Electric vehicles, as distributed energy storage resources, have broad prospects for participating in vehicle-to-everything (V2X) interactions. However, they encounter the problem of low user participation in practical applications. The key influencing factors are battery aging and range anxiety. In order to improve the enthusiasm of vehicle owners to participate in V2X technology, a multi-scenario V2X energy management system is designed based on battery aging perception capacity quantification. The battery health status is quantified as the "lifetime driving cycles", and a multi-layer perceptron neural network is used to achieve a fast linearized expression. At the same time, in order to simulate the psychological expectation of users experiencing endowment effects and having a premium in battery resource valuation, this paper establishes a cyber-physical-social system(CPSS) and enhances the response data based on conditional generative adversarial network(CGAN) through the artificial electric vehicle group in the virtual system to obtain the incentive strategy of the energy management system, ultimately achieving a win-win situation for both the user side and the grid side. Finally, a series of case studies show that the proposed scheme can effectively evaluate the impact of V2X interactions on battery life, alleviate range anxiety of users, and provide a reference for vehicle owners to participate in V2X interactions.