CN114492529B - Power battery system connection abnormity fault safety early warning method - Google Patents
Power battery system connection abnormity fault safety early warning method Download PDFInfo
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Abstract
The invention relates to the technical field of fault diagnosis, and discloses a power battery system connection abnormity fault safety early warning method, which comprises the following steps: step 1: analyzing to obtain original battery signal data; step 2: cleaning original battery signal data to obtain primary battery signal data; and 3, step 3: selecting to obtain target voltage data; and 4, step 4: performing feature extraction on the target voltage data by adopting a difference square sum method to obtain a difference accumulation matrix; and 5: calculating to obtain an average value difference matrix; step 6: traversing the column vector of the mean difference matrix to obtain the upper limit of the abnormal threshold of each battery cell; and 7: and traversing the characteristic vectors of the battery cells in the mean difference matrix, and determining whether the abnormal connection fault exists. The method can accurately identify the abnormal connection fault of the battery, can effectively reduce the false alarm rate of the fault, can quickly lock the fault at the beginning of the fault, and has higher fault judgment efficiency.
Description
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a power battery system connection abnormity fault safety early warning method.
Background
With the continuous decrease of global energy, more and more new energy is receiving more and more attention. At present, new energy vehicles are visible everywhere on roads, and more vehicle owners select to drive the new energy vehicles to go out. However, as more and more new energy vehicles continue to have safety problems, such as fire, slow charging speed, sudden failure and insufficient power, people begin to pay more attention to the safety problems of the new energy vehicles.
In new energy vehicles, a power battery system is one of the most important components, and the power battery system is used as an important energy source or a unique energy source of the new energy vehicle, and the operation safety of the power battery system is more closely related to the safety problem of the new energy vehicle. In the power battery system, the problem of abnormal connection failure of the power battery is particularly concerned. The abnormal connection fault of the power battery is caused by the fact that high-voltage connection (connection in a mode of bolts, welding and the like) between the battery cores is loosened, when the high-voltage connection loosening condition occurs, the contact internal resistance of the battery is increased, abnormal heat generation phenomenon is easily caused when large current is used, the battery cores are baked, and arc discharge phenomenon and even ignition problem are caused when the battery is serious, so that fire or explosion is caused.
The existing identification device or identification method for the abnormal connection fault of the battery is few and inaccurate, most of the existing identification devices or identification methods are only used for judging according to the change of the battery pressure difference, the judgment basis of the method is not reliable enough, the battery pressure difference change can be caused by the influence of other operation factors in the actual operation of the battery, so that more false alarm problems exist in the judgment, and the judgment accuracy is poor.
Disclosure of Invention
The invention aims to provide a power battery system connection abnormity fault safety early warning method, which can accurately and quickly identify battery connection abnormity faults, can effectively reduce the false alarm rate of faults and has higher fault judgment efficiency.
The basic scheme provided by the invention is as follows: a power battery system connection abnormal fault safety early warning method comprises the following steps:
step 1: analyzing to obtain original battery signal data;
step 2: cleaning original battery signal data to obtain primary battery signal data; selecting a target signal to identify connection abnormity;
and 3, step 3: selecting target voltage data in the obtained target signal;
and 4, step 4: performing feature extraction on the target voltage data by adopting a difference square sum method to obtain a difference accumulation matrix;
and 5: calculating an average difference matrix by using the difference accumulation matrix;
step 6: traversing the column vector of the mean difference matrix to obtain the upper limit of the abnormal threshold of each battery cell;
and 7: traversing the characteristic vectors of the battery cells in the mean difference matrix, and determining whether the abnormal connection fault exists according to a judgment strategy and an abnormal threshold upper limit.
