CN117890815B - Battery module assembly quality detection method and system - Google Patents
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Abstract
The invention relates to the technical field of battery detection, in particular to a method and a system for detecting the assembly quality of a battery module, wherein the method comprises the following steps: the method comprises the steps of collecting voltage data and current data when a battery module is charged, dividing a voltage signal interval, obtaining interval variation degree of each voltage signal interval, obtaining unit fluctuation amount of each voltage signal interval according to data distribution of the voltage signal interval, obtaining voltage main influence factors of all the voltage data by combining interval variation degree, obtaining range errors of the interval variation degree, obtaining voltage amplitude influence factors of all the voltage data according to the range errors, obtaining voltage influence factors by combining the voltage main influence factors and the voltage amplitude influence factors, and the like, obtaining current influence factors, and obtaining gain influence factors when the battery module is charged by combining the voltage influence factors and the current influence factors. The invention aims to improve the accuracy of the quality detection of the battery module and realize the quality detection of the assembled battery module.
Description
Technical Field
The invention relates to the technical field of battery detection, in particular to a method and a system for detecting the assembly quality of a battery module.
Background
Batteries are a common energy storage device that is capable of converting electrical energy into chemical energy and, when desired, chemical energy into electrical energy. The battery module is an integral unit assembled by a plurality of battery cells. Through assembled battery cell, the battery module can provide bigger electric capacity and higher voltage to satisfy various electric power demands. The battery module is widely applied to the fields of electric automobiles, energy storage systems, mobile equipment and the like. In order to improve the product delivery quality and the user satisfaction, the safety, the performance stability and the electrical connection reliability of each battery module are required to be ensured, the failure rate caused by poor assembly quality is reduced, the service efficiency of the battery is improved, and the service time of the battery is prolonged, so that the assembly quality of the battery module is required to be detected.
As for the assembly quality of the battery module, the assembly quality of the battery module may be reflected by evaluating the state of health evaluation value SOH at the time of charging the battery, which is defined as the ratio of the maximum available capacity to the rated capacity. The Kalman filtering algorithm is generally adopted to estimate the SOH value of the battery module, but the SOH value when the battery is charged is affected by the Kalman gain, so that the invention adopts the self-adaptive Kalman gain to improve the Kalman filtering algorithm, and the improved Kalman filtering algorithm is better suitable for estimating the state of health evaluation value SOH of the battery module.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a method and a system for detecting the assembly quality of a battery module, wherein the adopted technical scheme is as follows:
In a first aspect, an embodiment of the present invention provides a method for detecting assembly quality of a battery module, including the steps of:
Collecting voltage data and current data of the battery module during charging;
Dividing all collected voltage data into voltage signal intervals; obtaining the interval variation degree of each voltage signal interval; obtaining the unit fluctuation quantity of each voltage signal section according to the distribution of the data in the voltage signal section; combining the unit fluctuation quantity and interval variation degree of each voltage signal interval to obtain the voltage main influence factors of all voltage data;
Calculating the absolute value of the difference value of the variation degree of the adjacent interval according to the acquisition time sequence of the interval variation degree of each voltage signal interval; taking all absolute values of the differences as a differential sequence; obtaining range errors of interval variation degrees of all voltage signal intervals according to the occurrence probability of each element in the differential sequence; obtaining voltage amplitude influence factors of all voltage data according to the range error;
Combining the voltage main influence factors and the voltage amplitude influence factors to obtain voltage influence factors of all voltage data; obtaining a current influence factor by adopting an acquisition method which is the same as the voltage influence factor for the current data; and combining the voltage influence factor and the current influence factor to finish the quality detection of the assembled battery module.
Preferably, the dividing all collected voltage data into voltage signal intervals includes:
Sequencing all collected voltage data according to the collection time, obtaining maximum values in all sequenced voltage data, taking all voltage data from a first voltage data to a first maximum value, which contain the maximum values, as a voltage signal interval, taking all voltage data from the first maximum value to a second maximum value, which contain the first maximum value and the second maximum value, as a voltage signal interval, and dividing all voltage data into all voltage signal intervals by analogy.
