CSU’s work encompasses all aspects of the design, demonstration and sustainability impacts assessment of energy saving technologies including electrified vehicles and systems, and connected and autonomous vehicles.  Relevant recent projects include:

​Connected and Autonomous Vehicle System Integration (Sponsored by USDOE)
It is universally understood that higher bandwidth and shorter timescale predictions of near-term vehicle trajectories are necessary to enable many of the ways that CAVs can realize transportation system-level benefits.  For an example, CAVs have the potential to significantly improve the safety of personal mobility, but safety-driven use cases for CAVs require the input of second by second datasets that might include data regarding the presence of speed goals, obstacles, traffic, and more.  At present, these datasets must be derived from individual vehicles’ sensing and prediction algorithms, but they could also be derived or augmented by transportation infrastructure datasets.  Conceptual use cases might allow the infrastructure to enable and direct the platooning of high emissions vehicles through city streets, minimizing stopping and accelerations and thereby minimizing human health impacts.  In another example, CAVs have the potential to improve the fuel economy of vehicles through drive cycle prediction and predictive energy management system (EMS) optimization.  Although the optimization of TMS algorithms has been shown to improve metrics of mobility, and the optimization of EMS has been demonstrated to realize a fuel economy and emissions improvements, these effects have not been considered using synergistically.  This project fuses the concepts together and demonstrates their efficacy in solving real transportation problems using real-world, large-scale and inclusive mobility datasets.   

Prediction-based Powertrain Control for HEVs (Sponsored by Toyota)
Fuel economy (FE) improvements for hybrid electric vehicles using a predictive Optimal Energy Management Strategy (Optimal EMS) is an active subject of research. Recent developments have focused on real-time prediction-based control strategies despite the lack of research demonstrating the aspects of prediction that are most important for FE improvements. In this paper, driving-derived nonstochastic prediction errors are applied to a globally optimal control strategy implemented on a validated model of a 2010 Toyota Prius, and the FE results are reported for each type of prediction error. This paper first outlines the real-world drive cycle development, then the baseline model development that simulates a 2010 Toyota Prius, followed by an implementation of dynamic programming (DP) to derive the globally optimal control, and finally the use of the DP solution to evaluate prediction errors. FE comparisons are reported for perfect prediction, prediction errors from 14 alternate drive cycles, and prediction errors from 6 alternate vehicle parameters. The results show that FE improvements from the Optimal EMS are maintained under mispredicted stops, traffic, and vehicle parameters, while route changes and compounded drive cycle mispredictions may result in FE improvements being lost. Taken together, these results demonstrate that implementation of an Optimal EMS can result in a reliable FE improvement.  Click here for more information

Autonomous Vehicle Testing (Sponsored by CDOT)
Colorado State University, Colorado State Department of Transportation (CDOT) and partners have developed a project whose objectives are to support and promote collaborative research efforts in the field of autonomous technologies in work zone applications.  Our collective goal is improving the safety, efficiency and quality of work efforts by providing solutions and lessons learned for the integration of autonomous vehicle technologies in these applications. The industry, government and research agencies, leading to accelerated development of these beneficial technologies and improved sharing of knowledge. This project intends to further safety, efficiency, and quality of work done in this field for all relevant agencies.  Click here for more information

Weight Reduction on High Performance PHEV’s Through the use of Composite Rotating Components (Sponsored by US DOE)
The purpose of this research is to explore the use of composites in high performance PHEV’s to reduce weight and rotational mass. Implemented through the EcoCAR 3 Advanced Vehicle Technology Competition, this research will focus on the use of composites in the rear axle assembly of a 2016 Chevrolet Camaro. Goals of this project are to reduce part count and weight through composites while using a scalable manufacturing process. A composite splined shaft for use in the axle assembly will engineered and tested. 

