A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item EQUITY ISSUES IN ELECTRIC VEHICLE ADOPTION AND PLANNING FOR CHARGING INFRASTRUCTURE(2024) Ugwu, Nneoma; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Electric Vehicles (EVs) offer a sustainable solution to fossil fuel dependency and environmentalpollution from conventional vehicles, crucial for mitigating climate change. However, low market penetration among minority and low-income communities raises equity and environmental justice concerns. This dissertation examines EV adoption and charging station access disparities in Maryland, focusing on sociodemographic factors such as race and income. To address the lack of minority representation in existing EV research surveys, we conducted anonline survey targeting people of color (POC) and low-to-moderate-income households. We received 542 complete responses. Ordinal regression models were used to analyze factors influencing EV interest. We then performed a cumulative accessibility study of EV infrastructure in Maryland. Pearson correlation analysis was used to show the relationship between charging station accessibility and sociodemographics. Population density showed a strong positive correlation (0.87) with charging deployment. We found that Baltimore City, had the highest population density and the highest concentration of EV charging in Maryland. We conducted a case study of Baltimore City’s EV infrastructure investments and policy efforts. Charging stations were categorized based on speed, network, access, and facility type. Spatial analysis andZero-Inflated Poisson (ZIP) regression models at the block group level were employed to investigate the disparities in EV charging infrastructure distribution within the City across minority and non-minority communities. Our findings show substantial disparities in EV perceptions between POC and Whitecommunities. The survey revealed that POC were more than twice more likely than White respondents to indicate that the availability of charging stations affects their interest in EV adoption, while the case studies revealed that POC populations are less likely to have access to EV infrastructure, necessitating targeted investment in charging options and subsidies in these communities. Our study also found the need for policies fostering residential charging station deployment, particularly in minority communities. To ensure equitable EV adoption, strategic investments in economically disadvantaged and rural areas beyond centralized regions are vital. This study informs evidence-based policies prioritizing accessibility, equity, and inclusivity in promoting a cleaner and sustainable transportation landscape.Item PARAMETRIC AND NON-PARAMETRIC APPROACHES FOR THE PREDICTION OF THE DIFFUSION OF THE ELECTRIC VEHICLE(2020) Bas Vicente, Javier; Cirillo, Cinzia; Zofío Prieto, José Luis; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Driven by environmental awareness and new regulations for fuel efficiency, electric vehicles (EVs) have significantly evolved in the last decade, yet their market share is still much lower than expected. In addition to understanding the reasons for this slow market penetration, it is crucial to have appropriate tools to correctly predict the diffusion of this innovative product. Recent works in forecasting the EV market combine substitution and diffusion models, where discrete choice specifications are used to address the former, and Bass-type to account for the latter. However, these methodologies are not dynamic and do not consider the fact that innovation occurs through social channels among members of a social system. This research presents two advanced methodologies that make use of real data to evaluate the adoption of the EVs in the State of Maryland. The first consists of a disaggregated substitution model that considers social influence and social conformity, which is then embedded in a diffusion model to predict electric vehicle sales. The second, in contrast, relies on non-parametric machine learning techniques for the classification of potential EV purchasers. Both make use of data collected through a stated choice experiment specifically designed to capture the inclination of users towards EVs.Item GALLIUM NITRIDE BASED ONBOARD CHARGER FOR ELECTRIC VEHICLES(2019) Zhang, Zeyu; Khaligh, Alireza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Next generation of electric vehicles will be equipped with high power density and high efficiency onboard charging systems with bi-directional power flow. These benefits can be achieved by utilizing the emerging Wide Bandgap devices, planar magnetic solutions, innovative circuit topologies, and advanced control methods to enable MHz switching frequencies without sacrificing efficiency. However, the advantage of higher switching speed is gained at the expense of higher switching losses in both the semiconductors and the magnetics. Conventional circuit topologies, operation modes and control algorithms would no longer be effective in such conditions. Furthermore, the practical implementation of the system has shown more stringent requirements on the controller speed, layout parasitic and the thermal management. In this Ph.D. dissertation work, aforementioned challenges have been addressed, and the proposed innovations have been validated through design and development of a new bi-directional onboard charger using Gallium Nitride devices. The first part of this work has been focused on a thorough characterization of the front-end AC-DC power factor correction and rectification stage of an onboard charger, utilizing advanced planar magnetics and newly proposed soft-switching control methods. The second part of this work is focused on developing a CLLC DC-DC converter, to interface the AC-DC stage and the high-voltage traction battery. Extended Harmonic Approximation method has been developed and a novel “f-φ” control scheme is proposed to enhance the efficiency at high switching speed. This system allows insights into the practical implementation and evaluation of utilizing Wide Bandgap semiconductors to achieve high power density and efficiency for the transportation industry.Item Energy Management of a Battery-Ultracapacitor Hybrid Energy Storage System in Electric Vehicles(2016) Shen, Junyi; Khaligh, Alireza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Electric vehicle (EV) batteries tend to have accelerated degradation due to high peak power and harsh charging/discharging cycles during acceleration and deceleration periods, particularly in urban driving conditions. An oversized energy storage system (ESS) can meet the high power demands; however, it suffers from increased size, volume and cost. In order to reduce the overall ESS size and extend battery cycle life, a battery-ultracapacitor (UC) hybrid energy storage system (HESS) has been considered as an alternative solution. In this work, we investigate the optimized configuration, design, and energy management of a battery-UC HESS. One of the major challenges in a HESS is to design an energy management controller for real-time implementation that can yield good power split performance. We present the methodologies and solutions to this problem in a battery-UC HESS with a DC-DC converter interfacing with the UC and the battery. In particular, a multi-objective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. This optimization problem is numerically solved for standard drive cycle datasets using Dynamic Programming (DP). Trained using the DP optimal results, an effective real-time implementation of the optimal power split is realized based on Neural Network (NN). This proposed online energy management controller is applied to a midsize EV model with a 360V/34kWh battery pack and a 270V/203Wh UC pack. The proposed online energy management controller effectively splits the load demand with high power efficiency and also effectively reduces the battery peak current. More importantly, a 38V-385Wh battery and a 16V-2.06Wh UC HESS hardware prototype and a real-time experiment platform has been developed. The real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the real-time controller design for the battery-UC HESS. A battery State-of-Health (SoH) estimation model is developed as a performance metric to evaluate the battery cycle life extension effect. It is estimated that the proposed online energy management controller can extend the battery cycle life by over 60%.