Preferably, the obtaining the interval variation degree of each voltage signal interval includes:
Calculating the absolute value of the difference between each voltage data and the rated voltage in each voltage signal section, calculating the ratio of the absolute value of the difference to the number of the voltage data in the voltage signal section, and taking the ratio of all the voltage data in the voltage signal section as the average distance value of each voltage signal section relative to the rated voltage;
the expression of the interval variation degree of each voltage signal interval is:
wherein DF i represents the interval variation degree of the ith voltage signal interval, The average value of all the voltage data in the ith voltage signal section is represented, V i max represents the maximum value of the voltage data in the ith voltage signal section, V i min represents the minimum value of the voltage data in the ith voltage signal section, I represents the absolute value sign, F i represents the average distance value of the ith voltage signal section relative to the rated voltage, S i represents the number of the voltage data in the ith voltage signal section, and V i,j represents the jth voltage data in the ith voltage signal section.
Preferably, the obtaining the unit fluctuation amount of each voltage signal section according to the distribution of the data in the voltage signal section includes:
For each voltage signal section, calculating the difference value between the first voltage data and the last voltage data in the voltage signal section and the minimum voltage data in the voltage signal section, and taking the ratio of the sum value of the two difference values to the number of the voltage data in the voltage signal section as the unit fluctuation quantity of each voltage signal section.
Preferably, the obtaining the voltage main influence factor of all the voltage data by combining the unit fluctuation amount and the interval variation degree of each voltage signal interval includes:
For each voltage signal section, calculating the product of unit fluctuation quantity and section variation degree, calculating the information entropy of the product of all the voltage signal sections, taking the opposite number of the information entropy as an index of an exponential function based on a natural constant, and taking the calculation result of the exponential function as a voltage main influence factor of all the voltage data.
Preferably, the range error of the interval variation degree of all the voltage signal intervals is obtained according to the occurrence probability of each element in the differential sequence, and the expression is as follows:
Where RE represents a range error of the degree of section variation of all the voltage signal sections, n represents the number of the voltage signal sections, C k 'represents the k-th element value in the differential sequence, P (C k') represents the probability of occurrence of the k-th element value in the differential sequence, P represents the probability set corresponding to all the elements in the differential sequence, min () represents the minimum function, exp () represents the exponential function based on the natural constant.
Preferably, the voltage amplitude influencing factors of all the voltage data are obtained according to the range error, and the expression is:
Where D represents a voltage amplitude influence factor of all voltage data, C represents a section variation degree set of all voltage signal sections, NKMP (C) represents a longest non-overlapping substring in the acquired section variation degree set, round () represents a rounding-off function, n represents a number of voltage signal sections, countL represents a data length function, countL (NKMP (C)) represents a number of section variation degrees included in the longest non-overlapping substring, countMaxP (NKMP (C)) represents a number of maxima in the longest non-overlapping substring, countMax (NKMP (C)) represents a number of maxima in the longest non-overlapping substring, α represents an adjustment factor, and exp represents an exponential function based on a natural constant.
Preferably, the combining the voltage main influence factor and the voltage amplitude influence factor to obtain the voltage influence factors of all the voltage data includes:
Taking the inverse number of the reciprocal of the voltage influence factor as an index of an exponential function based on a natural constant, and taking the product of the calculation result of the exponential function and the voltage main influence factors of all the voltage data as the voltage influence factors of all the voltage data.
Preferably, the battery module assembly quality detection is completed by combining the voltage influence factor and the current influence factor, and the method comprises the following steps:
Taking the product of the voltage influence factor and the current influence factor as a gain influence factor when the battery module is charged, taking the gain influence factor as a gain coefficient of the Kalman filtering algorithm to finish the improvement of the Kalman filtering algorithm, taking the voltage and current data when the battery module is charged as the input of the improved Kalman filtering algorithm, and outputting the voltage and current data as a health state evaluation value when the battery module is charged, wherein if the health state evaluation value is larger than a preset threshold value, the battery module is good in assembly quality, otherwise, the battery module is good in assembly quality.
