Online energy consumption forecast for battery electric buses using a learning-free algebraic method

Zejiang Wang, Guanhao Xu, Ruixiao Sun, Anye Zhou, Adian Cook, Yuche Chen

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1931
Number of pages1
JournalScientific Reports
Volume15
Issue number1
DOIs
StatePublished - Jan 14 2025

Keywords

  • Algebraic derivative estimation
  • Battery electric bus
  • Energy consumption forecasting

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