The working principle and the advantages of the invention are as follows: firstly, the original battery signal data is cleaned, interference data are fully eliminated before feature extraction, data volume is simplified, feature extraction efficiency is improved, and overall fault judgment efficiency is improved. Secondly, a difference sum of squares method is adopted for feature extraction, and an extraction object is target voltage data, wherein the difference sum of squares method can effectively carry out difference accumulation on the target voltage data, and through the difference accumulation, the prominent expression mode of features can be expanded, so that feature values are easier to confirm, the efficiency and accuracy of feature extraction are higher, and accordingly, the overall fault judgment efficiency and accuracy are also higher.
Moreover, the difference square sum method adopts a difference calculation mode, when the characteristics are extracted, the relative size relation between the numerical values is calculated, specific numerical values are not calculated, the calculated amount is small, the calculation speed is high, meanwhile, compared with the specific numerical values, the relative relation is less influenced by external noise and is more stable, and the stability of the characteristic confirmation is beneficial to reducing the fault misjudgment rate. And the abnormal threshold upper limit is generated by traversing the column vector of the mean difference matrix, different abnormal threshold upper limits can be generated according to different mean difference matrices, the abnormal threshold upper limit is dynamic, the adaptability of the abnormal threshold upper limit to various power battery systems can be ensured, the threshold is properly set, the fault judgment is accurate and effective, and meanwhile, the fault misjudgment rate is reduced.
In addition, compared with conventional linear regression and other modes, the conventional mode usually needs to calculate each characteristic value, the resistance value and the like of each battery cell need to be specifically calculated, and then the resistance values and the like of each battery cell are compared with a set fixed threshold one by one, the calculated amount of the method is large, and the failure judgment efficiency is low; moreover, the fixed threshold does not have flexible matching, and is often set too large or too small, so that the fault cannot be determined in time or is determined by mistake frequently. The scheme does not have the problems, adopts a special characteristic extraction mode and sets a dynamic threshold value, and can accurately and quickly judge the abnormal connection fault.
Further, in step 2, the target signals include a time signal, a charge/discharge state signal, a voltage matrix signal, and a current signal.
By the arrangement, the target signal value is perfect, sufficient basic data values can be provided, and a sufficient data basis is provided for fault judgment.
Further, in step 4, when performing feature extraction, a sliding window method is also used for feature extraction.
The sliding window method is a common strategy for optimizing the algorithm, the sliding window method is adopted for feature extraction, the value of a certain point can be expanded to a section of interval containing the point, the interval is used for judgment, the accuracy of data can be effectively improved, meanwhile, the time complexity in feature extraction can be effectively reduced, and the extraction efficiency is improved.
Further, in step 4, the differential sum of squares method includes: and carrying out difference accumulation operation on the voltage matrix, carrying out difference on the cell voltage value at each moment and the cell voltage value at the previous moment, summing the squared difference obtained by difference to obtain a difference accumulation sum vector, and generating the difference accumulation matrix according to the difference accumulation sum vector.
The voltage difference values between the moments are differentiated, and then a difference accumulation matrix is accumulated and generated, the relative size between the numerical values can be effectively calculated in a difference mode, the characteristics are expressed and extracted through the relative size, the relative size is calculated and controlled between the adjacent moments, and the calculation fineness is high. And a difference accumulation matrix is formed by accumulation operation, so that the characteristics can be integrally expressed, the subsequent operation is convenient, and in addition, the relative relation among numerical values is calculated instead of specific numerical values, so the calculation efficiency is higher.
Further, in step 5, the mean difference matrix is obtained by performing a double-window sliding mean difference operation on the difference accumulation matrix.
By the arrangement, the difference accumulation matrix is subjected to further double-window sliding mean difference operation to obtain a mean difference matrix, the relative relation between the mean values can be calculated, the threshold value can be confirmed according to the relative relation of the mean values, and the threshold value obtained through the relative relation is better in stability and more accurate.
Further, in step 6, the abnormal threshold upper limit is a dynamic upper limit, and the dynamic upper limit changes according to real-time changes of the cell voltage.
