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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.

As the rigor of vehicle pollution regulations increase there is an increasing need to come up with unique and innovative ways of reducing the effective emissions of all vehicles. In this paper, we will describe our development of a carbon capture and sequestration system that can be used in-tandem with existing exhaust treatment used in convention vehicles or be used as a full replacement. This system is based on work done by researchers from NASA who were developing a next generation life support system and has been adapted here for use in a convention vehicle with minimal changes to the existing architecture. A prototype of this system was constructed and data will be presented showing the changes observed in the effective vehicle emissions to the atmosphere. This system has the potential to extract a significant portion of tailpipe emissions and convert them into a form that allows for safe, clean disposal without causing any harm to the environment.

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.

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.

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.

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.

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.