Optimizing Electric Vehicle Charging Station Placement Using Reinforcement Learning and Agent-Based Simulations

• URL: http://arxiv.org/abs/2511.01218v1
• Date: Mon, 03 Nov 2025 04:22:39 GMT
• Title: Optimizing Electric Vehicle Charging Station Placement Using Reinforcement Learning and Agent-Based Simulations
• Authors: Minh-Duc Nguyen, Dung D. Le, Phi Long Nguyen,
• Abstract summary: Reinforcement learning offers an innovative approach to identifying optimal charging station locations.<n>We propose a novel framework that integrates deep RL with agent-based simulations to model EV movement and estimate charging demand in real time.<n>Our approach employs a hybrid RL agent with dual Q-networks to select optimal locations and configure charging ports, guided by a hybrid reward function that combines deterministic factors with simulation-derived feedback.
• Score: 5.6037668742884135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid growth of electric vehicles (EVs) necessitates the strategic placement of charging stations to optimize resource utilization and minimize user inconvenience. Reinforcement learning (RL) offers an innovative approach to identifying optimal charging station locations; however, existing methods face challenges due to their deterministic reward systems, which limit efficiency. Because real-world conditions are dynamic and uncertain, a deterministic reward structure cannot fully capture the complexities of charging station placement. As a result, evaluation becomes costly and time-consuming, and less reflective of real-world scenarios. To address this challenge, we propose a novel framework that integrates deep RL with agent-based simulations to model EV movement and estimate charging demand in real time. Our approach employs a hybrid RL agent with dual Q-networks to select optimal locations and configure charging ports, guided by a hybrid reward function that combines deterministic factors with simulation-derived feedback. Case studies in Hanoi, Vietnam, show that our method reduces average waiting times by 53.28% compared to the initial state, outperforming static baseline methods. This scalable and adaptive solution enhances EV infrastructure planning, effectively addressing real-world complexities and improving user experience.

Related papers

• Large Language Model-Assisted Planning of Electric Vehicle Charging Infrastructure with Real-World Case Study [2.7788865512077225]
  The growing demand for electric vehicle (EV) charging infrastructure presents significant planning challenges.<n>The potential benefits of EV charging assignment, particularly in response to varying spatial-temporal patterns of charging demand, remain under-explored in infrastructure planning.<n>This paper proposes an integrated approach that jointly optimize investment decisions and charging assignments.
  arXiv  Detail & Related papers  (2025-11-24T12:45:10Z)
• Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networks [44.91945854166524]
  Wireless rechargeable sensor networks (WRSNs) with mobile charging capabilities offer a promising solution to extend network lifetime.<n>WRSNs face critical challenges from the inherent trade-off between maximizing the node survival rates and maximizing charging energy efficiency.
  arXiv  Detail & Related papers  (2025-10-24T03:30:00Z)
• Accelerating RL for LLM Reasoning with Optimal Advantage Regression [52.0792918455501]
  We propose a novel two-stage policy optimization framework that directly approximates the optimal advantage function.<n>$A$*-PO achieves competitive performance across a wide range of mathematical reasoning benchmarks.<n>It reduces training time by up to 2$times$ and peak memory usage by over 30% compared to PPO, GRPO, and REBEL.
  arXiv  Detail & Related papers  (2025-05-27T03:58:50Z)
• Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning [46.15490780173541]
  Electric Buses (EBs) represent a significant step toward sustainable development.<n>By utilizing Internet of Things (IoT) systems, charging stations can autonomously determine charging schedules based on real-time data.<n>However, optimizing EB charging schedules remains a critical challenge due to uncertainties in travel time, energy consumption, and fluctuating electricity prices.
  arXiv  Detail & Related papers  (2025-05-15T13:44:27Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• LLM-Enabled EV Charging Stations Recommendation [0.0]
  We propose RecomBot, which is a Large Language Model (LLM)-powered prompt-based recommender system.<n>It dynamically suggests optimal Charging Stations (CSs) using real-time heterogeneous data.<n>By leveraging natural language reasoning and fine-tuning EV-specific datasets, RecomBot enhances personalization, improves charging efficiency, and adapts to various EV types.
  arXiv  Detail & Related papers  (2025-04-29T19:57:05Z)
• Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards [3.867907469895697]
  We introduce a novel RL framework that combines the Deep Deterministic Policy Gradient approach with action masking and efficient MILP-driven policy guidance.<n>Our approach balances the exploration of continuous action spaces to meet user charging demands.<n>Our results show that the proposed approach is one of the first scalable and general approaches to solving the V2B energy management challenge.
  arXiv  Detail & Related papers  (2025-02-24T19:24:41Z)
• Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks [55.591662978280894]
  This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs) Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, often fall short due to their lack of convergence guarantees.
  arXiv  Detail & Related papers  (2023-10-13T21:26:16Z)
• Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning [55.08287089554127]
  Open Radio Access Network systems, with their base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability.<n>We propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments.<n>We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments.
  arXiv  Detail & Related papers  (2023-09-04T17:30:21Z)
• Actively Learning Costly Reward Functions for Reinforcement Learning [56.34005280792013]
  We show that it is possible to train agents in complex real-world environments orders of magnitudes faster. By enabling the application of reinforcement learning methods to new domains, we show that we can find interesting and non-trivial solutions.
  arXiv  Detail & Related papers  (2022-11-23T19:17:20Z)
• A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem [9.655068751758952]
  Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming. There are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. This paper proposes a hyper-heuristic approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning.
  arXiv  Detail & Related papers  (2022-06-07T11:10:38Z)
• Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning [6.37470346908743]
  We build on previous research on RL, based on a Markov decision process (MDP) to simultaneously coordinate multiple charging stations. We propose an improved cost function that essentially forces the learned control policy to always fulfill any charging demand that does not offer flexibility. We rigorously compare the newly proposed batch RL fitted Q-iteration implementation with the original (costly) one, using real-world data.
  arXiv  Detail & Related papers  (2022-03-03T11:22:27Z)
• Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization [2.580765958706854]
  This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems. We present an optimal service system including the design of SAEV vehicles and charging stations.
  arXiv  Detail & Related papers  (2020-10-16T05:06:58Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.