2024-05-31 チャルマース工科大学
As more and more people drive electric cars, congestion and queues can occur when many people need to charge at the same time. A new study from Chalmers University of Technology in Sweden shows how AI-controlled charging stations, through smart algorithms, can offer electric vehicle users personalised prices, and thus minimise both price and waiting time for customers. But the researchers point to the importance of taking the ethical issues seriously, as there is a risk that the artificial intelligence exploits information from motorists.
＜関連情報＞
- https://news.cision.com/chalmers/r/ai-controlled-stations-can-charge-electric-cars-at-a-personal-price,c3992121
- https://www.sciencedirect.com/science/article/abs/pii/S0968090X24000615
電気自動車のためのパーソナライズされたダイナミックプライシングポリシー： 強化学習アプローチ Personalized dynamic pricing policy for electric vehicles: Reinforcement learning approach
Sangjun Bae, Balázs Kulcsár, Sébastien Gros
Transportation Research Part C: Emerging Technologies Available online: 4 March 2024
DOI:https://doi.org/10.1016/j.trc.2024.104540
Highlights
- Applying reinforcement learning to pricing maximizes revenue in the EV charging market.
- New highway charging station pricing concept introduced for EVs.
- Personalized dynamic pricing yields higher revenue than ordinary pricing policy.
- Sharing personal information is crucial for EV users and may be misused by AI.
Abstract
With the increasing number of fast-electric vehicle charging stations (fast-EVCSs) and the popularization of information technology, electricity price competition between fast-EVCSs is highly expected, in which the utilization of public and/or privacy-preserved information will play a crucial role. Self-interest electric vehicle (EV) users, on the other hand, try to select a fast-EVCS for charging in a way to maximize their utilities based on electricity price, estimated waiting time, and their state of charge. While existing studies have largely focused on finding equilibrium prices, this study proposes a personalized dynamic pricing policy (PeDP) for a fast-EVCS to maximize revenue using a reinforcement learning (RL) approach. We first propose a multiple fast-EVCSs competing simulation environment to model the selfish behavior of EV users using a game-based charging station selection model with a monetary utility function. In the environment, we propose a Q-learning-based PeDP to maximize fast-EVCS’ revenue. Through numerical simulations based on the environment: (1) we identify the importance of waiting time in the EV charging market by comparing the classic Bertrand competition model with the proposed PeDP for fast-EVCSs (from the system perspective); (2) we evaluate the performance of the proposed PeDP and analyze the effects of the information on the policy (from the service provider perspective), and the robustness of the proposed approach; and (3) it can be seen that privacy-preserved information sharing can be misused by artificial intelligence-based PeDP in a certain situation in the EV charging market (from the customer perspective).
