{"title":"Strategic planning of charging infrastructure for shared electric vehicles: A multi-phase stochastic approach using reinforcement learning","authors":"Qiming Ye , Prateek Bansal , Bryan T. Adey","doi":"10.1016/j.scs.2025.106850","DOIUrl":null,"url":null,"abstract":"<div><div>The rapid electrification of ride-hailing mobility requires that fast-charging infrastructure be ready to support the dynamic charging needs of shared electric vehicle (SEV) fleets. Deploying city-wide fast-charging stations requires determining the timing, locations, and number of chargers at each station over multiple planning phases, while managing stochastic charging demand, induced demand from enhanced services, and constraints like grid capacity. In contrast to conventional deterministic and myopic methods that optimise charging stations’ configurations based on static demand and immediate benefits, this study formulates the problem as a stochastic sequential decision problem and applies reinforcement learning (RL) to approximate the optimal deployment plan considering long-term benefits and uncertainties. An agent-based model representing an AI gym environment is developed to train the RL model, simulating SEV interactions with charging stations to capture stochastic charging demand across space and time. RL maximises the expected charging service efficiency with the minimal number of fast chargers over the planning horizon. The model reduces the total number of chargers by 30.5% compared to the myopic benchmark, while achieving a 10.9% higher charging service efficiency. This multi-phase fast charger planning approach offers engineers and planners a novel tool for efficient long-term infrastructure allocation and policy development under stochastic charging demand.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"133 ","pages":"Article 106850"},"PeriodicalIF":12.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670725007231","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid electrification of ride-hailing mobility requires that fast-charging infrastructure be ready to support the dynamic charging needs of shared electric vehicle (SEV) fleets. Deploying city-wide fast-charging stations requires determining the timing, locations, and number of chargers at each station over multiple planning phases, while managing stochastic charging demand, induced demand from enhanced services, and constraints like grid capacity. In contrast to conventional deterministic and myopic methods that optimise charging stations’ configurations based on static demand and immediate benefits, this study formulates the problem as a stochastic sequential decision problem and applies reinforcement learning (RL) to approximate the optimal deployment plan considering long-term benefits and uncertainties. An agent-based model representing an AI gym environment is developed to train the RL model, simulating SEV interactions with charging stations to capture stochastic charging demand across space and time. RL maximises the expected charging service efficiency with the minimal number of fast chargers over the planning horizon. The model reduces the total number of chargers by 30.5% compared to the myopic benchmark, while achieving a 10.9% higher charging service efficiency. This multi-phase fast charger planning approach offers engineers and planners a novel tool for efficient long-term infrastructure allocation and policy development under stochastic charging demand.
期刊介绍:
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;