EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System

• URL: http://arxiv.org/abs/2304.04893v1
• Date: Mon, 10 Apr 2023 23:01:02 GMT
• Title: EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System
• Authors: Yanlin Qi, Gengchen Mai, Rui Zhu, and Michael Zhang
• Abstract summary: We present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, electric vehicle supply equipment, and electricity transmission network. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions.
• Score: 5.600639345303369
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Over the past decade, the electric vehicle industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, electric vehicle supply equipment, and electricity transmission network, to support decision-making related to EV technology development, infrastructure planning, and policy-making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV-relevant ontology modules from existing well-known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV-related knowledge into a single, easily accessible resource, the EVKG supports decision-makers in making informed choices about EV technology development, infrastructure planning, and policy-making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.

Related papers

• The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles [65.14115295214636]
  We specifically consider Internet of electric vehicles (IoEV) and we categorize GenAI for IoEV into four different layers. We introduce various GenAI techniques used in each layer of IoEV applications. Public datasets available for training the GenAI models are summarized.
  arXiv  Detail & Related papers  (2024-09-24T05:12:10Z)
• A Perspective on Foundation Models for the Electric Power Grid [53.02072064670517]
  Foundation models (FMs) currently dominate news headlines. We argue that an FM learning from diverse grid data and topologies could unlock transformative capabilities. We discuss a power grid FM concept, namely GridFM, based on graph neural networks and show how different downstream tasks benefit.
  arXiv  Detail & Related papers  (2024-07-12T17:09:47Z)
• Strategizing EV Charging and Renewable Integration in Texas [2.429429123532487]
  This study explores the convergence of electric vehicles (EVs), renewable energy, and smart grid technologies in the context of Texas. Research focuses on grid stability concerns, uncoordinated charging patterns, and the complicated relationship between EVs and renewable energy sources.
  arXiv  Detail & Related papers  (2023-10-25T23:34:25Z)
• Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms [49.37592437398933]
  "Smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation. We investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations. We propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging.
  arXiv  Detail & Related papers  (2023-10-18T18:38:59Z)
• Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control [42.17503767317918]
  This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments. A horizontal federated reinforcement learning (HFRL)-based method is proposed to fit various users' behaviors and dynamic environments. Simulation results illustrate that the proposed real-time EV charging/discharging control strategy can perform well among various factors.
  arXiv  Detail & Related papers  (2022-10-04T08:22:46Z)
• Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe [115.31507979199564]
  Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance. The core problems for BEV perception lie in (a) how to reconstruct the lost 3D information via view transformation from perspective view to BEV; (b) how to acquire ground truth annotations in BEV grid; and (d) how to adapt and generalize algorithms as sensor configurations vary across different scenarios.
  arXiv  Detail & Related papers  (2022-09-12T15:29:13Z)
• Driving and charging an EV in Australia: A real-world analysis [0.0]
  This study aims to collect data on real-world driving and charging patterns of 239 EVs across Australia. Data collection from current EV owners via an application programming interface platform began in November 2021 and is currently live.
  arXiv  Detail & Related papers  (2022-06-03T11:01:23Z)
• EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation [15.169440280225647]
  Electric vehicles (EVs) play an important role in reducing carbon emissions. As EV adoption accelerates, safety issues caused by EV batteries have become an important research topic. In order to benchmark and develop data-driven methods for this task, we introduce a large and comprehensive dataset of EV batteries. Our dataset is the first large-scale public dataset on real-world battery data, as existing data either include only several vehicles or is collected in the lab environment.
  arXiv  Detail & Related papers  (2022-01-28T10:06:39Z)
• An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning [50.85048976506701]
  This paper proposes a novel solution to range anxiety based on a federated-learning model. It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
  arXiv  Detail & Related papers  (2021-11-13T15:03:44Z)
• Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles [5.817576247456001]
  Battery electric vehicles (BEVs) exclusively use chemical energy stored in their battery packs for propulsion. We propose a novel framework for modelling energy consumption for BEVs (Fed-BEV) More specifically, a group of BEVs involved in the Fed-BEV framework can learn from each other to jointly enhance their energy consumption model.
  arXiv  Detail & Related papers  (2021-08-05T01:56:09Z)
• Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows [2.1399409016552347]
  We propose an end-to-end deep reinforcement learning framework to solve the EVRPTW. In particular, we develop an attention model incorporating the pointer network and a graph embedding technique to parameterize a policy for solving the EVRPTW. Our numerical studies show that the proposed model is able to efficiently solve EVRPTW instances of large sizes that are not solvable with any existing approaches.
  arXiv  Detail & Related papers  (2020-10-05T15:06:02Z)

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.