Title
A multi-step fast charging-based battery capacity estimation framework of real-world electric vehicles
Author
Zhang, D. (TU Delft DC systems, Energy conversion & Storage; Beijing Institute of Technology)
Wang, Zhenpo (Beijing Institute of Technology)
Liu, Peng (Beijing Institute of Technology)
She, Chengqi (Hunan University of Science and Technology)
Wang, Qiushi (Beijing Institute of Technology)
Zhou, Litao (Beijing Institute of Technology)
Qin, Z. (TU Delft DC systems, Energy conversion & Storage) 
Date
2024
Abstract
Accurately evaluating battery degradation is not only crucial for ensuring the safe and reliable operation of electric vehicles (EVs) but also fundamental for their intelligent management and maximum utilization. However, the non-linearity, non-measurability, and multi-stress coupled operating conditions have posed significant challenges for battery health prediction. This paper proposes a battery capacity estimation framework based on real-world operating data. Firstly, a comprehensive feature pool is constructed from the direct external features extracted during multi-step fast charging processes and the quantitative representation of operating conditions. Subsequently, a two-step feature engineering is introduced to select the most relevant features and eliminate the interference components. The battery capacity estimation framework is then implemented using machine learning methods. Validation results demonstrate that the proposed framework achieves superior estimation accuracy with lower computational expense compared to the modelling process without feature engineering. The MAPE and RMSE reach 1.18% and 1.98 Ah, respectively, representing reductions in errors of up to 8.53% and 11.21%. Collectively, the proposed framework paves the foundation for online health prognostics of batteries under practical operating conditions.
Subject
Lithium-ion battery
Capacity estimation
Multi-step fast charging
Machine learning
Real-world data
To reference this document use:
http://resolver.tudelft.nl/uuid:ddb484e7-190d-4c98-8ede-2ab732c7f299
DOI
https://doi.org/10.1016/j.energy.2024.130773
Embargo date
2024-08-22
ISSN
0360-5442
Source
Energy, 294
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2024 D. Zhang, Zhenpo Wang, Peng Liu, Chengqi She, Qiushi Wang, Litao Zhou, Z. Qin