Hybrid Electric Vehicle Simulation and Optimization (Sponsored by US Department of Energy)
Although the benefits of the PHEV concept have been validated through modeling and demonstration projects, the impact of vehicle design on PHEV performance is not well understood.  In general, the programs used to perform vehicle modeling are designed for fuel economy prediction, hardware in the loop simulation and low-level powertrain modeling.  The limitations of these tools have limited the role that vehicle design studies have played in defining the characteristics of PHEV designs.  For instance, the emphasis on optimization of vehicle fuel economy has excused researchers from modeling and developing the PHEV architectures that are being presently developed by automakers.  Whereas nearly all simulated PHEVs have been parallel, ZEV capable PHEVs, the automakers have been developing range extending and blended mode PHEVs to meet perceived customer requirements for cost, driveability, and reliability.  New tools are required to allow for rigorous design optimization of PHEVs to metrics of consumer acceptability, cost, environmental impact and real world performance.  Advancements will come with tools that can incorporate a larger design space, adaptive energy management strategies, and powertrain controls development for improved driveability, cost and battery life.  Click here for more information.

Plug in Vehicle HVAC Energy Consumption Modeling (Sponsored by Electric Power Research Institute)
Electric vehicles (EVs) are vehicles that are propelled by electric motors powered by rechargeable battery. They are generally asserted to have GHG emissions, driveability and life cycle cost benefits over conventional vehicles. Despite this, EVs face significant challenges due to their limited on-board energy storage capacity. In addition to providing energy for traction, the energy storage device operates HVAC systems for cabin conditioning. This results in reduced driving range. The factors such as local ambient temperature, local solar radiation, local humidity, duration and thermal soak have been identified to affect the cabin conditions. In this paper, the development of a detailed system-level approach to HVAC energy consumption in EVs as a function of transient environmental parameters is described. The resulting vehicle thermal comfort model is used to address several questions such as 1) How does day to day environmental conditions affect EV range? 2) How does frequency of EV range change geographically? 3) How does trip start time affect EV range? 4) Under what conditions does cabin preconditioning assist in increasing the EV range? 5) What percentage increase in EV range can be expected due to cabin preconditioning at a given location?  Click here for more information.Click here for more information.

Plug in Hybrid Vehicle Data Analysis Project (Sponsored by Electric Power Research Institute)
EPRI and its utility partners are interested in understanding the emissions and energy consumption benefits that accrue to utilities that have purchased and used medium duty PHEV trucks.  These 58 vehicles are manufactured and sold by Via Motors and Odyne Systems LLC, and have been instrumented by EPRI and the host utility through the EPRI Electrans Program.  The objective of this study is to repeatedly and quantitatively tabulate the emissions, electrical energy consumption, and fuel savings that are associated with these vehicles.  CSU will develop software that can allow for the emissions and energy accounting to occur on a monthly basis, using updated information from the Electrans database.  Click here for more information.

Plug in Hybrid Vehicle Autonomy, Sensing and Prediction for Fuel Economy Improvement (Sponsored by Toyota)
This study seeks to develop an understanding of the sensitivity of sensing and prediction-derived vehicle fuel economy improvements to prediction signal quality.  Various types of scenario control were selected for in-depth study.  For each scenario control, we developed real-world derived drive cycles to test and demonstrate the effectiveness of the scenario control.  Baseline models of the Prius HV were refined and used to develop a baseline fuel economy model.  Optimal scenario control policies were derived assuming perfect signal quality and were implemented in the baseline vehicle fuel economy model to demonstrate the effectiveness of the scenario control under ideal conditions.  Both the optimized and baseline vehicle models were then subjected to imperfections in the prediction signals with the objective of quantifying the absolute and relative performance of the scenario control policies, and the baseline vehicle control.  Click here for more information.

EcoCAR 3 (Sponsored by General Motors and US Department of Energy)
The Colorado State University’s Vehicle Innovation Team (CSU VIT) is one of 16 universities competing in the EcoCAR 3 competition. The competition is the latest in the Advanced Vehicle Technology Competitions and tasks the competing universities with producing a 2016 hybrid electric Chevrolet Camaro to increase economy and decrease emissions while maintaining stock vehicle performance.  Click here for more informationClick here for more information.  

