Research on vehicle-to-grid interaction system considering battery health status and range anxiety

Driven by the dual-carbon policy and rapid advancements in battery technology, the electric vehicle industry has witnessed remarkable growth. However, despite the promising prospects of EV participation in vehicle-to-grid interactions as energy storage resources, practical implementation faces challenges of low user engagement. Battery degradation and range anxiety emerge as critical barriers affecting user participation in vehicle-to-grid interaction activities. To enhance EV owners’ engagement and increase aggregated capacity for demand response, this study proposes a battery degradation-aware multiscenario vehicle-to-grid interaction management system designed to prolong battery lifespan, alleviate range anxiety, and strengthen grid response capabilities. The system quantifies battery health impacts through modified effective cycling counts induced by vehicle-to-grid interaction operations. A multilayer perceptron neural network is employed to achieve rapid linearized representation of state-of-health quantification. For charging optimization, personalized user preferences are incorporated to model range anxiety’s influence on decision-making processes, enabling rational behavioral modeling. Comprehensive case studies demonstrate that the proposed system enables swift assessment of vehicle-to-grid interaction impacts on battery longevity while optimizing engagement behaviors based on individualized preferences. This research provides a reliable framework for improving EV owners’ willingness to participate in vehicle-to-grid interaction interactions, offering valuable insights for enhancing grid-responsive EV cluster management.