The rapid adoption of electric vehicles (EVs) opens new opportunities because of their potential to mitigate greenhouse gas emissions, alleviate peak-hour energy management, and transform how energy is traded. However, a major limitation is that the energy supply cannot increase as needed to balance EVs' demand during peak hours. To address this problem, EVs can trade with other parties (e.g., EVs and smart buildings) by controlling their charging/discharging schedule for financial incentives, but this control decision is a burden to EV users. Automating this trading is a challenging task because of the diverse locations of EVs, the variation of charging/discharging energy amount, trading preferences, trading trustworthiness, and users' data privacy. Therefore, in this article, we design a secure and trustworthy vehicle-to-everything energy trading architecture to overcome these limitations. The goal of the proposed system is to increase the technological and the financial incentives f...

The proliferation of electric vehicles (EVs) and the advancement of vehicle-to-everything energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of EV users. This article proposes a novel federated reinforcement learning system combined with blockchain technology to maximize EV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of proof of state of charge as a consensus mechanism to determine the winning EVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves EV users' uti...