TY  - JOUR
AU  - Wang, Qiushi
AU  - Wang, Zhenpo
AU  - Liu, Peng
AU  - Zhang, Lei
AU  - Sauer, Dirk Uwe
AU  - Li, Weihan
TI  - Large-scale field data-based battery aging prediction driven by statistical features and machine learning
JO  - Cell reports / Physical science
VL  - 4
IS  - 12
SN  - 2666-3864
CY  - [New York, NY]
PB  - Elsevier
M1  - FZJ-2024-02543
SP  - 101720 -
PY  - 2023
N1  - Unterstützt durch das Projekt ‘‘COBALT-P’’ (16BZF314C) des BMKW
AB  - Accurately predicting battery aging is critical for mitigating performance degradation during battery usage. While the automotive industry recognizes the importance of utilizing field data for battery performance evaluation and optimization, its practical implementation faces challenges in data collection and the lack of field data-based prognosis methods. To address this, we collect field data from 60 electric vehicles operated for over 4 years and develop a robust data-driven approach for lithium-ion battery aging prediction based on statistical features. The proposed pre-processing methods integrate data cleaning, transformation, and reconstruction. In addition, we introduce multi-level screening techniques to extract statistical features from historical usage behavior. Utilizing machine learning, we accurately predict aging trajectories and worst-lifetime batteries while quantifying prediction uncertainty. This research emphasizes a field data-based framework for battery health management, which not only provides a vital basis for onboard health monitoring and prognosis but also paves the way for battery second-life evaluation scenarios.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001144156000001
DO  - DOI:10.1016/j.xcrp.2023.101720
UR  - https://juser.fz-juelich.de/record/1024892
ER  -