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

Authors

Ruixiao Sun
Yuche Chen
Abhishek Dubey
Philip Pugliese

Document Type

Journal Article

Publication Date

2021

Subject Area

mode - bus, infrastructure - vehicle, technology - alternative fuels, planning - methods

Keywords

Hybrid bus, fuel consumption

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.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part D Home Page:

http://www.sciencedirect.com/science/journal/13619209

Recommended Citation

Sun, R., Chen, Y., Dubey, A., & Pugliese, P. (2021). Hybrid electric buses fuel consumption prediction based on real-world driving data. Transportation Research Part D: Transport and Environment, Vol. 91, 102637.