为解决既有静态无线充电（Static Wireless Charging，SWC）设施布局方案容易忽略电池老化成本的问题，提出了计及电池老化特征的电动公交车SWC设施布局优化方法。首先考虑机会充电模式下电动公交车的运行特征，建立兼顾充电设施布设成本与电池老化成本的SWC设施布局优化模型，将电池荷电状态变化区间对电池老化速率的作用机理融入模型，同时引入累积能耗约束以确保SWC设施布局方案满足公交线路运营需求；然后针对模型的计算复杂性，提出一种改进禁忌搜索（Tabu Search，TS）算法对模型进行求解，依据模型特点构造算法的初始解及邻域结构；最后设计数值算例对模型和算法进行验证。研究结果表明：SWC设施布局对电池老化具有显著影响，相比不考虑电池老化特征的常规模型，文中提出的模型可以使年均总成本降低3.8%；在现阶段电池技术与成本条件下，电池老化成本占年均总成本的比例高达72.3%；文中提出的改进TS算法较原始TS算法在求解效率上有显著提升。文中还通过灵敏度分析探讨了模型的多个关键参数对优化结果的影响，发现电池荷电状态值上界、SWC设施充电功率与年均总成本呈显著的负相关，而电池单位容量成本、SWC设施布设成本、车辆单位行驶能耗与年均总成本呈不同程度的正相关。

As the existing layout schemes of static wireless charging (SWC) facilities often neglect battery degradation costs, this paper proposes a layout optimization method of SWC facilities for electric buses by considering battery degradation characteristics. Firstly, by considering the operation characteristics of electric buses under opportunity charging mode, a layout optimization method of SWC facilities is developed with simultaneous consideration of charger deployment costs and battery degradation costs, with the function mechanism of battery state of charge (SOC) variety ranges on battery degradation rate being integrated into the model, and with the accumulated energy consumption constraints being introduced in the model to ensure that the SWC layout scheme can satisfy the bus route operation demands. Then, an improved TS (Tabu Search) algorithm is presented to solve the model by overcoming its computational complexity, and the initial solution and neighborhood structure of the algorithm are constructed according to the model characteristics. Finally, a numerical example is designed to verify the model and algorithm. The results indicate that the layout of SWC facilities has significant effects on battery degradation; that the proposed model can reduce 3.8% of the total annualized cost, as compared with the conventional model that neglects the battery degradation characteristics; that the battery degradation cost accounts for up to 72.3% of the total annualized cost under current battery technology and cost conditions; and that the improved TS algorithm is better than the original one because it significantly improves the solution efficiency. Moreover, a sensitive analysis is conducted to explore the impacts of multiple critical factors on optimal results, finding that both the upper bound of battery SOC and the SWC facility charging power have significant negative correlation with the total annualized cost, while the battery’s unit capacity cost, the SWC facility layout cost and the vehicle energy consumption rate all have positive correlation with the total annualized cost in various degrees.