Lithium battery fault prediction method and system

Abstract

The invention relates to a lithium battery fault prediction method and system, and relates to the field of lithium battery fault diagnosis, and the method comprises the steps: recognizing the similarity between an actual condition graph and a standard condition graph, and carrying out the calculation to obtain the condition significance; and according to the plurality of voltage change saliency degrees, the plurality of current change saliency degrees and the plurality of temperature change saliency degrees, carrying out overcharge fault prediction on the lithium battery pack to obtain a first overcharge fault coefficient, and carrying out correction calculation on the first overcharge fault coefficient by adopting the condition saliency degree to obtain a second overcharge fault coefficient as a lithium battery fault prediction result. According to the invention, the technical problem that an existing lithium battery overcharge fault prediction method generally depends on a fixed threshold value or simple analysis based on a single parameter, and the prediction accuracy and reliability are insufficient can be solved; the overcharge fault prediction is carried out by fusing the multi-dimensional data, so that the accuracy and reliability of fault prediction can be improved, potential risks in the charging process can be identified in advance, and the use safety of the lithium battery is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of lithium battery fault diagnosis, and in particular to a lithium battery fault prediction method and system. Background Art

[0002] With the widespread application of lithium batteries in electric vehicles, energy storage systems, consumer electronics and other fields, the safety and reliability of batteries have become important concerns in the design and use process. If a lithium battery is overcharged during the charging process, it may cause battery damage, performance degradation, and even cause safety accidents such as fire. Therefore, accurate and real-time prediction of lithium battery overcharge failure is crucial to improving the safety of battery packs and extending their service life.

[0003] At present, existing lithium battery overcharge fault prediction methods usually rely on fixed thresholds or simple analysis based on a single parameter. For example, if the battery voltage exceeds 4.2V, the system will consider overcharging to have occurred and issue an alarm; or when the battery temperature exceeds a certain set value, it is considered that the battery may be at risk of overcharging, etc.; the lack of flexibility and adaptability leads to insufficient accuracy and reliability of fault prediction. Summary of the invention

[0004] The present invention aims to solve the technical problems that the existing lithium battery overcharge fault prediction methods usually rely on fixed thresholds or simple analysis based on a single parameter, and have insufficient prediction accuracy and reliability. A lithium battery fault prediction method and system are provided to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a lithium battery fault prediction method, comprising: during the charging process of a lithium battery pack, collecting the voltage, current and temperature of multiple lithium battery cells in the lithium battery pack to form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and obtaining the standard voltage sequence, standard current sequence and standard temperature sequence for charging the lithium battery cells, performing a change significance analysis to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances; obtaining the standard use condition parameters of the lithium battery pack to construct a standard condition graph, and collecting the actual use condition parameters of the current lithium battery pack to construct an actual condition graph, wherein a lithium battery pack consistency analysis is performed based on the multiple temperature change significances to obtain consistency condition parameters and construct the actual condition graph; identifying the similarity between the actual condition graph and the standard condition graph and calculating the condition significance; performing overcharge fault prediction of the lithium battery pack based on the multiple voltage change significances, multiple current change significances and multiple temperature change significances to obtain a first overcharge fault coefficient, and using the condition significance to perform correction calculation on the first overcharge fault coefficient to obtain a second overcharge fault coefficient as a lithium battery fault prediction result.

[0007] In a second aspect, the present invention provides a lithium battery fault prediction system, including: a change significance analysis module, which is used to collect voltages, currents and temperatures of multiple lithium battery cells in a lithium battery pack during the charging process of the lithium battery pack, form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and obtain standard voltage sequences, standard current sequences and standard temperature sequences for charging the lithium battery cells, and perform change significance analysis to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances; an actual condition map construction module, which is used to obtain standard usage condition parameters of the lithium battery pack, construct a standard condition map, and collect the actual usage conditions of the current lithium battery pack. The present invention relates to a lithium battery pack comprising: a component parameter, an actual condition diagram is constructed, wherein a consistency analysis of the lithium battery pack is performed according to the multiple temperature change significances to obtain consistency condition parameters and construct the actual condition diagram; a condition significance calculation module is used to identify the similarity between the actual condition diagram and the standard condition diagram and calculate the condition significance; an overcharge fault prediction module is used to predict the overcharge fault of the lithium battery pack according to the multiple voltage change significances, the multiple current change significances and the multiple temperature change significances to obtain a first overcharge fault coefficient, and the first overcharge fault coefficient is corrected and calculated using the condition significance to obtain a second overcharge fault coefficient as a lithium battery fault prediction result.

