电动公交车在运行过程中具有零排放、低能耗等优势，目前各个国家正在大力推进城市公交车辆的电动化，以减少交通系统碳排放，助力实现“双碳”目标。然而受资金约束以及燃油公交车尚未达到报废年限的影响，公交企业通常分批购买电动公交车来替换线路上的燃油公交车，导致线路上各辆公交车的电池健康程度以及续驶里程存在差异，使得车辆调度方案优化更加复杂。本研究针对电动公交线路各辆公交车电池健康程度存在差异的情况，考虑分时电价影响，以最小化每日的充电成本、车辆购置成本和电池损耗成本为目标，建立了单线路车辆调度方案优化模型。将优化模型重构为车辆行车计划优化与充电计划优化两个子问题，其中在外层考虑车辆运营强度差异对模拟退火算法的扰动策略进行改进，采用改进后的模拟退火算法求解车辆行车计划；在内层调用Gurobi求解车辆充电计划。最后，以某市一条实际电动公交线路为例验证方法的有效性，并与扰动策略中不考虑车辆运营强度差异的模拟退火算法进行比较。结果表明：本文设计的改进模拟退火算法使收敛速度提高31.8%，能够在短时间内求得质量较高的解；生成的调度方案不仅能够安排车辆优先在低电价时段充电，还可以缩小车队规模。

Electric buses (EBs) have the advantages of zero-emission and low energy consumption in operation. The electrification of urban buses is being vigorously promoted in many countries to reduce carbon emissions and promote the realization of the “Carbon peaking and Carbon neutrality” goals. However, due to financial constraints and the fact that fuel buses have not yet reached the end of life and bus companies usually replace fuel buses with EBs in batches, there are differences in the battery health degree and driving range of each bus on the line, which makes the optimization of the vehicle scheduling scheme more complicated. Considering the impact of battery differences in the state of health and time-of-use tariff, this paper proposed an optimized scheduling model for single-route, with the objective of minimizing average daily charging costs, EB acquisition costs, and battery loss costs. Then, the model was transformed into two sub-problems, the vehicle scheduling problem and the charging scheduling problem. In the outer layer, the vehicle scheduling problem was solved by the improved simulated annealing algorithm (ISAA), whose perturbation strategy is designed with the operating intensity differences among EBs. And Gurobi was employed to solve the charging scheduling problem in the inner layer. Finally, an actual EB route was taken as an example to verify the effectiveness of the method, and the method was compared with the simulated annealing algorithm in the perturbation strategy which does not consider differences in vehicle operating intensity. Results show that the ISAA can increase the convergence speed by 31.8% and achieve high-quality solutions in a short time. Moreover, the generated scheduling scheme can not only arrange EBs to be charged preferentially in the off-peak period of electricity prices but also reduce the EB fleet size.