TY - JOUR
T1 - Online energy consumption forecast for battery electric buses using a learning-free algebraic method
AU - Wang, Zejiang
AU - Xu, Guanhao
AU - Sun, Ruixiao
AU - Zhou, Anye
AU - Cook, Adian
AU - Chen, Yuche
N1 - Publisher Copyright:
© 2025. The Author(s).
PY - 2025/1/14
Y1 - 2025/1/14
N2 - 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.
AB - 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.
KW - Algebraic derivative estimation
KW - Battery electric bus
KW - Energy consumption forecasting
UR - http://www.scopus.com/inward/record.url?scp=85215758098&partnerID=8YFLogxK
U2 - 10.1038/s41598-024-82432-5
DO - 10.1038/s41598-024-82432-5
M3 - Article
C2 - 39809844
AN - SCOPUS:85215758098
SN - 2045-2322
VL - 15
SP - 1931
JO - Scientific Reports
JF - Scientific Reports
IS - 1
ER -