CN117031299A - Battery pack layered fault diagnosis method and system based on cumulative probability distribution - Google Patents
Battery pack layered fault diagnosis method and system based on cumulative probability distribution Download PDFInfo
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Abstract
The application discloses a battery pack layered fault diagnosis method and system based on cumulative probability distribution, comprising the following steps: obtaining a maximum voltage sequence and a minimum voltage sequence of a battery pack to be tested; calculating cumulative probability distributions for the maximum voltage sequence and the minimum voltage sequence, respectively; judging whether the battery pack has faults or not and the fault type based on the obtained cumulative probability distribution; for a battery pack judged to have no faults, acquiring a voltage sequence of battery monomers in the battery pack; and calculating the cumulative probability distribution of the battery cell voltage sequence, and judging whether the battery cell fails or not and the failure type based on the calculated cumulative probability distribution. The application establishes a layered fault diagnosis framework, can effectively detect the fault types of the battery pack and the battery single body, and can accurately detect the early faults of the battery single body.
Description
Technical Field
The application relates to the technical field of lithium ion battery fault diagnosis, in particular to a battery pack layered fault diagnosis method and system based on cumulative probability distribution.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The battery pack plays a vital role as an important energy storage device in the fields of electric automobiles, renewable energy power stations, portable electronic devices and the like. However, there are a series of potential failure hazards due to the battery pack being exposed to different operating environments and conditions of use over long periods of use.
Currently, battery fault diagnosis methods rely on traditional battery parameter measurements, such as voltage, current, and temperature. Although these methods can detect an abnormal state of the battery pack to some extent, they cannot accurately identify potential faults in time due to complicated chemical reactions and interactions inside the battery pack and provide necessary early warning at an early stage. In addition, existing fault diagnosis methods are generally limited by high equipment cost, slow response speed, and impact on equipment operation. This results in difficulty in real-time monitoring and fault diagnosis of the health status of the battery pack, thereby affecting the safety, reliability and life of the battery pack.
The existing battery fault diagnosis methods mainly include a threshold-based method, a model-based method, a data-driven-based method and the like.
The method based on the threshold value is to set various threshold values to realize battery fault detection, wherein the threshold values generally comprise parameters such as voltage, current, temperature, internal resistance and the like. Such as: the prior art discloses a fault identification method for an energy storage battery, which is characterized in that each safety threshold value is calculated through a weighted average method, and then the inconsistency of the voltage, the temperature and the internal resistance of each monomer in a battery module is predicted in real time and the fault is early-warned according to the threshold value. However, threshold-based approaches have many limitations: firstly, parameter fluctuation caused by early failure generally cannot reach a preset threshold value; secondly, it is challenging to select an appropriate threshold.
The method based on the model needs to establish an accurate battery model to realize fault detection, and is high in calculation cost, poor in robustness and difficult to realize on line.
The data driving-based method is a method for analyzing the behavior and the performance of a battery by utilizing actual observation data and a machine learning technology, mining the mode and the rule of battery faults and realizing the diagnosis of the battery faults. This approach requires a large amount of data to establish a potential relationship between battery parameters and faults, resulting in high computational costs, poor robustness, and difficult to implement on-line.
Disclosure of Invention
In order to solve the problems, the application provides a battery pack layered fault diagnosis method and system based on cumulative probability distribution, which convert numerous voltage data into cumulative probability values through a cumulative probability distribution algorithm, thereby relieving data redundancy, reducing calculation cost, identifying micro faults of a battery pack and realizing balance between diagnosis precision and diagnosis efficiency.
