Hybrid electric buses fuel consumption prediction based on real-world driving data

Ruixiao Sun; Yuche Chen; Abhishek Dubey; Philip Pugliese

doi:10.1016/j.trd.2020.102637

Hybrid electric buses fuel consumption prediction based on real-world driving data

Ruixiao Sun
, Yuche Chen
, Abhishek Dubey
, Philip Pugliese

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

62 Scopus citations

Abstract

Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. In this study, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

Original language	English
Article number	102637
Journal	Transportation Research Part D: Transport and Environment
Volume	91
DOIs	https://doi.org/10.1016/j.trd.2020.102637
State	Published - Feb 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
Fuel consumption prediction
Hybrid diesel transit bus

Access to Document

10.1016/j.trd.2020.102637

Cite this

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title = "Hybrid electric buses fuel consumption prediction based on real-world driving data",

abstract = "Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. In this study, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5{\textendash}60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1{\textendash}2\% for microscopic models and 5{\textendash}8\% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.",

keywords = "Artificial neural network, Fuel consumption prediction, Hybrid diesel transit bus",

author = "Ruixiao Sun and Yuche Chen and Abhishek Dubey and Philip Pugliese",

note = "Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",

year = "2021",

month = feb,

doi = "10.1016/j.trd.2020.102637",

language = "English",

volume = "91",

journal = "Transportation Research Part D: Transport and Environment",

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TY - JOUR

T1 - Hybrid electric buses fuel consumption prediction based on real-world driving data

AU - Sun, Ruixiao

AU - Chen, Yuche

AU - Dubey, Abhishek

AU - Pugliese, Philip

PY - 2021/2

Y1 - 2021/2

N2 - Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. In this study, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

AB - Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. In this study, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

KW - Artificial neural network

KW - Fuel consumption prediction

KW - Hybrid diesel transit bus

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DO - 10.1016/j.trd.2020.102637

M3 - Article

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JO - Transportation Research Part D: Transport and Environment

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ER -

Hybrid electric buses fuel consumption prediction based on real-world driving data

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