Battery-Degradation-Aware Routing to Nearest Feasible Charging Station for Electric Vehicles: A Simulation-Based Framework

This study presents a simulation-based framework for battery-degradation-aware routing in electric vehicles by integrating physics-informed battery state estimation with decision-level navigation logic. A hybrid estimation approach combining spatially distributed fiber-optic sensing with complementary Kalman filtering strategies is used to reconstruct core temperature, surface temperature, state-of-charge, and mechanical degradation indicators in real time. These estimated states are supplied directly to an intelligent routing module, enabling charging station selection that is both physically reachable and aware of thermal- and health-related constraints. The results demonstrate that routing decisions informed by battery state estimation consistently avoid high-risk thermal and swelling conditions while maintaining range feasibility. By explicitly incorporating mechanical degradation indicators into the routing logic, the framework addresses a key gap in prior studies where battery swelling and navigation were treated independently. Overall, the findings confirm that estimator-driven, degradation-aware routing can improve operational safety, reduce range anxiety, and support more reliable electric vehicle navigation. The study establishes a simulation-first foundation for future experimental validation, adaptive policy refinement, and broader deployment of battery-degradation-aware decision-making in electric mobility systems.