A Stable Matching Game for V2V Energy Sharing-A User Satisfaction Framework

Electric Vehicles (EVs) are being widely adopted to completely replace vehicles with internal combustion engines. However, the development of charging infrastructure to support the growing number of EVs has been lacking primarily due to the high cost of installation. Additionally, the immature and non-uniform deployment of charging stations has led to the absence of charging infrastructure in areas such as highways and rural areas. As a result, emerging concepts such as, EV-toHome (V2H), EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence. In this paper, the V2V energy sharing concept is exploited, where an intelligent and comprehensive framework to manage and allocate energy between EVs is proposed. This work presents a realistic modeling of the V2V energy sharing problem and proposes a two-layer matching approach that can efficiently match the EVs. The proposed approach not only optimizes the cast, but also the time, system energy efficiency, user satisfaction, and social welfare; hence making the approach more realistic and inclusive. Gale-Shapley is utilized to produce stable matchings, in the first layer, whereas a user-satisfaction model is devised to ensure realistic matchings, in the second layer. Additionally, a real-life dataset is developed from commercially available EVs and the performance of the system is evaluated using this dataset, in addition to realistic parameters derived from real-life data. The proposed approach is compared with a benchmark, where the results show that efficient, realistic, and effective V2V matches are achievable, paving the way for the adoption of such framework.

Automated summary

Electric Vehicles (EVs) are increasingly replacing vehicles with internal combustion engines, however, the lack of charging infrastructure and inconsistent deployment of charging stations in different areas have prompted the emergence of new concepts such as V2V charging. The proposed V2V energy sharing framework aims to efficiently manage and allocate energy between EVs, optimizing various factors for a more realistic and inclusive approach.