技术领域Technical field
本发明涉及电池管理技术领域，特别是涉及电池组故障预警方法、系统、设备及存储介质。The present invention relates to the technical field of battery management, and in particular to battery pack failure early warning methods, systems, equipment and storage media.
背景技术Background technique
本部分的陈述仅仅是提到了与本发明相关的背景技术，并不必然构成现有技术。The statements in this section merely mention the background technology related to the present invention and do not necessarily constitute prior art.
锂离子电池安全问题是制约锂离子电池进一步推广应用的主要制约因素。由于电池本身制造问题以及之后使用条件的影响，电池系统中可能出现各式各样的故障，这些故障严重影响锂离子电池的使用寿命和使用安全。Lithium-ion battery safety issues are the main constraints restricting the further promotion and application of lithium-ion batteries. Due to manufacturing problems of the battery itself and subsequent use conditions, various faults may occur in the battery system, which seriously affect the service life and safety of lithium-ion batteries.
发明人发现，现有的电池故障诊断无法准确实时诊断出电池故障的类型。The inventor found that existing battery fault diagnosis cannot accurately diagnose the type of battery fault in real time.
发明内容Contents of the invention
为了解决现有技术的不足，本发明提供了电池组故障预警方法及系统；In order to solve the deficiencies of the existing technology, the present invention provides a battery pack failure early warning method and system;
一方面，提供了电池组故障预警方法；On the one hand, it provides a battery pack failure early warning method;
电池组故障预警方法，包括：Battery pack failure early warning methods include:
获取电池组的电压数据，所述电压数据，包括：单体电压和组合体电压，所述组合体，是指相邻两个单体电池以及两个单体电池中间所连接的电阻；Obtain voltage data of the battery pack. The voltage data includes: single cell voltage and assembly voltage. The assembly refers to two adjacent single cells and the resistance connected between the two single cells;
对所述电池组的电压数据，采用滑动窗口进行扩展，得到二维的数据分析矩阵；The voltage data of the battery pack is expanded using a sliding window to obtain a two-dimensional data analysis matrix;
对二维的数据分析矩阵进行标准化处理，得到标准化后的矩阵；进而获得标准化后的矩阵所对应的协方差矩阵；所述标准化后的矩阵所对应的协方差矩阵包括五个子矩阵；Standardize the two-dimensional data analysis matrix to obtain a standardized matrix; then obtain a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix includes five submatrices;
计算每个子矩阵的最大特征值的期望；根据每个子矩阵的最大特征值的期望，确定电压传感器故障的故障指示器期望值；根据故障指示器期望值，确定电压传感器是否发生故障。Calculate the expectation of the maximum eigenvalue of each sub-matrix; determine the expected value of the fault indicator for voltage sensor failure based on the expectation of the maximum eigenvalue of each sub-matrix; determine whether the voltage sensor fails based on the expected value of the fault indicator.
另一方面，提供了电池组故障预警系统；On the other hand, a battery pack failure early warning system is provided;
电池组故障预警系统，包括：Battery pack failure early warning system, including:
获取模块，其被配置为：获取电池组的电压数据，所述电压数据，包括：单体电压和组合体电压，所述组合体，是指相邻两个单体电池以及两个单体电池中间所连接的电阻；Acquisition module, which is configured to: acquire voltage data of the battery pack. The voltage data includes: single cell voltage and assembly voltage. The assembly refers to two adjacent single cells and two single cells. The resistor connected in the middle;
扩展模块，其被配置为：对所述电池组的电压数据，采用滑动窗口进行扩展，得到二维的数据分析矩阵；An expansion module configured to: expand the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix;
处理模块，其被配置为：对二维的数据分析矩阵进行标准化处理，得到标准化后的矩阵；进而获得标准化后的矩阵所对应的协方差矩阵；所述标准化后的矩阵所对应的协方差矩阵包括五个子矩阵；A processing module configured to: standardize the two-dimensional data analysis matrix to obtain a standardized matrix; and then obtain a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix Includes five submatrices;
确定模块，其被配置为：计算每个子矩阵的最大特征值的期望；根据每个子矩阵的最大特征值的期望，确定电压传感器故障的故障指示器期望值；根据故障指示器期望值，确定电压传感器是否发生故障。a determination module configured to: calculate the expectation of the maximum eigenvalue of each sub-matrix; determine the expected value of the fault indicator of the voltage sensor fault according to the expectation of the maximum eigenvalue of each sub-matrix; determine whether the voltage sensor is faulty according to the expected value of the fault indicator malfunction.
