Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
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 (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).
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 45 references that are in the map. First posted on arXiv 1 Jan 2024.
Builds on
9 of 45 references.
References · 9
- 2021Short-term traffic prediction using physics-aware neural networkscited by 8
- 2017Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent Systemcited by 5
- 2016Smart Charging for Electric Vehicles: A Survey From the Algorithmic Perspectivecited by 3
- 2021Parameter and density estimation from real-world traffic data: A kinetic compartmental approachcited by 3
- 2014Group Activity Selection Problemcited by 2
- 2017Electrical Vehicle Charging Station Profit Maximization: Admission, Pricing, and Online Schedulingcited by 2
- 2014Unsplittable Load Balancing in a Network of Charging Stations Under QoS Guaranteescited by 1
- 2015Competitive Charging Station Pricing for Plug-in Electric Vehiclescited by 1
- 2020Dynamic Modeling and Real-time Management of a System of EV Fast-charging Stationscited by 1
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Citing papers per year: 2025: 1.
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- 2026Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches
- 2025Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction
- 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
- 2025Optimizing Electric Vehicle Charging Station Placement Using Reinforcement Learning and Agent-Based Simulations
- 2024Tacit algorithmic collusion in deep reinforcement learning guided price competition: A study using EV charge pricing game
- 2025A Hybrid Physics-Based and Reinforcement Learning Framework for Electric Vehicle Charging Time Prediction
- 2022Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning