Internal short circuit evaluation and corresponding failure mode analysis for lithium-ion batteries

Internal short circuit(ISC)is the major failure problem for the safe application of lithium-ion batteries,especially for the batteries with high energy density.However,how to quantify the hazard aroused by the ISC,and what kinds of ISC will lead to thermal runaway are still unclear.This paper investigates the thermal-electrical coupled behaviors of ISC,using batteries with Li(Ni_(1/3)CO_(1/3)Mn_(1/3))O_(2) cathode and composite separator.The electrochemical impedance spectroscopy of customized battery that has no LiPF6 salt is utilized to standardize the resistance of ISC.Furthermore,this paper compares the thermal-electrical coupled behaviors of the above four types of ISC at different states-of-charge.There is an area expansion phenomenon for the aluminum-anode type of ISC.The expansion effect of the failure area directly links to the melting and collapse of separator,and plays an important role in further evolution of thermal runaway.This work provides guidance to the development of the ISC models,detection algorithms,and correlated countermeasures.

Chair's Introduction to 2009 IEEE Circuits and Systems International Conference on Testing and Diagnosis

Based on the recommendation of ICTD＇09 TPC members, this Special Issue of the Journal of Electronic Science ＆ Technology of China （JESTC） contained 22 high quality papers selected from the Proceedings of 2009 IEEE Circuits and Systems International Conference on Testing and Diagnosis （ICTD ＇09） which is fully sponsored by the IEEE Circuits and Systems Society （CASS）, and is technically co-sponsored by the University of Electronic Science and Technology of China （UESTC）, the Chinese Institute of Electronics （CIE）, the China Instrument ＆ Control Society （CIS）, and organized by UESTC.

Data-driven short circuit resistance estimation in battery safety issues

Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.

Research on Eliminate Micro-Shorted MH/Ni Batteries

There are two methods for selecting micro-shorted MH/Ni batteries out from all formationed.cells.One is to judge by the decrease of open circuit voltage which takes longer standing time to eliminate efficiently and does not work very well when the coverage of open circuit voltage is big.Another is to judge by discharge capacity of charged cells, this can not only meet the requirement of MH/Ni battery stored in charged state, but also has the advantages of being easier to read out, good accuracy and taking shorting standing time etc, is a proper way to be used on the production line.

基于阻抗在线测量的锂离子电池过放电诱发内短路识别研究

锂离子电池的过放电行为可诱发内短路进而可能导致热失控。由于单体电池一致性差异和过放电导致的内短路初期电、热特征不明显,使得电压和温度等常规物理参数难以可靠实现故障预警,而阻抗可以反映电池内部信息,对故障状态具有较好的指示能力。该文通过分析锂离子电池过放电诱发内短路引起阻抗变化的机理,基于设计的阻抗在线测量装置,确定用于监测电池内短路故障的特征阻抗频率为70 Hz,获取过放电诱发内短路过程中特征频率下动态阻抗及阻抗变化率,提出一种基于动态阻抗特征的内短路在线识别方法并验证了方法的可靠性。实验结果表明,锂离子电池放电过程中动态阻抗半正弦变化特征可提前约144 s实现过放电预警,动态阻抗针状变化特征可提前约152 s实现内短路故障预警,动态阻抗明显回升特征可作为内短路发生的标志。此外,阻抗变化率特征有助于实现锂离子电池故障识别及预警,该方法在锂离子电池故障在线快速诊断中具有重要的应用潜力。

基于累积和事件段识别与改进谱聚类的锂离子电池储能系统内短路故障检测方法

锂离子电池系统的内短路故障可能导致严重安全事故,其检测受到在线检测实时性以及故障特征获得性制约,是当下锂离子电池储能系统安全运行亟待解决的问题。该文提出一种基于累积和(cumulative sum,CUSUM)事件段检测与改进谱聚类的锂离子电池储能系统内短路故障检测方法。首先,考虑内短路故障时的电压/温度变化特性,基于累积和事件突变点识别方法,识别疑似内短路故障事件段。其次,构建三维故障特征,刻画检测对象内短路故障特征属性。然后,构建基于Wasserstein测度的内短路故障特征距离矩阵,检测三维空间各点稀疏特性,客观划定故障聚类,实现内短路故障检测。搭建锂离子电池内短路实验平台、建立锂离子电池电–热耦合仿真模型,算例结果表明该文方法能够准确识别疑似内短路故障事件段,在不同串并联形式及故障类型下实现故障检测,证明了该文方法的正确性与可行性。

