论文标题
将用户偏好与外部奖励结合起来，以启用以驱动程序为中心和资源感知的电动汽车充电建议
Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation
论文作者
论文摘要
电动汽车（EV）充电建议既适合用户的偏好，又适应了不断变化的外部环境，这是一种减轻私人电动汽车驾驶员焦虑范围的成本效益策略。先前的研究着重于集中策略来实现优化的资源分配，这对于隐私 - 不断增长的出租车车队和固定的公共交通特别有用。但是，私人电动汽车司机寻求更个性化和资源感知的收费建议，以适应用户的偏好（何时何地收费），但充分适应充电供应和需求之间的时空不匹配。在这里，我们提出了一种新型的正规演员批评（RAC）充电建议方法，该方法将使每个EV驱动程序在用户偏好（历史充电模式）和外部奖励（驾驶距离和等待时间）之间取得最佳平衡。两个现实世界数据集的实验结果证明了我们对竞争方法的独特特征和出色的表现。
Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers. Previous studies focus on centralized strategies to achieve optimized resource allocation, particularly useful for privacy-indifferent taxi fleets and fixed-route public transits. However, private EV driver seeks a more personalized and resource-aware charging recommendation that is tailor-made to accommodate the user preference (when and where to charge) yet sufficiently adaptive to the spatiotemporal mismatch between charging supply and demand. Here we propose a novel Regularized Actor-Critic (RAC) charging recommendation approach that would allow each EV driver to strike an optimal balance between the user preference (historical charging pattern) and the external reward (driving distance and wait time). Experimental results on two real-world datasets demonstrate the unique features and superior performance of our approach to the competing methods.