In some embodiments, the following technical scheme is adopted:
a battery pack layered fault diagnosis method based on cumulative probability distribution, comprising:
obtaining a maximum voltage sequence and a minimum voltage sequence of a battery pack to be tested;
calculating cumulative probability distributions for the maximum voltage sequence and the minimum voltage sequence, respectively; judging whether the battery pack has faults or not and the fault type based on the obtained cumulative probability distribution;
for a battery pack judged to have no faults, acquiring a voltage sequence of battery monomers in the battery pack; calculating the cumulative probability distribution of the battery cell voltage sequence, and judging whether the battery cell fails or not and the failure type based on the calculated cumulative probability distribution;
for the single battery which is judged to have no faults, acquiring a voltage sequence of the single battery, and calculating an average voltage sequence of a battery pack; sequencing the voltage sequence and the average voltage sequence of the battery pack;
dividing the sequenced voltage sequence and the average voltage sequence into a plurality of data windows through a preset data window;
calculating the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average battery voltage sequences in each data window;
and calculating the total error between the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average voltage sequences of the battery packs in each data window, and judging whether the single battery fails or not and the failure type based on the total error.
The cumulative probability distribution of the single battery voltage sequences in each data window is calculated, and the cumulative probability distribution is specifically as follows:
dividing the voltage sequence of the single battery in each data window into a set number of numerical intervals with equal length; sequentially calculating the number of voltage data points falling in each numerical interval, and dividing the number by the total number of the data points to calculate the cumulative probability of the numerical interval; and summarizing the cumulative probability of all the numerical intervals to form the cumulative probability distribution of the single batteries in the data window.
In other embodiments, the following technical solutions are adopted:
a battery pack layered fault diagnosis system based on cumulative probability distribution, comprising:
the battery pack fault diagnosis module is used for acquiring a maximum voltage sequence and a minimum voltage sequence of the battery pack to be tested; calculating cumulative probability distributions for the maximum voltage sequence and the minimum voltage sequence, respectively; judging whether the battery pack has faults or not and the fault type based on the obtained cumulative probability distribution;
the first battery cell fault diagnosis module is used for acquiring a voltage sequence of battery cells in the battery pack which is judged to have no fault; and calculating the cumulative probability distribution of the battery cell voltage sequence, and judging whether the battery cell fails or not and the failure type based on the calculated cumulative probability distribution.
The second battery single cell fault diagnosis module is used for acquiring a voltage sequence of a single battery for the single battery which is judged to have no fault and calculating an average voltage sequence of a battery pack; sequencing the voltage sequence and the average voltage sequence of the battery pack; dividing the sequenced voltage sequence and the average voltage sequence into a plurality of data windows through a preset data window; calculating the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average battery voltage sequences in each data window; and calculating the total error between the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average voltage sequences of the battery packs in each data window, and judging whether the single battery fails or not and the failure type based on the total error.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor for implementing instructions; the memory is used to store a plurality of instructions adapted to be loaded by the processor and to perform the above-described cumulative probability distribution based battery pack hierarchical fault diagnosis method.
Compared with the prior art, the application has the beneficial effects that:
(1) The application establishes a layered fault diagnosis framework, can effectively detect the fault types of the battery pack and the battery single body, and can accurately detect the early faults of the battery single body.
Specifically, the application firstly calculates the cumulative probability distribution of the voltage sequence of the battery pack, and performs fault diagnosis on the battery pack; for the battery pack without faults, further diagnosing faults of the battery cells by using the cumulative probability distribution of the voltage sequences of the battery cells; the fault diagnosis method is simple, the calculation process is simple, and the rapid diagnosis of faults can be realized;
for the battery cells without faults, the data windows are divided, voltage fluctuation caused by the micro faults in each data window is more obvious, the cell voltage in the window and the average voltage of the battery pack are processed through an accumulated probability algorithm, and finally the micro fault diagnosis is realized by calculating the total error between the accumulated probability distributions of the cell voltage and the average voltage of the battery pack, so that the diagnosis precision is improved, and the hidden danger of the micro faults to the safety of the battery is avoided; the balance of fault diagnosis precision and efficiency is realized.