基于298/77 K循环处理回收PCB中非金属组分

为解决废印制电路板(PCB)中非金属组分回收难且二次污染率大的问题,引入298/77 K循环处理技术,首先分析温度改变对PCB内部结构所造成的影响及PCB内部力学性能的变化,测试静电分选和离心分选对PCB非金属组分回收率的实际影响,之后引入CaF_(2)作为精炼过程中的强氧化剂,测试PCB内部结构发生改变后的硅组分回收率,最后测定回收后硅元素的实际纯度。结果表明:PCB在经过298/77 K循环处理后,内部结构受温度循环影响发生明显变化;各项力学性能均实现明显下降;PCB非金属组分实际产出量和硅组分回收率,相比于传统处理方式实现了明显提升,且对原PCB中各项杂质有较好的去除效果,硅元素占比在97%以上。

基于主动方波激励检测锂离子电池早期内短路

锂离子电池内部短路(ISC)的早期检测对于避免潜在安全风险至关重要。现有的故障检测方法在检测早期短路时准确性较差,且需要大量的数据。以18650型锂离子电池为研究对象,通过在电池充电过程中施加微小的电流激励信号,借助极化压升与激励压升,发现锂离子电池在短路时压升异常规律。该方法在荷电状态(SOC)较低时效果更明显,对120Ω以内的短路检测有效,且适用范围可根据采样精度调配,可用于锂离子电池的常规短路检测。

针刺诱发电池热失控的研究进展综述

针刺测试是研究锂离子电池热失效机理和热失控特性的重要方法之一。本文汇总了目前广泛采用的针刺测试设置,并研究了不同变量对热失控演化的影响,包括针刺的深度、速度、位置以及针的材料和直径等,对针刺破坏下锂离子电池内短路及热失控行为进行机理性探索与分析。结果发现,针刺的深度与电池热失控行为的严重程度成正比,针刺的速度对电池热失控没有明显影响,针刺发生在活性材料较密集或电极附近时电池热失控更严重。针的材料和直径对电池热失控行为具有双重影响,需要结合实际情况对产热和散热情况分别进行分析。最后通过总结不同因素对电池热失控的影响,对减缓锂离子电池热失控以及更新其安全测试程序提出了新的见解,为后续的创新型研究工作提供思路。

基于电压曲线诊断锂离子电池的故障

锂离子电池在电动汽车中应用广泛,电池的不一致性故障与内短路故障是电动汽车的严重安全隐患。采用绝对中位差方法来放大电池组故障电池的电压特性;再利用非局部均值滤波算法进行滤波,得到平滑的故障电压特征;最后,通过动态时间归整(DTW)算法计算特征值曲线之间的相似度距离,实现故障电池自动定位。使用运行车辆电压数据进行验证,发现该诊断方法对两类故障车辆均具有良好的检测效果:一是故障电池DTW距离长期偏离其他电池的不一致性故障车辆;二是故障电池DTW距离短期偏离其他电池的内部短路故障车辆。与余弦相似度进行对比,验证所提方法的准确性更高。

变压器内部电弧故障快速检测方法研究与对比

变压器内部短路电弧故障是变压器最危险、最严重的故障,极有可能造成变压器油箱的形变和破裂,乃至变压器油的燃烧与爆炸,对人员生命安全、变电站财产安全、电网运行安全都造成了严重威胁。实现变压器内部短路电弧故障的快速检测有利于及时发出断路器分闸信号,从而缩短电弧燃弧时间,减小电弧能量,降低燃爆可能性。为寻找适用于电弧故障快速检测的信号及特征,首先搭建了基于电容电感串联谐振的油中工频大电流电弧实验平台,并实现了油中工频电弧的电压、电流、压力波、光、电磁脉冲、气泡形态等关键电弧特性的测量;发现压力波存在前期快速振荡-后期持续上升两阶段,光信号存在间隔约10 ms的脉冲,电弧电流过零点时必定会产生电磁脉冲,上述压力波、光和电磁脉冲3种信号及其特性有望应用于变压器内部短路电弧故障的快速检测;最后对比了这3种故障检测方法的优缺点,并指出这些方法在工程实际应用前急需进一步解决的问题。

