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)

Funding

This work was supported by the National Natural Science Foundation of China (grants 52272393 and 52122216 ). We thank L. Xie, Y. Gao, X. Zhang, X. Guan, and D. Zhang for discussions and Z. Zhao, L. Meng, M. Lv, Y. Shao, and J. Zhou for help with the experimental testing and data analysis. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the government. This work was supported by the National Natural Science Foundation of China (grants 52272393 and 52122216). We thank L. Xie, Y. Gao, X. Zhang, X. Guan, and D. Zhang for discussions and Z. Zhao, L. Meng, M. Lv, Y. Shao, and J. Zhou for help with the experimental testing and data analysis. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the government. Conceptualization, X.Y. and L.L.; methodology, X.Y.; investigation, Y.M. B.B. and L.G.; writing – original draft, X.Y.; writing – review & editing, J.H. and Y. Liu; funding acquisition, X.Y.; resources, H.Z. Y.J. and L.S.; supervision, X.Y. and J.H. Jilin University is in the process of applying for a US patent, application no. 17/646803 (January 3, 2022), related to this work, and it lists X.Y. and B.B. as the inventors.

FundersFunder number
National Natural Science Foundation of P.R. China52122216, 52272393
National Natural Science Foundation of P.R. China
J.H. Jilin University17/646803, 2022

    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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