In a second aspect, an embodiment of the present invention further provides a system for detecting assembly quality of a battery module, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The invention has at least the following beneficial effects:
Dividing voltage signal intervals according to voltage data, calculating the average distance value of each voltage signal interval relative to rated voltage, thus obtaining the interval variation degree of each voltage signal interval and reflecting the fluctuation condition of the voltage data relative to the rated voltage; calculating the unit fluctuation quantity of the voltage signal section through the voltage data of the voltage signal section, and calculating the voltage main influence factors of all the voltage data by using the unit fluctuation quantity and the section variation degree to show the overall fluctuation condition of the voltage data; constructing a set by utilizing the interval variation degree, constructing a differential sequence based on the interval variation degree set, obtaining range errors of the interval variation degree of all voltage signal intervals according to the differential sequence, eliminating errors among interval variation degree data, and improving the accuracy of a subsequent Kalman filtering algorithm; calculating to obtain a voltage side effect factor, and reflecting the periodicity between voltage data; the voltage influence factor is calculated through the voltage main influence factor and the voltage auxiliary influence factor, and the current influence factor is obtained by the same, the gain influence factor is calculated through the voltage influence factor and the current influence factor, the Kalman filtering algorithm is improved, the accuracy of the health state evaluation value of the Kalman filtering algorithm when the battery module is charged is improved, and the problem that the accuracy of the Kalman filtering algorithm is low when the health state evaluation value of the battery module is calculated due to the fact that the gain factor is fixed is solved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart illustrating steps of a method for detecting assembly quality of a battery module according to an embodiment of the present invention;
fig. 2 is a flowchart for obtaining a quality detection index of a battery module.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a specific implementation, structure, characteristics and effects of a method and a system for detecting the assembly quality of a battery module according to the invention, which are provided by the invention, with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The following specifically describes a specific scheme of a method and a system for detecting the assembly quality of a battery module provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a method for detecting assembly quality of a battery module according to an embodiment of the invention is shown, the method includes the following steps:
and S001, collecting voltage data and current data when the battery module is charged, and preprocessing the voltage data and the current data.
By installing a voltage sensor and a current sensor when the battery module is charged, voltage and current data when the battery module is charged are collected. The total time length of collection is from the fully discharged state of the battery module to the fully charged state of the battery module, the collection interval is once per second, and the collection interval implementer can set according to the actual situation, which is not limited in this embodiment. And arranging the collected voltage and current data according to the sequence of the collection time. Because the voltage and current data are collected, the voltage and current data may be interfered by factors such as environment, and in order to avoid the influence on subsequent analysis, the collected voltage and current data need to be preprocessed.
Since the NLM (Non-Local Means) algorithm has high-efficiency and accurate characteristics for noise processing, the NLM algorithm is adopted to denoise the acquired voltage and current data. Since the NLM algorithm is a known technology, the present embodiment is not described in detail here. The denoised voltage and current data are respectively recorded as V (1), V (2), …, V (N) and A (1), A (2), … and A (N), wherein N is the total collection times and also represents the required charging time (in seconds) of the battery module. The specific N value depends on different types of battery modules, and the embodiment is not limited herein.
Step S002, dividing the voltage signal intervals according to the voltage and current data when the battery module is charged, obtaining the interval variation degree and the unit fluctuation amount of each voltage signal interval, further obtaining the voltage main influence factors of all the voltage data, obtaining the range error of the interval variation degree of all the voltage signal intervals, obtaining the voltage influence factors of all the voltage data according to the range error and the voltage main influence factors, and the like, and obtaining the current influence factors.
Specifically, in this embodiment, voltage data and current data during charging of the battery module are collected first, voltage signal intervals are divided, interval variation degrees of each voltage signal interval are obtained, unit fluctuation amounts of each voltage signal interval are obtained according to data distribution of the voltage signal intervals, voltage main influence factors of all the voltage data are obtained by combining interval variation degrees, range errors of the interval variation degrees are obtained, voltage amplitude influence factors of all the voltage data are obtained according to the range errors, voltage main influence factors and voltage amplitude influence factors are obtained by combining the voltage main influence factors and the voltage amplitude influence factors, and so on, current influence factors are obtained, gain influence factors during charging of the battery module are obtained by combining the voltage influence factors and the current influence factors, and a specific battery module quality detection index obtaining flow chart is shown in fig. 2. The construction process of the gain influence factor during charging of the battery module comprises the following steps:
in general, when estimating the SOH (State of Health) of the battery module using the kalman filter algorithm, the kalman gain factor is an important factor for determining the SOH, and the kalman gain factor should be changed according to the obtained voltage and current data. When the voltage and current data change is smaller and the noise is smaller during the charging of the battery module, the Kalman gain coefficient should be increased, so that the Kalman filtering is more focused on the current observation value, and further, more accurate SOH estimation is obtained. On the contrary, when the battery module is charged, the voltage and current data change is larger, and the Kalman gain coefficient is reduced at the moment, so that the Kalman filtering is more focused on the estimated value, the sensitivity of the voltage and current data to noise can be reduced, and the assembly quality of the battery module is more stable.
