Privacy-Preserving V2V Charge Sharing Coordination using the Hungarian Algorithm

Electric Vehicles (EVs) are being widely adopted as a green alternative to fossil-based vehicles. However, the current charging infrastructure for EVs is inadequate to meet the growing charge demand. Vehicle-to-Vehicle (V2V) charging offers a promising solution that enables a charge supplier EV to provide charging services to a charge demander EV in a distributed manner. Nevertheless, V2V matching and charge scheduling can disclose sensitive location information about the drivers, such as their whereabouts and driving patterns. In this paper, we propose a privacy-preserving scheme for centralized optimal matching of demander EVs with supplier EVs, while protecting their sensitive information. In our scheme, charge demanders report to a matching server their encrypted location information and the requested energy quantities, whereas charge suppliers report encrypted charge costs such that the matching server can learn only the cost to match each demander to each supplier without revealing any location information or the exchanged charge amount. Then, the Hungarian algorithm is used to match demanders to suppliers while minimizing the total cost. The security analysis and simulation results show that our scheme can achieve optimal V2V matching while preserving drivers’ privacy with negligible computation overhead. Overall, our proposed scheme provides an effective solution for V2V charging, while maintaining privacy and confidentiality of sensitive drivers’ information.