Semantic Model for IoT-Enabled Electric Vehicle Services: Puzzling with Ontologies

Yoo, Min-Jung; Kolyvakis, Prodromos; Kiritsis, Dimitris; Werthmann, Dirk; Hellbach, Robert

doi:10.1109/ficloud.2016.61

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…For the evaluation purposes, 100 charging tasks have been randomly generated to improve the accuracy of the results and to overcome the inherent randomness of creating alternatives [51]. The 100 charging tasks have required duration (completion time) randomly sampled in the range (20, 95) minutes, while the increase in the stored energy of the battery is randomly sampled in the range (30,180) Ah.…”

Section: Evaluation Methodsmentioning

confidence: 99%

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A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver

2020

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During the last decade, the technologies related to electric vehicles (EVs) have captured both scientific and industrial interest. Specifically, the subject of the smart charging of EVs has gained significant attention, as it facilitates the managed charging of EVs to reduce disturbances to the power grid. Despite the presence of an extended literature on the topic, the implementation of a framework that allows flexibility in the definition of the decision-making objectives, along with user-defined criteria is still a challenge. Towards addressing this challenge, a framework for the smart charging of EVs is presented in this paper. The framework consists of a heuristic algorithm that facilitates the charge scheduling within a charging station (CS), and the analytic hierarchy process (AHP) to support the driver of the EV selecting the most appropriate charging station based on their needs of transportation and personal preferences. The communications are facilitated by the Open Platform Communications–Unified Architecture (OPC–UA) standard. For the selection of the scheduling algorithm, the genetic algorithm and particle swarm optimisation have been evaluated, where the latter had better performance. The performance of the charge scheduling is evaluated, in various charging tasks, compared to the exhaustive search for small problems.

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Section: Evaluation Methodsmentioning

confidence: 99%

“…The application of the OPC-UA for V2G communications has been introduced in [7], where its semantic modelling capabilities are utilised to support heterogeneous information models. The importance of the semantic data representation within the V2G communications has been highlighted in [30,31].…”

Section: Background and Related Workmentioning

confidence: 99%

A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver

2020

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“…Similarly, Yoo et al [29] present a semantic model to gather data from electric vehicles enabling interoperability between different IoT data provides. They also discuss how this interoperability can enable new services, such as a charging location search optimised with traffic information.…”

Section: Iot Semantic Interoperabilitymentioning

confidence: 99%

“…The complete list of 136 facts and 87 competency questions is available in the requirements folder of the ontology repository. 29…”

Section: Requirements Validationmentioning

confidence: 99%

Urban IoT ontologies for sharing and electric mobility

Scrocca

Baroni

Celino

2023

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Cities worldwide are facing the challenge of digital information governance: different and competing service providers operating Internet of Things (IoT) devices often produce and maintain large amounts of data related to the urban environment. As a consequence, the need for interoperability arises between heterogeneous and distributed information, to enable city councils to make data-driven decisions and to provide new and effective added value services to their citizens. In this paper, we present the Urban IoT suite of ontologies, a common conceptual model to harmonise the data exchanges between municipalities and service providers, with specific focus on the sharing mobility and electric mobility domains.

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