Modelling the energy consumption of electric vehicles under uncertain and small data conditions

Yang Liu, Qi Zhang, Cheng Lyu and Zhiyuan Liu

Transportation Research Part A: Policy and Practice, 2021, vol. 154, issue C, 313-328

Abstract: This study models the energy consumption of electric vehicles (EVs) under uncertain and small data conditions by combining the machine learning method and the idea of controlled experiments. We propose a Machine Learning-Control Variable model, termed the MLCV model, to estimate the trip energy consumption of EVs. Different data augmentation methods, ensemble methods, sampling factors are adopted as the parameters of the proposed method. Through parameter search, the accuracy of the base learner can be further improved. Our method utilizes real driving behaviours that are generated by real drivers and collected in a complex urban environment, making the approach generalizable. The experimental results demonstrate that the proposed MLCV model is superior to existing machine learning models in terms of estimation accuracy.

Keywords: Energy consumption; Data augmentation; Test time augmentation; Ensemble strategy (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.tra.2021.10.009

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