[0008] In a third aspect, the present invention further provides an electronic device, comprising:

[0009] At least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can perform the steps of any one of the methods described in the first aspect above.

[0010] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed, the steps of the method described in any one of the first aspects are implemented.

[0011] The beneficial effects of the present invention are as follows: during the charging process of the lithium battery pack, the voltage, current and temperature of multiple lithium battery cells in the lithium battery pack are collected to form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and the standard voltage sequence, standard current sequence and standard temperature sequence for charging the lithium battery cells are obtained, and a change significance analysis is performed to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances; on the other hand, the standard use condition parameters of the lithium battery pack are obtained to construct a standard condition diagram, and the actual use condition parameters of the current lithium battery pack are collected to construct an actual condition diagram, wherein the consistency analysis of the lithium battery pack is performed according to the multiple temperature change significances to obtain consistency condition parameters, and the actual condition diagram is constructed. an actual condition diagram; then identifying the similarity between the actual condition diagram and the standard condition diagram, and calculating the condition significance; further predicting the overcharge fault of the lithium battery pack according to the multiple voltage change significances, the multiple current change significances, and the multiple temperature change significances, and obtaining a first overcharge fault coefficient; finally, using the condition significance to perform a correction calculation on the first overcharge fault coefficient, and obtaining a second overcharge fault coefficient as a lithium battery fault prediction result; that is, by fusing multi-dimensional data to predict lithium battery overcharge faults, the accuracy and reliability of overcharge fault prediction can be improved, so that potential risks in the charging process can be identified in advance, serious consequences caused by overcharge faults can be avoided, and the safety of lithium battery use can be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of a flow chart of a lithium battery fault prediction method provided by the present invention;

[0013] Figure 2 A schematic diagram of the structure of a lithium battery fault prediction system provided by the present invention;

[0014] Figure 3 A schematic diagram of the structure of an electronic device provided by the present invention;

[0015] Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0016] In the accompanying drawings, the components represented by the reference numerals are described as follows:

[0017] Change significance analysis module 01, actual condition map construction module 02, condition significance calculation module 03, overcharge fault prediction module 04, electronic device 500, memory 510, processor 520, first computer program 511, computer readable storage medium 600, second computer program 611. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0019] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0020] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0021] Embodiment 1, as Figure 1 As shown, an embodiment of the present invention provides a lithium battery fault prediction method, which specifically includes the following steps:

[0022] S100: During the charging process of the lithium battery pack, the voltage, current and temperature of multiple lithium battery cells in the lithium battery pack are collected to form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and the standard voltage sequence, standard current sequence and standard temperature sequence for charging the lithium battery cells are obtained, and a change significance analysis is performed to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances.

[0023] Furthermore, step S100 of the present application also includes:

[0024] S110: During the charging process of the lithium battery pack, the voltage, current and temperature of multiple lithium battery cells at multiple time nodes before the charge coefficient reaches the full threshold are collected to form multiple voltage sequences, multiple current sequences and multiple temperature sequences; S120: The standard voltage, standard current and standard temperature at the multiple time nodes are obtained to construct a standard voltage sequence, a standard current sequence and a standard temperature sequence; S130: The deviations of the multiple voltage sequences, multiple current sequences and multiple temperature sequences from the standard voltage sequence, standard current sequence and standard temperature sequence are calculated respectively to obtain multiple voltage deviation sequences, multiple current deviation sequences and multiple temperature deviation sequences.

[0025] Specifically, the data collection time interval is configured, and the data collection time interval can be set according to the fault prediction accuracy requirement. The higher the fault prediction accuracy requirement, the shorter the data collection time interval. For example, the data collection time interval is set to 1 minute, that is, data collection is performed every 1 minute. Then, during the charging process of the lithium battery pack, the voltage, current and temperature of multiple lithium battery cells at multiple time nodes before the charge factor reaches the full threshold are collected. The charge factor (SOC) is a value that describes the ratio of the current power of the battery to the maximum power, usually expressed as a percentage. During the charging process, the SOC of the battery gradually increases from 0%. When the SOC reaches 100%, the battery is fully charged. During the charging process, the voltage, current and temperature of the battery will change dynamically. Then, the voltage, current and temperature of multiple lithium battery cells at multiple time nodes are arranged in the order of the time nodes to obtain multiple voltage sequences, multiple current sequences and multiple temperature sequences, wherein each lithium battery cell includes a voltage sequence, a current sequence and a temperature sequence.

