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

Yuche Chen; Yunteng Zhang; Ruixiao Sun

doi:10.1016/j.trd.2021.102969

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

Yuche Chen, Yunteng Zhang, Ruixiao Sun

Research output: Contribution to journal › Article › peer-review

85 Scopus citations

Abstract

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. 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.

Original language	English
Article number	102969
Journal	Transportation Research Part D: Transport and Environment
Volume	98
DOIs	https://doi.org/10.1016/j.trd.2021.102969
State	Published - Sep 2021
Externally published	Yes

Funding

This material is based upon work supported by the Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), under Award Number DE-EE0008467. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Specifically, neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof.

Keywords

Artificial neural network
Electric bus
Energy consumption prediction

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10.1016/j.trd.2021.102969

Cite this

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title = "Data-driven estimation of energy consumption for electric bus under real-world driving conditions",

abstract = "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. 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.",

keywords = "Artificial neural network, Electric bus, Energy consumption prediction",

author = "Yuche Chen and Yunteng Zhang and Ruixiao Sun",

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TY - JOUR

T1 - Data-driven estimation of energy consumption for electric bus under real-world driving conditions

AU - Chen, Yuche

AU - Zhang, Yunteng

AU - Sun, Ruixiao

PY - 2021/9

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N2 - 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. 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.

AB - 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. 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.

KW - Artificial neural network

KW - Electric bus

KW - Energy consumption prediction

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DO - 10.1016/j.trd.2021.102969

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

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