Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction

Linas Petkevicius, Simonas Saltenis, Alminas Civilis, Kristian Torp

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

14 Citations (Scopus)
64 Downloads (Pure)


The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We envision a two-Tier architecture to produce such predictions. First, a routing and travel-Time-prediction subsystem generates a suggested route and predicts how the speed will vary along the route. Next, the expected energy use is predicted from the speed profile and other contextual characteristics, such as weather information and slope. To this end, the paper proposes deep-learning models that are built from EV tracking data. First, as the speed profile of a route is one of the main predictors for energy use, different simple ways to build speed profiles are explored. Next, eight different deep-learning models for energy-use prediction are proposed. Four of the models are probabilistic in that they predict not a single-point estimate but parameters of a probability distribution of energy use on the route. This is particularly relevant when predicting EV energy use, which is highly sensitive to many input characteristics and, thus, can hardly be predicted precisely. Extensive experiments with two real-world EV tracking datasets validate the proposed methods. The code for this research has been made available on GitHub.

Original languageEnglish
Title of host publicationProceedings of 17th International Symposium on Spatial and Temporal Databases, SSTD 2021
Number of pages11
PublisherAssociation for Computing Machinery
Publication date23 Aug 2021
ISBN (Electronic)9781450384254
Publication statusPublished - 23 Aug 2021
Event17th International Symposium on Spatial and Temporal Databases, SSTD 2021 - Virtual, Online, United States
Duration: 23 Aug 202125 Aug 2021


Conference17th International Symposium on Spatial and Temporal Databases, SSTD 2021
Country/TerritoryUnited States
CityVirtual, Online

Bibliographical note

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  • Deep neural network
  • E-Vehicle energy consumption
  • Probabilistic model
  • Sequential data
  • Spatio-Temporal data


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