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

Xiaoyu Li, Tengyuan Wang, Jiaxu Li, Yong Tian and Jindong Tian
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Xiaoyu Li: College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Tengyuan Wang: College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Jiaxu Li: College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Yong Tian: College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Jindong Tian: College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China

Energies, 2022, vol. 15, issue 11, 1-17

Abstract: 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.

Keywords: electric bus; energy consumption; physical model; CatBoost; fusion model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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