Saudi Cultural Missions Theses & Dissertations
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Item Restricted HYBRID MACHINE LEARNING APPROACHES FOR SOC AND RUL ESTIMATION IN BATTERY MANAGEMENT SYSTEMS(Oakland University, 2024) Hawsawi, Tarik Abdullah; Zohdy, MohamedWith the fast development of electric vehicles (EVs), new technologies are needed to manage batteries more efficiently to optimize performance and more profound and longer battery use. A significant problem that must be solved successfully is accurate estimation of the State-of-Charge (SoC) to avoid fully discharging a battery. It shortens battery life and prolongs the time it takes to charge the battery. This dissertation introduces a new approach that uses Edge Computing and real-time predictive analytics to assess the status of EV batteries and send alerts when necessary, thus facilitating energy efficiency. The Edge Impulse platform is used to predict the Remain Useable Life RUL of batteries with enhanced accuracy using EON-Tuner and DSP processing blocks, enhancing computational capability and making it feasible for edge devices. Since traditional SoC estimations include tools like Kalman filters and Extended Kalman filters, which are effective but have a considerable drawback in estimating the SoC with changing battery parameters, this study proposes a multi-variable optimization method. The method enhances performance prediction after key parameters are iteratively adjusted, thus resolving the emergence hypotheses of most existing techniques. The system was designed and tested on Jupyter Notebook, and performance indicators of accuracy, MSE, and efficiency further validated the design. This study helps ensure proper energy use and long battery life for e-vehicles, which promotes clean energy use.6 0Item Embargo Optimized Dynamic Electric Vehicles Charging in Smart Cities(University of Maryland Baltimore County, 2024-11-22) Alaskar, Shorooq Sulaiman; Younis, MohamedRecent technological advances have fueled interest in the development of smart cities where the convenience and health of inhabitants are core objectives. Increased automation and reduced green gas emission are prime means for achieving these objectives. No wonder that there is a major push for the adoption of electrical vehicles. Yet, the deployment of electric vehicles (EVs) is slow-paced. One of the primary obstacles hindering the widespread adoption of EVs is range anxiety, which refers to the fear that an EV will not have sufficient battery charge to reach its destination or a nearby charging station. Additionally, long charging times pose a significant barrier, as recharging an EV battery can take considerably longer compared to refueling a conventional gasoline vehicle, making it less convenient for users. Furthermore, the scarcity of charging infrastructure capable of handling a large number of EVs exacerbates these concerns, deterring potential buyers due to worries about accessibility and convenience. Most of the existing charging techniques require EVs to remain stationary while being charged. Wireless charging has emerged as a viable solution that mitigates such a shortcoming by enabling dynamic EV-to-EV charging. It can also expand the driving range of automobiles if the battery can be continuously charged while the vehicle is in motion, hence extending the traveled distance. EV-to-EV charging provides drivers with greater temporal and spatial flexibility, specifically in densely populated urban regions, while aiding in the reduction of energy consumption. It can also mitigate the burden of the grid during peak loads and optimize power usage during off-peak hours. EV-to-EV charging not only adds convenience to drivers, but it enables a new business model as well, where mobile suppliers may sell their energy to EVs on the road. This dissertation addresses the challenge of the dynamic charging and routing of EVs in smart cities. We first present RIMEC, a Routing for Increased Mobile Energy Charging algorithm that determines an optimized EV travel route for utilizing the Mobile Energy Disseminator (MEDs) in order to maximize the potential energy that can be harvested while minimizing the impact on travel time. Second, we introduce an EV-to-EV charging framework for energy suppliers. The framework opts to maximize the profit, while considering battery degradation and the overhead cost. The optimization is modeled as a time-space network and a dynamic programming-based solution strategy is pursued to optimally pair and route the energy supplier (ES) and requester (ER). Specifically, ES is incentivized to rendezvous ERs at encounter nodes to dispense the requested energy through platooning. The complexity of the problem arises from nonstationary consumers and service supply, which make monitoring and synchronizing the movement of ES and ER spatiotemporally challenging. Third, we tackle the problem of dynamic EV-to-EV charging that aims to maximize the supplier's profitability within a specific timeframe, taking into account overhead costs. Finally, we address the multi-supplier, multi-requester routing problem. We formulate the optimization problem mathematically as a mixed-integer program and develop a local search based-heuristic algorithm. Our objective is to optimize system-level metrics, including profitability and throughput. The simulation results validate the effectiveness of our proposed approach, demonstrating significant improvements in maximizing overall profit while minimizing energy consumption, travel time, and distance for requesters.24 0Item Restricted DATA-DRIVEN EV CHARGING INFRASTRUCTURE PLANNING: ADAPTIVE SIMULATION AND STRATEGIC DEPLOYMENT IN VIRGINIA(Geroge Mason University, 2024-04-29) Zaylaee, Mohammed; Ji, WenyingAbstract The rapid adoption of electric vehicles (EVs) necessitates innovative approaches to manage and optimize the deployment of EV charging infrastructure. This scholarly paper integrates two complementary studies focusing on the dynamic modeling of EV charging behaviors and the strategic evaluation of charging station coverage in Virginia. The first part of the thesis employs advanced spatial analysis techniques to assess the current network of public EV charging stations in Virginia. Using a comprehensive dataset from Virginia Clean Cities, this analysis identifies regions with insufficient charging facilities, particularly in areas exhibiting high growth potential in the EV market. Techniques such as buffer and coverage analysis are utilized to map and visualize the distribution and capacity of existing infrastructure, thereby pinpointing areas where expansion is most needed. This spatial investigation highlights underserved areas by comparing infrastructure against critical benchmarks, facilitating targeted policy actions to bridge infrastructure