Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach
Journal article, 2024

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).

Electric vehicle

Reinforcement learning

Personalized dynamic pricing

Fast-electric vehicle charging station

Game theory

Author

Sangjun Bae

Sejong cyber university

Balázs Adam Kulcsár

Chalmers, Electrical Engineering, Systems and control

Sébastien Gros

Norwegian University of Science and Technology (NTNU)

Chalmers, Electrical Engineering, Systems and control

Transportation Research, Part C: Emerging Technologies

0968-090X (ISSN)

Vol. 161 104540

ServiCe OPtimization of charging station for Electrified vehicles

Swedish Electromobility Centre, 2017-10-18 -- 2019-10-17.

E-Laas: Energy optimal urban Logistics As A Service

Swedish Energy Agency (2023-00021), 2023-05-02 -- 2025-04-30.

European Commission (EC) (F-ENUAC-2022-0003), 2023-05-01 -- 2025-04-30.

Areas of Advance

Transport

Subject Categories

Transport Systems and Logistics

Electrical Engineering, Electronic Engineering, Information Engineering

Control Engineering

DOI

10.1016/j.trc.2024.104540

More information

Latest update

8/9/2024 9