基于孤立森林算法的锂离子电池微内短路故障诊断方法

电池系统的内短路故障是造成电池热失控和火灾事故的主要原因之一,因此有必要对电池内短路故障进行诊断对事故做出早期预警。孤立森林算法是一种无监督的异常检测算法,被广泛应用于异常数据识别领域。根据串联电池组中内短路电池的电压会与正常电池发生偏离的特点,本文提出了基于孤立森林算法的锂离子电池微内短路故障诊断方法。为了对方法进行验证,本文构建了串联电池组进行了不同短路电阻和充放电工况的短路实验,并在实际运行工况下对一个锂离子电池储能系统进行了电池短路实验,然后利用孤立森林算法对实验数据进行分析诊断。结果表明,对于循环充放电工况,孤立森林算法对短路电阻为1000Ω的短路故障诊断精准率超过了74%,召回率超过了76%,准确率超过了91%;在模拟电动汽车实际驾驶的动态工况中,算法对300Ω的短路故障诊断精准率和召回率超过了86%,准确率超过了95%;算法对电池储能系统在实际运行工况下25Ω的内短路故障检测召回率大于98%。实验结果表明,孤立森林算法可以在多种工况下对锂离子电池微内短路故障进行有效检测,被检测出的锂离子电池内短路电阻达到了千欧姆数量级。

基于电化学机理模型的锂离子电池早期内短路电热特征研究

锂离子电池的内短路故障是诱发其热失控的主要原因之一,早期内短路特征研究能够为电池管理系统的故障诊断和安全预警提供支撑,对提高电动汽车的安全性具有重要意义。构建了锂离子电池内短路电化学机理模型,实现了不同内短路阻值下的锂枝晶内短路故障模拟。结果表明,由锂枝晶导致的电池内短路产热98%以上来源于正负极产生的焦耳热,早期内短路过程中正负极集流体表面的温升小于1.5 K,不显著的外部热特征无法用于早期内短路故障诊断。与正常电池相比,内短路故障将使得电池充电速度变慢,放电速度变快,端电压异常下降,上述电特征可以为构建早期内短路故障诊断方法提供依据。

应用阻抗谱的锂电池模组早期内短路故障诊断

针对锂电池模组内部内短路故障单体诊断困难、检测效率低下的问题,提出应用电荷传递内阻和固体电解质界面膜内阻比值变化幅度的锂电池模组早期内短路故障诊断方法。所提方法基于电化学阻抗谱对内短路故障演化过程中锂电池内部电化学反应变化进行监测,利用弛豫时间分布方法针对阻抗谱特征进行量化分析。在阻抗谱由单体维度到模组维度正向推衍的基础上,筛选满荷电状态下电荷传递内阻和固体电解质界面膜内阻作为主要的评估指标,并根据阈值诊断模型搭建实验平台进行验证。实验结果表明:欧姆内阻和电荷传递内阻随着内短路故障程度的加深逐渐增大,固体电解质界面膜内阻整体呈现出逐渐减小的趋势;当模组电池数控制在8节以下时,通过阈值诊断模型对存在故障单体的模组识别准确率可以达到100%。通过对阻抗谱特征的精确辨识与分析,该方法能够有效实现锂电池模组早期内短路故障的准确诊断。

Internal short circuit detection method for battery pack based on circuit topology

Internal short circuit(ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ISCr detection method based on the symmetrical loop circuit topology(SLCT) is introduced. The SLCT ensures that every battery has the same priority in the circuit and every battery will contribute the same amount of short-circuit current to the ISCr once the ISCr happens. The ISCr battery could be identified by the combination of the ratio of the short-circuit currents and the sign of the short-circuit currents. The recursive least square method is adopted for the real-time application and the optimized ammeters allocation is derived from the mathematic deduction. The battery pack based on the individual DP(dual polarization) battery model is established to verify the ISCr detection method. The 1–1000 Ω s ISCr(the early stage ISCr) can be effectively detected within 1–125 s. The SLCT provides the possibility of new battery pack designs and new battery management methods. The proposed ISCr detection method shows excellent effectiveness and efficiency on the identification of the ISCr battery in the early stage.

