Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks

Ramachandran, A.; Balakrishna, Ashwin; Kundzicz, Peter; Anirudh, Neti,

doi:10.48550/arxiv.1804.00714

Cited by 2 publications

(2 citation statements)

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“…Neural networks were employed in a study to forecast specific CS utilization data based on the station's actual placement within a network, providing immediate predictions of average utilization data for proposed architectures without the need for executing costly models. This approach assists developers in quickly testing multiple charging infrastructure placements to determine the best design according to their goals [46]. Another study compared three regression methods, RF, gradient boosting (GB), and XGBoost, using supervised ML on a dataset to determine the most influential variables affecting charging network management.…”

Section: Previous Approachesmentioning

confidence: 99%

Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities

Alanazi,

Alshammari,

Azam

2023

Sustainability

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Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To overcome these barriers and ensure equitable EV usage, a comprehensive understanding of the intricate interplay among social, economic, and environmental factors influencing the placement of charging stations is crucial. This study investigates the key variables that contribute to demographic disparities in the accessibility of EV charging stations (EVCSs). We analyze the impact of various factors, including EV percentage, geographic area, population density, available electric vehicle supply equipment (EVSE) ports, electricity sources, energy costs, per capita and average family income, traffic patterns, and climate, on the placement of EVCSs in nine selected US states. Furthermore, we employ predictive modeling techniques, such as linear regression and support vector machine, to explore unique nuances in EVCS installation. By leveraging real-world data from these states and the identified variables, we forecast the future distribution of EVCSs using machine learning. The linear regression model demonstrates exceptional effectiveness, achieving 90% accuracy, 94% precision, 89% recall, and a 91% F1 score. Both graphical analysis and machine learning converge on a significant finding: Texas emerges as the most favorable state for optimal EVCS placement among the studied areas. This research enhances our understanding of the multifaceted dynamics that govern the accessibility of EVCSs, thereby informing the development of policies and strategies to accelerate EV adoption, reduce emissions, and promote social inclusivity.

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Section: Previous Approachesmentioning

confidence: 99%

Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities

Alanazi,

Alshammari,

Azam

2023

Sustainability

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“…An optimal CSs distribution method should tactically formulate the distribution problem and efficiently employ functional algorithms. Many studies have been conducted to facilitate easy access to charging points for EV users by identifying the proper siting of charging points utilizing methods, such as genetic algorithm [35], fuzzy neural network [36], linear programming [6,37], and so on. Accordingly, one recently proposed solution tried to optimally combine Chicken Swarm Optimization along with Teaching Learning Based Optimization algorithms to exploit the essential properties of each algorithm in order to increase the introduced solution's efficacy [38].…”

Section: Charging Stations Distributionmentioning

confidence: 99%

A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation

Teimoori

Yassine

2022

Sustainability

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Over the last few years, Electric Vehicles (EVs) have been gaining interest as a result of their ability to reduce vehicle emissions. Developing an intelligent system to manage EVs charging demands is one of the fundamental aspects of this technology to better adapt for all-purpose transportation utilization. It is necessary for EVs to be connected to the Smart Grid (SG) to communicate with charging stations and other energy resources in order to control charging schedules, while Artificial Intelligent (AI) techniques can be beneficial for improving the system, they can also raise security and privacy threats. In recent years, privacy preservation methodologies have been introduced to ensure data security. Federated Learning (FL) and blockchain technology are two emerging strategies to address information protection concerns. Therefore, in this article, a comprehensive literature review is proposed to analyze existing EVs energy management challenges and solutions and present potential future research directions for EVs charging/discharging coordination applications.

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Predicting Electric Vehicle Charging Station Usage: Using Machine Learning to Estimate Individual Station Statistics from Physical Configurations of Charging Station Networks

Cited by 2 publications

References 0 publications

Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities

Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities

A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation

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