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Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles

Author

Listed:
  • Yan Ding

    (State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Zhe Ji

    (State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
    University of Science and Technology of China, Hefei 230026, China
    Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Peng Liu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Zhiqiang Wu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Gang Li

    (State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China)

  • Dingsong Cui

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Yizhong Wu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Sha Xu

    (Beijing Bitnei Corp., Ltd., Beijing 100081, China)

Abstract

With the requirement of reduced carbon emissions and air pollution, it has become much more important to monitor the oil quality used in heavy-duty vehicles, which have more than 2/3 transportation emissions. Some gas stations may provide unqualified fuel, resulting in uncontrollable emissions, which is a big challenge for environmental protection. Based on this focus, a gas station recognition method is proposed in this paper. Combining the CART algorithm with the DBSCAN clustering algorithm, the locations of gas stations were detected and recognized. Then, the oil quality analysis of these gas stations could be effectively evaluated from oil stability and vehicle emissions. Massive real-world data operating in Tangshan, China, collected from the Heavy-duty Vehicle Remote Emission Service and Management Platform, were used to verify the accuracy and robustness of the proposed model. The results illustrated that the proposed model can not only accurately detect both the time and location of the refueling behavior but can also locate gas stations and evaluate the oil quality. It can effectively assist environmental protection departments to monitor and investigate abnormal gas stations based on oil quality analysis results. In addition, this method can be achieved with a relatively small calculation effort, which makes it implementable in many different application scenarios.

Suggested Citation

  • Yan Ding & Zhe Ji & Peng Liu & Zhiqiang Wu & Gang Li & Dingsong Cui & Yizhong Wu & Sha Xu, 2021. "Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles," Energies, MDPI, vol. 14(23), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8011-:d:692119
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    References listed on IDEAS

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    1. Xinyu Liang & Shaojun Zhang & Ye Wu & Jia Xing & Xiaoyi He & K. Max Zhang & Shuxiao Wang & Jiming Hao, 2019. "Air quality and health benefits from fleet electrification in China," Nature Sustainability, Nature, vol. 2(10), pages 962-971, October.
    2. Kaizhi Liang & Zhaosheng Zhang & Peng Liu & Zhenpo Wang & Shangfeng Jiang, 2019. "Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-17, December.
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