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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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.