Data-driven remaining driving range estimation and analysis framework for electric vehicles under real-world conditions11Zhiyu Mao (corresponding author) e-mail: zhymao@dicp.ac.cnZhaosheng Zhang (corresponding author) e-mail: zhangzhaosheng@bit.edu.cnZhongwei Chen (corresponding author) e-mail: zwchen@dicp.ac.cn☆

Litao Zhou, Zhenpo Wang, Zhiyu Mao, Qiushi Wang, Dayu Zhang, Zhaosheng Zhang and Zhongwei Chen

Applied Energy, 2026, vol. 402, issue PB, No S0306261925016113

Abstract: Range anxiety is one of the major issues impeding the widespread adoption of electric vehicles. Accurate estimation of the remaining driving range (RDR) can effectively address this problem. However, in real-world operations, the coupling effects of various factors such as driving behavior, ambient temperature, and battery aging present significant challenges to accurate RDR estimation. In this study, a novel data-driven framework is proposed to achieve real-time online estimation and analysis of the remaining driving range for real-world vehicles. Firstly, methods for calculating energy consumption and state of health (SOH) based on real vehicle operation data are proposed. Then, stepwise estimation of the energy consumption rate and RDR is conducted using random forest regression methods. Finally, a multidimensional analysis of driving range capability is achieved through the estimation model. The proposed framework was validated on passenger vehicle and bus fleets in different cities over a three-year period. The results show that the prediction accuracy for the driving range reached a mean relative error of 5.39 % and 5.45 %, respectively. By adjusting driving behavior, the driving range can be improved by over 30 % for passenger vehicles and over 10 % for buses.

Keywords: Lithium-ion batteries; electric vehicles; machine learning; remaining driving range estimation (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925016113
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:402:y:2026:i:pb:s0306261925016113

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.126881

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().