Applications of Machine Learning in the Planning of Electric Vehicle Charging Stations and Charging Infrastructure: A Review

Panda, Bhagyashree; Rajabi, Mohammad Sadra; Rajaee, Alimohammad

doi:10.1007/978-3-030-72322-4_202-1

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Additionally, neural networks can be used for fault detection and diagnosis, allowing for proactive maintenance and minimizing downtime. By continuously learning from data, neural network-based optimization algorithms can adapt to changing conditions and improve charging efficiency over time [12].…”

Section: Neural Network Applicationsmentioning

confidence: 99%

Optimization of Wireless Charging Techniques in Electric Vehicle Applications through Machine Learning

Archana L. Rane,

Mariyam E. Maniyar,

Rashika A. Ghavate

2024

Int Res J Adv Engg Hub

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Membrane antenna technology is recognized for its potential to achieve a large aperture while maintaining a lightweight and compact design, making it suitable for spaceborne applications. Over the past few decades, extensive research has been conducted on both active and passive membrane antennas.so The demand for wide-ranging antennas in future space missions has increasingly stimulated the advancement of deployable membrane-based RF sensor configurations due to their minimal mass and compact storage capacity. However, the practical implementation of these antennas in spaceborne applications has been limited due to various challenges, such as the need for accurate surface maintenance, reliable operation in orbit, and compatibility with the environment. This article provides a comprehensive overview of the development and current advancements in spaceborne membrane antennas. Spaceborne membrane antenna configurations are primarily classified into Curved surface membrane antennas, parabolic membrane antenna, and planar membrane antenna configurations. Additionally, it covers topics related to radiofrequency design, deployment mechanisms, materials, experiments, applications, and analysis methods. By carefully examining the advantages and challenges associated with current membrane antennas, this paper aims to offer insights into the existing issues and future trends in the field of spaceborne membrane antennas. Through a review of membrane-based RF sensor configurations, guidance for research and applications of space membrane antennas is provided.

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Section: Neural Network Applicationsmentioning

confidence: 99%

Optimization of Wireless Charging Techniques in Electric Vehicle Applications through Machine Learning

Archana L. Rane,

Mariyam E. Maniyar,

Rashika A. Ghavate

2024

Int Res J Adv Engg Hub

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“…EVs, driven by clean energy sources, emit harmless byproducts instead of exhaust gases, thereby improving air quality in cities and promoting the health of their residents [3,4]. In addition to their positive environmental impact, EVs play a vital role in future smart grids by conserving energy, reducing carbon emissions, and promoting sustainability [5,6]. The adoption of EVs by consumers has been increasing steadily, with global sales surpassing 10 million in 2022.…”

Section: Introductionmentioning

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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Intelligent Neural Network-Based Energy Management for Microgrids with Prioritized EV Charging

Rele,

Patil

2023

2023 6th Asia Conference on Energy and Electrical Engineering (ACEEE)

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Applications of Machine Learning in the Planning of Electric Vehicle Charging Stations and Charging Infrastructure: A Review

Cited by 3 publications

References 44 publications

Optimization of Wireless Charging Techniques in Electric Vehicle Applications through Machine Learning

Optimization of Wireless Charging Techniques in Electric Vehicle Applications through Machine Learning

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

Intelligent Neural Network-Based Energy Management for Microgrids with Prioritized EV Charging

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