储能电池外部短路的损伤与失效边界及其预测

doi:10.3901/JME.2023.04.113

机械工程学报 ›› 2023, Vol. 59 ›› Issue (4): 113-124.doi: 10.3901/JME.2023.04.113

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储能电池外部短路的损伤与失效边界及其预测

熊瑞, 孙万洲, 杨瑞鑫, 孙逢春

北京理工大学机械与车辆学院北京 100081

收稿日期:2022-04-09 修回日期:2022-08-11 出版日期:2023-02-20 发布日期:2023-04-24
通讯作者: 熊瑞(通信作者),男,1985年出生,教授,博士研究生导师。主要研究方向为动力/储能电池管控基础理论和关键技术。E-mail:rxiong@bit.edu.cn
作者简介:孙万洲,男,1996年出生,硕士研究生。主要研究方向为电池安全管理。E-mail:sunwzh_bit@163.com;杨瑞鑫,男,1988年出生,博士研究生。主要研究方向为新能源汽车电池管理。E-mail:yangruixin@bit.edu.cn;孙逢春,男,1958年出生,教授,博士研究生导师,中国工程院院士。主要研究方向为车辆电传动,车辆动力学。E-mail:sunfch@bit.edu.cn
基金资助:
国家重点研发计划(2021YFB2402002)和国家自然科学基金(51922006)资助项目。

Damage and Failure Boundaries and Prediction of External Short Circuit in Lithium-ion Battery

XIONG Rui, SUN Wanzhou, YANG Ruixin, SUN Fengchun

School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081

Received:2022-04-09 Revised:2022-08-11 Online:2023-02-20 Published:2023-04-24

摘要/Abstract

摘要： 储能电池外部短路故障发生伴随着电池大倍率放电、内部热量快速聚集、温度极速升高，是一种典型的电-热耦合滥用工况。针对电池外部短路：试验研究了不同初始条件下的外部短路特性，明确了短路开始至电池电压和电流突降为零的时间为外部短路失效边界；进一步分析了失效前短路时间与老化的耦合特性，应用电化学阻抗谱揭示了固体电解质膜增长为外部短路电池容量衰退的主因，且主要受电池温度影响。明确了短路开始至电池内部温度达到80 ℃的时间为外部短路损伤边界，为获取电池内部温度，构建了圆柱形电池集总参数热模型，建立了电池内部温度估计算法，误差<±2 ℃。应用长短时记忆神经网络算法构建了电池外部短路损伤边界和失效边界的预测模型。以外部短路前1 s和短路后2 s的电流、电压及环境温度作为输入，结果表明损伤边界预测误差<3.5%，失效边界预测误差<2%。该模型的应用能够加深外部短路对电池损伤的认识，为电池的安全监测提供有力保障。

关键词: 储能电池, 外部短路, 损伤边界, 失效边界, 长短时记忆神经网络

Abstract: The occurrence of external short circuit(ESC) fault of energy storage battery is accompanied by high-rate discharge current, internal heat accumulates rapidly, and battery temperature rises rapidly, which is a typical electro-thermal coupling abuse condition. For ESC of batteries: The characteristics of ESC under different initial conditions were experimentally studied, the time from the start of ESC to voltage and current of battery drop to 0 was defined as ESC failure boundary, and the coupling characteristics of short circuit time and aging are further analyzed. By using electrochemical impedance spectroscopy(EIS), it is determined that the growth of solid electrolyte interphase(SEI) is the main reason for the capacity decline of ESC batteries, which is mainly affected by temperature. Further, taking the time from the start of ESC to the internal temperature of battery reaching the 80 ℃ as damage boundary. To obtain the internal temperature of battery, a lumped parameter thermal model of was constructed, and the internal temperature of battery was estimated based on a PID observer, the estimation error is within 2 ℃. A long-short term memory(LSTM) neural network is constructed to predict the damage and failure boundary of battery. Taking the current, voltage and ambient temperature 1 s before and 2 s after the ESC as input, the results show that the prediction error of damage boundary is within 3.5%, and the prediction error of failure boundary is within 2%. The application of this model can deepen the understanding of battery damage caused by external short circuit, and provide a strong guarantee for battery safety monitoring.

Key words: energy storage battery, external short circuit, damage boundary, failure boundary, LSTM neural network

中图分类号:

TM912

熊瑞, 孙万洲, 杨瑞鑫, 孙逢春. 储能电池外部短路的损伤与失效边界及其预测[J]. 机械工程学报, 2023, 59(4): 113-124.

XIONG Rui, SUN Wanzhou, YANG Ruixin, SUN Fengchun. Damage and Failure Boundaries and Prediction of External Short Circuit in Lithium-ion Battery[J]. Journal of Mechanical Engineering, 2023, 59(4): 113-124.

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储能电池外部短路的损伤与失效边界及其预测

Damage and Failure Boundaries and Prediction of External Short Circuit in Lithium-ion Battery

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