  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.