[0026] Next, the standard voltage, standard current and standard temperature within the multiple time nodes are obtained, and the standard voltage, standard current and standard temperature within the multiple time nodes are arranged in the order of the time nodes to obtain a standard voltage sequence, a standard current sequence and a standard temperature sequence. Further, based on the standard voltage sequence, the standard current sequence and the standard temperature sequence, the deviations of the multiple voltage sequences, the multiple current sequences and the multiple temperature sequences are calculated respectively, that is, the deviations between the actual voltage, current, temperature and the standard values ​​in each lithium battery cell at the same time node are calculated to obtain multiple voltage deviation sequences, multiple current deviation sequences and multiple temperature deviation sequences, wherein the voltage deviation reflects the difference between the actual voltage and the standard voltage of each lithium battery cell; the current deviation reflects the difference between the actual charging current and the standard current of each battery cell; the temperature deviation reflects the difference between the actual temperature and the standard temperature of each battery cell. By calculating the deviation sequence of voltage, current and temperature, the state changes of each cell during the lithium battery charging process can be monitored in real time, and potential faults and abnormal conditions can be identified in time.

[0027] S140: performing change significance analysis according to the multiple voltage deviation sequences, the multiple current deviation sequences, and the multiple temperature deviation sequences to obtain multiple voltage change significances, multiple current change significances, and multiple temperature change significances.

[0028] Furthermore, step S140 of the present application also includes:

[0029] S141: Select a first selected voltage deviation within the first voltage deviation sequence, and randomly select a first random voltage deviation, calculate the ratio of the first selected voltage deviation to the first random voltage deviation, and obtain the first selected voltage change significance; S142: Continue to calculate to obtain multiple first selected voltage change significances, and calculate the average to obtain the first voltage change significance; S143: Continue to calculate to obtain multiple voltage change significances, multiple current change significances, and multiple temperature change significances based on the multiple voltage deviation sequences, multiple current deviation sequences, and multiple temperature deviation sequences.

[0030] Specifically, first, any one of the multiple voltage deviation sequences is randomly selected as the first voltage deviation sequence, and the voltage deviation of the first time node in the first voltage deviation sequence is selected as the first selected voltage deviation, wherein the first time node refers to the time node with the highest ranking, that is, the first node; then, the voltage deviation of any time node other than the first time node is randomly selected in the first voltage deviation sequence and is set as the first random voltage deviation; further, the ratio of the first selected voltage deviation and the first random voltage deviation is calculated, and the voltage deviation ratio is set as the first selected voltage change significance, wherein a larger significance value indicates that the selected voltage deviation has a greater change than the randomly selected voltage deviation, which means that the voltage fluctuation during charging is abnormal or there is a risk of large deviation in the battery.

[0031] Further using the same method, continue to calculate the ratio of the first selected voltage deviation to other random voltage deviations, obtain multiple first selected voltage change significances, and perform mean calculation on the multiple first selected voltage change significances to obtain the first voltage change significance. Then continue to perform significance calculation based on the multiple voltage deviation sequences, multiple current deviation sequences, and multiple temperature deviation sequences to obtain multiple voltage change significances, multiple current change significances, and multiple temperature change significances, wherein the voltage change significance reflects the degree of battery voltage change of each lithium battery cell during the charging process, the current change significance reflects the degree of current change of each lithium battery cell during the charging process, and the temperature change significance reflects the degree of change of the battery temperature of each lithium battery cell during the charging process.

[0032] S200: Obtain standard usage condition parameters of the lithium battery pack, construct a standard condition diagram, and collect actual usage condition parameters of the current lithium battery pack to construct an actual condition diagram, wherein consistency analysis of the lithium battery pack is performed based on the multiple temperature change significances, consistency condition parameters are obtained, and the actual condition diagram is constructed.

[0033] Furthermore, step S200 of the present application also includes:

[0034] S210: Obtain standard usage condition parameters of a lithium battery pack, wherein the standard usage condition parameters include standard ambient temperature, standard charging voltage, and standard consistency parameters; S220: Set the standard ambient temperature, standard charging voltage, and standard consistency parameters as preset values, as pixel values ​​of image data, and construct a standard condition map; S230: Collect actual usage condition parameters of the current lithium battery pack, wherein the actual usage condition parameters include actual ambient temperature, actual charging voltage, and actual consistency parameters, and the actual consistency parameters are obtained by calculating the variance of the multiple temperature change significances; S240: Calculate the ratio of the actual ambient temperature, actual charging voltage, and actual consistency parameters to the standard ambient temperature, standard charging voltage, and standard consistency parameters, multiply by the preset value, as the pixel value of the image data, and construct an actual condition map.

