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Article

Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model

1
College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
2
National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
3
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Academic Editors: Alon Kuperman, Alessandro Lampasi and Michele Pastorelli
Energies 2022, 15(11), 4160; https://doi.org/10.3390/en15114160
Received: 18 March 2022 / Revised: 23 May 2022 / Accepted: 2 June 2022 / Published: 6 June 2022
(This article belongs to the Topic Energy Storage and Conversion Systems)
The energy consumption of electric vehicles is closely related to the problems of charging station planning and vehicle route optimization. However, due to various factors, such as vehicle performance, driving habits and environmental conditions, it is difficult to estimate vehicle energy consumption accurately. In this work, a physical and data-driven fusion model was designed for electric bus energy consumption estimation. The basic energy consumption of the electric bus was modeled by a simplified physical model. The effects of rolling drag, brake consumption and air-conditioning consumption are considered in the model. Taking into account the fluctuation in energy consumption caused by multiple factors, a CatBoost decision tree model was constructed. Finally, a fusion model was built. Based on the analysis of electric bus data on the big data platform, the performance of the energy consumption model was verified. The results show that the model has high accuracy with an average relative error of 6.1%. The fusion model provides a powerful tool for the optimization of the energy consumption of electric buses, vehicle scheduling and the rational layout of charging facilities. View Full-Text
Keywords: electric bus; energy consumption; physical model; CatBoost; fusion model electric bus; energy consumption; physical model; CatBoost; fusion model
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MDPI and ACS Style

Li, X.; Wang, T.; Li, J.; Tian, Y.; Tian, J. Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model. Energies 2022, 15, 4160. https://doi.org/10.3390/en15114160

AMA Style

Li X, Wang T, Li J, Tian Y, Tian J. Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model. Energies. 2022; 15(11):4160. https://doi.org/10.3390/en15114160

Chicago/Turabian Style

Li, Xiaoyu, Tengyuan Wang, Jiaxu Li, Yong Tian, and Jindong Tian. 2022. "Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model" Energies 15, no. 11: 4160. https://doi.org/10.3390/en15114160

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