybrid Committee has codified the concept of the utility factor in SAE J2841 - Utility Factor Definitions for Plug-In Hybrid Electric Vehicles Using Travel Survey Data. The J2841 utility factor weights the energy consumption from each energy source according to a model of US driving behavior derived statistics from the 2001 National Highway Transportation Survey, and the assumption that each vehicle begins the day trip fully charged and does not charge over the course of the day. This paper examines the sensitivity of the J2841 utility factor to more detailed models of vehicle charging behavior and proposes a utility factor model and simulation method as an alternative to J2841.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

With the recent adoption of fuel economy and emissions regulations for medium and heavy duty vehicles by the Environmental Protection Agency and the National Highway Traffic Safety Agency, new technologies to meet these targets are being developed. One such vehicle architecture that could meetthese regulations is hydraulic hybridization. Medium- and heavy-duty vehicles which operate on high intensity drive cycles (city buses, delivery vehicles, refuse vehicles, etc.) are good candidates for hydraulic hybridization due to the drive cycle intensity and the speeds at which they operate. This paper, through MATLAB/Simulink modeling, investigates the overall effectiveness of hydraulic hybridization through the selection of individual hydraulic parameters.

Application of predictive optimal energy management strategies to improve fuel economy in hybrid electric vehicles is an active subject of research. Acceleration events during a drive cycle provide particularly attractive opportunities for predictive optimal energy management because of their high energy cost and limited variability, which enables optimal control trajectories to be computed in advance. In this research, dynamic-programming derived optimal control matrices are implemented during a drive cycle on a validated model of a 2010 Toyota Prius to simulate application of pre-computed control to improve fuel economy over a baseline model. This article begins by describing the development of the vehicle model and the formulation of optimal control, both of which are simulated over the New York City drive cycle to establish baseline and upper-limit fuel economies. Then, optimal control strategies are computed for acceleration events in the drive cycle.

Connected and Automated Vehicles (CAV) provide new prospects for energy-efficient driving due to their improved information accessibility, enhanced processing capacity, and precise control. The idea of the Eco-Driving (ED) control problem is to perform energy-efficient speed planning for a connected and automated vehicle using data obtained from high-resolution maps and Vehicle-to-Everything (V2X) communication. With the recent goal of commercialization of autonomous vehicle technology, more research has been done to the investigation of autonomous eco-driving control. Previous research for autonomous eco-driving control has shown that energy efficiency improvements can be achieved by using optimization techniques. Most of these studies are conducted through simulations, but many more physical vehicle integrated test application studies are needed.

Driven by consumer demand and environmental regulations, market share for plug-in hybrid electric vehicles (PHEVs) continues to increase. An opportunity remains to develop PHEVs that also meet consumer demand for performance. As a participant in the EcoCAR 3 competition, Colorado State University’s Vehicle Innovation Team (CSU VIT) has converted a 2016 Chevy Camaro to a PHEV architecture with the aim of improving efficiency and emissions while maintaining drivability and performance. To verify the vehicle and its capabilities, the CSU Camaro is rigorously tested by means of repeatable circumstances of physical operation while Controller Area Network (CAN) loggers record various measurements from several sensors. This data is analyzed to determine consistent output and coordination between components of the electrical charge and discharge system, as well as the traditional powertrain.

EcoCAR2 is a three year project in which a 2013 Chevrolet Malibu will be redesigned to reduce emissions and be more energy efficient without sacrificing performance, safety, or consumer appeal. The competition includes 15 universities across North America and is headline sponsored by General Motors and the U.S. Department of Energy. Extensive modeling work guided the Colorado State University (CSU) Vehicle Innovation Team (VIT) to choose an all-electric vehicle architecture with a range extending hydrogen fuel cell. The team has followed the EcoCAR2 vehicle design process (VDP) in the development of the powertrain, energy storage, controls, and auxiliary systems. Details on the design process and results for these subsystems and a discussion of the integration challenges are presented.

