File(s) not publicly available
Data Privacy of Wireless Charging Vehicle to Grid (V2G) Networks with Federated Learning
journal contribution
posted on 2022-09-29, 02:36 authored by Shiva PokhrelShiva Pokhrel, Mohammad Belayet HossainWireless charging vehicle to grid (V2G) system is not-so-futuristic. It can maintain the power supply balance and stabilize the grid through a wireless link between vehicles using batteries based on energy supply-demand statistics. Most of the existing privacy preservation methods are based on data tampering approaches (e.g., encryption, adding noise, aggregation), which are not practicable over V2G due to complexity in billing and inaccurate state estimation. In this paper, we develop an adaptive demand-side energy management framework by employing federated learning-based privacy preservation for the wireless charging V2G systems. Our framework learns the temporal evolution of energy consumption of dynamic charging electric vehicles in a distributed fashion and exploits the reinforcement learning model for cost-saving and reward maximization. The convergence and the privacy preservation properties of the framework are demonstrated with extensive evaluations.
History
Journal
IEEE Transactions on Vehicular TechnologyPublisher DOI
ISSN
0018-9545eISSN
1939-9359Publication classification
C1 Refereed article in a scholarly journalUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC