Development of a framework for projecting line-haul truck technology adoption and greenhouse gas emissions in the U.S. using a System-of-Systems methodology
thesisposted on 09.03.2020, 19:36 by Ana Isabel Guerrero de la PenaAna Isabel Guerrero de la Pena
In order to displace diesel fuel consumption and reduce greenhouse gas emissions in the line-haul freight transportation system, a strong uptake of low and zero emission vehicle technologies must be incentivized by manufacturers and policymakers alike. A simulation tool that can project a wide array of future scenarios and predict the effects of freight transportation system evolution on mixed technology adoption trajectories is needed. This tool can assist the system stakeholders identify the level of innovation and policies necessary to increase the economic attractiveness of cleaner technologies and therefore incentivize the market to reduce system-wide emissions.
In this thesis I present a simulation framework for projecting adoption and utilization of emerging technologies and network-wide emissions in a line-haul freight transportation system network. A System-of-Systems engineering methodology is followed to realize the definition, abstraction and simulation of the system. This results in a framework capable of modeling the evolution of system factors with respect to time and their influence across a set of representative heterogeneous line-haul fleets operating on a regional network. A constrained mixed-integer linear program is used to represent the decision-making process for heterogeneous fleets selecting vehicles and allocating them on freight delivery routes to minimize total cost of ownership. The proposed model is parametrized and validated using a Design of Experiments (DOE) and historical adoption data. The results of this SoS model demonstrate 90% accuracy in prediction outcome when modeling historical technology adoption across a set of 12 heterogeneous representative fleets over an 11-year period. The formulation is then implemented to project alternative powertrain technology adoption and utilization trends for a set of line-haul fleets. Alternative powertrain technologies include compressed and liquefied natural gas engines, diesel-electric hybrid, battery electric, and hydrogen fuel cell. Future policies, economic factors, and availability of fueling and charging infrastructure are input assumptions to the proposed modeling framework. Three mixed-adoption scenarios, including BE, HFC, and CNG vehicle market penetration, are identified by the DOE study to demonstrate the potential to reduce cumulative CO2 emissions by more than 25% between 2018-2028. Next, the framework is exercised to project powertrain adoption, utilization, and emissions from 2019-2035 given a set of assumptions for the impact different levels of autonomy may have on purchase costs, vehicle efficiency, driver wages, vehicle reliability, and hours of service regulations. The proposed model formulation, which predicts both adoption and utilization, can enable stakeholders with a deeper understanding of how and why different levels of autonomy impact the broader freight transportation network. Finally, the framework is extended to predict adoption and utilization behaviors upon introduction of intra-fleet 2-vehicle platooning. A study on the effects of platooning fuel efficiency and freight demand on adoption, utilization, and resulting network emissions is presented.
Degree TypeDoctor of Philosophy
Campus locationWest Lafayette
Advisor/Supervisor/Committee ChairNeera Jain
Additional Committee Member 2Gregory Shaver
Additional Committee Member 3Daniel DeLaurentis
Additional Committee Member 4Satish Ukkusuri
Line-haul truckingautonomous commercial vehiclestechnology adoptionbattery electric vehiclesheavy-duty vehiclesClass 8 vehiclesdiesel powertrainSystem-of-Systems Architecturestrucking CO2 emissionsCNG vehiclesHydrogen fuel cell electric vehiclesAutonomous VehiclesAutomotive Engineering not elsewhere classifiedHybrid Vehicles and PowertrainsInterdisciplinary Engineering not elsewhere classifiedMechanical Engineering