Plug-in Hybrid Electric Vehicles (PHEV) offer the benefits of both home charging from grid electricity and extended range from fuels. Fuel cell PHEVs in a range-extending (FCEREV) configuration build upon the advantages of PHEV by producing zero emissions while driving. The Colorado State University Vehicle Innovation Team (CSU VIT) successfully designed, built, and demonstrated a FCEREV named ‘H2eV’ for Year Two of the 3-year EcoCAR 2 collegiate competition. The demonstrated FCEREV is based on the 2013 Chevrolet Malibu and features a 15 kW Polymer Electrolyte Membrane fuel cell system, an 18.9 kWh/177 kW Li-Ion battery, and a 145 kW motor for all-electric drive. Operational data was taken during driving on a closed course, following a cycle that approximates the Environmental Protection Agency's 5-cycle test procedure. This paper provides an overview of the CSU VIT's FCEREV and a detailed analysis of vehicle performance during its successful demonstration.

Hydrogen Fuel Cell Plug in Hybrid Electric Vehicles (FCPHEV) offer the potential for zero tailpipe-emission personal transportation with extended range over many battery electric vehicles. As part of the EcoCAR 2 vehicle design competition Colorado State University (CSU) has undergone a complete vehicle design process for a FCPHEV. EcoCAR 2 is a three-year collegiate engineering competition challenging universities in North America to reduce the environmental impacts of a Chevrolet Malibu without compromising performance, safety and consumer acceptability. The detailed design phase is outlined and explained in this paper including component specification, safety, and control. The CSU FCPHEV is intended to serve as a demonstration for how hydrogen and electricity can meet future transportation needs for passenger vehicles.

High accuracy hybrid vehicle fuel consumption (FC) and emissions models used in practice today are the product of years of research, are physics based, and bear a large computational cost. However, it may be possible to replace these models with a non-physics based, higher accuracy, and computationally efficient versions. In this research, an alternative method is developed by training and testing a time series artificial neural network (ANN) using real world, on-road data for a hydraulic hybrid truck to predict instantaneous FC and emissions. Parameters affecting model fidelity were investigated including the number of neurons in the hidden layer, specific training inputs, dataset length, and hybrid system status. The results show that the ANN model was computationally faster and predicted FC within a mean absolute error of 0-0.1%. For emissions prediction the ANN model had a mean absolute error of 0-3% across CO2, CO, and NOx aggregate predicted concentrations.

Vehicle control using prediction based optimal energy management has been demonstrated to achieve better fuel economy resulting in economic, environmental, and societal benefits. However, research focusing on prediction derivation for use in optimal energy management is limited despite the existence of hundreds of optimal energy management research papers published in the last decade. In this work, multiple data sources are used as inputs to derive a prediction for use in optimal energy management. Data sources include previous drive cycle information, current vehicle state, the global positioning system, travel time data, and an advanced driver assistance system (ADAS) that can identify vehicles, signs, and traffic lights. To derive the prediction, the data inputs are used in a nonlinear autoregressive artificial neural network with external inputs (NARX).

Test methods and data acquisition system specifications are described for measurements of the energy consumption of the control system of a servo-pump continuously variable transmission (CVT). Dynamic measurements of the power consumption of the servo-pump CVT control system show that the control system draws approximately 18.9 W-hrs of electrical energy over the HWFET cycle and 13.6 W-hrs over the 505 cycle. Sample results are presented of the dynamic power consumption of the servo-pump system under drive cycle conditions. Steady state measurements of the control power draw of the servo-pump CVT show a peak power consumption of 271 W, including lubrication power. The drive-cycle averaged and steady state energy consumption of the servo-pump CVT are compared to conventional CVT pump technologies.

The transportation sector contributes significantly to emissions and air pollution globally. Emission models of modern vehicles are important tools to estimate the impact of technologies or controls on vehicle emission reductions, but developing a simple and high-fidelity model is challenging due to the variety of vehicle classes, driving conditions, driver behaviors, and other physical and operational constraints. Recent literature indicates that neural network-based models may be able to address these concerns due to their high computation speed and high-accuracy of predicted emissions. In this study, we seek to expand upon this initial research by utilizing several deep neural networks (DNN) architectures such as a recurrent neural network (RNN) and a convolutional neural network (CNN). These DNN algorithms are developed specific to the vehicle-out emissions prediction application, and a comprehensive assessment of their performances is done.

