Online energy consumption forecast for battery electric buses using a learning-free algebraic method
Abstract Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-lear...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82432-5 |
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| author | Zejiang Wang Guanhao Xu Ruixiao Sun Anye Zhou Adian Cook Yuche Chen |
| author_facet | Zejiang Wang Guanhao Xu Ruixiao Sun Anye Zhou Adian Cook Yuche Chen |
| author_sort | Zejiang Wang |
| collection | DOAJ |
| description | Abstract Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. It eliminates the time-consuming and computationally expensive offline training. Consequently, its prediction performance is not constrained by the quantity and quality of the training data. Moreover, the method can swiftly adapt to new situations not included in the previous driving cycles, which makes it especially suitable for emerging transport modes, e.g., on-demand transit services. In addition, its online execution only involves algebraic calculations, yielding superior calculation efficiency. Using real-world data, we comprehensively compare the performance of the proposed learning-free algebraic method with multiple representative machine-learning-based methods. Finally, the advantages and limitations of the proposed method are discussed in detail. |
| format | Article |
| id | doaj-art-c8d3defa868d4bc3b3581a4acaaeda54 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c8d3defa868d4bc3b3581a4acaaeda542025-01-19T12:20:45ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-82432-5Online energy consumption forecast for battery electric buses using a learning-free algebraic methodZejiang Wang0Guanhao Xu1Ruixiao Sun2Anye Zhou3Adian Cook4Yuche Chen5Department of Mechanical Engineering, The University of Texas at DallasBuildings and Transportation Science Division, Oak Ridge National LaboratoryBuildings and Transportation Science Division, Oak Ridge National LaboratoryBuildings and Transportation Science Division, Oak Ridge National LaboratoryBuildings and Transportation Science Division, Oak Ridge National LaboratoryDepartment of Civil and Environmental Engineering, University of South CarolinaAbstract Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. It eliminates the time-consuming and computationally expensive offline training. Consequently, its prediction performance is not constrained by the quantity and quality of the training data. Moreover, the method can swiftly adapt to new situations not included in the previous driving cycles, which makes it especially suitable for emerging transport modes, e.g., on-demand transit services. In addition, its online execution only involves algebraic calculations, yielding superior calculation efficiency. Using real-world data, we comprehensively compare the performance of the proposed learning-free algebraic method with multiple representative machine-learning-based methods. Finally, the advantages and limitations of the proposed method are discussed in detail.https://doi.org/10.1038/s41598-024-82432-5Algebraic derivative estimationBattery electric busEnergy consumption forecasting |
| spellingShingle | Zejiang Wang Guanhao Xu Ruixiao Sun Anye Zhou Adian Cook Yuche Chen Online energy consumption forecast for battery electric buses using a learning-free algebraic method Scientific Reports Algebraic derivative estimation Battery electric bus Energy consumption forecasting |
| title | Online energy consumption forecast for battery electric buses using a learning-free algebraic method |
| title_full | Online energy consumption forecast for battery electric buses using a learning-free algebraic method |
| title_fullStr | Online energy consumption forecast for battery electric buses using a learning-free algebraic method |
| title_full_unstemmed | Online energy consumption forecast for battery electric buses using a learning-free algebraic method |
| title_short | Online energy consumption forecast for battery electric buses using a learning-free algebraic method |
| title_sort | online energy consumption forecast for battery electric buses using a learning free algebraic method |
| topic | Algebraic derivative estimation Battery electric bus Energy consumption forecasting |
| url | https://doi.org/10.1038/s41598-024-82432-5 |
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