Efficient and privacy preserving supplier matching for electric vehicle charging

Electric Vehicle (EV) charging takes longer time and happens more frequently compared to refueling of fossil-based vehicles. This requires in-advance scheduling on charging stations depending on the route of the demander EVs for efficient resource allocation. However, such scheduling and frequent charging may leak sensitive information about the users which may expose their driving patterns, whereabouts, schedules, etc. The situation is compounded with the proliferation of EV chargers such as V2V charging where any two EVs can charge each other through a charging cable. In such cases, the matching of these EVs is typically done in a centralized manner which exposes private information to third parties which do the matching. To address this issue, in this paper, we propose an efficient and privacy-preserving distributed matching of demander EVs with charge suppliers (i.e., public/private stations, V2V chargers) using bichromatic mutual nearest neighbor (BMNN) assignments. To this end, we use partially homomorphic encryption-based BMNN computation through local communication (e.g., DSRC or LTE-direct) between users while hiding their locations. The proposed matching algorithm provides not only a satisfactory assignment for all parties but also achieves an efficient matching in dynamic environments where new demanders and suppliers show up and some leave. The simulation results indicate that the proposed matching of suppliers and demanders can be achieved in a distributed fashion within reasonable computation and convergence times while preserving privacy of users. Moreover, due to the nature of its design, it provides a more efficient matching process for dynamic environments compared to standard stable matching algorithm, reducing the average waiting time for users until matching. (C) 2018 Elsevier B,V. All rights reserved.