Joint optimization of vehicle scheduling and charging strategies for electric buses to reduce battery degradation.

This article focuses on a joint optimization model for electric bus (EB) vehicle scheduling and charging strategies that incorporates both explicit charging costs and implicit battery degradation costs. Using graph theory, the authors develop a mixed-integer linear programming (MILP) model that minimizes total operating costs by optimizing bus-trip assignments and charging schedules while considering state of charge (SoC) constraints and charging facility limitations. Numerical experiments based on a bus line in Nanjing, China, demonstrate that adjusting vehicle scheduling and charging strategies can reduce battery degradation costs by 7.45% and total costs by 6%, despite a slight increase in charging costs. The study highlights that battery degradation costs significantly exceed charging costs and that maintaining a low initial SoC is beneficial for cost savings, whereas increasing the number of buses has minimal impact. The model is extensible to include additional operational factors and aims to provide practical insights for sustainable EB fleet management.