The EcoCAR3 competition challenges student teams to redesign a 2016 Chevrolet Camaro to reduce environmental impacts and increase energy efficiency while maintaining performance and safety that consumers expect from a Camaro. Energy management of the new hybrid powertrain is an integral component of the overall efficiency of the car and is a prime focus of Colorado State University’s (CSU) Vehicle Innovation Team. Previous research has shown that error-less predictions about future driving characteristics can be used to more efficiently manage hybrid powertrains. In this study, a novel, real-world implementable energy management strategy is investigated for use in the EcoCAR3 Hybrid Camaro. This strategy uses a Nonlinear Autoregressive Artificial Neural Network with Exogenous inputs (NARX Artificial Neural Network) trained with real-world driving data from a selected drive cycle to predict future vehicle speeds along that drive cycle.

Transportation vehicle and network system efficiency can be defined in two ways: 1) reduction of travel times across all the vehicles in the system, and 2) reduction in total energy consumed by all the vehicles in the system. The mechanisms to realize these efficiencies are treated as independent (i.e., vehicle and network domains) and, when combined, they have not been adequately studied to date. This research aims to integrate previously developed and published research on Predictive Optimal Energy Management Strategies (POEMS) and Intelligent Traffic Systems (ITS), to address the need for quantifying improvement in system efficiency resulting from simultaneous vehicle and network optimization. POEMS and ITS are partially independent methods which do not require each other to function but whose individual effectiveness may be affected by the presence of the other. In order to evaluate the system level efficiency improvements, the Mobility Energy Productivity (MEP) metric is used.

Vehicular design, especially for hybrid electric vehicles, is multifaceted and necessarily objective oriented. Whether designing for total cost, performance, societal impact, or any other factor there can be a number of possible solutions but limited optimal solutions. While many efforts to achieve particular vehicle characteristics through systems engineering achieve acceptable designs, they are extremely resource consuming and often restricted to utilization of a handful of available components. Design complexity often exists when designers must choose between different vehicle architectures or powertrain characteristics. Evaluating design options equivalently often entails undergoing multiple design iterations to fully understand the strengths and weaknesses of selected concepts. Through the use of numerical vehicle modeling, simulation, and optimization many theoretical vehicle configurations can be compared quickly and inexpensively.

Standard operation of autonomous vehicles on public roads results in significant exposure to high levels of risk. There is a significant need to develop metrics that evaluate safety of an automated system without reliance on the rate of vehicle accidents and fatalities compared to the number of miles driven; a proactive rather than a reactive metric is needed. Resilience engineering is a new paradigm for safety management that focuses on evaluating complex systems and their interaction with the environment. This paper presents the overall methodology of resilience engineering and the resilience assessment grid (RAG) as an evaluation tool to measure autonomous systems' resilience. This assessment tool was used to evaluate the ability to respond to the system. A Pure Pursuit controller was developed and utilized as the path tracking control algorithm, and the Carla simulator was used to implement the algorithm and develop the testing environment for this methodology.

There are numerous activities in the automotive industry in which a vehicle drives a pre-defined route multiple times such as portable emissions measurement systems testing or real-world electric vehicle range testing. The speed profile is not the same for each drive cycle due to uncontrollable real-world variables such as traffic, stoplights, stalled vehicles, or weather conditions. It can be difficult to compare each run accurately. To this end, this paper presents a method to compare and quantify the repeatability of real-world on-road vehicle driving schedules using dynamic time warping (DTW). DTW is a well-developed computational algorithm which compares two different time-series signals describing the same underlying phenomenon but occurring at different time scales. DTW is applied to real-world, on-road drive cycles, and metrics are developed to quantify similarities between these drive cycles.

The design of vehicles, particularly hybrid and other advanced technology vehicles, is typically complex and benefits from systems engineering processes. Vehicle modeling and simulation have become increasingly important system design tools to improve the accuracy, repeatability, and flexibility of the design process. In developing vehicle computational models and simulation, there is an inevitable compromise between the level of detail and the development/computational cost. The tradeoff is specific to the requirements of each vehicle design effort. The assumptions and detail limitations used for vehicle simulations lead to a varying degree of result uncertainty for each design effort. This paper provides a literature review to investigate the state of the art vehicle simulation methods, and quantifies the uncertainty associated with components that are commonly allocated uncertainty.