The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Reinforcement Learning for Real-Time Pricing and Scheduling Control in EV Charging Stations
Abstract
This article proposes a reinforcement-learning (RL) approach for optimizing charging scheduling and pricing strategies that maximize the system objective of a public electric vehicle (EV) charging station. The proposed algorithm is “online” in the sense that the charging and pricing decisions made at each time depend only on the observation of past events, and is “model-free” in the sense that the algorithm does not rely on any assumed stochastic models of uncertain events. To cope with the challenge arising from the time-varying continuous state and action spaces in the RL problem, we first show that it suffices to optimize the total charging rates to fulfill the charging requests before departure times. Then, we propose a feature-based linear function approximator for the state-value function to further enhance the efficiency and generalization ability of the proposed algorithm. Through numerical simulations with real-world data, we show that the proposed RL algorithm achieves on average 138.5% higher charging-station profit than representative benchmark algorithms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
Introduction to Stochastic Programming
Birge, J. R., & Louveaux, F. (2011) · Springer Series in Operations Research and Financial Engineering · doi:10.1007/978-1-4614-0237-4 · Cited by 6,469
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Baxter, L. A. (1995) · Technometrics · doi:10.1080/00401706.1995.10484354 · Cited by 2,415
Reinforcement Learning and Dynamic Programming Using Function Approximators
Busoniu, L., Babuska, R., De Schutter, B., & Ernst, D. (2017) · CRC Press eBooks · doi:10.1201/9781439821091 · Cited by 919
A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches
Deng, R., Yang, Z., Chow, M.-Y., & Chen, J. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2414719 · Cited by 917
Energy use, cost and CO2 emissions of electric cars
van Vliet, O., Brouwer, A. S., Kuramochi, T., van den Broek, M., & Faaij, A. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2010.09.119 · Cited by 381
Smart Charging for Electric Vehicles: A Survey From the Algorithmic Perspective
Wang, Q., Liu, X., Du, J., & Kong, F. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2518628 · Cited by 273
A Heuristic Operation Strategy for Commercial Building Microgrids Containing EVs and PV System
Liu, N., Chen, Q., Liu, J., Lu, X., Li, P., Lei, J., & Zhang, J. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2364553 · Cited by 263
Cited by
Tuchnitz, F., Ebell, N., Schlund, J., & Pruckner, M. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116382 · Cited by 175
Real-time feedback based online aggregate EV power flexibility characterization
Yan, D., Huang, S., & Chen, Y. (2024) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2023.3324705 · Cited by 51
Han, Y., Meng, J., & Luo, Z. (2024) · Electronics · doi:10.3390/electronics13214235 · Cited by 14
Generalizable Causal Reinforcement Learning for Out-of-Distribution Environments
Huang, S., Hu, J., Chen, H., Cui, P., Piao, H., Sun, L., & Yang, B. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3556029 · Cited by 4
Metadata
- Authors
- Wang, S., Bi, S., & Zhang, Y. A.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-02
- Volume
- 17
- Issue
- 2
- Pages
- 849-859
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2989127100
- Cited by
- 281
- References
- 21
- FWCI
- 5.64
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Reinforcement Learning for Real-Time Pricing and Scheduling Control in EV Charging Stations. https://app.cochat.ai/papers/10.1109/tii.2019.2950809
- The paper
- Wang, S., Bi, S., & Zhang, Y. A. (2021). Reinforcement Learning for Real-Time Pricing and Scheduling Control in EV Charging Stations. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2950809
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
