Associate professor
Supervisor of Master's Candidates
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Impact Factor:7.847
DOI number:10.1109/JAS.2022.106010
Journal:IEEE/CAA Journal of Automatica Sinica
Abstract:This letter presents a multi-feature fusion-based method for estimating the instantaneous energy consumption of electric buses. More specifically, to improve the accuracy of instantaneous energy consumption estimation of electric buses, we propose a new energy consumption estimation method based on random forest regression (RFR) with multi-feature fusion. The multi-feature includes driving behavior, vehicle status, and external environment. The experimental results show that the absolute mean average percentage error (MAPE) value of the method proposed in this paper is 7.10%, which has a higher estimation accuracy than several state-of-the-art methods.
Indexed by:Journal paper
Discipline:Engineering
Document Type:J
Volume:10
Issue:10
Page Number:2035–2037
Translation or Not:no
Date of Publication:2023-10-01
Included Journals:SCI
Links to published journals:https://www.ieee-jas.net/en/article/doi/10.1109/JAS.2022.106010
