Electric vehicle charging and discharging scheduling strategy under dynamic traffic network considering battery health

In order to enhance user engagement in power grid scheduling, this paper proposes a battery health assessment method based on electric vehicle charging curves, combined with a traffic network model to dynamically determine EVs’ state-of-charge distribution. First, by analyzing EV charging curves, an algorithm is introduced that accurately evaluates battery health, relying on characteristic changes observed during the charging and discharging processes. Second, a dynamic traffic network model is designed to monitor and predict the state-of-charge distribution at various charging stations in real time, thereby enabling more rational allocation of power resources and improving energy efficiency. Finally, the Kepler optimization algorithm is employed to solve the charging strategy, aiming to balance battery health and grid load. Simulation results show that the proposed method effectively predicts EV battery health status while optimizing the state-of-charge distribution among charging stations, thus reducing grid load fluctuations and enhancing both the stability and operational efficiency of the power grid.