EcoCAR 2 – Plugging in to the Future (Sponsored by General Motors and US Department of Energy) 
EcoCAR 2 is a three-year collegiate engineering competition and the only program of its kind. The mission of EcoCAR 2 is to educate the next generation of automotive engineers through an unparalleled hands-on, real-world engineering experience. The competition challenges 16 North American universities to reduce the environmental impact of vehicles without compromising performance, safety and consumer acceptability.  Shaped by the greatest design changes in the history of the automotive industry, EcoCAR 2 requires students to explore a variety of PHEV powertrain architectures and follow a real-world engineering regimen modeled after GM’s Global Vehicle Development Process (GVDP). EcoCAR 2 teams will utilize a vehicle donated by General Motors, as the integration platform for their advanced vehicle design.  CSU students will work in teams of ME, ECE, and College of Business seniors and graduate students to design, construct and test a full-sized plug-in hybrid electric vehicle.  Click here for more information.

Investigation of Battery End-of-Life Conditions for Plug-in Hybrid Electric Vehicles (Sponsored by EPRI and US Department of Transportation)
Plug-in hybrid electric vehicles (PHEVs) capable of drawing tractive energy from the electric grid represent an energy efficient alternative to conventional vehicles.  After several thousand charge depleting cycles, PHEV traction batteries can be subject to energy and power degradation which has the potential to affect vehicle performance and efficiency.  This study seeks to understand the effect of battery degradation and the need for battery replacement in PHEVs through the experimental measurement of lithium ion battery lifetime under PHEV-type driving and charging conditions.  The dynamic characteristics of the battery performance over its lifetime are then input into a vehicle performance and fuel consumption simulation to understand these effects as a function of battery degradation state, and as a function of vehicle control strategy.  The results of this study show that active management of PHEV battery degradation by the vehicle control system can improve PHEV performance and fuel economy relative to a more passive baseline.  Simulation of the performance of the PHEV throughout its battery lifetime shows that battery replacement will be neither economically incentivized nor necessary to maintain performance in PHEVs.  These results have important implications for techno-economic evaluations of PHEVs which have treated battery replacement and its costs with inconsistency.  Click here for more information.

Analysis of plug-in hybrid electric vehicle utility factors (Sponsored by EPRI, US Department of Energy)
Plug-in hybrid electric vehicles (PHEVs) are hybrid electric vehicles that can be fueled from both conventional liquid fuels and grid electricity. To represent the total contribution of both of these fuels to the operation, energy use, and environmental impacts of PHEVs, researchers have developed the concept of the utility factor. As standardized in documents such as SAE J1711 and SAE J2841, the utility factor represents the proportion of vehicle distance travelled that can be allocated to a vehicle test condition so as to represent the real-world driving habits of a vehicle fleet. These standards must be used with care so that the results are understood within the context of the assumptions implicit in the standardized utility factors. These studies analyze and derive alternatives to the standard utility factors from the 2001 and 2009 National Highway Transportation Survey, so as to understand the sensitivity of PHEV performance to assumptions regarding charging frequency, vehicle characteristics, driver characteristics, and means of defining the utility factor. Through analysis of these alternative utility factors, these studies identify areas where analysis, design, and policy development for PHEVs can be improved through the use of alternative utility factor calculations. Click here for more information, or Click here for more information.

Fuel Cell Powered Plug in Hybrid Electric Vehicle Characterization and Impacts (Sponsored by EPRI, California Air Resources Board (CARB) and South Coast Air Quality Management District (SCAQMD))
The PFCV (Plug-in Fuel Cell Vehicle) can be understood as a FCV to which a grid-rechargeable (plug-in) battery system has been added that can provide significant driving range by releasing energy stored in the battery. Alternatively, a PFCV can be understood as a BEV to which a fuel cell system has been added to provide fast refueling and vehicle driving range significantly beyond the limits of the battery system Finally, the PFCV can also be viewed as an ICE-powered grid charged Plug-in Hybrid Electric Vehicle (PHEV) in which the ICE has been replaced with a fuel cell system. In 2009 a small group of experts with experience in AETV propulsion were invited to meet at the Electric Power Research Institute (EPRI) to discuss the advantages PFCVs might offer over FCVs, BEVs and ICE-powered PHEVs. These proposed value propositions for PFCVs are documented in Appendix A. The group concluded that the basic promise of PFCVs warranted closer analysis because of a number of prospectively favorable attributes. Based on this preliminary assessment, EPRI submitted a proposal to the California Air Resources Board (CARB) and the South Coast Air Quality Management District (SCAQMD) to conduct a preliminary (Phase 1) study of the PFCV concept. If justified by the findings, Phase 1 work also was to include development of a plan for a detailed (Phase 2) analysis of the PFCV concept and its comparison with leading AETVs.  Click here for more information.