再一方面，还提供了一种电子设备，包括：In another aspect, an electronic device is also provided, including:
存储器，用于非暂时性存储计算机可读指令；以及Memory for non-transitory storage of computer-readable instructions; and
处理器，用于运行所述计算机可读指令，a processor for executing the computer readable instructions,
其中，所述计算机可读指令被所述处理器运行时，执行上述第一方面所述的方法。Wherein, when the computer readable instructions are executed by the processor, the method described in the first aspect is executed.
再一方面，还提供了一种存储介质，非暂时性存储计算机可读指令，其中，当非暂时性计算机可读指令由计算机执行时，执行第一方面所述方法的指令。In yet another aspect, a storage medium is also provided that non-transitoryly stores computer-readable instructions, wherein when the non-transitory computer-readable instructions are executed by a computer, the instructions of the method described in the first aspect are executed.
再一方面，还提供了一种计算机程序产品，包括计算机程序，所述计算机程序当在一个或多个处理器上运行的时候用于实现上述第一方面所述的方法。In another aspect, a computer program product is also provided, including a computer program, which is used to implement the method described in the first aspect when running on one or more processors.
上述技术方案具有如下优点或有益效果：The above technical solution has the following advantages or beneficial effects:
(1)本发明基于非冗余测量拓扑，使用N个电压传感器测量N串电池电压和N+1个等效连接电阻的电压，结构紧凑，检测效率高；(1) The present invention is based on a non-redundant measurement topology and uses N voltage sensors to measure the voltage of N strings of batteries and the voltage of N+1 equivalent connection resistors. It has a compact structure and high detection efficiency;
(2)本发明采用基于数据驱动的故障特征识别方法，由于固有的正交性，该方法对电池电压变化的不一致性敏感，而对电流突变引起的电压变化不敏感，及时性高，误报率低；(2) The present invention adopts a data-driven fault feature identification method. Due to the inherent orthogonality, this method is sensitive to the inconsistency of battery voltage changes, but is insensitive to voltage changes caused by sudden changes in current. It has high timeliness and is prone to false alarms. rate is low;
(3)本发明使用有限长度的时间窗口限制故障指示器的有效时间，故障发生后，故障指示器做出反应，并在固定时间后恢复，避免被故障阻塞，影响新故障的及时诊断，从而避免故障漏报。(3) This invention uses a time window of limited length to limit the effective time of the fault indicator. After a fault occurs, the fault indicator responds and recovers after a fixed time to avoid being blocked by the fault and affecting the timely diagnosis of new faults, thereby Avoid missed fault reports.
附图说明Description of the drawings
构成本发明的一部分的说明书附图用来提供对本发明的进一步理解，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。The description and drawings that constitute a part of the present invention are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.
图1为实施例一的方法流程图；Figure 1 is a method flow chart of Embodiment 1;
图2为实施例一的冗余交叉测量拓扑示意图；Figure 2 is a schematic diagram of the redundant cross measurement topology of Embodiment 1;
图3(a)为实施例一的短路故障诊断结果-故障评估量曲线；Figure 3(a) is the short-circuit fault diagnosis result-fault evaluation curve of Embodiment 1;
图3(b)为实施例一的短路故障诊断结果-特征向量各元素曲线；Figure 3(b) shows the short-circuit fault diagnosis result of Embodiment 1 - the curve of each element of the characteristic vector;
图4(a)为实施例一的电连接故障诊断结果-故障评估量曲线；Figure 4(a) is the electrical connection fault diagnosis result-fault evaluation quantity curve of Embodiment 1;
图4(b)为实施例一的电连接故障诊断结果-特征向量各元素曲线；Figure 4(b) is the electrical connection fault diagnosis result-curve of each element of the characteristic vector in Embodiment 1;
图5(a)为实施例一的电压传感器故障诊断结果-故障评估量曲线；Figure 5(a) is the voltage sensor fault diagnosis result-fault evaluation quantity curve of Embodiment 1;
图5(b)为实施例一的电压传感器故障诊断结果-特征向量各元素曲线。Figure 5(b) shows the fault diagnosis result of the voltage sensor in Embodiment 1 - the curve of each element of the characteristic vector.
