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处于高度劣化状态且健康状态范围广泛的锂离子电池的老化特性数据。

Ageing characterization data of lithium-ion battery with highly deteriorated state and wide range of state-of-health.

作者信息

Xia Zhiyong, Abu Qahouq Jaber A

机构信息

Department of Electrical and Computer Engineering, College of Engineering, University of Alabama, Tuscaloosa, AL 35487 United States.

出版信息

Data Brief. 2021 Dec 16;40:107727. doi: 10.1016/j.dib.2021.107727. eCollection 2022 Feb.

Abstract

This paper presents ageing characterization data of two lithium-ion battery cells that have been put through a deep ageing process. At the end of ageing process, the values of state-of-health (SOH) of the battery cells drop down to around 15%. The battery cells are aged using a developed autonomous ageing platform which performs functions such as constant current (CC) discharging, CC charging, and constant voltage (CV) charging. Each time the battery cell completes 30 ageing cycles, battery performance tests including dc impedance measurement, minimum impedance measurement, and capacity calibration are conducted to characterize the ageing or health status of the battery cell. The collected battery dc impedance data, minimum impedance data, capacity data, and CC-CV charging time for the deeply aged battery cells are presented in this paper. The presented data has the potential to help in identifying battery ageing behavior patterns. It can also be utilized to investigate the correlation or relationship between different battery ageing characterization data and to develop SOH estimation methods for lithium-ion batteries with high degradation conditions, for examples, for second-use battery and when battery health exhibits unexpected faster deterioration.

摘要

本文展示了两个经历深度老化过程的锂离子电池的老化特性数据。在老化过程结束时,电池的健康状态(SOH)值降至约15%。使用一个开发的自主老化平台对电池进行老化,该平台执行诸如恒流(CC)放电、CC充电和恒压(CV)充电等功能。每次电池完成30个老化循环后,进行包括直流阻抗测量、最小阻抗测量和容量校准在内的电池性能测试,以表征电池的老化或健康状态。本文给出了深度老化电池的收集到的电池直流阻抗数据、最小阻抗数据、容量数据以及CC-CV充电时间。所呈现的数据有助于识别电池老化行为模式。它还可用于研究不同电池老化特性数据之间的相关性或关系,并为高退化条件下的锂离子电池开发SOH估计方法,例如用于二次使用电池以及电池健康状况出现意外更快恶化的情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6299/8715160/6b0b5a68c6ee/gr1.jpg

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