[0035] Specifically, first, the standard usage condition parameters of the lithium battery pack are obtained, wherein the standard usage condition parameters include standard ambient temperature, standard charging voltage, and standard consistency parameters. The standard consistency parameters characterize the uniformity of temperature performance of each battery cell in the battery pack during the charging process, which can be expressed by temperature variance and is used to reflect the consistency of temperature distribution between battery cells.

[0036] Next, the standard ambient temperature, standard charging voltage, and standard consistency parameters are mapped to the pixel values ​​of the image data. For example, the standard ambient temperature (such as 25°C) is set as a preset grayscale value of a certain image area in the image, such as a grayscale value of 100, to represent the environmental condition; the standard charging voltage (such as 4.2V) is set as a preset pixel value in the image, and the same pixel value preset value (such as a grayscale value of 100) can be assigned to this standard charging voltage, indicating the ideal voltage condition during the charging process; the standard consistency parameter (temperature variance) is set to 0, indicating that the temperatures of all cells in the battery pack are completely consistent, which is the optimal state. When the temperature variance is 0, it is mapped to another preset pixel value (such as a grayscale value of 100), indicating that the consistency of the battery pack is very good; a standard condition map is constructed based on the pixel values ​​set above, wherein the standard condition map includes three areas, each of which represents a standard condition parameter.

[0037] Then, the actual use condition parameters of the current lithium battery pack are collected, wherein the actual use condition parameters include the actual ambient temperature, the actual charging voltage and the actual consistency parameter, and the actual consistency parameter is obtained by calculating the variance of the multiple temperature change significances, that is, the variance of the multiple temperature change significances is set as the actual consistency parameter. Further calculate the ratio of the actual ambient temperature to the standard ambient temperature, set it as the temperature parameter ratio, calculate the ratio of the actual charging voltage to the standard charging voltage, set it as the voltage parameter ratio, and calculate the ratio of the actual consistency parameter to the standard consistency parameter, and set it as the consistency parameter ratio; and multiply the temperature parameter ratio, the voltage parameter ratio and the consistency parameter ratio by the pixel value preset value (such as the gray value of 100) as the pixel value of the image data to construct an actual condition map, wherein the gray value (i.e., pixel value) of each area in the image represents the difference between the actual condition and the standard condition of the battery pack during the charging process. When the actual ambient temperature, voltage and consistency parameters of the battery differ greatly from the standard conditions, the difference in pixel values ​​(i.e., gray values) between the actual condition map and the standard condition map is large, and obvious changes will appear in the image, indicating that an abnormality or failure may occur during the battery charging process. By constructing the actual condition diagram, the difference between the battery state during charging and the standard state can be intuitively reflected, and at the same time, it provides a basis for the next step of condition significance.

[0038] S300: Identify the similarity between the actual condition graph and the standard condition graph, and calculate and obtain the conditional saliency.

[0039] Furthermore, step S300 of the present application also includes:

[0040] S310: Collect a set of sample actual condition graphs and a set of sample standard condition graphs; S320: Use a twin neural network to supervise the training of a condition graph similarity identifier using the set of sample actual condition graphs and the set of sample standard condition graphs until the training converges; S330: Input the actual condition graph and the standard condition graph into the converged condition graph similarity identifier, and identify the output to obtain similarity; S340: Subtract the similarity from 1 to obtain conditional saliency.

[0041] Specifically, the historical operation records of similar battery packs of the lithium battery pack are queried, and a set of sample actual condition graphs and a set of sample standard condition graphs are collected. Then, a conditional image similarity identifier is constructed based on the twin neural network. The twin neural network is a neural network architecture that includes two identical neural network branches that share the same weights and receive two inputs. During training, the network compares the similarity of the two inputs and optimizes by calculating the difference between them. Then, the sample actual conditional image set and the sample standard conditional image set are used as training data to supervise the training of the conditional image similarity identifier. First, the image pairs in the standard conditional image set and the actual conditional image set are input into the two branches of the twin network, and the similarity value of the network output is calculated and compared with the true label (the similarity between the actual conditional image and the standard conditional image). The true similarity between the actual conditional image and the standard conditional image can be calculated as the ratio of the sum of the grayscale values ​​of all pixels in the actual conditional image to the sum of the grayscale values ​​of all pixels in the standard conditional image. Then, the contrast loss function is used to calculate the loss value, and the network weights are optimized by back propagation, and training is continued until the loss function converges to obtain a trained conditional image similarity identifier. During the training process, the twin neural network will gradually learn how to judge the similarity between the actual conditional image and the standard conditional image by comparing their pixel values ​​and features.