具体实施方式Detailed ways
应该指出，以下详细说明都是示例性的，旨在对本发明提供进一步的说明。除非另有指明，本文使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的相同含义。It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
本发明属于电池管理技术领域，公开一种电池系统早期故障诊断方法，可以检测、区分并定位电池系统是否发生电池短路故障、电连接故障以及电压传感器故障。方法主要包含以下步骤：使用非冗余交叉测量拓扑实时监测电池单体电压和连接电阻的电压；设立时间窗口和“先入先出”数据缓存，将固定时间内的检测数据整理为Mk矩阵；根据统计方法对Mk进行特征匹配，以获取故障类型和故障位置。The invention belongs to the technical field of battery management and discloses an early fault diagnosis method for a battery system, which can detect, distinguish and locate whether a battery short-circuit fault, an electrical connection fault and a voltage sensor fault occur in the battery system. The method mainly includes the following steps: using a non-redundant cross measurement topology to monitor the battery cell voltage and the voltage of the connecting resistor in real time; establishing a time window and a "first in, first out" data cache, and organizing the detection data within a fixed period into an Mk matrix; Feature matching is performed on Mk according to statistical methods to obtain the fault type and fault location.
实施例一Embodiment 1
本实施例提供了电池组故障预警方法；This embodiment provides a battery pack failure early warning method;
如图1所示，电池组故障预警方法，包括：As shown in Figure 1, battery pack failure early warning methods include:
S101：获取电池组的电压数据，所述电压数据，包括：单体电压和组合体电压，所述组合体，是指相邻两个单体电池以及两个单体电池中间所连接的电阻；S101: Obtain voltage data of the battery pack. The voltage data includes: single cell voltage and assembly voltage. The assembly refers to two adjacent single cells and the resistance connected between the two single cells;
S102：对所述电池组的电压数据，采用滑动窗口进行扩展，得到二维的数据分析矩阵；S102: Expand the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix;
S103：对二维的数据分析矩阵进行标准化处理，得到标准化后的矩阵；进而获得标准化后的矩阵所对应的协方差矩阵；所述标准化后的矩阵所对应的协方差矩阵包括五个子矩阵；S103: Standardize the two-dimensional data analysis matrix to obtain a standardized matrix; then obtain a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix includes five submatrices;
S104：计算每个子矩阵的最大特征值的期望；根据每个子矩阵的最大特征值的期望，确定电压传感器故障的故障指示器期望值；根据故障指示器期望值，确定电压传感器是否发生故障。S104: Calculate the expectation of the maximum eigenvalue of each sub-matrix; determine the expected value of the fault indicator of the voltage sensor failure based on the expectation of the maximum eigenvalue of each sub-matrix; determine whether the voltage sensor fails based on the expected value of the fault indicator.
进一步地，所述方法还包括：Further, the method also includes:
S105：对二维的数据分析矩阵进行归一化处理，得到归一化矩阵；进而获得归一化矩阵所对应的协方差矩阵；所述归一化矩阵所对应的协方差矩阵包括三个新子矩阵；S105: Normalize the two-dimensional data analysis matrix to obtain a normalized matrix; then obtain a covariance matrix corresponding to the normalized matrix; the covariance matrix corresponding to the normalized matrix includes three new submatrix;
S106：计算每个新子矩阵的最大特征值的期望；根据每个新子矩阵的最大特征值的期望，确定早期电池短路故障阈值和早期电连接故障阈值；根据早期电池短路故障阈值，确定电池组是否发生电池短路故障；根据早期电连接故障阈值，确定电池组是否发生电连接故障。S106: Calculate the expectation of the maximum eigenvalue of each new sub-matrix; determine the early battery short-circuit fault threshold and early electrical connection failure threshold according to the expectation of the maximum eigenvalue of each new sub-matrix; determine the battery based on the early battery short-circuit fault threshold Whether a battery short-circuit failure occurs in the battery pack; determine whether an electrical connection failure occurs in the battery pack based on the early electrical connection failure threshold.
进一步地，所述方法还包括：Further, the method also includes:
S107：通过最大特征值所对应的特征向量元素的最小值，确定故障发生的位置。S107: Determine the location of the fault through the minimum value of the eigenvector element corresponding to the maximum eigenvalue.
