Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving

Xinmei Yuan, Jiangbiao He, Yutong Li, Yu Liu, Yifan Ma, Bo Bao, Leqi Gu, Lili Li, Hui Zhang, Yucheng Jin, Long Sun

Research output: Contribution to journalArticlepeer-review

Abstract

Standard energy-consumption testing, providing the only publicly available quantifiable measure of battery electric vehicle (BEV) energy consumption, is crucial for promoting transparency and accountability in the electrified automotive industry; however, significant discrepancies between standard testing and real-world driving have hindered energy and environmental assessments of BEVs and their broader adoption. In this study, we propose a data-driven evaluation method for standard testing to characterize BEV energy consumption. By decoupling the impact of the driving profile, our evaluation approach is generalizable to various driving conditions. In experiments with our approach for estimating energy consumption, we achieve a 3.84% estimation error for 13 different multiregional standardized test cycles and a 7.12% estimation error for 106 diverse real-world trips. Our results highlight the great potential of the proposed approach for promoting public awareness of BEV energy consumption through standard testing while also providing a reliable fundamental model of BEVs.

Original languageEnglish
Article number100950
JournalPatterns
Volume5
Issue number4
DOIs
StatePublished - Apr 12 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s)

Keywords

  • data-driven
  • driving behavior
  • driving cycle
  • eco-driving
  • electric vehicle
  • energy consumption
  • estimation
  • evaluation
  • modeling
  • standard test

ASJC Scopus subject areas

  • General Decision Sciences

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