Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting

Sao, Ashutosh; Tempelmeier, Nicolas; Demidova, Elena

doi:10.1109/itsc48978.2021.9565097

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…There has been some recent focus on forecasting how busy the charging stations (CS) are in certain areas to ensure that the EVs can plan their journeys conveniently [11], [12]. However, the existing research in this direction, primarily founded upon machine learning based methods, does not address the privacy concerns involved in such predictive techniques and does not consider situations where there may arise unprecedented traffic congestion (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Atmaca,

Biswas,

Maple

et al. 2024

IEEE Open J. Veh. Technol.

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Electric vehicles (EVs) are becoming more popular due to environmental consciousness. However, the limited availability of charging stations (CSs) compared to the number of EVs on the road has increased range anxiety and CS queries during trips. Simultaneously, personal data use for various forms of analytics is growing at an unprecedented rate, raising concerns about privacy violations. One standard for formalising location privacy is geo-indistinguishability as a generalisation of local differential privacy. However, the noise has to be carefully calibrated considering the implications of potential utility loss. In this paper, we introduce the notion of approximate geo-indistinguishability (AGeoI) which allows the EVs to obfuscate the individual query locations while ensuring that they remain within their preferred area of interest. It is vital because journeys are often sensitive to a sharp drop in quality of service (QoS), which has a high cost for the extra distance to be covered. Our proposed method combines the application of AGeoI and the generation of dummy data to provide two-fold privacy protection (individual query locations and the trajectory of the entire journeys) for EVs while preserving a high level of QoS. Analytical insights and experiments are used to demonstrate that a very high percentage of EVs get "privacy for free" and that the utility loss caused by the gain in privacy guarantees is minuscule. Aside from harbouring a high QoS for the EVs, using the iterative Bayesian update, our method allows for a private and precise prediction occupancies of CSs which is vital in unprecedented traffic congestion scenarios and efficient route-planning.

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Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Atmaca,

Biswas,

Maple

et al. 2024

IEEE Open J. Veh. Technol.

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“…Maximising the profit through optimal pricing and scheduling, utilizing various parameters like number of vehicles visited EVCS, demand response, the parking time at the station and the various pricing mechanism with reinforcement learning is proposed in [16]. A geographic specific learning is conducted recently [17] in predicting the availability of charging stations and similar inputs are used in [18] to predict the occupancy of the charging station. Consumed energy, number of charging transactions, charging time, facilities at the station, location of the station and the repeatability of the same vehicle using the same charging station have been used in [19] to predict the popularity of a given charging station.…”

Section: Introductionmentioning

confidence: 99%

Communication frame work in an electric vehicle charging station supporting solar energy management

George

Dixit

Dawnee

et al. 2022

IJEECS

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Exploiting Renewable energy to the maximum extent possible in an electric vehicle charging station (EVCS) is the key in supporting the anticipated carbon reduction from the electric vehicles (EVs). Knowing the expected load and the solar energy in advance at the EVCS can be crucial in framing a proper energy management strategy. Selection of suitable parameters associated with the participating EVs and EVCS are vital in utilizing them for predicting the probable EV load and expected solar energy for a given period under consideration. A prototype EVCS with smart communication infrastructure is developed considering solar pv as the energy source. Real time communication of the parameters between multiple agents has been established effectively using an interactive website, cloud server and an short message service (SMS) application programming interface (API). The data generated from the prototype models have been utilized in a random forest regression (RFR) classifier model in order to predict the probable solar energy and the expected EV load for every minute duration. The integrated communication frame work is found to be less complex to implement for an autonomous direct current (DC EVCS). The details provided at the graphical user interface (GUI) designed at the EVCS can be instrumental in developing a proper energy management strategy.

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“…Geographic data plays an essential role in a range of real-world applications on the Web, including machine learning models estimating travel time or charging demand for electric vehicles, recommending points of interest and predicting traffic accidents (e.g., [2], [9]). Such applications rely on rich representations of a variety of geographic entities including monuments, roads and charging stations.…”

Section: Introductionmentioning

confidence: 99%

Creating knowledge graphs for geographic data on the web

et al. 2022

Self Cite

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Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.

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Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting

Cited by 8 publications

References 21 publications

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Communication frame work in an electric vehicle charging station supporting solar energy management

Creating knowledge graphs for geographic data on the web

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