Predicting Electric Vehicle Energy Consumption from Field Data Using Machine Learning
Artikel i vetenskaplig tidskrift, 2024

This study addresses the challenge of accurately forecasting the energy consumption of electric vehicles (EVs), which is crucial for reducing range anxiety and advancing strategies for charging and energy optimization. Despite the limitations of current forecasting methods, including empirical, physics-based, and data-driven models, this paper presents a novel machine learning-based prediction framework. It integrates physics-informed features and combines offline global models with vehicle-specific online adaptation to enhance prediction accuracy and assess uncertainties. Our framework is tested extensively on data from a real-world fleet of EVs. While the leading global model, quantile regression neural network (QRNN), demonstrates an average error of 6.30%, the online adaptation further achieves a notable reduction to 5.04%, with both surpassing the performance of existing models significantly. Moreover, for a 95% prediction interval, the online adapted QRNN improves coverage probability to 91.27% and reduces the average width of prediction intervals to 0.51. These results demonstrate the effectiveness and efficiency of utilizing physics-based features and vehicle-based online adaptation for predicting EV energy consumption.

electric vehicles

energy consumption

Machine learning


Qingbo Zhu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Yicun Huang

Chalmers, Elektroteknik, System- och reglerteknik

Chih Feng Lee

Polestar Performance AB

Peng Liu

Beijing Institute of Technology

Jin Zhang

Beijing Institute of Technology

Torsten Wik

Chalmers, Elektroteknik, System- och reglerteknik

IEEE Transactions on Transportation Electrification

2332-7782 (eISSN)

Vol. In Press






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