Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time

Precise charging time prediction can effectively mitigate the inconvenience to drivers induced by inevitable charging behavior throughout trips. Although the effectiveness of the machine learning (ML) algorithm in predicting future outcomes has been established in a variety of applications (transportation sector), the investigation into electric vehicle (EV) charging time prediction is almost new. This calls for the investigation of the ML algorithm to predict EV charging time. The study developed an EV charging time prediction model based on two years of charging event data collected from 500 EVs in Japan. To predict EV charging time, this paper employed three ML algorithms: extreme learning machine (ELM), feed-forward neural network (FFNN), and support vector regression (SVR). Furthermore, ML algorithms parameters are optimized by a metaheuristic techniques: the gray wolf optimizer (GWO), particle swarm optimizer (PSO), and genetic algorithm (GA) to achieve higher accuracy and robustness. The prediction results reveal that GWO-based ML models yielded better results compared to other models.

• Record URL:
  https://doi.org/10.1080/19427867.2022.2111902
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19427867
• Supplemental Notes:
  • © 2022 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Ullah, Irfan
  • Liu, Kai
  • Yamamoto, Toshiyuki
  • Shafiullah, Md
  • Jamal, Arshad
• Publication Date: 2023-9

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 889-906
• Serial:
  • Transportation Letters: The International Journal of Transportation Research
  • Volume: 15
  • Issue Number: 8
  • Publisher: Taylor & Francis
  • ISSN: 1942-7867
  • EISSN: 1942-7875
  • Serial URL: http://www.tandfonline.com/toc/ytrl20/current

Subject/Index Terms

• TRT Terms: Electric vehicle charging; Electric vehicles; Machine learning; Optimization; Time duration; Travel behavior
• Geographic Terms: Japan
• Subject Areas: Data and Information Technology; Energy; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01897170
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 23 2023 4:52PM