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Browsing by Author "Trinko, David A., author"

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    Modeling and simulation to investigate the electrification potential of medium- and heavy-duty vehicle fleets
    (Colorado State University. Libraries, 2023) Trinko, David A., author; Bradley, Thomas H., advisor; Quinn, Jason C., committee member; Simske, Steven, committee member; Hurrell, James, committee member
    This project involves developing and integrating new modeling tools to simulate the dynamics of electric medium- and heavy-duty fleet vehicle adoption. A technical and economic modeling tool, combining a data-driven hardware cost model with a cost-optimal charging strategy microsimulation, enables tailored analysis of the costs and benefits of electrifying individual fleets. Next, a novel text synthesis process, applied to a curated corpus of literature, quantifies trade-offs between technical, economic, and other factors in the fleet vehicle procurement decision. The outcomes of these tasks combine with knowledge from recent literature on fleet decision processes to specify the vehicle procurement model used by fleets in an agent-based model of the medium- and heavy-duty electric vehicle market. This model embodies an especially disaggregated approach to adoption modeling, internalizing factors and dynamics that conventional adoption models externalize. In particular, explicitly modeling the formation and diffusion of opinions among agents enables experiments that conventional models cannot support. Demonstrations show, for example, that increasing the extent of interactions between populations with different proclivities to electric vehicles has an asymmetrical outcome. High-proclivity electric vehicle adoption is generally unaffected as interactions increase, but low-proclivity adoption is accelerated. By representing individual fleets' requirements and costs at a high level of detail, incorporating an adoption decision model informed by a wide body of empirical research, and broadening the array of variables and dynamics available for experimentation, this integrated model offers a new way to understand the urgent challenge of eliminating emissions from the most emissions-intensive transportation sectors.
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    Predictive energy management strategies for hybrid electric vehicles applied during acceleration events
    (Colorado State University. Libraries, 2019) Trinko, David A., author; Bradley, Thomas H., advisor; Quinn, Jason C., committee member; Anderson, Charles W., committee member
    The emergence and widespread adoption of vehicles with hybrid powertrains and onboard computing capabilities have improved the feasibility of utilizing predictions of vehicle state to enable optimal energy management strategies (EMS) to improve fuel economy. Real-world implementation of optimal EMS remains challenging in part because of limits on prediction accuracy and computation speed. However, if a finite set of EMS can be pre-derived offline, instead of onboard the vehicle in real time, fuel economy improvements may be possible using hardware that is common in current production vehicles. Acceleration events (AE) are attractive targets for this kind of EMS application due to their high energy cost, probability of recurrence, and limited variability. This research aims to understand how a finite set of EMS might be derived and applied to AEs based on predictions of basic AE attributes to achieve reliable fuel economy improvements. Models of the 2010 Toyota Prius are used to simulate fuel economy for a variety of control strategies, including baseline control, optimal EMS control derived via dynamic programming, and pre-derived control applied with approximate prediction to AEs. Statistical methods are used to identify correlations between AE attributes, optimal powertrain control, and fuel economy results. Then, key AE attributes are used to define AE categorization schemes of various resolutions, in which one pre-derived EMS is applied to every AE in a category. Last, the control strategies are simulated during a variety of drive cycles to predict real-world fuel economy results. By simulating fuel economy improvement for AEs both in isolation and in the context of drive cycles, it was concluded that applying pre-derived EMS to AEs based on predictions of initial and final velocity is likely to enable reliable fuel economy benefits in low-aggression driving.