[0042] Then the actual condition graph and the standard condition graph are input into the converged condition graph similarity identifier for similarity identification, and the similarity between the two is output; and the similarity is subtracted from 1, and the difference between the two is taken as the condition significance, where the condition significance reflects the gap between the working state of the battery pack during the charging process and the ideal state. The larger the condition significance, the greater the difference between the actual conditions and the standard conditions, which means that the probability of overcharging failure of the battery pack during the charging process is greater and the risk is higher.

[0043] S400: Predict an overcharge fault of a lithium battery pack based on the multiple voltage change significances, the multiple current change significances, and the multiple temperature change significances to obtain a first overcharge fault coefficient, and use the conditional significance to perform a correction calculation on the first overcharge fault coefficient to obtain a second overcharge fault coefficient as a lithium battery fault prediction result.

[0044] Furthermore, step S400 of the present application also includes:

[0045] S410: Based on the overcharge fault detection data of the same model lithium battery pack in the historical time, a sample voltage change significance set, a sample current change significance set and a sample temperature change significance set are collected, and the proportion of overcharge faults of the lithium battery pack under different sample voltage change significances, sample current change significances and sample temperature change significances are collected to obtain a sample overcharge fault coefficient set; S420: Using the sample voltage change significance set, sample current change significance set, sample temperature change significance set and sample overcharge fault coefficient set as supervised training data, train the overcharge fault predictor until the training converges; S430: Combine the multiple voltage change significances, multiple current change significances and multiple temperature change significances to obtain multiple groups of input data, input them into the overcharge fault predictor respectively, and output multiple predicted overcharge fault coefficients; S440: Calculate the mean of the multiple predicted overcharge fault coefficients to obtain a first overcharge fault coefficient.

[0046] Specifically, based on the overcharge fault detection data of the same model of lithium battery packs in historical time, the sample voltage change significance set, the sample current change significance set and the sample temperature change significance set are collected; then the proportion of overcharge faults in the lithium battery pack under different sample voltage change significances, sample current change significances and sample temperature change significances is counted, that is, the ratio of the number of overcharge faults to the total number of charging times, and the fault proportion is set as the sample overcharge fault coefficient to obtain the sample overcharge fault coefficient set.

[0047] An overcharge fault predictor is constructed based on a BP neural network. The overcharge fault predictor is a model that uses machine learning technology to predict overcharge faults during the charging process of a lithium battery pack. The model includes an input layer, multiple hidden layers, and an output layer. The input data of the input layer are voltage change significance, current change significance, and temperature change significance, and the output data of the output layer is an overcharge fault coefficient. The sample voltage change significance set, the sample current change significance set, the sample temperature change significance set and the sample overcharge fault coefficient set are further used as supervised training data to perform supervised training on the overcharge fault predictor. First, the input (voltage, current, temperature significance) in the training data is passed to the input layer of the network. Then, each input value is multiplied by the weight of the input layer, added with the bias, and processed by the activation function to obtain the output of the hidden layer. The output of the hidden layer is then passed to the output layer, and the predicted overcharge fault coefficient is obtained through weighted summation and activation function calculation; then the loss function is used to calculate the difference between the network output and the true label (overcharge fault coefficient), and the error of each hidden layer and input layer is calculated by back propagation through the error of the output layer. The weight and bias of the network are adjusted according to the error of each layer through the gradient descent method. Through multiple iterations, the BP neural network gradually updates the weight and bias, so that the predicted overcharge fault coefficient becomes closer and closer to the true label until the loss function converges to obtain a trained overcharge fault predictor.

[0048] Then, the multiple voltage change significances, multiple current change significances and multiple temperature change significances are combined, that is, the change significance of each lithium battery cell is combined into a set of input data to obtain multiple sets of input data; then the multiple sets of input data are respectively input into the overcharge fault predictor, multiple predicted overcharge fault coefficients are output, and the average of the multiple predicted overcharge fault coefficients is calculated, and the average calculation result is set as the first overcharge fault coefficient, wherein the larger the overcharge fault coefficient, the greater the probability of overcharge fault occurring in the lithium battery pack and the higher the risk.

[0049] Furthermore, step S400 of the present application also includes:

[0050] S450: Calculate the sum of 1 and the conditional significance as a correction coefficient; S460: Multiply the correction coefficient by the first overcharge fault coefficient to obtain a second overcharge fault coefficient as a lithium battery fault prediction result.

