# Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach

arXiv: https://arxiv.org/abs/2401.00661 · HTML: https://mlsanity.com/paper/2401.00661/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 9 of 9)

- [Short-term traffic prediction using physics-aware neural networks](https://mlsanity.com/paper/2109.10253/) (2021)
- [Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System](https://mlsanity.com/paper/1711.06871/) (2017)
- [Smart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective](https://mlsanity.com/paper/1607.07298/) (2016)
- [Parameter and density estimation from real-world traffic data: A kinetic compartmental approach](https://mlsanity.com/paper/2101.11485/) (2021)
- [Group Activity Selection Problem](https://mlsanity.com/paper/1401.8151/) (2014)
- [Electrical Vehicle Charging Station Profit Maximization: Admission, Pricing, and Online Scheduling](https://mlsanity.com/paper/1705.02116/) (2017)
- [Unsplittable Load Balancing in a Network of Charging Stations Under QoS Guarantees](https://mlsanity.com/paper/1409.6673/) (2014)
- [Competitive Charging Station Pricing for Plug-in Electric Vehicles](https://mlsanity.com/paper/1511.07907/) (2015)
- [Dynamic Modeling and Real-time Management of a System of EV Fast-charging Stations](https://mlsanity.com/paper/2012.09349/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning for routing: A guided review of recent developments and future directions](https://mlsanity.com/paper/2507.00218/) (2025)

## 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).