Battery health evaluation using a short random segment of constant current charging
Artikel i vetenskaplig tidskrift, 2022

Accurately evaluating the health status of lithium-ion batteries (LIBs) is significant to enhance the safety, efficiency, and economy of LIBs deployment. However, the complex degradation processes inside the battery make it a thorny challenge. Data-driven methods are widely used to resolve the problem without exploring the complex aging mechanisms; however, random and incomplete charging-discharging processes in actual applications make the existing methods fail to work. Here, we develop three data-driven methods to estimate battery state of health (SOH) using a short random charging segment (RCS). Four types of commercial LIBs (75 cells), cycled under different temperatures and discharging rates, are employed to validate the methods. Trained on a nominal cycling condition, our models can achieve high-precision SOH estimation under other different conditions. We prove that an RCS with a 10mV voltage window can obtain an average error of less than 5%, and the error plunges as the voltage window increases.

Författare

Zhongwei Deng

Chongqing University

Xiaosong Hu

Chongqing University

Yi Xie

Chongqing University

Le Xu

Chongqing University

Penghua Li

Chongqing University of Posts and Telecommunications

Xianke Lin

Ontario Tech

Xiaolei Bian

Kungliga Tekniska Högskolan (KTH)

iScience

25890042 (eISSN)

Vol. 25 5 104260

Ämneskategorier

Annan kemiteknik

Sannolikhetsteori och statistik

Datorseende och robotik (autonoma system)

DOI

10.1016/j.isci.2022.104260

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Senast uppdaterat

2024-01-03