[0051] Specifically, on the other hand, 1 and the conditional significance are added, and the sum of the two is used as the correction coefficient. Then, the correction coefficient is multiplied by the first overcharge fault coefficient, and the product of the two is used as the second overcharge fault coefficient, and the second overcharge fault coefficient is used as the lithium battery fault prediction result. By setting the correction coefficient according to the conditional significance and correcting the first overcharge fault coefficient, the charging condition interference during the charging process can be effectively considered, and the accuracy and reliability of overcharge fault prediction can be further improved.

[0052] A lithium battery fault prediction method provided by an embodiment of the present invention has at least the following technical effects:

[0053] During the charging process of the lithium battery pack, the voltage, current and temperature of multiple lithium battery cells in the lithium battery pack are collected to form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and the standard voltage sequence, standard current sequence and standard temperature sequence for charging the lithium battery cells are obtained, and a change significance analysis is performed to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances; on the other hand, the standard use condition parameters of the lithium battery pack are obtained to construct a standard condition diagram, and the actual use condition parameters of the current lithium battery pack are collected to construct an actual condition diagram, wherein the consistency analysis of the lithium battery pack is performed according to the multiple temperature change significances to obtain consistency condition parameters and construct the actual condition diagram; Then, the similarity between the actual condition graph and the standard condition graph is identified, and the condition significance is calculated; further, based on the multiple voltage change significances, multiple current change significances, and multiple temperature change significances, the overcharge fault prediction of the lithium battery pack is performed to obtain a first overcharge fault coefficient; finally, the first overcharge fault coefficient is corrected and calculated using the condition significance to obtain a second overcharge fault coefficient as a lithium battery fault prediction result; that is, by fusing multi-dimensional data to perform lithium battery overcharge fault prediction, the accuracy and reliability of overcharge fault prediction can be improved, so that potential risks in the charging process can be identified in advance, serious consequences caused by overcharge faults can be avoided, and the safety of lithium battery use can be significantly improved.

[0054] Embodiment 2, as Figure 2As shown, based on the same inventive concept as the lithium battery fault prediction method provided in Example 1, an embodiment of the present invention further provides a lithium battery fault prediction system, including: a change significance analysis module 01, which is used to collect the voltage, current and temperature of multiple lithium battery cells in the lithium battery pack during the charging process of the lithium battery pack, form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and obtain the standard voltage sequence, standard current sequence and standard temperature sequence for charging the lithium battery cells, and perform change significance analysis to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances; an actual condition map construction module 02, which is used to obtain standard usage condition parameters of the lithium battery pack, construct a standard condition map, and Collect the actual use condition parameters of the current lithium battery pack and construct an actual condition diagram, wherein the consistency analysis of the lithium battery pack is performed according to the multiple temperature change significances to obtain consistency condition parameters and construct the actual condition diagram; a condition significance calculation module 03 is used to identify the similarity between the actual condition diagram and the standard condition diagram and calculate the condition significance; an overcharge fault prediction module 04 is used to predict the overcharge fault of the lithium battery pack according to the multiple voltage change significances, multiple current change significances and multiple temperature change significances to obtain a first overcharge fault coefficient, and the first overcharge fault coefficient is corrected and calculated using the condition significance to obtain a second overcharge fault coefficient as a lithium battery fault prediction result.

[0055] Furthermore, the lithium battery fault prediction system also includes: during the charging process of the lithium battery pack, collecting the voltage, current and temperature of multiple lithium battery cells in multiple time nodes before the charge coefficient reaches the full threshold, forming multiple voltage sequences, multiple current sequences and multiple temperature sequences; obtaining the standard voltage, standard current and standard temperature in the multiple time nodes, and constructing a standard voltage sequence, a standard current sequence and a standard temperature sequence; respectively calculating the deviations of the multiple voltage sequences, multiple current sequences and multiple temperature sequences from the standard voltage sequence, standard current sequence and standard temperature sequence, and obtaining multiple voltage deviation sequences, multiple current deviation sequences and multiple temperature deviation sequences; performing a change significance analysis based on the multiple voltage deviation sequences, multiple current deviation sequences and multiple temperature deviation sequences, and obtaining multiple voltage change significances, multiple current change significances and multiple temperature change significances.

