High-Resolution Data-Driven Energy Consumption Prediction for Battery-Electric Buses Using Boosting Algorithms.
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| Title: | High-Resolution Data-Driven Energy Consumption Prediction for Battery-Electric Buses Using Boosting Algorithms. |
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| Authors: | Wu, Yong1 (AUTHOR), Xin, Zhichao1,2 (AUTHOR), Li, Jiachang1 (AUTHOR), Ma, Zhenliang2 (AUTHOR), Xing, Jianping1 (AUTHOR) xingjp@sdu.edu.cn |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 9, p2058. 25p. |
| Subject Terms: | *Boosting algorithms, *Energy consumption forecasting, *Real-time computing, *Electric motor buses, *Metadata, *Machine learning, *Public transit |
| Geographic Terms: | Jinan (Shandong Sheng, China) |
| Abstract: | Accurate prediction of energy consumption is essential for the operation and charging management of battery-electric buses. Existing prediction studies are often constrained by incomplete or low-resolution input data, limiting their robustness under real-world operating conditions. This paper presents a high-resolution, sensor-rich energy consumption modeling framework using second-by-second operational data and tests on an electric bus fleet operating on Route 49 in Jinan, China. The dataset integrates synchronized measurements of vehicle kinematics, powertrain variables, and thermal conditions, providing a substantially more complete description of bus operation against previous studies. Boosting-based machine learning models are developed to predict the instantaneous power demand, and their performance is evaluated in comparison with a physics-based energy model and other variants of machine learning models. Results show that the data-driven boosting models demonstrate excellent explanatory power ( R 2 values of up to 0.99 (training) and 0.95 (test)) and remain reliable under nonlinear operating conditions. Feature and SHAP analyses identify physically consistent energy drivers, supporting the applicability of the approach to real-world public transport operations. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Accurate prediction of energy consumption is essential for the operation and charging management of battery-electric buses. Existing prediction studies are often constrained by incomplete or low-resolution input data, limiting their robustness under real-world operating conditions. This paper presents a high-resolution, sensor-rich energy consumption modeling framework using second-by-second operational data and tests on an electric bus fleet operating on Route 49 in Jinan, China. The dataset integrates synchronized measurements of vehicle kinematics, powertrain variables, and thermal conditions, providing a substantially more complete description of bus operation against previous studies. Boosting-based machine learning models are developed to predict the instantaneous power demand, and their performance is evaluated in comparison with a physics-based energy model and other variants of machine learning models. Results show that the data-driven boosting models demonstrate excellent explanatory power ( R 2 values of up to 0.99 (training) and 0.95 (test)) and remain reliable under nonlinear operating conditions. Feature and SHAP analyses identify physically consistent energy drivers, supporting the applicability of the approach to real-world public transport operations. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19092058 |