基于弛豫电压的锂离子电池内短路分级检测方法

为了提高锂离子电池在运行过程中的内短路故障检测效率,提出基于弛豫电压的锂离子电池内短路分级及检测方法。首先根据内短路电池暂停放电后的弛豫电压曲线特征,基于支持向量机模型对电池内短路等级进行分类。然后根据分类结果,提出相应的电池内短路故障检测方法。对于中期内短路电池,立即令其退出电池系统;对于早期内短路电池,利用卡尔曼滤波(Kalman filtering,KF)算法实时计算电池荷电状态(state of charge,SOC)偏差;对于无短路电池,保持原检测措施。最后对所提分类及检测方法进行实验验证。实验结果表明该分类方法的正确率受弛豫电压序列的采样总时间长度和采样间隔时间影响,增加恒流恒压充电阶段获取的特征数据能进一步提高内短路分类结果的正确率,实时检测电池SOC偏差的方法能及时发现异常的早期内短路电池。

Thermal runaway evolution of a 280 Ah lithium-ion battery with LiFePO_(4) as the cathode for different heat transfer modes constructed by mechanical abuse

Lithium iron phosphate batteries have been increasingly utilized in recent years because their higher safety performance can improve the increasing trend of recurring thermal runaway accidents.However,the safety performance and mechanism of high-capacity lithium iron phosphate batteries under internal short-circuit challenges remain to be explored.This work analyzes the thermal runaway evolution of high-capacity LiFePO_(4) batteries under different internal heat transfer modes,which are controlled by different penetration modes.Two penetration cases involving complete penetration and incomplete penetration were detected during the test,and two modes were performed incorporating nails that either remained or were removed after penetration to comprehensively reveal the thermal runaway mechanism.A theoretical model of microcircuits and internal heat conduction is also established.The results indicated three thermal runaway evolution processes for high-capacity batteries,which corresponded to the experimental results of thermal equilibrium,single thermal runaway,and two thermal runaway events.The difference in heat distribution in the three phenomena is determined based on the microstructure and material structure near the pinhole.By controlling the heat dissipation conditions,the time interval between two thermal runaway events can be delayed from 558 to 1417 s,accompanied by a decrease in the concentration of in-situ gas production during the second thermal runaway event.

An Online Adaptive Internal Short Circuit Detection Method of Lithium-Ion Battery

Internal short circuit(ISC)is a critical cause for the dangerous thermal runaway of lithium-ion battery(LIB);thus,the accurate early-stage detection of the ISC failure is critical to improving the safety of electric vehicles.In this paper,a model-based and self-diagnostic method for online ISC detection of LIB is proposed using the measured load current and terminal voltage.An equivalent circuit model is built to describe the characteristics of ISC cell.A discrete-time regression model is formulated for the faulty cell model through the system transfer function,based on which the electrical model parameters are adapted online to keep the model accurate.Furthermore,an online ISC detection method is exploited by incorporating an extended Kalman filter-based state of charge estimator,an abnormal charge depletion-based ISC current estimator,and an ISC resistance estimator based on the recursive least squares method with variant forgetting factor.The proposed method shows a self-diagnostic merit relying on the single-cell measurements,which makes it free from the extra uncertainty caused by other cells in the system.Experimental results suggest that the online parameterized model can accurately predict the voltage dynamics of LIB.The proposed diagnostic method can accurately identify the ISC resistance online,thereby contributing to the early-stage detection of ISC fault in the LIB.

磷酸铁锂电池内短路检测方法研究综述

锂离子电池内短路是锂离子电池热失控事故中最常见的诱因之一,也是机械滥用、电滥用、热滥用等不当使用行为的共性环节,它是潜在的安全威胁,已成为制约储能系统安全运行的重要隐患。本研究首先从电池内部结构缺陷、机械滥用、电滥用和热滥用四种情形出发,详细分析储能电池内短路的形成机理。随后,综述当前国内外在储能电池内短路检测领域的研究进展,并对常见的检测方法进行归纳总结,这些方法主要包括模型驱动法、数据驱动法、多方法融合内短路检测法以及新技术内短路检测法,同时,对这些方法的适用场景、优势与局限性进行了对比。最后,总结分析目前锂电池内短路检测方法存在的不足,并对未来的内短路诊断方法的研究进行展望。

可变速抽蓄机组转子绕组新型主保护方案性能分析

丰宁可变速抽蓄机组的投运使得其转子绕组保护方案的配置备受关注。运用多回路分析法,对湘潭电机厂制造的12 kW交流励磁样机所有实际可能发生的转子绕组内部故障进行了仿真,在此基础上对基于定子分支谐波环流的转子绕组新型主保护方案的性能进行了分析,并进一步明确了新型转子绕组主保护方案不能动作故障类型及相应的改进措施,为后续国产化示范项目中可变速抽蓄机组的稳定运行奠定基础。