Vehicle Simulation and Optimization in Comparison Studies (Sponsored by EPRI and US Department of Energy)
To meet these objectives CSU proposes to construct models of vehicle architectures using Modelica.  A qualitative comparison of modeling architecture considered is shown in Table 1.  As an open source modeling language, Modelica utilizes object-oriented modeling to simulate real-world systems.  All of the most current and past versions of both OpenModelica and the Modelica libraries are available for download at their respective websites (www.ida.liu.se/labs/pelab/modelica/OpenModelica.html and www.modelica.org).  Modelica is a modeling architecture that follows class definitions to combine variables, constants and equations into systems that can be easily and quickly simulated, controlled and optimized.  Modelica is also a modeling language that allows for the combination of multiple physical domains including, but not limited to, mechanical, electrical and thermal, ideal for PHEV’s.  The non-causal capabilities of the modeling program allow for the description of PHEV components using simple equation definitions without having to worry about solution structure.  Modelica automatically determines the simulation layout and solution methods based on the classes that components have been created in.  Controls can also either be implemented within the simulation itself or external to it depending on the functionality desired.  These simulations allow for both the optimization of the system and the controls for idealized cross-architectural comparisons, or the ability to separate the controls and base the comparisons on the basic operations of the systems alone.  Click here for more information.

PHEV Policy Analyses (Sponsored by EPRI, and the Kingdom of Saudi Arabia, Ministry of Higher Education)
PHEVs will be an important component of the US vehicle fleet as mass-production by GM and others begins in the 2011 model year.  The effect of PHEVs on CAFE costs of compliance has not been studied in detail, to date.  This paper describes an analysis which quantifies the incremental costs of CAFE compliance for PHEVs and compares those costs to the costs of compliance for HEVs and for the regulator’s (NHTSA’s) preferred alternative technology portfolio.  To date, the methods with which CAFE regulations will quantify PHEV fuel economy has not been finalized.  This study examines the impacts of PHEV fuel economy metrics on the incremental costs of CAFE compliance.  In sum, the results of this work will inform regulators and automakers as to the role that PHEVs can play in achieving the petroleum reduction goals of CAFE regulations with lower compliance costs.  This type of analysis is timely but will also help to provide a new baseline for the incremental costs and benefits of PHEVs including the benefits associated with reduced CAFE costs of compliance, thereby informing future work from academics and regulators.  Click here for more information.

HEV Total Cost of Ownership Analyses (Sponsored by EPRI, and the Kingdom of Saudi Arabia – Ministry of Higher Education) 
Motor vehicles represent one of the widely owned assets in the US. A vehicle’s ownership cost includes fixed expenses to purchase and own the vehicle and variable costs to use and operate the vehicle. Policymakers, analysts and consumers are interested in understanding the total ownership costs of various vehicle types and technologies so as to understand their relative consumer preference and valuation. Plug-in hybrid electric vehicles are an advanced technology vehicle that is presently in limited production, but whose relative cost of ownership is not well-defined. A few studies have attempted to calculate the costs and benefits of PHEVs but none consider the cost and benefits of PHEVs at a level of detail comparable to what has been performed for other vehicle technologies. In order to understand the costs and benefits of PHEVs purchase and use, this study constructs a comprehensive ownership cost model. The model is then used to analyze different PHEV designs within four vehicle classes. This study then performs a sensitivity analysis to understand the sensitivity of total ownership cost and payback period to model parameters and the modeled components of ownership costs. Results show that a more comprehensive PHEV ownership cost model has a lower net cost of ownership than studies to date, resulting in a shorter payback period and higher consumer preference.