(2) The method converts the complicated voltage data into the cumulative probability value through the cumulative probability distribution algorithm, thereby relieving the data redundancy, reducing the data calling difficulty and improving the algorithm calculation efficiency and the diagnosis efficiency.
Additional features and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
FIG. 1 is a flow chart of a hierarchical fault diagnosis of a battery pack based on cumulative probability distribution in an embodiment of the application;
FIG. 2 is a schematic diagram of a process for calculating a cumulative probability distribution of a voltage sequence according to an embodiment of the present application;
FIG. 3 is a graph illustrating voltage curves for a battery pack UDDS operating mode according to an embodiment of the present application;
FIG. 4 is a graph showing the maximum and minimum voltage sequences of a battery pack according to an embodiment of the present application;
FIG. 5 is a graph showing cumulative probability distribution corresponding to a maximum voltage sequence in an embodiment of the present application;
FIG. 6 is a cumulative probability distribution diagram corresponding to a minimum voltage sequence according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a battery fault diagnosis result according to an embodiment of the present application;
FIG. 8 is a graph showing the cell voltage sequence of the 7 th cell in accordance with the embodiment of the present application;
FIG. 9 is a schematic diagram of a battery cell fault diagnosis result according to an embodiment of the present application;
FIG. 10 is a plot of voltage sequence for cell at section 6 in an embodiment of the application;
fig. 11 is a schematic diagram of a diagnosis result of a micro fault of a battery cell according to an embodiment of the application.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. 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 application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
Probability analysis is a mathematical and statistical tool used to study and quantify the likelihood and uncertainty of occurrence of events. The algorithm based on the probability analysis has wide application in the fields of financial investment, engineering reliability analysis, risk assessment and the like. The cumulative probability distribution algorithm provided by the application can convert a time sequence into a non-time sequence, and has potential application value in the field of battery fault diagnosis.
Voltage is an important parameter of a battery that can dynamically reflect certain battery characteristics. The fluctuation or change in voltage over time may be represented as a time series, which in some specific cases can be used to characterize a battery failure. When a short-circuit fault occurs in a battery cell at a certain time, the cell voltage suddenly drops at that time. Conversely, if the cell voltage suddenly increases at a certain time, this phenomenon may represent an open circuit failure or a sensor failure of the cell.
Referring to fig. 2, the process of performing data structure optimization on the voltage data sequence based on the cumulative probability distribution algorithm in this embodiment is specifically as follows:
the voltage sequence to be processed is obtained, and the voltage sequence can be a maximum voltage sequence and a minimum voltage sequence in the battery pack or can be a voltage sequence of a battery cell.
Extracting a maximum voltage value and a minimum voltage value in a voltage sequence to be processed;
based on the maximum voltage value and the minimum voltage value, arranging all voltage data in ascending order or descending order, and storing the formed new sequence;
setting 10 numerical intervals with equal length according to the maximum voltage value, the minimum voltage value and the new sequence;
based on the new sequence and the numerical intervals, sequentially calculating the number of voltage data points falling in each numerical interval, and dividing the number by the total number of the data points to calculate the cumulative probability;
and summarizing all the accumulated probabilities to obtain the accumulated probability distribution condition of the voltage sequence to be processed.
