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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.

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.

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.

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.

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.

Modern vehicles have incorporated numerous safety-focused Advanced Driver Assistance Systems (ADAS) in the last decade including smart cruise control and object avoidance. In this paper, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy. We combine ADAS sensor data with a previously developed prediction model, dynamic programming optimal energy management control, and a validated model of a 2010 Toyota Prius to explore fuel economy. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results.

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.

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.

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).

EcoCAR 2 is the premiere North American collegiate automotive competition that challenges 15 North American universities to redesign a 2013 Chevrolet Malibu to decrease the environmental impact of the Malibu while maintaining its performance, safety, and consumer appeal. The EcoCAR 2 project is a three year competition headline sponsored by General Motors and U.S. Department of Energy. In Year 1 of the competition, extensive modeling guided the Colorado State University (CSU) Vehicle Innovation Team (VIT) to choose an all-electric vehicle powertrain architecture with range extending hydrogen fuel cells, to be called the Malibu H2eV. During this year, the CSU VIT followed the EcoCAR 2 Vehicle Design Process (VDP) to develop the H2eV's electric and hydrogen powertrain, energy storage system (ESS), control systems, and auxiliary systems.

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.

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.

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.

Even when charging at the highest rates currently available, Electric Vehicles (EVs) add range at substantially lower rates than Internal Combustion Engine Vehicles (ICVs) do while fueling. In addition, DC charging comes at a cost premium and leads to accelerated battery degradation. EV users able to rely on AC charging during long dwells at home or work may experience cost and time savings relative to ICV users with similar driving patterns. However, EV users unable to charge during long dwells will face higher charging costs and higher dedicated charging time. An important question is how occupants of Multi-Unit Dwellings (MUDs), which provide some AC Electric Vehicle Supply Infrastructure (EVSE) but not enough for all cars to charge at once, will be effected. In this paper the authors’ previously published method for quantifying EV user inconvenience due to charging is extended to deal with stochastic charger availability.