Browsing by Subject "Electric vehicles"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Benefits and Barriers to Electrification of the Freight System in Minnesota - White Paper
    (Center for Transportation Studies, University of Minnesota, 2022-05) Khani, Alireza; Davazdah Emami, Behnam
    This white paper provides a brief overview of benefits and barriers to the electrification of freight vehicles in Minnesota.
  • Thumbnail Image
    Item
    Evaluation of the E-TRAN Vehicle Propulsion Concept
    (1994-01) Hennessey, Michael P.; Donath, Max
    The viability of the patented E-TRAN electric roadway and vehicle concept was examined from an engineering systems point of view. Specific recommendations are made regarding the end-usage and development of the propulsion concept. Based on this study, two research areas were identified and investigated in more detail: (a) quantify the auxiliary power needs due to power input discontinuities and (b) the dynamic effects of road pantograph bounce. Auxiliary power needs arise because of power input discontinuities, either due to: (1) power strip segment failures, (2) lane changing, and/or (3) E-TRAN grid discontinuities, which includes getting the vehicle to and from the grid. Simulation results indicate that power strip segment failures will have the least effect on system performance. E-TRAN grid discontinuities will have serious effects on the system while the effects of lane changing will affect performance at a level in between the other two. The dynamic effects of a road pantograph in contact with a road mounted power strip was also studied, first using simulated models and then verified by experiment. From a mechanical point of view, key issues that affect the design include friction, wear and dynamic bounce effects. Since good correspondence was achieved between the experimentally measured and simulated support forces and pantograph angular displacement, the models can be used for future design analysis.
  • Thumbnail Image
    Item
    Exploring the benefits of minimobility in the urban context: The case of central Stockholm
    (Journal of Transport and Land Use, 2021) Riggs, William; Shukla, Shivani
    Over the past decade, there has been rapid growth in the development and infusion of new and disruptive transportation. Some of the pivotal emergent technologies range from micro-mobility and bikeshare to ridesourcing that is set to utilize automated vehicles. This paper introduces and defines minimobility that falls between a regular ridesourcing/taxi option and micromobility, and also providing critical logistics services during the era of COVID-19. In Central Stockholm the platform has provided a safe and environmentally friendly mode choice that occupies limited space and efficiently serves on the congested city network. We explore potential economic and environmental benefits of minimobility, discussing the advantages and disadvantages of deploying such a service. While we demonstrate a general increase in VMT, consistent with other work showing increased travel from new mobility, due to the electric platform this increase in customer access to mobility results in minimal GHG impacts. This informs how planners and engineers can explore minimobility platforms not only as reduced emissions solutions to urban transit issues but as tools to increase total mobility particularly for the most vulnerable.
  • Thumbnail Image
    Item
    Identifying and Optimizing Electric Vehicle Corridor Charging Infrastructure for Medium and Heavy-Duty Trucks
    (Minnesota Department of Transportation, 2023-06) Khani, Alireza; Emami, Behnam Davazdah; Garcia, Fernando; Popenhagen, Brandi
    This project studies the benefits and barriers of increased adoption of medium-duty and heavy-duty electric trucks, referred to as e-trucks, and presents a methodology for optimizing the location of e-truck charging stations in Minnesota. In general, e-trucks provide zero tailpipe emissions and lower operating and maintenance costs. However, some barriers to adopting e-trucks include higher initial purchase costs, lack of charging and maintenance infrastructure, limited range, and charging time. The methods presented in this study aim to address the charging infrastructure planning, which provides information about e-truck charging activities, changes in vehicle miles traveled (VMT), and potential operating cost savings.
  • Thumbnail Image
    Item
    Scalable Learning and Energy Management for Power Grids
    (2019-01) Zhang, Liang
    Contemporary power grids are being challenged by unprecedented levels of voltage fluctuations, due to large-scale deployment of electric vehicles (EVs), demand-response programs, and renewable generation. Nonetheless, with proper coordination, EVs and responsive demands can be controlled to enhance grid efficiency and reliability by leveraging advances in power electronics, metering, and communication modules. In this context, the present thesis pioneers algorithmic innovations targeting timely opportunities emerging with future power systems in terms of learning, load control, and microgrid management. Our vision is twofold: advancing algorithms and their performance analysis, while contributing foundational developments to guarantee situational awareness, efficiency, and scalability of forthcoming smart power grids. The first thrust to this end deals with real-time power grid monitoring that comprises power system state estimation (PSSE), state forecasting, and topology identification modules. Due to the intrinsic nonconvexity of the PSSE task, optimal PSSE approaches have been either sensitive to initialization or computationally expensive. To bypass these hurdles, this thesis advocates deep neural networks (DNNs) for real-time PSSE. By unrolling an iterative physics-based prox-linear PSSE solver, a novel model-specific DNN with affordable training and minimal tuning effort is developed. To further enable system awareness even ahead of the time horizon, as well as to endow the DNN-based estimator with resilience, deep recurrent neural networks (RNNs) are also pursued for state forecasting. Deep RNNs leverage the long-term nonlinear dependencies present in the historical voltage time series to enable forecasting, and they are easy to implement. Finally, multi-kernel learning based partial correlations accounting for nonlinear dependencies between given nodal measurements are leveraged to unveil connectivity of power grids. The second thrust leverages the obtained state and topology information to design optimal load control and microgrid management schemes. With regards to EV load control, a decentralized protocol relying on the Frank-Wolfe algorithm is put forth to manage the heterogeneous charging loads. The novel paradigm has minimal computational requirements, and is resilient to lost updates. When higher levels of EV load exceed prescribed voltage limits, the underlying grid needs to be taken into account. In this context, communication-free local reactive power control and optimal decentralized energy management schemes, are developed based on the proximal gradient method and the alternating direction method of multipliers, respectively.