Firstly, selecting voltage data for analysis, obtaining the maximum value of the preprocessed voltage data, namely, aiming at the voltage data arranged according to the sequence of acquisition time, if the voltage data on the left side and the right side are smaller than the current voltage data, marking the current voltage data as the maximum value, dividing all the preprocessed voltage data into voltage signal intervals according to the maximum value of the voltage data, for example, the voltage data are 220, 222, 219, 218, 220, 223, 221, 219, 222, 224, 222, 219, 218, 220, 221 and 223, the maximum value of the voltage data is 222 at the second position, 223 at the sixth position and 224 at the tenth position, the divided voltage signal intervals are [220, 222], [222, 219, 220, 223], [223, 221, 219, 222, 224], [224, 222, 219, 218, 220, 221 ] except the first voltage signal interval and the last voltage signal interval, the first element and the last element of other voltage signal intervals are the maximum value, and the first element and the last element of the first voltage signal interval are the last element of the voltage signal, and the last element of the voltage signal is the first element of the last element of the voltage signal, and the last element of the voltage signal is the last element of the voltage signal.
The divided voltage signal intervals are denoted as V i (i represents the ith voltage signal interval), the number of elements in each voltage signal interval is denoted as S i, the maximum value in the voltage signal interval is denoted as V i max, the minimum value is denoted as V i min, and the interval variation degree of each voltage signal interval is constructed by the above indexes, and the specific expression is:
Wherein F i represents the average distance value of the ith voltage signal section relative to the rated voltage; s i represents the number of voltage data in the ith voltage signal section; v i,j denotes the jth voltage data in the ith voltage signal section; v i denotes the rated voltage in the ith voltage signal section; the expression absolute value function is taken. It should be noted that the rated voltage is the rated voltage of the battery module, and the embodiment is not limited thereto depending on the different types of battery modules.
DF i represents the interval variation degree of the ith voltage signal interval; Representing the average value of all voltage data in the ith voltage signal interval; v i max represents the maximum value of the voltage data in the i-th voltage signal section, and V i min represents the minimum value of the voltage data in the i-th voltage signal section; the expression absolute value function is taken.
The more severe the voltage data change is when the battery module is charged, the more obvious the fluctuation of the voltage data is, the larger the difference between the voltage data in the voltage signal interval and the rated voltage is, namely |V i,j-Vi | is, so that the larger the value of the average distance value F i of the voltage signal interval relative to the rated voltage is, the further the Kalman gain coefficient should be increased, and the Kalman filtering algorithm is more focused on the current observation value; for the voltage data in the voltage signal section, the larger the fluctuation is, the larger the variation degree of the voltage data is, namely the difference value and the extremely difference value between the voltage data and the average value in the voltage signal section are increased, namelyV i max-Vi min increases, which means that the greater the degree of variation of the voltage signal in the voltage signal section, the greater the value of the section variation degree DF i of the voltage signal section. The larger the DF i value is, the higher the fluctuation amplitude and the fluctuation complexity of the voltage signal section are, so the Kalman gain coefficient is further increased, and the influence of the voltage and current data on the SOH evaluation value of the battery module is reduced.
The fluctuation range of the voltage data is not only represented by the fluctuation condition of the voltage data, but also represented by the unit fluctuation amount of the voltage data. The unit fluctuation amount of the voltage data can be obtained by analyzing the unit fluctuation amplitude of the voltage data in the voltage signal section. The voltage main influence factor can be obtained through the unit fluctuation quantity and the interval variation degree of the voltage signal interval, and the specific expression is as follows:
Wherein U i represents the unit fluctuation amount of the ith voltage signal section; s i represents the number of voltage data in the ith voltage signal section; v i,1 denotes the first voltage data in the i-th voltage signal section; v i,Si denotes the last voltage data in the ith voltage signal interval; min () represents taking a minimum function;
U represents the voltage main influencing factor of all voltage data; n represents the number of voltage signal intervals; DF i represents the interval variation degree of the ith voltage signal interval; p (DF i×Ui) represents the probability of occurrence of DF i×Ui in n voltage signal intervals; exp () represents an exponential function based on a natural constant, log 2 represents a logarithmic function based on 2, The opposite number of the information entropy representing the product of the unit fluctuation amount and the interval variation degree is the information entropy of the prior art, and the detailed description of the embodiment is omitted here.
If the size of the voltage signal interval is fixed, when the increasing and decreasing amplitude of the voltage data in the voltage signal interval is larger, the sum V i,1-min(Vi)+Vi,Si-min(Vi of the difference value between the voltage data at two ends and the minimum value is larger, and the value of the unit fluctuation U i in the voltage signal interval is increased; if the increasing amplitude of the voltage data in the voltage signal section is fixed, the smaller the length of the voltage signal section, namely, the smaller the S i is, so that the value of the unit fluctuation U i in the voltage signal section is increased; if the product of the interval variation degree of the voltage signal interval and the unit fluctuation quantity, that is, the more times that DF i×Ui appears in all the voltage signal intervals, the smaller the information quantity carried by the voltage signal interval is, if the information entropy is larger, the more chaotic the voltage data distribution is proved, and the smaller the value of the voltage main influence factor U is calculated.
The section variation degree DF i of each voltage signal section is time-sequentially formed into a section variation degree set C. Since the voltage data is obtained by measurement, certain measurement errors may exist in the voltage data of each voltage signal interval, and the accuracy of the kalman gain coefficient can be improved by eliminating the measurement errors.
Arranging elements in the interval variation degree set C according to the acquisition time sequence, calculating the absolute value of the difference of adjacent interval variation degrees by adopting a first-order linear difference method, taking all the absolute value of the difference as a differential sequence, acquiring the differential sequence of the interval variation degree set, marking as C ', counting the occurrence probability of each element in the differential sequence C', marking as P (C k '), forming the probability of each element of the differential sequence C' into a probability set P, and constructing the range error RE of the interval variation degree of all the voltage signal intervals according to the analysis, wherein the specific expression is as follows:
Wherein RE represents the range error of the interval variation degree of all voltage signal intervals; n represents the number of voltage signal intervals; c k' represents the value of the kth element in the differential sequence; p (C k') represents the probability of occurrence of the kth element value in the differential sequence; p represents a probability set corresponding to all elements in the differential sequence; min () represents taking a minimum function; exp () represents an exponential function based on a natural constant.
If the frequency of occurrence of the element C k ' in the differential sequence C ' is greater, the weight exp (P (C k ') -min (P)) carried by the element C k ' is greater than that carried by other elements, so that the value of the range error RE is close to C k '. The smaller the value exp (min (P)) of the lowest frequency of occurrence in the differential sequence, the larger the value of the range error RE of the section variation degree of the voltage signal section.
If the periodicity of the data is stronger, the data often has obvious repeated patterns and regular fluctuations, which represent that the data change is mainly affected by inherent regularity, and the regularity is determined by mechanisms or characteristics inside the system, and is not caused by external environment or interference factors. For the kalman gain factor, the more periodic the data, the larger the kalman gain factor should be, because the more periodic the data, the less affected it is by other factors. The periodicity of the voltage data is analyzed, and the voltage side effect factor of the voltage data is calculated, wherein the specific expression is as follows:
Wherein D represents the voltage amplitude influencing factors of all the voltage data, C represents the interval variation degree set of all the voltage signal intervals, NKMP (C) represents the longest non-overlapping substring in the obtained interval variation degree set, and round () represents a rounding-off function; n represents the number of voltage signal intervals; countL denotes a fetch data length function; countL (NKMP (C)) represents the number of the degree of inter-section mutation included in the longest non-overlapping substring, countMaxP (NKMP (C)) represents the number of the maximum value in the longest non-overlapping substring, countMax (NKMP (C)) represents the number of the maximum value in the longest non-overlapping substring, α represents the adjustment factor, exp represents an exponential function based on a natural constant, in this embodiment α=4, and the practitioner can set himself according to the actual situation, which is not limited in this embodiment; it should be noted that, in the formula for calculating the voltage side effect factor, NKMP is an improved KMP (Knuth Morris Pratt) algorithm in this embodiment, when matching is performed, the matching is not performed completely, but is performed roughly, that is, when the difference between two data is smaller than the range error RE, the two data are regarded as the same data, for example, if re=0.5, the data 49, 49.6, 50 are judged, at this time, the NKMP algorithm is successful for 49.6 and 50 when matching, and the matching is unsuccessful for 49 and 49.6. In this embodiment, the input of NKMP algorithm is set C, and the output is the longest non-overlapping substring in set C, and since KMP algorithm is a known technology, this embodiment is not described in detail here.
