Search Results

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.

Prediction based optimal energy management systems are a topic of high interest in the automotive industry as an effective, low-cost option for improving vehicle fuel efficiency. With the continuing development of connected and autonomous vehicle (CAV) technology there are many data streams which may be leveraged by transportation stakeholders. The Suite of CAVs-derived data streams includes advanced driver-assistance (ADAS) derived information about surrounding vehicles, vehicle-to-vehicle (V2V) communications for real time and historical data, and vehicle-to-infrastructure (V2I) communications. The suite of CAVs-derived data streams have been demonstrated to enable improvements in system-level safety, emissions and fuel economy.

Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide low error velocity prediction. We developed an LSTM deep neural network that uses different groups of datasets collected in Fort Collins, Colorado.

A fuel cell powered airplane has been designed and constructed at the Georgia Insitute of Technology to develop an understanding of the design and implementation challenges of fuel cell-powered unmanned aerial vehicles (UAVs). A custom 448W net output proton exchange membrane fuel cell powerplant has been constructed and tested. A demonstrator aircraft was designed and built to accommodate this powerplant and the fuel cell powered aircraft has performed seven test flights to date. Test data show that the aircraft performance validates the models used for design and optimization and that the fuel cell aircraft is capable of longer endurance, higher performance test flights.

Optimal energy management of hybrid electric vehicles has previously been shown to increase fuel economy (FE) by approximately 20% thus reducing dependence on foreign oil, reducing greenhouse gas (GHG) emissions, and reducing Carbon Monoxide (CO) and Mono Nitrogen Oxide (NOx) emissions. This demonstrated FE increase is a critical technology to be implemented in the real world as Hybrid Electric Vehicles (HEVs) rise in production and consumer popularity. This review identifies two research gaps preventing optimal energy management of hybrid electric vehicles from being implemented in the real world: sensor and signal technology and prediction scope and error impacts. Sensor and signal technology is required for the vehicle to understand and respond to its environment; information such as chosen route, speed limit, stop light locations, traffic, and weather needs to be communicated to the vehicle.

Previous research has demonstrated an increase in Fuel Economy (FE) using an optimal controller based on limited foreknowledge using methods such as Engine Equivalent Minimization Strategy (ECMS) and Stochastic Dynamic Programming (SDP) with stochastic error in the prediction signal considerations. This study seeks to quantify the sensitivity of prediction-derived vehicle FE improvements to prediction signal quality assuming optimal control. In this research, a hill pattern and route type identification scenario control subjected to varying prediction signal quality is selected for in depth study. This paper describes the development of a baseline Toyota Prius Hybrid Vehicle (HV) simulation models, real world drive cycles and real-world disturbances, and an optimal controller incorporating a prediction of vehicle power requirements.

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.

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.

The environmental implications of converting vehicles powered by Internal Combustion Engines (ICE) to battery powered and hybrid battery/ICE powered are evaluated for the case of Chile, one of the worldwide leaders in the production of lithium (Li) required for manufacturing of Li-ion batteries. The economic and environmental metrics were evaluated by techno-economic analysis (TEA) and Life Cycle Assessment (LCA) tools - SuperPro Designer and Gabi®/GREET® models. The system boundary includes both the renewable and nonrenewable energy sources available in Chile and well-to-pump energy consumptions and GHG emissions due to Li mining and Li-ion battery manufacturing. All the major input data required for TEA and LCA were generated using Autonomie vehicle modeling software. This study compares economic and environmental indicators of three vehicle models for the case of Chile including compact, mid-size, and a light duty truck.

Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into whether near-term technologies can be utilized to improve FE and the impact of real-world prediction error on potential FE improvements. In this study, a speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication with real-world driving data and a drive cycle database was developed to understand if incorporating near-term technologies could be utilized in a predictive energy management strategy to improve vehicle FE. This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a validated high-fidelity fuel economy model of a Toyota Prius.

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.

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.

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.

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.

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.

Practical applications of recently developed sensor fusion algorithms perform poorly in the real world due to a lack of proper evaluation during development. Existing evaluation metrics do not properly address a wide variety of testing scenarios. This issue can be addressed using proactive performance measurements such as the tools of resilience engineering theory rather than reactive performance measurements such as root mean square error. Resilience engineering is an established discipline for evaluating proactive performance on complex socio-technical systems which has been underutilized for automated vehicle development and evaluation. In this study, we use resilience engineering metrics to assess the performance of a sensor fusion algorithm for vehicle localization. A Kalman Filter is used to fuse GPS, IMU and LiDAR data for vehicle localization in the CARLA simulator.

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.

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.

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.