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Data Privacy of Wireless Charging Vehicle to Grid (V2G) Networks with Federated Learning

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posted on 29.09.2022, 02:36 authored by Shiva PokhrelShiva Pokhrel, Mohammad Belayet HossainMohammad Belayet Hossain
Wireless 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.

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Journal

IEEE Transactions on Vehicular Technology

ISSN

0018-9545

eISSN

1939-9359

Publication classification

C1 Refereed article in a scholarly journal

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