Electric Vehicles and Infrastructure

Neueste Paper

• Electric Vehicle Charging Station Location Selection using Geospatial Artificial Intelligence (GeoAI)

  Eun Hak Lee, Euntak Lee · 28. September 2026

  As electric vehicle (EV) adoption increases, ensuring efficient and well-distributed charging infrastructure has become a critical challenge. While many EV charging station location problem (CSLP) studies focus on minimizing costs or travel distance, it is crucial to consider the surrounding geospat…

• Uncovering Residential PV-EV Co-Adoption from Smart-Meter Data: Load Archetypes and Detection for Demand-Side Planning

  Jack Zheng, Hao Wang · 25. September 2026

  The increasing adoption of electric vehicles (EVs) and rooftop photovoltaic (PV) systems is reshaping residential electricity demand and creating new challenges for demand-side management (DSM), tariff design, and low-voltage network planning. Much of the existing literature examines EV charging or …

• LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems

  Yang Zhang, Lindong Xie, Chongyu Wang, Gaojunjie Li, Siqi Bu, Edward Chung · 15. September 2026

  In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, station profitability, and smart grid stability presents a complex multi-objective challenge. Existing Multi-Agent Reinforcement Learning (MARL) approache…

• EVTradeMatch: A Mobility-Aware Multi-Objective Matching Framework for EV--EV Energy Trading

  Md. Mahfujur Rahman, Alistair Barros, Raja Jurdak, Darshika Koggalahewa · 11. September 2026

  Peer-to-peer energy trading among electric vehicles (EVs) can improve charging flexibility under limited charging infrastructure, but effective EV--EV trading requires coordinated provider--consumer matching under journey-specific conditions. This paper proposes EVTradeMatch, a prediction-guided mul…

• Three-sided mobility-energy market design as a multiperiod stochastic assignment game

  Hai Yang, Joseph Y. J. Chow · 9. September 2026

  As mobility service providers (MSPs) and energy providers (EPs) expand electric vehicle ecosystems, models are needed to understand their interactions within a three-sided market. Existing frameworks often overlook the temporal interdependencies between mobility and charging demands. We address this…

• Conditional Diffusion Models for Energy-Efficient Driving

  Hemanth Neelgund Ramesh, Andr\'e Snoeck, Chyi-Fu Hong, Shijing Sun · 31. August 2026

  Electrification of commercial delivery fleets is shifting fleet routing from distance- and time-based optimization toward energy-aware decision-making. Existing sequence models primarily provide deterministic point estimates or limited uncertainty summaries, which do not capture the range of plausib…

• A Behavior-Guided Online Probabilistic Forecasting Method for Electric vehicle Charging Loads

  Chenghan Li, Qingxiang Liu, Yinliang Xu, Yuxuan Liang · 26. August 2026

  Electric vehicle (EV) charging loads exhibit strong behavioral heterogeneity and temporal variability, posing significant challenges for online probabilistic forecasting under evolving operating conditions. In particular, persistent charging patterns may differ substantially across stations, while r…

• Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble framework for preventive maintenance and low-carbon mobility

  Cande Lian (School of Management, Foshan University, Foshan, China), Wentao Zeng (School of Management, Foshan University, Foshan, China), Jiabin Wu (School of Management, Foshan University, Foshan, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Wei Zhou (Department of Civil and Environmental Engineering, National University of Singapore) · 24. Juli 2026

  Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and humidity increasingly raises equipment fault risk and undermines the resilience of urban energy and mobility services. Sh…

• Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches

  Xavier Rate, Eloann Le Guern, Rapha\"el F\'eraud, Fatma Salem, Melissa Chiknoun, Eymeric Giabicani, Mehdi Feki, Patrick Maill\'e, Guy Camilleri, Anne Blavette, Hamid Benhamed · 1. Juli 2026

  The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources. To enable efficient integration of EVs while minimizing …

• When Agents Meet Electric Bus Fleet Operations: Pricing Behavior, Trade-offs, and Policy Implications in an Aggregator Framework

  J\^onatas Augusto Manzolli, Ali Eslami, Luis Miranda-Moreno, Jiangbo Yu · 26. Juni 2026

  Agentic systems are changing how complex operational tasks are coordinated, introducing a new paradigm for connecting heterogeneous data sources and automating processes. Electric bus fleets provide a relevant test case. Their operation requires continuous coordination between service reliability, b…

• GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks

  Nanhong Liu, Mucun Sun, Jie Zhang · 19. Juni 2026

  Electric vehicle charging stations (EVCSs) can expose distribution feeders to cyberattacks. While machine learning methods, including graph neural networks, can localize which bus is compromised, significant challenges remain in data sharing and model training. For example, privacy regulations grant…

• Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times

  Giuseppe Gabriele, Fabio Pavirani, Seyed Soroush Karimi Madahi, Chris Develder · 18. Juni 2026

  The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability. Smart control of EV charging -- e.g., based on reinforcement learning (RL) -- can alleviate these issues by learning temporal and contextual patterns from historical d…

• Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration

  Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon, Shafkat Khan Siam, Shafiuzzaman K Khadem, Krishnendu Guha, Mayeen U Khandaker, Md. Noor-A-Rahim · 26. Mai 2026

