Mao, LangFotouhi, AbbasShateri, NedaEwin, Nathan2021-07-212021-07-212021-07-20Mao L, Fotouhi A, Shateri N, Ewin N. (2022) A multi-mode electric vehicle range estimator based on driving pattern recognition. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Volume 236, Issue 6, March 2022, pp. 2677–26970954-4062https://doi.org/10.1177/09544062211032994https://dspace.lib.cranfield.ac.uk/handle/1826/16908Limited driving range and availability of charging infrastructures are still among the main barriers of adoption of electric vehicles (EVs) in the market. Combination of those limiting factors causes ‘range anxiety’ in EV users. While different EV battery technologies and charging infrastructures are under development, one short-term solution to reduce EV users’ range anxiety is to provide the EV user with an accurate range estimation. In this study, an EV range estimation technique is proposed that recognises the current driving pattern and then classifies it into one of the predefined clusters (driving modes). The future energy consumption per kilometre is then tuned according to the average energy consumption of each cluster. Having an updated energy consumption rate, the EV range is calculated based on the battery state-of-charge. Different features are considered for driving pattern clustering where ‘average speed’ and ‘average power’ were identified as the best choices for this application. The effectiveness of the proposed EV range estimator is validated using real driving data that gives an average error of 9% in EV energy consumption estimation aheadenAttribution 4.0 InternationalEnergy ConsumptionClusteringDriving Pattern RecognitionElectric Vehicle RangeArticle