gaps. The second part introduces a novel adaptive simulation framework that enhances traditional models by incorporating Bayesian inference with (MCMC). Together, these studies provide a comprehensive approach to understanding and improving the EV charging infrastructure. By combining detailed spatial analysis with adaptive simulation techniques, this scholarly paper offers actionable insights that can drive the sustainable growth of the EV market through more informed decision-making and strategic deployment of resources.188 0Item Restricted EXPLORING SUCESS FACTORS OF ADOPTING ADVANCED MANUFACTURING TECHNOLOGY FOR ELECTRICAL VEHICLES INDUSTRY IN SAUDI ARABIA APPLYING THE TECHNOLOGY ACCEPTANCE MODEL (TAM)(Al Fatais, Abdullah, 2023) Al Fatais, Abdullah Mohammed; Korwowski, WaldemarBased on the Technology Acceptance Model (TAM), the study explores the success factors of adopting Advanced Manufacturing Technology (AMT) for the Electrical Vehicles (EVs) industry in Saudi Arabia. The study assesses the impact of eight factors on AMT adoption and implementation success. The dimensions include Training & Education, Planning, Management, Technology, Business, Economic, Policies & Regulations, and Social. The study analyzes the sample including people with careers related to advanced manufacturing in Saudi Arabia, either in the public sector, private sector, industrial sector, and academia. Furthermore, an online questionnaire was used to collect data from the participants. Additionally, a Systematic Literature Review (SLR) was conducted to analyze the existing literature in addition to the utilization of TAM for data analysis. This study aims to evaluate the readiness of the Saudi industrial sector to adopt EVs manufacturing technologies. Moreover, this study is expected to use a reasonable sample size for analysis purposes which can result in solid conclusions and practical recommendations.18 0Item Restricted Unlocking the Potential of Electric Vehicles to Enhance Large-Scale Photovoltaic Solar Energy Integration into Grid Systems - A Case for Saudi Arabia(2023-06-09) Alotaibi, Saleh; Omer, SiddigTraditionally, the Kingdom of Saudi Arabia (KSA) has relied heavily on fossil fuels to meet its energy demands. However, due to environmental concerns and economic incentives, the Kingdom is planning to escalate renewable energy generation, notably from Solar Photovoltaic (PV), and to promote the widespread adoption of electric vehicles (EVs). One of the issues associated with solar energy is its intermittent nature, and its large-scale integration could pose a challenge to the grid system; therefore strategies to alleviate this problem are required. This thesis investigates the potential of EVs to support the country’s renewable energy aspirations by enhancing large-scale PV energy integration into the Saudi grid. This study begins by considering the current energy situation in KSA, the development of solar PV in the country, and the challenges associated with large-scale solar PV integration into the grid. It then reviews the state-of-the-art in EVs, their related charging infrastructure, and Vehicle-to-Grid (V2G) technology, which enables EVs to feed energy back to the grid when demand is high. This is a particularly useful feature in the Saudi context, where demand for electricity fluctuates heavily between summers and winters (and between day-time and night-time) due to the need for air conditioning during the excessive summer heat. The study goes on to explore the current status of EVs in Saudi Arabia and the factors that are most likely to affect their widespread adoption in the country. It finds that battery-driven electric vehicles (BEVs) are more likely to find acceptance than other EV types in KSA due to their zero emissions, better safety levels, and the fact that owners can benefit financially by exporting their excess energy via V2G. The study then considers the opinions of Saudi citizens to determine the factors and features that they deem important in EVs, which were gathered via a survey conducted for this purpose. Responses from 1012 potential EV drivers were analysed in order to identify and rank their preferences, leading to the identification of the features most likely to influence Saudi citizens’ adoption of EVs. The main barriers identified related to infrastructure, performance, financial, social, and policy, but the most important were the lack of charging infrastructure and the additional load placed on the grid. Concerns about the safety and performance of EV batteries in high temperatures and EVs ability to perform in desert conditions emerged as novel concerns related to the Saudi context. These findings were validated in a workshop conducted by the Saudi Standards, Metrology, and Quality Organization (SASO) via a focus group of EV users who shared their experience of using EVs in KSA. Their responses were in line with the earlier survey, and three major themes emerged related to EV infrastructure, battery performance, and lack of effective policy. The study then assesses the viability of implementing a solar-powered system to support EVs in major Saudi cities. After analysing several cities in terms of their potential to generate electricity using solar PV and expected increases in the numbers of EVs by 2030, Riyadh was identified as the most suitable location for a case study to test the viability of three models through simulation. These included Model 1 (PV Plant Only), Model 2 (PV Plant and Charging Stations), and Model 3 (PV, Charging Stations and V2G). Techno-economic analysis of these models in low, medium and high EV uptake scenarios (LUS, MUS and HUS) revealed that Model 1 (PV Only) would be the cheapest option at around $1 billion in LUS. However, Model 3 (PV plant, charging stations and V2G) has the best Internal Rate of Return (IRR) (around 28.2% in HUS) and would reduce emissions most significantly (by around 5.5 mtCO2 in HUS). The study concludes that EVs have considerable potential to enhance large-scale PV energy integration into the Saudi grid and proposes the construction of solar PV plants together with charging stations and V2G technology as a cost-effective alternative to PV plants alone. It also recommends that EV manufacturers demonstrate that their vehicles are safe and effective in hot desert climates if they wish to penetrate the Saudi market. In order to promote a widespread EV implementation, the government and all other authorities should develop policies to support EV adoption, including by providing subsidies or other incentives to EV users. The data gathered for this study and the research findings will be of interest to policymakers, EV manufacturers, entities charged with providing EV infrastructure, solar PV and EV businesses, and any sector working towards the Vision 2030 goals.21 0