Fine-grained RNN with transfer learning for energy consumption estimation on EVs

Hua, Y., Sevegnani, M. , Yi, D., Birnie, A. and McAslan, S. (2022) Fine-grained RNN with transfer learning for energy consumption estimation on EVs. IEEE Transactions on Industrial Informatics, 18(11), pp. 8182-8190. (doi: 10.1109/TII.2022.3143155)

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263049.pdf - Accepted Version



Electric vehicles (EVs) are increasingly becoming an environmentally-friendly option in current transportation systems thanks to reduced fossil fuel consumption and carbon emission. However, the more widespread adoption of EVs has been hampered by two factors: the lack of charging infrastructure and the limited cruising range. Energy consumption estimation is crucial to address these challenges as it provides the foundations to enhance charging-station deployment, improve eco-driving behaviour, and extend the EV cruising range. We propose an EV energy consumption estimation method capable of achieving accurate estimation despite insufficient EV data and ragged driving trajectories. It consists of three distinct features: knowledge transfer from Internal Combustion Engine/Hybrid Electric Vehicles (ICE/HEV) to EVs, segmentation-aided trajectory granularity, time-series estimation based on bidirectional recurrent neural network. Experimental evaluation shows our method outperforms other machine learning benchmark methods in estimating energy consumption on a real-world vehicle energy dataset.

Item Type:Articles
Additional Information:This work is supported by the Engineering and Physical Sciences Research Council, under PETRAS SRF grant MAGIC (EP/S035362/1) and the University of Glasgow Impact Acceleration Account.
Glasgow Author(s) Enlighten ID:Sevegnani, Dr Michele and HUA, Dr YINING
Authors: Hua, Y., Sevegnani, M., Yi, D., Birnie, A., and McAslan, S.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Industrial Informatics
ISSN (Online):1941-0050
Published Online:14 January 2022
Copyright Holders:Copyright © 2022 IEEE
First Published:First published i18(11): 8182-8190
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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