A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management

Tahir Emre Kalaycı, Bor Bricelj, Marko Lah, Franz Pichler, Matthias K. Scharrer and Jelena Rubeša-Zrim
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Tahir Emre Kalaycı: Virtual Vehicle Research GmbH, Inffeldgasse 21A, 8010 Graz, Austria
Bor Bricelj: Virtual Vehicle Research GmbH, Inffeldgasse 21A, 8010 Graz, Austria
Marko Lah: Virtual Vehicle Research GmbH, Inffeldgasse 21A, 8010 Graz, Austria
Franz Pichler: Virtual Vehicle Research GmbH, Inffeldgasse 21A, 8010 Graz, Austria
Matthias K. Scharrer: Virtual Vehicle Research GmbH, Inffeldgasse 21A, 8010 Graz, Austria
Jelena Rubeša-Zrim: Virtual Vehicle Research GmbH, Inffeldgasse 21A, 8010 Graz, Austria

Sustainability, 2021, vol. 13, issue 3, 1-17

Abstract: Today, the automotive and transportation sector is undergoing a transformation process to meet the requirements of sustainable and efficient operations. This transformation mainly reveals itself by electric vehicles, hybrid electric vehicles, and electric vehicle sharing. One significant, and the most expensive, component in electric vehicles is the batteries, and the management of batteries is crucial. It is essential to perform constant monitoring of behavior changes for operational purposes and quickly adjust components and operations to these changes. Thus, to address these challenges, we propose a knowledge graph-based data integration framework for simplifying access and analysis of data accumulated through the operations of vehicles and related transportation systems. The proposed framework aims to enable the effortless analysis and navigation of integrated knowledge and the creation of additional data sets from this knowledge to use during the application of data analysis and machine learning. The knowledge graph serves as a significant component to simplify the extraction, enrichment, exploration, and generation of data in this framework. We have developed it according to the human-centered design, and various roles of the data science and machine learning life cycle can use it. Its main objective is to streamline the exploration and interaction with the integrated data to maximize human productivity. Finally, we present a battery use case to show the feasibility and benefits of the proposed framework. The use case illustrates the usage of the framework to extract knowledge from raw data, navigate and enrich it with additional knowledge, and generate data sets.

Keywords: anomaly detection; battery data management; data integration; data analysis; intelligent transport systems; knowledge graphs; machine learning techniques (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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