进一步地，如图2所示，所述S101：获取电池组的电压数据，所述电压数据，包括：单体电压和组合体电压，所述组合体，是指相邻两个单体电池以及两个单体电池中间所连接的电阻，具体包括：Further, as shown in Figure 2, the S101: Obtain the voltage data of the battery pack. The voltage data includes: single cell voltage and assembly voltage. The assembly refers to two adjacent single cells and The resistance connected between the two single cells includes:
电池组，包括：依次连接的电池组负极、第1个连接点、电阻R0,1、第2个连接点、第1电池单体、第3个连接点、电阻R1,2、第4个连接点、第2电池单体、第5个连接点、电阻R2,3、第6个连接点、第3电池单体、第7个连接点、电阻R3,4、第8个连接点、第4电池单体……第2n-1个连接点、电阻Rn-1,n、第2n个连接点、第n电池单体、第2n+1个连接点、电阻Rn,b、第2n+2个连接点、电池组正极；n大于等于1；The battery pack includes: the negative electrode of the battery pack, the first connection point, the resistor R0,1 , the second connection point, the first battery cell, the third connection point, the resistor R1,2 , and the fourth connection point, which are connected in sequence. connection point, 2nd battery cell, 5th connection point, resistor R2,3 , 6th connection point, 3rd battery cell, 7th connection point, resistor R3,4 , 8th connection point, the 4th battery cell... the 2n-1th connection point, resistance Rn-1,n , the 2nth connection point, the nth battery cell, the 2n+1th connection point, resistance Rn,b, The 2n+2 connection point, the positive pole of the battery pack; n is greater than or equal to 1;
在第2个连接点和第5个连接点之间连接第一电压传感器，第一电压传感器采集的电压为在第4个连接点和第7个连接点之间连接第二电压传感器，第二电压传感器采集的电压为/>在第6个连接点和第9个连接点之间连接第三电压传感器，第三电压传感器采集的电压为/>以此类推；Connect the first voltage sensor between the 2nd connection point and the 5th connection point. The voltage collected by the first voltage sensor is Connect the second voltage sensor between the 4th connection point and the 7th connection point. The voltage collected by the second voltage sensor is/> Connect the third voltage sensor between the 6th connection point and the 9th connection point. The voltage collected by the third voltage sensor is/> And so on;
在第2n个连接点和第2n+3个连接点之间连接第n电压传感器，第n电压传感器采集的电压为The nth voltage sensor is connected between the 2nth connection point and the 2n+3 connection point. The voltage collected by the nth voltage sensor is
得到测量数据向量其中，n为串联的电池数量；Get the measurement data vector Among them, n is the number of batteries connected in series;
根据测量拓扑和基尔霍夫电压定律，可知测量数据向量的分解式为：According to the measurement topology and Kirchhoff's voltage law, it can be seen that the decomposition of the measurement data vector is:
其中，是电压传感器测量的电压值向量，/>是电池端电压向量，Rn,n+1为第n个单体电池与第n+1个单体电池之间的等效连接电阻，Rb,1是与电池组负极连接的电阻，Rn,b是与电池组正极连接的电阻；每个电压传感器用于测量2个相邻单体电池和它们之间的等效连接电阻的电压。in, is the voltage value vector measured by the voltage sensor,/> is the battery terminal voltage vector, Rn,n+1 is the equivalent connection resistance between the nth single cell and the n+1th single cell, Rb,1 is the resistance connected to the negative electrode of the battery pack, Rn,b is the resistance connected to the positive electrode of the battery pack; each voltage sensor is used to measure the voltage of two adjacent single cells and the equivalent connection resistance between them.
应理解地，由电流变化引起的电压检测量的变化与电流保持高度一致，而故障导致的电压检测量的变化与电流相关性较差，这一特性可用于检测和区分电池系统故障，每个单体电池包括一个电芯或多个并联电芯。It should be understood that changes in voltage detection caused by current changes are highly consistent with current, while changes in voltage detection caused by faults have poor correlation with current. This feature can be used to detect and distinguish battery system faults, each A single battery consists of one cell or multiple cells connected in parallel.
进一步地，所述S102：对所述电池组的电压数据，采用滑动窗口进行扩展，得到二维的数据分析矩阵，具体包括：Further, S102: Expand the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix, which specifically includes:
设定滑动窗口的尺寸和滑动步长w，将滑动窗口在测量数据向量Vs上滑动，每滑动一步就得到一个二维数据分析子矩阵，最后得到二维的数据分析矩阵Mk：Set the size of the sliding window and the sliding step w, slide the sliding window on the measurement data vector Vs , and get a two-dimensional data analysis sub-matrix with each sliding step, and finally get the two-dimensional data analysis matrix Mk :
Mk＝[Vs|k-w+1,Vs|k-w+2,...,Vs|k]T (2)Mk =[Vs|k -w+1 ,Vs|k -w+2 ,...,Vs|k ]T (2)
其中，in,
Vs|k表示在时间窗口内的第k个时刻下的es状态。Vs|k represents the stateof es at the kth moment in the time window.
