Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles With Federated Learning

Thorgeirsson, Adam Thor; Scheubner, Stefan; Funfgeld, Sebastian; Gauterin, Frank

doi:10.1109/ojvt.2021.3065529

Cited by 33 publications

(15 citation statements)

References 37 publications

(48 reference statements)

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“…Thorgeirsson et al have proposed the federated learning‐based energy demand prediction of EVs 11 . The federated learning model improves the driver‐individual learning standard and reduces the travel time of EVs.…”

Section: Related Workmentioning

confidence: 99%

“…Thorgeirsson et al have proposed the federated learning-based energy demand prediction of EVs. 11 The federated learning model improves the driver-individual learning standard and reduces the travel time of EVs. The measured data are stored in cloud storage, and the performance analyzed based on scoring rules provides higher performance than the linear model.…”

Section: Algorithms In Ev Energy Managementmentioning

confidence: 99%

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An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Bhaskar¹,

Prasanth

Saranya³

2022

Int J Communication

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In today's world, electric vehicles (EVs) play a significant role in transportation automation systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV generally depends on electric charges where the appropriate usage, charging, and energy management are the key constraints in EVs. To overcome these issues, proper energy management is essential in current EV management. In this paper, a novel blockchain-based secure energy management has been proposed to provide efficient energy management in transportation automation. Primarily, the EVs have connected to the Internet of Things (IoT) sensors for collecting information like charging level, distance to be traveled, and location of the EVs. This information has been processed by an information center and transferred to the random forest classifier to identify the price of charging. Afterward, it can be transferred to the power scheduling algorithm for finding the nearest charging location (shortest distance) and time of charging to a specific EV. Finally, this information is stored in blocks to mitigate the misleading of EVs and to offer secure price transactions between the users and charging stations. The results manifest that the proposed scheme provides improved EV management with 94.5% of accuracy and maintains 10% lesser communication overhead as compared with existing state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Algorithms In Ev Energy Managementmentioning

confidence: 99%

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Bhaskar¹,

Prasanth

Saranya³

2022

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Since the charging stations are not available everywhere, even in the major cities, thus, it is important to estimate the driving range of an EV in an efficient manner before planning a driving route. An interesting work targeting the domain of privacy preserving demand range and energy demand predic-tion for EVs with the help of decentralized federated learning has been presented by authors in [78]. Instead of using the traditional federated averaging (FedAvg), authors worked over using modified FedAvg-Gaussian (FedAG) for efficient energy demand prediction.…”

Section: A Private Energy Managementmentioning

confidence: 99%

Privacy Preserving Machine Learning for Electric Vehicles: A Survey

Sani¹,

Hassan²,

Chen³

2022

Preprint

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In the recent years, the interest of individual users in modern electric vehicles (EVs) has grown exponentially. An EV has two major components, which make it different from traditional vehicles, first is its environment friendly nature because of being electric, and second is the interconnection ability of these vehicles because of modern information and communication technologies (ICTs). Both of these features are playing a key role in the development of EVs, and both academia and industry personals are working towards development of modern protocols for EV networks. All these interactions, whether from energy perspective or from communication perspective, both are generating a tremendous amount of data every day. In order to get most out of this data collected from EVs, research works have highlighted the use of machine/deep learning techniques for various EV applications. This interaction is quite fruitful, but it also comes with a critical concern of privacy leakage during collection, storage, and training of vehicular data. Therefore, alongside developing machine/deep learning techniques for EVs, it is also critical to ensure that they are resilient to private information leakage and attacks. In this paper, we begin with the discussion about essential background on EVs and privacy preservation techniques, followed by a brief overview of privacy preservation in EVs using machine learning techniques. Particularly, we also focus on an in-depth review of the integration of privacy techniques in EVs and highlighted different application scenarios in EVs. Alongside this, we provide a a very detailed survey of current works on privacy preserving machine/deep learning techniques used for modern EVs. Finally, we present the certain research issues, critical challenges, and future directions of research for researchers working in privacy preservation in EVs.

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“…To protect the privacy of sensitive data that will be frequently exchanged between the charging stations and the vehicles, and also to reduce the communication overhead, the authors have used FL that allows the charging stations to share the data without exposing the sensitive data. Similarly, the authors in [92] applied an enhanced FL averaging algorithm that learns regression models and probabilistic neural networks in a privacy-preserving and communication-efficient manner to address the predictive uncertainty of traditional FL based models.…”

Section: Federated Learning Enabled Big Data In Smart Transportationmentioning

confidence: 99%

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Gadekallu,

Pham,

Huynh-The

et al. 2021

Preprint

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Big data has remarkably evolved over the last few years to realize an enormous volume of data generated from newly emerging services and applications and a massive number of Internet-of-Things (IoT) devices. The potential of big data can be realized via analytic and learning techniques, in which the data from various sources is transferred to a central cloud for central storage, processing, and training. However, this conventional approach faces critical issues in terms of data privacy as the data may include sensitive data such as personal information, governments, banking accounts. To overcome this challenge, federated learning (FL) appeared to be a promising learning technique. However, a gap exists in the literature that a comprehensive survey on FL for big data services and applications is yet to be conducted. In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL for big data. In particular, we extensively review the use of FL for key big data services, including big data acquisition, big data storage, big data analytics, and big data privacy preservation. Subsequently, we review the potential of FL for big data applications, such as smart city, smart healthcare, smart transportation, smart grid, and social media. Further, we summarize a number of important projects on FL-big data and discuss key challenges of this interesting topic along with several promising solutions and directions.

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Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles With Federated Learning

Cited by 33 publications

References 37 publications

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Privacy Preserving Machine Learning for Electric Vehicles: A Survey

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

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