The upper limit of the abnormal threshold is dynamically adjusted in real time according to the voltage value of the battery cell, the upper limit of the abnormal threshold is matched with the voltage of the battery cell in real time, compared with a fixed threshold, the scheme is set in such a way that the set threshold is effective under different conditions,
further, in step 7, when the decision strategy is that the eigenvectors of the electric core in the average difference matrix are traversed, it is determined whether a plurality of consecutive eigenvalues are all greater than the upper limit of the abnormal threshold, and if yes, it is determined that the connection of the electric core is abnormal.
According to the arrangement, the connection abnormity is judged according to the continuous characteristic values, and compared with the method of judging according to a single characteristic value, the method has the advantages that the basis for judging the connection abnormity is more sufficient, and the misjudgment rate is lower.
Further, the method also comprises the step 8: and outputting the abnormal time value and the abnormal cell number of the abnormal connection and giving an alarm according to the judgment result of the abnormal connection fault.
Set up like this, can confirm to connect the relevant numerical value of unusual trouble to the output is reported to the police, helps the maintainer in time and fix a position trouble electricity core fast.
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Fig. 1 is a schematic flow chart of an embodiment of a power battery system connection abnormal fault safety warning method according to the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
the embodiment is basically as shown in the attached figure 1: a power battery system connection abnormal fault safety early warning method comprises the following steps:
step 1: analyzing to obtain original battery signal data;
specifically, in this embodiment, corresponding battery signal data is obtained by analyzing a message log of the power battery system to be tested, which conforms to the GB32960 protocol, where the battery signal data is original battery signal data; the obtained data are fully complete and accord with the standard, and the operation is effective. Preferably, the original battery signal data can be acquired from vehicle-mounted software of a vehicle in which the power battery system to be tested is positioned, so that the scheme is more abundant in overall acquisition application scene and stronger in universality.
Step 2: cleaning original battery signal data to obtain primary battery signal data; selecting a target signal to perform connection abnormity identification;
specifically, when the original battery signal data is cleaned, abnormal characters and invalid data, such as NAN, are deleted, and corresponding data with voltage signal data greater than 6V and less than 1V are removed, so as to reduce the original battery signal data and obtain effective primary battery signal data. The target signals comprise time signals, charge and discharge state signals, voltage matrix signals and current signals.
And step 3: and selecting target voltage data in the obtained target signal.
Specifically, the target voltage data is voltage data in a discharge state, and the corresponding current value is greater than 10A and the corresponding current value is less than-5A. By the arrangement, the screening of the target voltage data is performed based on the current value, and the connection abnormity is more obvious under the condition of a large current value, so that the fault judgment is more convenient.
In analogy, other threshold values which are similar to the current value conditions of 20A and-20A, the current value conditions of 15A and-15A and the like and meet the high-current standard can be set, and the adjustability and the flexibility of the whole method are good.
And 4, step 4: performing feature extraction on the target voltage data by adopting a difference square sum method to obtain a difference accumulation matrix;
specifically, when feature extraction is performed, a sliding window method is also adopted for feature extraction. In this embodiment, the voltage matrix signal data V has N columns, each column represents one cell, and there are N cells. The specific row number represents time, and a differential accumulation sum matrix is obtained by performing sliding window calculation on the voltage matrix signal data V, where the length of the sliding window may be set according to an actual situation, for example, if data acquired by a general battery system is 10s once, a time interval between two data points is 10s, and the length M of the sliding window is defined as 10.
The differential square sum method comprises the following steps: performing difference accumulation operation on the voltage matrix to make difference between the cell voltage value at each moment and the cell voltage value at the previous moment, i.e. { v } 2 -v 1 ,v 3 -v 2 ……v n -v n-1 }; and the difference value of M rows obtained by difference is squared and added to obtain difference accumulation sum vector, then according to step length M and according to difference accumulation sum vector the above-mentioned difference accumulation operation can be continuously implementedThe difference accumulation matrix D is obtained by performing continuous accumulation, and each point in the difference accumulation matrix D represents the accumulation of the difference in a period of time.
