Dynamic Pricing Based Electric Vehicle Charging Station Location Strategy Using Reinforcement Learning

Li, Yanbin; Wang, JiaNi; Wang, WeiYe; Liu, Chang; Li, Yun

doi:10.2139/ssrn.4376344

2023

DOI: 10.2139/ssrn.4376344

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Dynamic Pricing Based Electric Vehicle Charging Station Location Strategy Using Reinforcement Learning

Yanbin Li

JiaNi Wang

WeiYe Wang

et al.

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GRouteNet: A GNN-Based Model to Optimize Pathfinding and Smart Charging Management for Autonomous Guided Vehicles

Kazmi,

Akber

2024

Symmetry

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Autonomous Guided Vehicles (AGVs) play an important role in the automation of material handling and transportation tasks in modern industrial and logistics systems. However, suboptimal path planning and longer waiting times at charging stations significantly affect the operational efficiency of these vehicles. To address these challenges, we leverage the capabilities of Graph Neural Networks (GNN) to find the optimal paths for AGVs. In this paper, we propose GRouteNet, a GNN-based model that effectively finds the shortest path for AGVs. The proposed model utilizes the message-passing mechanism of GNN to determine the neighbor nodes and then aggregates this information to find the shortest path. We compare the results of GRouteNet with a couple of existing state-of-the-art pathfinding models and show that the path length computed by GRouteNet is up to 45% shorter compared to the existing models. Furthermore, we propose a Shortest Charging Time First (SCTF) scheduling algorithm to reduce the long waiting times in the queues at the charging stations. The proposed SCTF algorithm prioritizes the charging of AGVs based on their charging time and charges the AGVs with the shortest charging time first. We compare the results of SCTF with the first-come-first-serve scheduling algorithm and show that SCTF reduces the waiting times at the charging stations by up to 42%.

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GRouteNet: A GNN-Based Model to Optimize Pathfinding and Smart Charging Management for Autonomous Guided Vehicles

Kazmi,

Akber

2024

Symmetry

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Dynamic Pricing Based Electric Vehicle Charging Station Location Strategy Using Reinforcement Learning

Cited by 1 publication

References 0 publications

GRouteNet: A GNN-Based Model to Optimize Pathfinding and Smart Charging Management for Autonomous Guided Vehicles

GRouteNet: A GNN-Based Model to Optimize Pathfinding and Smart Charging Management for Autonomous Guided Vehicles

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