As a specific example, assume a battery pack consisting of m battery cells, with the total number of voltage data points for each cell being t. The voltage sequence of each cell in the battery is defined as:
taking the 1 st section monomer as an example, the voltage sequence of the monomer is defined as:
v 1 ＝{v 1 (1),v 1 (2),v 1 (3),…,v 1 (t-1),v 1 (t)} (2)
the monomer voltage data points are arranged in ascending order based on the voltage minima and maxima, and the new sequence is stored into set D. Set D is defined as:
based on set D, 10 equal-length numerical intervals are defined as:
sequentially calculating the number K of voltage data points falling in each numerical interval i+1 And dividing the number by the total number of data points to calculate the cumulative probability p i+1 ：
Summarizing all the cumulative probabilities, and distributing the cumulative probability of the 1 st monomer P 1 The definition is as follows:
P 1 ＝{p 1 ,p 2 ,p 3 ,…,p l } (6)
based on the above-mentioned voltage sequence data structure optimization method, in one or more embodiments, a battery pack layered fault diagnosis method based on cumulative probability distribution is disclosed, and in combination with fig. 1, the method specifically includes the following steps:
(1) Method A: applying an accumulated probability distribution algorithm to a maximum voltage sequence and a minimum voltage sequence of the battery pack for rapid detection and diagnosis of battery pack faults; the specific process is as follows:
(1-1) obtaining a maximum voltage sequence and a minimum voltage sequence of a battery pack to be tested;
in this embodiment, the maximum voltage sequence of the battery pack consists of the maximum voltages of the individual cells in the battery pack; the minimum voltage sequence of the battery pack consists of the minimum voltages of the individual cells in the battery pack.
The relationship between the maximum voltage data point and the cell voltage data point of the battery is defined as:
the relationship between the minimum voltage data point and the cell voltage data point of the battery pack is defined as:
the cell maximum voltage sequence of the battery is:
v max ＝{v max (1),v max (2),…,v max (t)}
the cell minimum voltage sequence of the battery pack is as follows:
v min ＝{v min (1),v min (2),…,v min (t)}
wherein v is max (1)、v max (2) And v max (t) represents the maximum 1 st, 2 nd and t th voltage values, respectively, among all the monomers; v min (1)、v min (2) And v min (t) represents the smallest 1 st, 2 nd and t th voltage values, respectively, among all the monomers; v m (1)、v m (2) And v m (t) represents the 1 st, 2 nd and t th voltage values of the m-th monomer, respectively.
(1-2) calculating the cumulative probability distribution of the maximum voltage series and the minimum voltage series by using the method for calculating the cumulative probability distribution of the voltage series to be processed as the voltage series to be processed; the specific process will not be described in detail.
(1-3) judging whether the battery pack fails and the type of the failure based on the obtained cumulative probability distribution; the method comprises the following steps:
selecting the middle numerical value interval as a reference numerical value interval;
if the accumulated probability of one or a plurality of numerical intervals in the reference numerical interval or the numerical interval positioned before the reference numerical interval is lower than a set threshold value, the monomer has a short circuit fault;
if the cumulative probability of one or a plurality of numerical intervals which are positioned in the numerical intervals after the reference numerical interval is lower than a set threshold value, the battery pack has an open circuit fault.
In this embodiment, the set threshold is selected to be r=0.008.
Such as: assuming 10 total value intervals, selecting the 5 th value interval as a reference value interval; if the cumulative probability of one or more numerical value intervals in the 1 st to 5 th numerical value intervals is lower than the set threshold value of 0.008, the monomer has a short circuit fault; if the cumulative probability of one or more numerical value intervals in the 6 th to 10 th numerical value intervals is lower than the set threshold value of 0.008, the battery pack has an open circuit fault.
(2) Method B: the cumulative probability distribution algorithm is applied to the method A to detect the single voltage sequence without faults and is used for the rapid detection and diagnosis of single faults; the specific process is as follows:
(2-1) extracting a voltage sequence of the single body in which the failure is not detected by the method A;
(2-2) calculating the cumulative probability distribution of the battery cell voltage sequence by using the method for calculating the cumulative probability distribution of the voltage sequence to be processed, and taking the battery cell voltage sequence as the voltage sequence to be processed; the process comprises the following steps: reordering the single battery voltage sequences according to a set order; dividing the reordered voltage sequence into a set number of equal-length numerical intervals; calculating the cumulative probability of each numerical interval; and summarizing the cumulative probability of all the numerical intervals to form the cumulative probability distribution of the single battery. The detailed steps are not repeated.