[0056] Furthermore, the lithium battery fault prediction system also includes: selecting a first selected voltage deviation within a first voltage deviation sequence, and randomly selecting a first random voltage deviation, calculating the ratio of the first selected voltage deviation to the first random voltage deviation, and obtaining a first selected voltage change significance; continuing to calculate to obtain multiple first selected voltage change significances, and calculating the average to obtain the first voltage change significance; continuing to calculate to obtain multiple voltage change significances, multiple current change significances, and multiple temperature change significances based on the multiple voltage deviation sequences, multiple current deviation sequences, and multiple temperature deviation sequences.

[0057] Furthermore, the lithium battery fault prediction system also includes: obtaining standard usage condition parameters of a lithium battery pack, wherein the standard usage condition parameters include standard ambient temperature, standard charging voltage, and standard consistency parameters; setting the standard ambient temperature, standard charging voltage, and standard consistency parameters as preset values, as pixel values ​​of image data, to construct a standard condition map; collecting actual usage condition parameters of the current lithium battery pack, wherein the actual usage condition parameters include actual ambient temperature, actual charging voltage, and actual consistency parameters, and the actual consistency parameters are obtained by calculating the variance of the multiple temperature change significances; calculating the ratio of the actual ambient temperature, actual charging voltage, and actual consistency parameters to the standard ambient temperature, standard charging voltage, and standard consistency parameters, multiplying by the preset value, as the pixel value of the image data, to construct an actual condition map.

[0058] Furthermore, the lithium battery fault prediction system also includes: collecting a set of sample actual condition graphs and a set of sample standard condition graphs; using a twin neural network, using the set of sample actual condition graphs and the set of sample standard condition graphs, supervised training of a condition graph similarity identifier until the training converges; inputting the actual condition graph and the standard condition graph into the converged condition graph similarity identifier, identifying the output to obtain similarity; subtracting the similarity from 1 to obtain conditional significance.

[0059] Furthermore, the lithium battery fault prediction system also includes: based on the overcharge fault detection data of the same model lithium battery pack in the historical time, collecting a sample voltage change significance set, a sample current change significance set and a sample temperature change significance set, and collecting the proportion of overcharge faults occurring in the lithium battery pack under different sample voltage change significances, sample current change significances and sample temperature change significances to obtain a sample overcharge fault coefficient set; using the sample voltage change significance set, sample current change significance set, sample temperature change significance set and sample overcharge fault coefficient set as supervised training data to train an overcharge fault predictor until the training converges; combining the multiple voltage change significances, multiple current change significances and multiple temperature change significances to obtain multiple groups of input data, respectively inputting them into the overcharge fault predictor, and outputting multiple predicted overcharge fault coefficients; calculating the mean of the multiple predicted overcharge fault coefficients to obtain a first overcharge fault coefficient.

[0060] Furthermore, the lithium battery fault prediction system also includes: calculating the sum of 1 and the conditional significance as a correction coefficient; multiplying the correction coefficient by the first overcharge fault coefficient to obtain a second overcharge fault coefficient as a lithium battery fault prediction result.

[0061] For example 3, please refer to Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: during the charging process of the lithium battery pack, the voltage, current, and temperature of multiple lithium battery cells in the lithium battery pack are collected to form multiple voltage sequences, multiple current sequences, and multiple temperature sequences, and a standard voltage sequence, a standard current sequence, and a standard temperature sequence for charging the lithium battery cells are obtained, and a change significance analysis is performed to obtain multiple voltage change significances, multiple current change significances, and multiple temperature change significances; the lithium battery is obtained. The standard usage condition parameters of the battery pack are collected to construct a standard condition diagram, and the actual usage condition parameters of the current lithium battery pack are collected to construct an actual condition diagram, wherein the consistency analysis of the lithium battery pack is performed according to the multiple temperature change significances to obtain consistency condition parameters and construct the actual condition diagram; the similarity between the actual condition diagram and the standard condition diagram is identified and the condition significance is calculated; the overcharge fault prediction of the lithium battery pack is performed according to the multiple voltage change significances, the multiple current change significances and the multiple temperature change significances to obtain a first overcharge fault coefficient, and the first overcharge fault coefficient is corrected and calculated using the condition significance to obtain a second overcharge fault coefficient as the lithium battery fault prediction result.

