Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation

• URL: http://arxiv.org/abs/2210.12693v1
• Date: Sun, 23 Oct 2022 10:52:51 GMT
• Title: Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation
• Authors: Chengyin Li, Zheng Dong, Nathan Fisher, and Dongxiao Zhu
• Abstract summary: 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. Here we propose a novel Regularized Actor-temporal (RAC) charging recommendation approach that would allow each EV driver to strike an optimal balance between the user preference and the external reward. Experimental results on two real-world datasets demonstrate unique features and superior performance of our approach to the competing methods.
• Score: 9.009978844120514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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