将误差的影响抽象为向量e，将公式(1)简化为m＝Ax1+Bx2+e＝m*+e，其中m*表示向量m的无故障部分，e为测量误差，可认为是满足的白噪声，并且Abstract the impact of the error as a vector e, and simplify formula (1) to m=Ax1 +Bx2 +e=m* +e, where m* represents the fault-free part of the vector m, and e is the measurement error, which can be considered satisfy of white noise, and
进一步地，所述S103：对二维的数据分析矩阵进行标准化处理，得到标准化后的矩阵；进而获得标准化后的矩阵所对应的协方差矩阵；所述标准化后的矩阵所对应的协方差矩阵包括五个子矩阵，具体包括：Further, the S103: Standardize the two-dimensional data analysis matrix to obtain a standardized matrix; then obtain a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix includes Five sub-matrices, including:
将二维的数据分析矩阵Mk标准化，令表示无故障、无误差分量，其中Standardize the two-dimensional data analysis matrix Mk , let represents no fault and no error component, where
其中，是Yk的标准差对角阵，和/>分别是x1和x2的平均值向量。in, is the standard deviation diagonal matrix of Yk , and/> are the mean vectors of x1 and x2 respectively.
添加故障量，可以得到：Adding the fault amount, we get:
其中，代表无故障分量；fk代表时间窗口中的故障分量，它包含了故障持续时间的特性；ξ代表故障权重，只有故障通道非零，并且||ξ||2＝1；/>为误差分量，代表电压传感器的正常测量误差。in, represents the fault-free component; fk represents the fault component in the time window, which contains the characteristics of the fault duration; ξ represents the fault weight, only the fault channel is non-zero, and ||ξ||2 = 1; /> is the error component, representing the normal measurement error of the voltage sensor.
的协方差矩阵为/>为了分析方便，将其拆分为5份， The covariance matrix of is/> For the convenience of analysis, it is divided into 5 parts.
其中 in
应理解地，电压传感器故障模式匹配。当某个电压传感器发生故障时，可认为其检测值叠加了故障分量f，则有m＝m*+ξf。m为实际检测值，m*为无故障分量，ξf为故障分量，ξ为故障权重，只有故障通道非零，并且||ξ||2＝。It should be understood that voltage sensor failure modes match. When a voltage sensor fails, its detection value can be considered to be superimposed with the fault component f, then m=m* +ξf. m is the actual detection value, m* is the fault-free component, ξf is the fault component, ξ is the fault weight, only the fault channel is non-zero, and ||ξ||2 =.
应理解地，故障检测指标与协方差矩阵Ck的特征值分布直接相关。因此，对特征值的期望展开研究，以深入了解故障幅度和检测阈值之间的相关性。分别推导每个子矩阵{Ci}的下界。It should be understood that the fault detection index is directly related to the eigenvalue distribution of the covariance matrix Ck . Therefore, the expectation of eigenvalues is studied to gain insights into the correlation between fault magnitude and detection threshold. Derive the lower bound for each sub-matrix {Ci } separately.
进一步地，所述S104：计算每个子矩阵的最大特征值的期望，具体包括：Further, the S104: Calculate the expectation of the maximum eigenvalue of each sub-matrix, specifically including:
(1)C1,k(1)C1,k
根据矩阵乘法交换律，将C1,k表述为：According to the commutative law of matrix multiplication, C1,k is expressed as:
其中，in,
经过特征值分解，可得：After eigenvalue decomposition, we can get:
其中，为按降序排列的/>的特征值对角矩阵，/>为对应的特征向量。in, Sorted in descending order/> Diagonal matrix of eigenvalues, /> is the corresponding feature vector.
上式等号右侧部分的特征值满足：Eigenvalues of the right part of the equal sign in the above equation satisfy:
当时，/>在/>内严格单调递增，可知when When,/> in/> is strictly monotonically increasing, it can be seen that
对于最大特征值For the largest eigenvalue
即：Right now:
那么，的期望满足：So, The expectations are met:
其中，tf为滑动窗口内的故障持续时间。Among them, tf is the fault duration within the sliding window.
根据Weyl不等式，Ck最大特征值的下限为：According to Weyl's inequality, the lower limit of the maximum eigenvalue of Ck is:
Weyl不等式的定义如下：Weyl's inequality is defined as follows:
设A,B∈Mn是Hermite矩阵，设各个特征值λi(A),λi(B),λi(A+B)均按照递增顺序排列，则对每个k＝1,2,…,n有：Assume A, B∈Mn is a Hermite matrix, and assume that the eigenvalues λi (A), λi (B), and λi (A+B) are arranged in increasing order, then for each k = 1, 2, …, n has:
λk(A)+λ1(B)≤λk(A+B)≤λk(A)+λn(B)λk (A)+λ1 (B)≤λk (A+B)≤λk (A)+λn (B)
(2)C2,k+C3,k(2)C2,k +C3,k
由于初始故障情况下ξl＝1，因此C2,k+C3,k可以展开为：Since ξl =1 in the initial fault condition, C2,k +C3,k can be expanded to:
其中，是/>的第i列。令/>C2,k+C3,k的特征多项式可表示为in, Yes/> The i-th column. Order/> The characteristic polynomial of C2,k +C3,k can be expressed as
通过数学归纳法得到：Obtained through mathematical induction:
那么，C2,k+C3,k的非零特征值是：Then, the non-zero eigenvalues of C2,k +C3,k are:
其中因此，/>的期望满足：in Therefore,/> The expectations are met:
因此，therefore,
(3)C4,k(3)C4,k
将C4,k展开可得：Expanding C4,k we get:
其特征值满足Its characteristic value satisfy
其中，和/>分别是/>和/>的第j列。in, and/> They are/> and/> The jth column.
