Battery health evaluation using a short random segment of constant current charging

Zhongwei Deng; Xiaosong Hu; Yi Xie; Le Xu; Penghua Li; Xianke Lin; Xiaolei Bian

doi:10.1016/j.isci.2022.104260

Battery health evaluation using a short random segment of constant current charging

iScience. 2022 Apr 12;25(5):104260. doi: 10.1016/j.isci.2022.104260. eCollection 2022 May 20.

Authors

Zhongwei Deng¹, Xiaosong Hu¹, Yi Xie¹, Le Xu¹, Penghua Li², Xianke Lin³, Xiaolei Bian⁴

Affiliations

¹ College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.
² College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
³ Department of Mechanical Engineering, Ontario Tech University, ON L1G 0C5, Canada.
⁴ Department of Chemical Engineering, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.

Abstract

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.

Keywords: Electrochemical energy storage; Electrochemistry; Engineering.