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

Chen, Yuche; Zhang, Yunteng; Sun, Ruixiao

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

Other · Fri Jul 09 04:00:00 EDT 2021 · Transportation Research. Part D, Transport and Environment

OSTI ID:1824217

Chen, Yuche ^[1]; Zhang, Yunteng ^[2]; Sun, Ruixiao ^[2]

Univ. of South Carolina, Columbia, SC (United States); Vanderbilt University
Univ. of South Carolina, Columbia, SC (United States)

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, we 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. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. Here, 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. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

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Research Organization:: Vanderbilt Univ., Nashville, TN (United States); Univ. of Houston, TX (United States); Chattanooga Area Regional Transportation Authority, Chattanooga, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office

DOE Contract Number:: EE0008467

OSTI ID:: 1824217

Country of Publication:: United States

Language:: English

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