令并且：make and:
那么So
其中因此，in therefore,
由于因此，because therefore,
因此，可以得到：Therefore, we can get:
(4)C5,k(4)C5,k
由于每个电压通道的噪声水平几乎相同，令是C5,k最小的特征值，/>是归一化后的(n-1)-1ETE的最小特征值，可以得到：Since the noise level of each voltage channel is almost the same, let is the smallest eigenvalue of C5,k ,/> is the minimum eigenvalue of (n-1)-1 ET E after normalization, we can get:
即Right now
根据大维随机矩阵理论中的Marchenko-Pastur定律，因为n,w→+∞，c＝(n-1)/w∈(0,1]，因此：According to the Marchenko-Pastur law in large-dimensional random matrix theory, Because n,w→+∞, c=(n-1)/w∈(0,1], therefore:
将故障指示器设计为Design the fault indicator to
其中，λi,k为时间窗口内的协方差矩阵Ck的特征值，/>为其在时间窗口内的平均值，/>为其在时间窗口内的标准差Among them, λi,k is the time window Eigenvalues of the covariance matrix Ck ,/> is its average value within the time window,/> is its standard deviation within the time window
进一步地，S104：根据每个子矩阵的最大特征值的期望，确定电压传感器故障的故障指示器期望值，具体包括：Further, S104: Determine the expected value of the fault indicator of the voltage sensor fault based on the expectation of the maximum eigenvalue of each sub-matrix, specifically including:
由公式(5)、公式(8)、公式(10)、公式(11)、公式(12)可得，电压传感器故障的故障指示器期望值为：It can be obtained from formula (5), formula (8), formula (10), formula (11), and formula (12) that the expected value of the fault indicator for voltage sensor fault is:
其中，为λj,k的期望值。in, is the expected value of λj,k .
进一步地，S104：根据故障指示器期望值，确定电压传感器是否发生故障，具体包括：若故障指示器的值小于故障指示器期望值，说明系统发生电压传感器故障。Further, S104: Determine whether the voltage sensor fails based on the expected value of the fault indicator, specifically including: if the value of the fault indicator is less than the expected value of the fault indicator, it means that a voltage sensor failure occurs in the system.
进一步地，S105：对二维的数据分析矩阵进行归一化处理，得到归一化矩阵；进而获得归一化矩阵所对应的协方差矩阵；所述归一化矩阵所对应的协方差矩阵包括三个新子矩阵Further, S105: Normalize the two-dimensional data analysis matrix to obtain a normalized matrix; and then obtain a covariance matrix corresponding to the normalized matrix; the covariance matrix corresponding to the normalized matrix includes Three new submatrices
假设初期短路故障f出现在第l个通道x1,l，差分测量矢量描述为：Assuming that the initial short-circuit fault f occurs in the l-th channel x1,l , the differential measurement vector is described as:
m＝A(x1+ξf)+Bx2+e；m=A(x1 +ξf)+Bx2 +e;
滑动窗口中的样本矩阵Mk归一化为：The sample matrix Mk in the sliding window is normalized to:
的协方差矩阵表示为/>其中 The covariance matrix of is expressed as/> in
短路故障位于电池单体电压矢量x1处，连接故障位于接触电阻电压矢量x2处。这两个电气故障的位置相似，其统计分析方法也类似。The short circuit fault is located at the battery cell voltage vector x1 , and the connection fault is located at the contact resistance voltage vector x2 . The locations of these two electrical faults are similar, and so are their statistical analysis methods.
结合(12)(14)，则有Combining (12)(14), we have
进一步地，S106：计算每个新子矩阵的最大特征值的期望，具体包括：Further, S106: Calculate the expectation of the maximum eigenvalue of each new sub-matrix, specifically including:
若要则/>其中i∈{1,…,n-1}，δsc是短路故障的检测阈值。To then/> Where i∈{1,…,n-1}, δsc is the detection threshold of short circuit fault.
