Fine-grained RNN with Transfer Learning for Energy Consumption Estimation on EVs

Yining Hua, Michele Sevegnani, Dewei Yi, Andrew Birnie, Steve Mcaslan

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)8182-8190
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Issue number11
Early online date14 Jan 2022
Publication statusPublished - Nov 2022

Bibliographical note

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.


  • electric vehicle
  • energy consumption estimation
  • trajectory segmentation
  • transfer learning
  • recurrent neural network


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