Aging characteristics-based health diagnosis and remaining useful life prognostics for lithium-ion batteries
Artikel i vetenskaplig tidskrift, 2019

This paper developed methods for improving the practicability of battery health diagnosis and remaining useful life prognostics. Battery state of health was estimated using a feature extraction-based method based on the charging voltage curve. Battery remaining useful life was predicted by identifying recognizable aging stages. Acceleration aging test data for 9 cells at constant current rates including 0.5C, 1C, 1.5C, and 2C, and dynamic current rates were used to validate the developed methods. The capacity estimates were accurate with estimation errors less than 1% at most cycles. The remaining useful life was predicted within 0.3 s at dynamic current rates, with the prediction errors at most cycles less than 10 after 300 cycles and the 95% confidence intervals covering about 20 cycles for each prediction.

Lithium-ion battery

Electric vehicles

Aging characteristics

State of health diagnosis

Remaining useful life prognostics

Författare

Yongzhi Zhang

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Beijing Institute of Technology

Rui Xiong

Beijing Institute of Technology

Hongwen He

Beijing Institute of Technology

Xiaobo Qu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Michael Pecht

A. James Clark School of Engineering

eTransportation

25901168 (eISSN)

Vol. 1 100004

Ämneskategorier

Biomedicinsk laboratorievetenskap/teknologi

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik

DOI

10.1016/j.etran.2019.100004

Mer information

Senast uppdaterat

2020-11-16