Interdisciplinary semantic integration of battery electric vehicle charging infrastructure data

Electric mobility comprises the interactions between transportation and power grid infrastructures. Knowledge integration in such interdisciplinary contexts comes with important challenges. Researchers dealing with this subject often face data coming from diverse sources. Even when data exchange standards are followed strictly, bringing them together can lead to costly data integration tasks and the risk of misinterpretation. The FAIR guiding principles for scientific data management and stewardship are guidelines intended to aid researchers and organizations in enabling the computer actionability of their data. As part of our research, we explore ways in which their implementation can be used to manage data in interdisciplinary contexts. In our work related to charging infrastructure, we performed three main tasks. First, we evaluate the syntax and semantics of existing open-data publications associated with charging infrastructure. Secondly, using this knowledge we develop demonstrative data models and ontologies from our understanding of the analysed data and publications. We show how the same information can be expressed in different data models and how an ontology can be used to bring together these representations. Lastly, based on our interpretation of the FAIR principles we propose some concrete solutions on how to avoid problems of reproducibility and interoperability. In future applications, we intend to use ontologies in microservice architectures that can be coupled with other methodologies such as the ones coming from disciplines like machine learning, operations research, and agent-based modelling.