Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Modeling and Verification of Eco-Driving Evaluation

  • Lin Liu (School of Automation, Chongqing University of Posts and Telecommunications) ;
  • Nenglong Hu (School of Automation, Chongqing University of Posts and Telecommunications) ;
  • Zhihu Peng (School of Automation, Chongqing University of Posts and Telecommunications) ;
  • Shuxian Zhan (School of Automation, Chongqing University of Posts and Telecommunications) ;
  • Jingting Gao (School of Automation, Chongqing University of Posts and Telecommunications) ;
  • Hong Wang (School of Automation, Chongqing University of Posts and Telecommunications)
  • Received : 2022.01.21
  • Accepted : 2022.03.29
  • Published : 2024.06.30
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Abstract

Traditional ecological driving (Eco-Driving) evaluations often rely on mathematical models that predominantly offer subjective insights, which limits their application in real-world scenarios. This study develops a robust, data-driven Eco-Driving evaluation model by integrating dynamic and distributed multi-source data, including vehicle performance, road conditions, and the driving environment. The model employs a combination weighting method alongside K-means clustering to facilitate a nuanced comparative analysis of Eco-Driving behaviors across vehicles with identical energy consumption profiles. Extensive data validation confirms that the proposed model is capable of assessing Eco-Driving practices across diverse vehicles, roads, and environmental conditions, thereby ensuring more objective, comprehensive, and equitable results.

Keywords

Acknowledgement

This research was supported by the 2018 "College Student Scientific Research Training Program" at Chongqing University of Posts and Telecommunications (Grant No. A2018-97).

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