VF represents the voltage influencing factors of all the voltage data; u represents the voltage main influencing factor of all voltage data.
When the period of the voltage signal interval is higher, the length CountL (NKMP (C)) of the longest non-overlapping substring of the interval variation is larger, and the calculated period length is longerThe larger the value of the non-overlapping substring length CountL (NKMP (C)) with the longest interval variation degree is, the larger the voltage side effect factor D of all the voltage data is, and the weight of the voltage side effect factor is calculatedThe larger the value of (c) is so that the voltage side effect factor D of all the voltage data is larger. The larger the values of the main voltage influence factor U and the auxiliary voltage influence factor D of all the voltage data are, the larger the calculated value of the voltage influence factor VF is.
According to the above steps, and so on, for all the current data, the current influence factor AF is obtained according to the same calculation steps as the voltage influence factor AF, and the gain influence factor F when the battery module is charged is calculated by combining the current influence factor AF and the voltage influence factor VF, where f=af×vf.
The gain factor F in the Kalman filtering algorithm is replaced by the gain factor F in the charging process of the battery module, so that the Kalman filtering algorithm is improved, and the improved Kalman filtering algorithm enables the SOH evaluation value of the battery module to be more sensitively adapted to the battery state change, and a more accurate SOH evaluation value is obtained.
Step S003, the SOH value of the battery module is obtained through an improved Kalman filtering algorithm, and the assembly quality of the battery module is evaluated according to the SOH value.
The voltage and current data of the battery module during charging are used as the input of the improved Kalman filtering algorithm, the output is the health state evaluation value of the battery module during charging, if the health state evaluation value is larger than the preset threshold value T, the battery module assembly quality is good, otherwise, the battery module assembly quality is good, in this embodiment, T=98%, and the operator can set the battery module assembly quality according to the actual situation, and the embodiment is not limited to this. The kalman filtering algorithm is a known technology, and the detailed description of this embodiment is omitted here.
Based on the same inventive concept as the above method, the embodiment of the invention further provides a battery module assembly quality detection system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to realize the steps of any one of the above battery module assembly quality detection methods.
In summary, the embodiment of the invention solves the problem of low accuracy in calculating the state of health evaluation value of the battery module due to the fixed gain coefficient of the Kalman filtering algorithm, and improves the accuracy of the state of health evaluation value of the Kalman filtering algorithm in calculating the charging of the battery module.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. The battery module assembly quality detection method is characterized by comprising the following steps:
Collecting voltage data and current data of the battery module during charging;
Dividing all collected voltage data into voltage signal intervals; obtaining the interval variation degree of each voltage signal interval; obtaining the unit fluctuation quantity of each voltage signal section according to the distribution of the data in the voltage signal section; combining the unit fluctuation quantity and interval variation degree of each voltage signal interval to obtain the voltage main influence factors of all voltage data;
Calculating the absolute value of the difference value of the variation degree of the adjacent interval according to the acquisition time sequence of the interval variation degree of each voltage signal interval; taking all absolute values of the differences as a differential sequence; obtaining range errors of interval variation degrees of all voltage signal intervals according to the occurrence probability of each element in the differential sequence; obtaining voltage amplitude influence factors of all voltage data according to the range error;
Combining the voltage main influence factors and the voltage amplitude influence factors to obtain voltage influence factors of all voltage data; obtaining a current influence factor by adopting an acquisition method which is the same as the voltage influence factor for the current data; the voltage influence factor and the current influence factor are combined to finish the quality detection of the assembled battery module;
the obtaining the interval variation degree of each voltage signal interval comprises the following steps:
Calculating the absolute value of the difference between each voltage data and the rated voltage in each voltage signal section, calculating the ratio of the absolute value of the difference to the number of the voltage data in the voltage signal section, and taking the ratio of all the voltage data in the voltage signal section as the average distance value of each voltage signal section relative to the rated voltage;
the expression of the interval variation degree of each voltage signal interval is:
wherein DF i represents the interval variation degree of the ith voltage signal interval, Representing the average of all voltage data in the ith voltage signal interval,Represents the maximum value of the voltage data in the ith voltage signal interval,Representing the minimum value of the voltage data in the ith voltage signal interval,The absolute value is represented, F i represents the average distance value of the ith voltage signal section relative to the rated voltage, S i represents the number of voltage data in the ith voltage signal section, and V i,j represents the jth voltage data in the ith voltage signal section;
The range error of the interval variation degree of all the voltage signal intervals is obtained according to the occurrence probability of each element in the differential sequence, and the expression is as follows:
Wherein RE represents the range error of the interval variation degree of all the voltage signal intervals, n represents the number of the voltage signal intervals, C 'k represents the k element value in the differential sequence, P (C' k) represents the probability of the k element value in the differential sequence, P represents the probability set corresponding to all the elements in the differential sequence, The representation takes the function of the minimum value,An exponential function based on a natural constant is represented.
