Electric vehicles have been rapidly increasing in usage, but stations to charge them have not always kept up with demand, so efficient routing of vehicles to stations is critical to operating at maximum efficiency. Deciding which stations to recommend drivers to is a complex problem with a multitude of possible recommendations, volatile usage patterns and temporally extended consequences of recommendations. Reinforcement learning offers a powerful paradigm for solving sequential decision-making problems, but traditional methods may struggle with sample efficiency due to the high number of possible actions. By developing a model that allows complex representations of actions, we improve outcomes for users of our system by over 30% when compared to existing baselines in a simulation. If implemented widely, these better recommendations can globally save over 4 million person-hours of waiting and driving each year.
Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning
School of Engineering, University of Guelph（圭尔夫大学工程学院）；EthicalAI（伦理AI）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Context-Aware Agentic Power Resources Optimisation in EV using Smart2ChargeApp
Faculty of Computing, Engineering and the Built Environment（计算、工程与建成环境学院）；Birmingham City University（伯明翰城市大学）；Department of Information Systems, College of Computing and Informatics（信息系统系，计算与信息学院）；The University of Sharjah（沙迦大学）
EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading
School of Information Systems, Queensland University of Technology（昆士兰科技大学信息系统学院）；School of Computer Science, Queensland University of Technology（昆士兰科技大学计算机科学学院）
Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能工程学院）；Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能工程学院广东省智能交通系统重点实验室）
