Next-generation EV Charging lots management framework

With the rapid adoption of Electric Vehicles (EVs), the need for an efficient and robust charging infrastructure has become essential to support the growing demand for E-Mobility services. However, the current EV charging stations face several challenges, including long waiting times, charging delays, uneven charge scheduling, and an unequal distribution of charging stations. These issues are particularly prevalent during peak hours, leading to increased queues and extended waiting times for EVs. To address these challenges, this project aims to design a comprehensive framework for an advanced online EV charging slot booking system. The proposed system leverages a stochastic queuing model to optimize the charging process at stations. By formulating an objective function that considers charging time, cost, queuing delay, and distance, the system aims to minimize these inefficiencies while maximizing the overall user experience. Additionally, the project introduces a cloud-based Charging Station Management platform that will network and manage multiple charging stations, facilitating real-time charging forecasts and efficient scheduling. This server-based approach will reduce waiting times, improve resource allocation, and help prevent EVs from running out of battery on the road. Ultimately, the proposed system aims to provide a cost-effective, scalable, and user-friendly solution for optimizing EV charging infrastructure.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-04
DOI
https://doi.org/10.5281/zenodo.15591503
Primary Topic
Enzyme Structure and Function
Type
article
Field-Weighted Citation Impact
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article

Next-generation EV Charging lots management framework

Zenodo (CERN European Organization for Nuclear Research)
Enzyme Structure and Function
article

Next-generation EV Charging lots management framework

article en

Abstract

With the rapid adoption of Electric Vehicles (EVs), the need for an efficient and robust charging infrastructure has become essential to support the growing demand for E-Mobility services. However, the current EV charging stations face several challenges, including long waiting times, charging delays, uneven charge scheduling, and an unequal distribution of charging stations. These issues are particularly prevalent during peak hours, leading to increased queues and extended waiting times for EVs. To address these challenges, this project aims to design a comprehensive framework for an advanced online EV charging slot booking system. The proposed system leverages a stochastic queuing model to optimize the charging process at stations. By formulating an objective function that considers charging time, cost, queuing delay, and distance, the system aims to minimize these inefficiencies while maximizing the overall user experience. Additionally, the project introduces a cloud-based Charging Station Management platform that will network and manage multiple charging stations, facilitating real-time charging forecasts and efficient scheduling. This server-based approach will reduce waiting times, improve resource allocation, and help prevent EVs from running out of battery on the road. Ultimately, the proposed system aims to provide a cost-effective, scalable, and user-friendly solution for optimizing EV charging infrastructure.

Zenodo (CERN European Organization for Nuclear Research)
Vels University (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 63%
Enzyme Structure and Function
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Next-generation EV Charging lots management framework · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS