This paper presents a smart charge management strategy for an on-demand electric shuttle operating at the National Renewable Energy Laboratory (NREL) campus and supported by an inductive charger at the vehicle's waiting spot. A new control algorithm has been proposed for mitigating the demand charges incurred from the wireless charger. It monitors the shuttle, wireless charger, renewable energy generation, and other loads and regulates charging behavior for demand charge mitigation. Within the control algorithm, an energy prediction is made to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller is designed and optimized using a Simulink model for the entire system. It is then implemented and tested in real time at the NREL campus using online cloud services. Two vehicle-use cases—charge-sustaining and charge-depletion operation—are tested under different campus power profiles and drive cycles to assess the controller's performance. The proposed controller showed a robust performance under different driving scenarios, with high correlation between simulation and experimental data. The results show that proper demand response can be achieved, with an average of 94% reduction of charging loads during peak demand events.

中文翻译：

---

实时实现按需穿梭服务的智能无线充电以减轻按需收费

本文为在国家可再生能源实验室（NREL）校园内运行并在车辆候车点得到感应充电器支持的按需电动穿梭车提出了一种智能充电管理策略。已经提出了一种新的控制算法，用于减轻无线充电器产生的需求费用。它监视航天飞机，无线充电器，可再生能源发电和其他负载，并调节充电行为以减轻需求电量。在控制算法内，进行能量预测以估计车辆的机动性需求，并在操作期间保持不间断的服务，同时仍使峰值需求最小化。拟议的控制器是使用Simulink模型针对整个系统进行设计和优化的。然后使用在线云服务在NREL校园中对其进行实时实施和测试。在不同的园区功率配置文件和行驶周期下测试了两种车辆用例（电荷保持和电荷消耗操作），以评估控制器的性能。所提出的控制器在不同的驾驶情况下表现出鲁棒的性能，在仿真和实验数据之间具有高度的相关性。结果表明，可以实现适当的需求响应，在高峰需求事件期间平均减少94％的充电负荷。与仿真和实验数据之间具有高度相关性。结果表明，可以实现适当的需求响应，在高峰需求事件期间平均减少94％的充电负荷。与仿真和实验数据之间具有高度相关性。结果表明，可以实现适当的需求响应，在高峰需求事件期间平均减少94％的充电负荷。