And 5: calculating an average difference matrix by using the difference accumulation matrix;
the mean difference matrix is obtained by performing a double-window sliding mean difference operation on the difference accumulation matrix. Specifically, the dual window sliding mean differential operation is: taking the first k points in the difference accumulation matrix D as a front window; taking the last k points in the difference accumulation matrix D as a back window to respectively calculate the mean value, and carrying out difference operation to obtain a mean value difference matrix S; wherein k refers to setting according to an actual application scene.
Step 6: traversing the column vector of the mean difference matrix to obtain the upper limit of the abnormal threshold of each battery cell;
specifically, when traversing the column vector of the mean difference matrix S, 75 quantiles and 25 quantiles of the feature vector are obtained, i.e., for the column vector { d } 1 ,d 2 ……d l Sorting and counting the number of bits i = L × 75% (L is the number of rows), rounding up if L × 75% is not an integer, and finally 75 quantiles q 75 Is the average of the i-th term and the (i + 1) -th term, i.e. q 75 ＝(d i +d i+1 )/2. The calculation mode of 25 quantiles is the same as the above, and finally the upper limit thre of the abnormal threshold is obtained up ＝c×(q 75 -q 25 ) The abnormal threshold upper limit is a dynamic upper limit, and the dynamic upper limit changes according to the real-time change of the cell voltage.
And 7: traversing the characteristic vectors of the battery cells in the mean difference matrix, and determining whether the abnormal connection fault exists according to a judgment strategy and an abnormal threshold upper limit.
Specifically, the judgment strategy is to judge whether a plurality of continuous characteristic values are larger than the upper limit of the abnormal threshold value when traversing the characteristic vector of the battery cell in the mean difference matrix, and if so, judge that the connection of the battery cell is abnormal.
And 8: and outputting the abnormal time value and the abnormal cell number of the abnormal connection and giving an alarm according to the judgment result of the abnormal connection fault. For example: for N electricity in the mean difference matrix STraversing the characteristic vector of the core, and judging whether continuous m values exist and satisfy the condition that all the values are larger than the upper limit thre of the abnormal threshold up I.e. s ij >thre up I represents time, j represents jth battery cell, if the conditions are met, the battery cell is judged to have abnormal connection, and abnormal time t is output i And the battery cell number j and gives an alarm.
According to the power battery system connection abnormity fault safety early warning method provided by the embodiment, the sufficient data cleaning step and the data extraction step are set, so that the total amount of data participating in subsequent calculation is effectively reduced, the data participating in the subsequent calculation are all effective data, the fault judgment accuracy is improved, and meanwhile, the calculation efficiency is improved.
And the design adopts a difference square sum method to extract the characteristics, and compared with the conventional linear regression and other modes for calculating specific values, the scheme does not calculate the specific resistance value of the battery cell, but calculates the relative relation between the resistance values. In an actual power battery system, the value change caused by abnormal connection is small, if the judgment is carried out by only depending on specific values, the fault cannot be judged in time, and the early warning effect cannot be achieved. And this scheme is from more obvious and stable numerical value relative relation relatively, because the position of each electricity core is arranged differently, the hookup location is different for the resistance of different electricity cores has different big or small relations, according to this relative big or small relation, can obtain effectual characteristic value that can supply the fault to judge fast, and the calculated amount is less, and relative relation value is compared in numerical value itself also more stable, helps accurate and discern the battery and connects unusual trouble fast.