(2-3) judging whether the single cell fails or not and the failure type of the failure according to the cumulative probability distribution of the voltage sequences of the single cells; the specific judging method comprises the following steps:
selecting the middle numerical value interval as a reference numerical value interval;
if the accumulated probability of one or a plurality of numerical intervals in the reference numerical interval or the numerical interval positioned before the reference numerical interval is lower than a set threshold value, the monomer has a short circuit fault;
if the cumulative probability of one or a plurality of numerical intervals which are positioned in the numerical intervals after the reference numerical interval is lower than a set threshold value, the single body has an open circuit fault.
In this embodiment, the set threshold is selected to be r=0.008.
(3) Method C: applying the cumulative probability distribution algorithm to the method A and the method B to detect the fault-free single voltage sequence for detecting and diagnosing finer single micro faults; the specific process is as follows:
(3-1) extracting a voltage sequence of the unit cells in which the faults are not detected by the method a and the method B, and calculating an average voltage of the battery pack; sequencing the voltage sequence and the average voltage sequence of the battery pack;
(3-2) dividing a plurality of data windows according to a preset data window size (100 seconds);
(3-3) calculating the cumulative probability distribution of the single cell voltages and the average voltage of the battery pack in different data windows, and taking the sequenced single cell voltage sequence and the average voltage sequence of the battery pack as the to-be-processed voltage sequence by adopting the method for calculating the cumulative probability distribution of the to-be-processed voltage sequence, wherein the method comprises the following steps of:
dividing and setting a number of numerical intervals with equal length based on the voltage sequence of the single battery in each data window; sequentially calculating the number of voltage data points falling in each numerical interval, and dividing the number by the total number of the data points to calculate the cumulative probability of the numerical interval; and summarizing the cumulative probability of all the numerical intervals to form the cumulative probability distribution of the single batteries in the data window.
(3-4) calculating a total error σ between the cell voltage cumulative probability distribution and the battery pack average voltage cumulative probability distribution within each numerical window; the specific process is as follows:
the single voltage cumulative probability distribution and the average voltage cumulative probability distribution of the battery pack are respectively composed of 10 cumulative probability values, each cumulative probability value is calculated to obtain a cumulative probability error, and all the cumulative probability errors on the 10 cumulative probability values are added to obtain a total error.
(3-5) judging whether the single body fails or not and the failure type of the failure according to the cumulative probability distribution and the total error sigma; the specific judging method comprises the following steps:
calculating an error between the single cell voltage sequence cumulative probability distribution and the battery pack average voltage sequence cumulative probability distribution in each data window; determining a data window corresponding to the maximum error, and selecting a middle data window as a reference data window;
if the total error sigma > t and the data window corresponding to the maximum error is positioned before or in the reference data window, the single battery has a short circuit fault;
if the total error sigma > t and the data window corresponding to the maximum error is positioned behind the reference data window, the single battery has a short circuit fault; t is a preset threshold, and the value of this embodiment is 0.15.
Such as: dividing 10 data windows in total, and selecting the 5 th data window as a reference data window; if the data window corresponding to the maximum error is the 4 th, the single body has a short circuit fault; if the data window corresponding to the maximum error is 6 th, the single body has an open circuit fault.
The method and effects of the present embodiment are described below in a comparative manner and specifically by way of example:
the battery pack consisted of 8 cells in series, cell 1, cell 2, cell 3, cell 4, cell 5, cell6, cell7 and Cell8, respectively, and was subjected to UDDS operating mode experiments. Under the UDDS condition, the measured cell voltage curves of the battery are shown in fig. 3. As shown in fig. 3, cell 4 is caused to have an open-circuit failure and a short-circuit failure (second and third dotted line boxes in fig. 3), cell6 is caused to have a short-circuit failure (first dotted line box in fig. 3), and Cell7 is caused to have a short-circuit failure (fourth dotted line box in fig. 3). The other monomers work normally without faults.
