The Open V2X Management Platform: An intelligent charging station management system

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2024-12-06 DOI:10.1016/j.is.2024.102494

Christos Dalamagkas, V.D. Melissianos, George Papadakis, Angelos Georgakis, Vasileios-Martin Nikiforidis, Kostas Hrissagis-Chrysagis

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引用次数: 0

Abstract

We present an open-source web-based system, called Open V2X Management Platform (O-V2X-MP), which facilitates the management of charging points for electric vehicles with the goal of realizing Vehicle-to-Everything (V2X) scenarios. First, we describe its backend, which comprises several components connected through a microservices architecture leveraging Docker containers. Then, we elaborate on its frontend, which provides numerous functionalities for common users (i.e., EV drivers) and administrators. Finally, we demonstrate its data analytics capabilities, showing that O-V2X-MP can seamlessly integrate AI pipelines from the Python ecosystem. In particular, we examine two tasks of particular interest for charging point operators: (i) the clustering of EV drivers into profiles of predictable behavior, and (ii) the prediction of the overall daily load for each individual charging station. In our experiments, we use proprietary and public real-world data, verifying the high effectiveness achieved in both tasks.

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开放式V2X管理平台：智能充电站管理系统

我们提出了一个基于web的开源系统，称为开放V2X管理平台（O-V2X-MP），该系统有助于电动汽车充电点的管理，目标是实现车联网（V2X）场景。首先，我们描述它的后端，它由利用Docker容器的微服务架构连接的几个组件组成。然后，我们详细介绍了它的前端，它为普通用户（即电动汽车司机）和管理员提供了许多功能。最后，我们展示了它的数据分析能力，表明O-V2X-MP可以无缝地集成来自Python生态系统的人工智能管道。特别是，我们研究了充电点运营商特别感兴趣的两个任务：(i)将电动汽车驾驶员聚类到可预测的行为剖面中，以及（ii）预测每个单独充电站的总体日负荷。在我们的实验中，我们使用专有和公开的真实世界数据，验证了在这两个任务中实现的高效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.