mlsanityData to 7 Oct 2026

Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control

arXiv 2210.01452 ↗First posted 4 Oct 2022eess.SY cs.LG cs.MAcited by 4

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Abstract (arXiv)

With the recent advances in mobile energy storage technologies, electric vehicles (EVs) have become a crucial part of smart grids. When EVs participate in the demand response program, the charging cost can be significantly reduced by taking full advantage of the real-time pricing signals. However, many stochastic factors exist in the dynamic environment, bringing significant challenges to design an optimal charging/discharging control strategy. This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments to maximize EV users' benefits. We first formulate this problem as a Markov decision process (MDP). Then we consider EV users with different behaviors as agents in different environments. Furthermore, a horizontal federated reinforcement learning (HFRL)-based method is proposed to fit various users' behaviors and dynamic environments. This approach can learn an optimal charging/discharging control strategy without sharing users' profiles. Simulation results illustrate that the proposed real-time EV charging/discharging control strategy can perform well among various stochastic factors.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 32 references that are in the map. First posted on arXiv 4 Oct 2022.

Builds on

16 of 32 references.

References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  4. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  5. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  6. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  7. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  8. 2022Federated Reinforcement Learning with Environment Heterogeneitycited by 61
  9. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 40
  10. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  11. 2017On-line Building Energy Optimization using Deep Reinforcement Learningcited by 27
  12. 2020Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surfacecited by 21
4 more
  1. 2022Trustworthy Federated Learning via Blockchaincited by 15
  2. 2016Dynamic Scheduling for Charging Electric Vehicles: A Priority Rulecited by 6
  3. 2022Differentially Private Federated Learning via Reconfigurable Intelligent Surfacecited by 5
  4. 2017Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stationscited by 3

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.

4 papers.

  1. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  2. 2023An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Controlcited by 1
  3. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewardscited by 1
  4. 2024Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networkscited by 0