1. Method A-based battery pack fault rapid detection and diagnosis
(1) Extracting the maximum/minimum voltage sequence of the battery pack
As shown in fig. 4, a battery pack maximum/minimum voltage sequence is extracted. Abnormal voltage fluctuations of cell 4 are maintained in the stack maximum voltage sequence and abnormal voltage fluctuations of cells 4, 6 and 7 are maintained in the stack minimum voltage sequence.
(2) Calculating cumulative probability distribution for maximum voltage sequence and minimum voltage sequence
As shown in fig. 5, the cumulative probability distribution of the maximum voltage sequence is calculated. As shown in fig. 6, the cumulative probability distribution of the minimum voltage sequence is calculated.
(3) And judging whether the battery pack fails or not and the failure type of the failure according to the cumulative probability distribution of the maximum voltage sequence and the minimum voltage sequence.
As shown in fig. 5, the cumulative probability of the 5 th to 10 th numerical intervals is lower than the threshold, and these numerical intervals cover a higher voltage interval, which is (4.08 v,4.17 v), and the battery pack has an open circuit fault. As shown in fig. 6, the cumulative probability of the 1 st to 3 rd numerical intervals is lower than the threshold, and these numerical intervals cover a lower voltage interval (3.89 v,3.95 v), and the battery pack has a short circuit fault. As shown in fig. 7, by performing the fault diagnosis and localization, it can be diagnosed that an open circuit fault exists at 621s and a short circuit fault exists in the vicinity of 659s and 1108 s.
The diagnostic time for the rapid detection of battery pack failure based on method a was only 0.0003 seconds. It is noted that the method a can complete the fault detection task only by running the cumulative probability distribution algorithm once for the maximum/minimum voltage sequences.
2. Method B-based rapid detection and diagnosis of battery cell faults
(1) Extracting voltage sequences of battery cells
In this specific case, the 7 th cell is taken as an example, and the voltage sequence of the cell is extracted.
(2) Calculating cumulative probability distribution of cell voltage sequences
As shown in fig. 8, the cumulative probability distribution of the 7 th cell voltage is calculated.
(3) Judging whether the battery pack fails or not and the failure type of the failure according to the cumulative probability distribution of the maximum voltage sequence and the minimum voltage sequence
As shown in fig. 8, the cumulative probability of the 1 st to 4 th numerical intervals is lower than the threshold, and these numerical intervals cover a higher voltage interval (3.92 v,3.98 v), and the battery pack has an open circuit fault. As shown in fig. 9, by performing the fault diagnosis and localization, it can be diagnosed that there is a short-circuit fault in the vicinity of 1108 s.
The diagnostic time for the cell failure detection based on method B was only 0.001 seconds. It is noted that the method B can complete the fault detection task by only running the algorithm once for each single voltage sequence.
3. Method C-based detection and diagnosis of small faults of battery cells
(1) Extracting voltage sequence of battery cell and average voltage sequence of battery pack
As shown in fig. 10, in this specific case, taking the 6 th cell as an example, the voltage sequence and the average voltage sequence of the cell are extracted.
(2) Dividing a plurality of data windows according to a preset data window size (100 seconds)
As shown in fig. 10, a plurality of data windows are divided.
(3) Calculating a cell voltage cumulative probability distribution and a battery pack average voltage cumulative probability distribution within different data windows
Taking the 4 th data window as an example, as shown in fig. 11, a cell voltage cumulative probability distribution and a battery pack average voltage cumulative probability distribution are calculated.
(4) Calculating a total error sigma between the cell voltage cumulative probability distribution and the average voltage cumulative probability distribution of the battery pack within each numerical window
Taking the 4 th data window as an example, as shown in fig. 11, the total error σ=1.000 between the cell voltage cumulative probability distribution and the battery pack average voltage cumulative probability distribution.