根据矩阵迹的可加性，可得由于特征值期望满足可得：According to the additivity of the matrix trace, we can get Since the eigenvalues are expected to satisfy Available:
对于C2,k，根据矩阵迹与期望的交换律，可得：For C2,k , According to the commutative law of matrix trace and expectation, we can get:
对于C3,k，根据矩阵迹的交换律，其中，/>表示/>的第l列，因此，/>则有：For C3,k , according to the commutative law of matrix trace, Among them,/> Express/> Column l of , therefore,/> Then there are:
进一步地，S106：根据每个新子矩阵的最大特征值的期望，确定早期电池短路故障阈值和早期电连接故障阈值；根据早期电池短路故障阈值，确定电池组是否发生电池短路故障；根据早期电连接故障阈值，确定电池组是否发生电连接故障，具体包括：Further, S106: Determine the early battery short-circuit fault threshold and the early electrical connection fault threshold according to the expectation of the maximum eigenvalue of each new sub-matrix; determine whether a battery short-circuit fault occurs in the battery group according to the early battery short-circuit fault threshold; according to the early electrical connection fault threshold Connection failure threshold determines whether an electrical connection failure occurs in the battery pack, including:
对于早期的电池短路故障，结合(15)(16)(17)(18)，有For early battery short circuit fault, combined with (15)(16)(17)(18), we have
电连接故障的数学表达与短路故障相似。因此，对于早期电连接故障，有The mathematical expression of an electrical connection fault is similar to that of a short circuit fault. Therefore, for early electrical connection failures, there are
如果公式(19)成立，则表示发生了电池短路故障；If formula (19) is established, it means that a battery short-circuit fault has occurred;
如果公式(20)成立，则表示发生了电连接故障。If formula (20) is established, it means that an electrical connection failure has occurred.
当故障评估量低于相应检测阈值时，说明系统发生了相应的故障。When the fault evaluation amount is lower than the corresponding detection threshold, it means that a corresponding fault has occurred in the system.
进一步地，S107：通过特征向量元素的最小值，确定故障发生的位置，具体包括：Further, S107: Determine the location of the fault through the minimum value of the feature vector element, specifically including:
当发生电压传感器故障时，特征向量vm,k中最小的元素索引设为i，则故障发生在第i个电压传感器；When a voltage sensor fault occurs, the smallest element index in the feature vector vm,k is set to i, then the fault occurs at the i-th voltage sensor;
当发生短路故障时，找到特征向量vm,k中小于平均值的一对相邻元素，其索引设为{i-1，i}，则故障发生在第i个电池单体；When a short-circuit fault occurs, find a pair of adjacent elements in the feature vector vm,k that are smaller than the average value, and set their index to {i-1, i}, then the fault occurs in the i-th battery cell;
当发生电连接故障时，特征向量vm,k中最小的元素索引设为i，则故障发生在第i个电池单体和第i+1个电池单体之间。When an electrical connection failure occurs, the smallest element index in the feature vector vm,k is set to i, and the failure occurs between the i-th battery cell and the i+1-th battery cell.
应理解地，根据分析结果，协方差矩阵第i行对特征值λm,k的贡献可以通过相应特征向量vm,k的第i项来衡量。故障判定后，可以通过标准化离群特征值λ1,k的贡献来确定故障发生的位置。It should be understood that according to the analysis results, the contribution of the i-th row of the covariance matrix to the eigenvalue λm,k can be measured by the i-th term of the corresponding eigenvector vm,k . After the fault is determined, the location of the fault can be determined by the contribution of the normalized outlier eigenvalue λ1,k .
本发明在由5串电池组成的电池系统中进行了测试，故障检测与定位结果如图3(a)、图3(b)、图4(a)、图4(b)、图5(a)、图5(b)。The present invention was tested in a battery system composed of 5 strings of batteries. The fault detection and positioning results are shown in Figure 3(a), Figure 3(b), Figure 4(a), Figure 4(b), and Figure 5(a). ), Figure 5(b).
