Data-driven estimation of energy consumption for electric bus under real-world driving conditions

Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, the authors developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. The authors propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. A K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. The authors compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of their models.

• Record URL:
  https://doi.org/10.1016/j.trd.2021.102969
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1361920921002674
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Chen, Yuche
  • Zhang, Yunteng
  • Sun, Ruixiao
• Publication Date: 2021-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 102969
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 98
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Buses; Data analysis; Electric vehicles; Energy consumption; Estimating
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01781730
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 20 2021 2:52PM