(5) Judging whether the single body fails or not and the failure type of the failure according to the accumulated probability distribution and the total error sigma
Taking the 4 th data window as an example, the total error sigma is more than 0.15, the numerical value interval with the largest error is the 4 th numerical value interval, the corresponding voltage interval is smaller, and the single body has short circuit fault.
The diagnosis precision of the battery cell fault detection based on the method C is higher than that of the method A and the method B. It is noted that the method C needs to run the algorithm multiple times for each single voltage sequence to complete the fault detection task.
Example two
In one or more embodiments, a battery pack layered fault diagnosis system based on a cumulative probability distribution is disclosed, comprising:
the battery pack fault diagnosis module is used for acquiring a maximum voltage sequence and a minimum voltage sequence of the battery pack to be tested; calculating cumulative probability distributions for the maximum voltage sequence and the minimum voltage sequence, respectively; judging whether the battery pack has faults or not and the fault type based on the obtained cumulative probability distribution;
the first battery cell fault diagnosis module is used for acquiring a voltage sequence of battery cells in the battery pack which is judged to have no fault; and calculating the cumulative probability distribution of the battery cell voltage sequence, and judging whether the battery cell fails or not and the failure type based on the calculated cumulative probability distribution.
The second battery single cell fault diagnosis module is used for acquiring a voltage sequence of a single battery for the single battery which is judged to have no fault and calculating an average voltage sequence of a battery pack; sequencing the voltage sequence and the average voltage sequence of the battery pack; dividing the sequenced voltage sequence and the average voltage sequence into a plurality of data windows through a preset data window; calculating the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average battery voltage sequences in each data window; and calculating the total error between the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average voltage sequences of the battery packs in each data window, and judging whether the single battery fails or not and the failure type based on the total error.
The specific implementation of each module is the same as that in the first embodiment, and will not be described in detail.
Example III
In one or more embodiments, a terminal device is disclosed that includes a server including a memory, a processor, and a computer program stored on the memory and executable on the processor, which when executed implements the cumulative probability distribution-based battery pack layered fault diagnosis method of embodiment one. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software.
While the foregoing description of the embodiments of the present application has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the application, but rather, it is intended to cover all modifications or variations within the scope of the application as defined by the claims of the present application.
Claims (10)
1. A battery pack layered fault diagnosis method based on cumulative probability distribution, comprising:
obtaining a maximum voltage sequence and a minimum voltage sequence of a battery pack to be tested;
calculating cumulative probability distributions for the maximum voltage sequence and the minimum voltage sequence, respectively; judging whether the battery pack has faults or not and the fault type based on the obtained cumulative probability distribution;
for a battery pack judged to have no faults, acquiring a voltage sequence of battery monomers in the battery pack; and calculating the cumulative probability distribution of the battery cell voltage sequence, and judging whether the battery cell fails or not and the failure type based on the calculated cumulative probability distribution.
2. The battery pack layered fault diagnosis method based on cumulative probability distribution according to claim 1, further comprising:
for the single battery which is judged to have no faults, acquiring a voltage sequence of the single battery, and calculating an average voltage sequence of a battery pack; sequencing the voltage sequence and the average voltage sequence of the battery pack;
dividing the sequenced voltage sequence and the average voltage sequence into a plurality of data windows through a preset data window;
calculating the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average battery voltage sequences in each data window;
and calculating the total error between the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average voltage sequences of the battery packs in each data window, and judging whether the single battery fails or not and the failure type based on the total error.
3. The battery pack layered fault diagnosis method based on the cumulative probability distribution according to claim 2, wherein the cumulative probability distribution of the cell voltage sequence in each data window is calculated, specifically:
dividing the voltage sequence of the single battery in each data window into a set number of numerical intervals with equal length; sequentially calculating the number of voltage data points falling in each numerical interval, and dividing the number by the total number of the data points to calculate the cumulative probability of the numerical interval; and summarizing the cumulative probability of all the numerical intervals to form the cumulative probability distribution of the single batteries in the data window.