实施例二Embodiment 2
本实施例提供了电池组故障预警系统，包括：This embodiment provides a battery pack failure early warning system, including:
获取模块，其被配置为：获取电池组的电压数据，所述电压数据，包括：单体电压和组合体电压，所述组合体，是指相邻两个单体电池以及两个单体电池中间所连接的电阻；Acquisition module, which is configured to: acquire voltage data of the battery pack. The voltage data includes: single cell voltage and assembly voltage. The assembly refers to two adjacent single cells and two single cells. The resistor connected in the middle;
扩展模块，其被配置为：对所述电池组的电压数据，采用滑动窗口进行扩展，得到二维的数据分析矩阵；An expansion module configured to: expand the voltage data of the battery pack using a sliding window to obtain a two-dimensional data analysis matrix;
处理模块，其被配置为：对二维的数据分析矩阵进行标准化处理，得到标准化后的矩阵；进而获得标准化后的矩阵所对应的协方差矩阵；所述标准化后的矩阵所对应的协方差矩阵包括五个子矩阵；A processing module configured to: standardize the two-dimensional data analysis matrix to obtain a standardized matrix; and then obtain a covariance matrix corresponding to the standardized matrix; the covariance matrix corresponding to the standardized matrix Includes five submatrices;
确定模块，其被配置为：计算每个子矩阵的最大特征值的期望；根据每个子矩阵的最大特征值的期望，确定电压传感器故障的故障指示器期望值；根据故障指示器期望值，确定电压传感器是否发生故障。a determination module configured to: calculate the expectation of the maximum eigenvalue of each sub-matrix; determine the expected value of the fault indicator of the voltage sensor fault according to the expectation of the maximum eigenvalue of each sub-matrix; determine whether the voltage sensor is faulty according to the expected value of the fault indicator malfunction.
此处需要说明的是，上述获取模块、扩展模块、处理模块和确定模块对应于实施例一中的步骤S101至S104，上述模块与对应的步骤所实现的示例和应用场景相同，但不限于上述实施例一所公开的内容。需要说明的是，上述模块作为系统的一部分可以在诸如一组计算机可执行指令的计算机系统中执行。It should be noted here that the above-mentioned acquisition module, expansion module, processing module and determination module correspond to steps S101 to S104 in Embodiment 1. The examples and application scenarios implemented by the above-mentioned modules and the corresponding steps are the same, but are not limited to the above. Contents disclosed in Embodiment 1. It should be noted that the above-mentioned modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.
上述实施例中对各个实施例的描述各有侧重，某个实施例中没有详述的部分可以参见其他实施例的相关描述。The description of each embodiment in the above embodiments has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.
所提出的系统，可以通过其他的方式实现。例如以上所描述的系统实施例仅仅是示意性的，例如上述模块的划分，仅仅为一种逻辑功能划分，实际实现时，可以有另外的划分方式，例如多个模块可以结合或者可以集成到另外一个系统，或一些特征可以忽略，或不执行。The proposed system can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the above modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system. A system, or some features can be ignored, or not implemented.
实施例三Embodiment 3
本实施例还提供了一种电子设备，包括：一个或多个处理器、一个或多个存储器、以及一个或多个计算机程序；其中，处理器与存储器连接，上述一个或多个计算机程序被存储在存储器中，当电子设备运行时，该处理器执行该存储器存储的一个或多个计算机程序，以使电子设备执行上述实施例一所述的方法。This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in the first embodiment.
应理解，本实施例中，处理器可以是中央处理单元CPU，处理器还可以是其他通用处理器、数字信号处理器DSP、专用集成电路ASIC，现成可编程门阵列FPGA或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。It should be understood that in this embodiment, the processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices. , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
存储器可以包括只读存储器和随机存取存储器，并向处理器提供指令和数据、存储器的一部分还可以包括非易失性随机存储器。例如，存储器还可以存储设备类型的信息。在实现过程中，上述方法的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。The memory may include read-only memory and random access memory and provide instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information. During the implementation process, each step of the above method can be completed by instructions in the form of hardware integrated logic circuits or software in the processor.
实施例一中的方法可以直接体现为硬件处理器执行完成，或者用处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器、闪存、只读存储器、可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。为避免重复，这里不再详细描述。The method in Embodiment 1 can be directly implemented by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module may be located in a storage medium that is mature in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, or the like. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.
本领域普通技术人员可以意识到，结合本实施例描述的各示例的单元及算法步骤，能够以电子硬件或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented with electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each specific application, but such implementations should not be considered to be beyond the scope of the present invention.
实施例四本实施例还提供了一种计算机可读存储介质，用于存储计算机指令，所述计算机指令被处理器执行时，完成实施例一所述的方法。Embodiment 4 This embodiment also provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the method described in Embodiment 1 is completed.
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.
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| US20220176133A1 (en)* | 2019-07-02 | 2022-06-09 | Nalu Medical, Inc. | Stimulation apparatus |
| CN114384424A (en)* | 2022-01-25 | 2022-04-22 | 山东大学 | Data-driven battery pack early fault real-time diagnosis method and system |
| CN115327438A (en)* | 2022-08-22 | 2022-11-11 | 山东大学 | Energy storage battery pack short-circuit fault diagnosis method, battery management method and system |
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