Short-term electric vehicle battery swapping demand prediction: Deep learning methods

Shengyou Wang, Anthony Chen, Pinxi Wang, Chengxiang Zhuge

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Battery swap stations have become an important alternative to general charging posts. Predicting battery swapping demand at the station level would be helpful for real-time operation of stations. This paper first provided insights into battery swapping demand patterns by analyzing a real-world dataset which contained 2,529 battery swapping events collected from 36 battery swap stations in Beijing from 31st July to 20th August 2019. Further, we developed a series of deep learning methods to predict the EV battery swapping demand, particularly considering temporal demand patterns obtained from the dataset. The deep learning models were Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Units, and Bidirectional Gated Recurrent Units. The results showed that the four deep learning models outperformed typical machine learning methods (e.g., support vector regression). An ablation study indicated that incorporating temporal battery swapping demand patterns into the deep learning methods could greatly improve model performance.

Original languageEnglish
Article number103746
JournalTransportation Research Part D: Transport and Environment
Publication statusPublished - Jun 2023


  • Battery swapping demand
  • Deep learning methods
  • Electric vehicle
  • Short-term prediction
  • Spatial big data analysis

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation
  • General Environmental Science


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