2. The method for detecting the assembly quality of a battery module according to claim 1, wherein the dividing all collected voltage data into voltage signal intervals comprises:
Sequencing all collected voltage data according to the collection time, obtaining maximum values in all sequenced voltage data, taking all voltage data from a first voltage data to a first maximum value, which contain the maximum values, as a voltage signal interval, taking all voltage data from the first maximum value to a second maximum value, which contain the first maximum value and the second maximum value, as a voltage signal interval, and dividing all voltage data into all voltage signal intervals by analogy.
3. The method for detecting the assembly quality of a battery module according to claim 1, wherein the step of obtaining the unit fluctuation amount of each voltage signal section according to the distribution of the data in the voltage signal section comprises the steps of:
For each voltage signal section, calculating the difference value between the first voltage data and the last voltage data in the voltage signal section and the minimum voltage data in the voltage signal section, and taking the ratio of the sum value of the two difference values to the number of the voltage data in the voltage signal section as the unit fluctuation quantity of each voltage signal section.
4. The method for detecting the assembly quality of a battery module according to claim 1, wherein the step of obtaining the voltage main influence factor of all the voltage data by combining the unit fluctuation amount and the interval variation degree of each voltage signal interval comprises the steps of:
For each voltage signal section, calculating the product of unit fluctuation quantity and section variation degree, calculating the information entropy of the product of all the voltage signal sections, taking the opposite number of the information entropy as an index of an exponential function based on a natural constant, and taking the calculation result of the exponential function as a voltage main influence factor of all the voltage data.
5. The method for detecting the assembly quality of a battery module according to claim 1, wherein the voltage amplitude influencing factors of all the voltage data are obtained according to the range error, and the expression is as follows:
wherein D represents the voltage amplitude influencing factors of all the voltage data, C represents the interval variation degree set of all the voltage signal intervals, NKMP (C) represents the longest non-overlapping substring in the obtained interval variation degree set, The rounding function, n denotes the number of voltage signal intervals, countL denotes the data length function, countL (NKMP (C)) denotes the number of intervals of varying degree included in the longest non-overlapping substring, countMaxP (NKMP (C)) denotes the number of maxima in the longest non-overlapping substring, countMax (NKMP (C)) denotes the number of maxima in the longest non-overlapping substring, α denotes the adjustment factor, exp denotes an exponential function based on natural constants.
6. The method for detecting the assembly quality of a battery module according to claim 1, wherein the step of combining the voltage main influence factor and the voltage amplitude influence factor to obtain the voltage influence factors of all the voltage data comprises the steps of:
Taking the inverse number of the reciprocal of the voltage influence factor as an index of an exponential function based on a natural constant, and taking the product of the calculation result of the exponential function and the voltage main influence factors of all the voltage data as the voltage influence factors of all the voltage data.
7. The method for detecting the assembly quality of a battery module according to claim 1, wherein the step of combining the voltage influence factor and the current influence factor to complete the detection of the assembly quality of the battery module comprises the steps of:
Taking the product of the voltage influence factor and the current influence factor as a gain influence factor when the battery module is charged, taking the gain influence factor as a gain coefficient of the Kalman filtering algorithm to finish the improvement of the Kalman filtering algorithm, taking the voltage and current data when the battery module is charged as the input of the improved Kalman filtering algorithm, and outputting the voltage and current data as a health state evaluation value when the battery module is charged, wherein if the health state evaluation value is larger than a preset threshold value, the battery module is good in assembly quality, otherwise, the battery module is good in assembly quality.
8. A battery module assembly quality detection system comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1-7 when executing the computer program.
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