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.

本研究提出了基于正则化 Actor-Critic 算法的电动汽车充电推荐方法，该方法可为私人电动汽车驾驶者提供个性化、资源感知的充电推荐服务，并在实际数据集上表现出对比方法所不具备的优越性能，可为缓解电动汽车驾驶者的续航焦虑提供一种成本效益的解决策略。