4. The battery pack layered fault diagnosis method based on the cumulative probability distribution according to claim 2, wherein the total error between the cumulative probability distribution of the voltage sequences of the single cells and the cumulative probability distribution of the average voltage sequences of the battery packs in each data window is calculated by:
the single voltage cumulative probability distribution and the average voltage cumulative probability distribution of the battery pack are respectively composed of a set number of cumulative probability values, a cumulative probability difference is calculated on each cumulative probability value, and all the obtained cumulative probability differences are added to obtain a total error.
5. The battery pack layered fault diagnosis method based on cumulative probability distribution according to claim 2, wherein judging whether a single battery has a fault and a fault type based on the total error is specifically:
calculating an error between the single cell voltage sequence cumulative probability distribution and the battery pack average voltage sequence cumulative probability distribution in each data window; determining a data window corresponding to the maximum error, and selecting a middle data window as a reference data window;
if the total error sigma > t and the data window corresponding to the maximum error is positioned before or in the reference data window, the single battery has a short circuit fault;
if the total error sigma > t and the data window corresponding to the maximum error is positioned behind the reference data window, the single battery has a short circuit fault; t is a preset threshold.
6. The battery pack layered fault diagnosis method based on the cumulative probability distribution according to claim 1, wherein the cumulative probability distribution of the single cell voltage sequence is calculated, specifically:
reordering the single battery voltage sequences according to a set order;
dividing the reordered voltage sequence into a set number of equal-length numerical intervals;
calculating the cumulative probability of each numerical interval;
and summarizing the cumulative probability of all the numerical intervals to form the cumulative probability distribution of the single battery.
7. The method for hierarchical fault diagnosis of a battery pack based on cumulative probability distribution according to claim 6, wherein determining whether a battery cell has failed based on the calculated cumulative probability distribution is specifically:
selecting the middle numerical value interval as a reference numerical value interval;
if the accumulated probability of one or a plurality of numerical intervals in the reference numerical interval or the numerical interval positioned before the reference numerical interval is lower than a set threshold value, the monomer has a short circuit fault;
if the cumulative probability of one or a plurality of numerical intervals which are positioned in the numerical intervals after the reference numerical interval is lower than a set threshold value, the single body has an open circuit fault.
8. A battery pack layered fault diagnosis system based on cumulative probability distribution, comprising:
the battery pack fault diagnosis module is used for acquiring a maximum voltage sequence and a minimum voltage sequence of the battery pack to be tested; calculating cumulative probability distributions for the maximum voltage sequence and the minimum voltage sequence, respectively; judging whether the battery pack has faults or not and the fault type based on the obtained cumulative probability distribution;
the first battery cell fault diagnosis module is used for acquiring a voltage sequence of battery cells in the battery pack which is judged to have no fault; and calculating the cumulative probability distribution of the battery cell voltage sequence, and judging whether the battery cell fails or not and the failure type based on the calculated cumulative probability distribution.
9. The battery pack hierarchical fault diagnosis system based on cumulative probability distribution according to claim 8, further comprising:
the second battery single cell fault diagnosis module is used for acquiring a voltage sequence of a single battery for the single battery which is judged to have no fault and calculating an average voltage sequence of a battery pack; sequencing the voltage sequence and the average voltage sequence of the battery pack;
dividing the sequenced voltage sequence and the average voltage sequence into a plurality of data windows through a preset data window;
calculating the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average battery voltage sequences in each data window;
and calculating the total error between the cumulative probability distribution of the single battery voltage sequences and the cumulative probability distribution of the average voltage sequences of the battery packs in each data window, and judging whether the single battery fails or not and the failure type based on the total error.
10. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing a plurality of instructions, wherein the instructions are adapted to be loaded by the processor and to perform the battery pack hierarchical fault diagnosis method based on cumulative probability distribution of any one of claims 1-7.
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