  The rapid growth of Electric Vehicle (EV) adoption challenges power distribution networks through peak load spikes, voltage instability, and transformer overloads from uncoordinated charging. While Model Predictive Control (MPC) and standard Reinforcement Learning (RL) methods have addressed these i…

• Federated Learning for Early Prediction of EV Charging Demand

  Vasilis Perifanis, Foteini Nikolaidou, Nikolaos Pavlidis, Panagiotis Thomakos, Andreas Sendros · 7. Mai 2026

  Accurate forecasting of electric vehicle (EV) charging demand is critical for grid stability, infrastructure planning, and real-time charging optimization. In this work, we study the problem of early prediction of charging demand, where the total energy of a session is estimated using only informati…

• A Grid-Aware Agent-Based Model for Analyzing Electric Vehicle Charging Systems

  Khalil Al-Rahman Youssefi, Marija Gojkovic, Walter Stefanutti, Mika Auer, Melanie Schranz · 1. Mai 2026

  This paper presents a configurable, grid-aware Agent-Based Model (ABM) for the systematic analysis of electric vehicle (EV) charging systems under configurable infrastructure and operational conditions. The model integrates heterogeneous EV behavior, charging column constraints, and a shared Energy …

• Learning to Route Electric Trucks Under Operational Uncertainty

  Stavros Orfanoudakis, Ziyan Li, Ruixiao Yang, Nikolay Aristov, Pedro P. Vergara, Chuchu Fan, Elenna Dugundji · 30. April 2026

  Electric truck operations require routing decisions that remain feasible under limited battery range, long charging times, travel and energy consumption, and competition for shared charging infrastructure. These features make electric truck routing a coupled logistics and energy problem, limiting th…

• Spatio-temporal modelling of electric vehicle charging demand

  Kaoutar Bouaachra, Yvenn Amara-Ouali, Yannig Goude, Rapha\"el Lachieze-Rey · 23. April 2026

  Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to rely on legacy benchmarks; such as the Palo Alto (2020) dataset; that fail to reflect the scale and behavioral diversity of modern charging networks. …

• A Digital Twin Framework for Decision-Support and Optimization of EV Charging Infrastructure in Localized Urban Systems

  Bui Khanh Linh Do, Thanh H. Nguyen, Nghi Huynh Quang, Doanh Nguyen-Ngoc, Laurent El Ghaoui · 20. April 2026

  As Electric Vehicle (EV) adoption accelerates in urban environments, optimizing charging infrastructure is vital for balancing user satisfaction, energy efficiency, and financial viability. This study advances beyond static models by proposing a digital twin framework that integrates agent-based dec…

• The causal relation between off-street parking and electric vehicle adoption in Scotland

  Bernardino D'Amico, Achille Fonzone, Emma Hart · 13. April 2026

  The transition to electric mobility hinges on maximising aggregate adoption while also facilitating equitable access. This study examines whether the 'charging divide' between households with and without off-street parking reflects a genuine infrastructure constraint or a by-product of socio-economi…

• EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading

  Md Mahfujur Rahmana, Alistair Barros, Raja Jurdak, Darshika Koggalahewa · 31. März 2026

  Peer-to-peer energy trading among electric vehicles (EVs) has been increasingly studied as a promising solution for improving supply-side resilience under growing charging demand and constrained charging infrastructure. While prior studies on EV-EV energy trading and related EV research have largely…

• Impacts of Electric Vehicle Charging Regimes and Infrastructure Deployments on System Performance: An Agent-Based Study

  Jiahua Hu, Hai L. Vu, Wynita Griggs, Hao Wang · 19. März 2026

  The rapid growth of electric vehicles (EVs) requires more effective charging infrastructure planning. Infrastructure layout not only determines deployment cost, but also reshapes charging behavior and influences overall system performance. In addition, destination charging and en-route charging repr…

• Autonomous Edge-Deployed AI Agents for Electric Vehicle Charging Infrastructure Management

  Mohammed Cherifi · 11. März 2026

  Public EV charging infrastructure suffers from significant failure rates -- with field studies reporting up to 27.5% of DC fast chargers non-functional -- and multi-day mean time to resolution, imposing billions in annual economic burden. Cloud-centric architectures cannot achieve the latency, relia…

• Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach

  Chuanlin Zhang, Junkang Feng, Chenggang Cui, Pengfeng Lin, Hui Chen, Yan Xu, A. M. Y. M. Ghias, Qianguang Ma, Pei Zhang · 3. März 2026

  With the growing adoption of electric vehicles (EVs), understanding user charging behavior has become critical for grid stability and transportation planning. This study investigates the behavioral heterogeneity of EV taxi drivers by analyzing the interaction between psychological traits and situati…

• On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning

  Andreas Tritsarolis, Gil Sampaio, Nikos Pelekis, Yannis Theodoridis · 25. Februar 2026

  The wide spread of new energy resources, smart devices, and demand side management strategies has motivated several analytics operations, from infrastructure load modeling to user behavior profiling. Energy Demand Forecasting (EDF) of Electric Vehicle Supply Equipments (EVSEs) is one of the most cri…

• Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling

  Mohammad Partohaghighi, Roummel Marcia, Bruce J. West, YangQuan Chen · 16. Februar 2026

  Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-client variation induced by diverse operating conditions. Conventional FedAvg and many advanced methods can suffer from exce…