In addition, the scheme adopts a quantile multiple method to confirm the upper limit of the dynamic abnormal threshold, and the dynamic threshold of the real-time adjustment of the cell voltage value is set in a breakthrough manner. Compare in the fixed threshold value that sets up in conventional scheme, the connection trouble also can be discerned to the fixed threshold value, but the fixed threshold value often sets up partially greatly in order to can fully cover different battery pressure difference scenes when setting up, so, the fixed threshold value often is not timely enough to the judgement of trouble, and when its judgement takes effect, the abnormal condition of connection often takes place for a long time, can't reach the early warning purpose. According to the scheme, the dynamic threshold can adjust the upper limit of the threshold in real time, the validity and accuracy of a fault judgment range are guaranteed, the fault can be judged in time at the beginning of the occurrence of the abnormal connection fault, the fault early warning timeliness rate can be improved, and the fault false alarm rate can be reduced.
The foregoing are embodiments of the present invention and are not intended to limit the scope of the invention to the particular forms set forth in the specification, which are set forth in the claims below, but rather are to be construed as the full breadth and scope of the claims, as defined by the appended claims, as defined in the appended claims, in order to provide a thorough understanding of the present invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several variations and modifications can be made, which should also be considered as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the utility of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (5)
1. A power battery system connection abnormity fault safety early warning method is characterized by comprising the following steps:
step 1: analyzing to obtain original battery signal data;
step 2: cleaning original battery signal data to obtain primary battery signal data; selecting a target signal to perform connection abnormity identification;
and step 3: selecting target voltage data in the obtained target signal;
and 4, step 4: performing feature extraction on the target voltage data by adopting a difference square sum method to obtain a difference accumulation matrix; when the feature extraction is carried out, the feature extraction is also carried out by adopting a sliding window method;
the voltage matrix signal data V has N columns, each column represents one battery cell, and N battery cells are total; the row number represents time, and a differential accumulation sum matrix is obtained by carrying out sliding window calculation on voltage matrix signal data V, wherein the length of the sliding window is M;
the differential sum-of-squares method comprises: performing a differential accumulation operation on the voltage matrix to differentiate the cell voltage value at each time from the cell voltage value at the previous time, i.e.
(ii) a The difference values of M rows obtained by difference are subjected to square summation to obtain a difference accumulation sum vector through accumulation, then the difference accumulation operation is continuously executed according to the step length M and the difference accumulation sum vector to obtain a difference accumulation matrix D through continuous accumulation, and each point in the difference accumulation matrix D represents the difference accumulation in a period of time;
and 5: calculating an average difference matrix according to the difference accumulation matrix; the mean difference matrix is obtained by carrying out double-window sliding mean difference operation on the difference accumulation matrix;
the dual window sliding mean difference operation is: taking the first k points in the difference accumulation matrix D as a front window; taking the last k points in the difference accumulation matrix D as a back window to respectively calculate the mean value, and carrying out difference operation to obtain a mean value difference matrix S;
and 6: traversing the column vector of the mean difference matrix to obtain the upper limit of the abnormal threshold of each battery cell;
and 7: traversing the characteristic vectors of the battery cells in the mean difference matrix, and determining whether the abnormal connection fault exists according to a judgment strategy according to the upper limit of the abnormal threshold.
2. The power battery system connection abnormity, fault and safety early warning method according to claim 1, wherein in step 2, the target signal comprises a time signal, a charge-discharge state signal, a voltage matrix signal and a current signal.
3. The power battery system connection abnormity fault safety early warning method according to claim 1, wherein in step 6, the abnormity threshold upper limit value is a dynamic upper limit value, and the dynamic upper limit value changes according to real-time change of the cell voltage.
4. The power battery system connection abnormity fault safety warning method according to claim 1, wherein in step 7, when the judgment strategy is traversing the eigenvectors of the battery cell in the mean difference matrix, whether a plurality of continuous eigenvalues are all larger than an abnormity threshold upper limit is judged, and if yes, the battery cell is judged to have connection abnormity.
5. The power battery system connection abnormity fault safety early warning method according to claim 4, characterized by further comprising the step 8: and outputting the abnormal time value and the abnormal cell number of the abnormal connection and giving an alarm according to the judgment result of the abnormal connection fault.
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