[0062] Example 4, please refer to Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, the following steps are implemented: during the charging process of the lithium battery pack, the voltage, current and temperature of multiple lithium battery cells in the lithium battery pack are collected to form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and a standard voltage sequence, a standard current sequence and a standard temperature sequence for charging the lithium battery cells are obtained, and a change significance analysis is performed to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances; standard use condition parameters of the lithium battery pack are obtained, and a standard Condition diagram, and collect actual use condition parameters of the current lithium battery pack to construct an actual condition diagram, wherein, according to the multiple temperature change significances, the lithium battery pack consistency analysis is performed to obtain consistency condition parameters and construct the actual condition diagram; the similarity between the actual condition diagram and the standard condition diagram is identified, and the condition significance is calculated to obtain; according to the multiple voltage change significances, multiple current change significances and multiple temperature change significances, the overcharge fault prediction of the lithium battery pack is performed to obtain a first overcharge fault coefficient, and the first overcharge fault coefficient is corrected and calculated using the condition significance to obtain a second overcharge fault coefficient as the lithium battery fault prediction result.

[0063] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0064] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0068] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A lithium battery failure prediction method, characterized in that: Methods include: During the charging process of the lithium battery pack, the voltage, current and temperature of multiple lithium battery cells in the lithium battery pack are collected to form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and the standard voltage sequence, standard current sequence and standard temperature sequence for charging the lithium battery cells are obtained, and a change significance analysis is performed to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances; Obtain standard usage condition parameters of the lithium battery pack, construct a standard condition diagram, and collect actual usage condition parameters of the current lithium battery pack to construct an actual condition diagram, wherein a consistency analysis of the lithium battery pack is performed according to the plurality of temperature change significances, consistency condition parameters are obtained, and the actual condition diagram is constructed; Identify the similarity between the actual condition graph and the standard condition graph, and calculate the conditional saliency; Based on the multiple voltage change significances, multiple current change significances and multiple temperature change significances, an overcharge fault prediction of the lithium battery pack is performed to obtain a first overcharge fault coefficient, and the first overcharge fault coefficient is corrected and calculated using the conditional significance to obtain a second overcharge fault coefficient as a lithium battery fault prediction result.

2. A lithium battery failure prediction method according to claim 1, characterized in that: The voltage, current and temperature of multiple lithium battery cells in the lithium battery pack are collected to form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and the standard voltage sequence, standard current sequence and standard temperature sequence for charging the lithium battery cells are obtained, and the change significance analysis is performed to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances, including: During the charging process of the lithium battery pack, the voltage, current and temperature of multiple lithium battery cells at multiple time points before the charge factor reaches the full threshold are collected to form multiple voltage sequences, multiple current sequences and multiple temperature sequences; Acquire the standard voltage, standard current and standard temperature in the multiple time nodes, and construct a standard voltage sequence, a standard current sequence and a standard temperature sequence; Respectively calculating deviations between the multiple voltage sequences, the multiple current sequences, and the multiple temperature sequences and the standard voltage sequence, the standard current sequence, and the standard temperature sequence to obtain multiple voltage deviation sequences, multiple current deviation sequences, and multiple temperature deviation sequences; A change significance analysis is performed according to the multiple voltage deviation sequences, multiple current deviation sequences, and multiple temperature deviation sequences to obtain multiple voltage change significances, multiple current change significances, and multiple temperature change significances.

3. A lithium battery fault prediction method according to claim 2, characterized in that: According to the multiple voltage deviation sequences, multiple current deviation sequences and multiple temperature deviation sequences, a change significance analysis is performed to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances, including: Selecting a first selected voltage deviation in a first voltage deviation sequence, and randomly selecting a first random voltage deviation, calculating a ratio of the first selected voltage deviation to the first random voltage deviation, and obtaining a first selected voltage change significance; Continue to calculate to obtain multiple first selected voltage change significances, and calculate the average to obtain the first voltage change significance; Continuously calculate and obtain a plurality of voltage change significances, a plurality of current change significances, and a plurality of temperature change significances according to the plurality of voltage deviation sequences, the plurality of current deviation sequences, and the plurality of temperature deviation sequences.

4. A lithium battery failure prediction method according to claim 1, characterized in that: Obtain the standard use condition parameters of the lithium battery pack, build a standard condition diagram, and collect the actual use condition parameters of the current lithium battery pack to build an actual condition diagram, including: Obtaining standard usage condition parameters of a lithium battery pack, wherein the standard usage condition parameters include standard ambient temperature, standard charging voltage, and standard consistency parameters; The standard ambient temperature, standard charging voltage, and standard consistency parameters are set as preset values ​​as pixel values ​​of image data to construct a standard condition map; Collecting actual use condition parameters of the current lithium battery pack, wherein the actual use condition parameters include actual ambient temperature, actual charging voltage and actual consistency parameters, and the actual consistency parameters are obtained by calculating the variance of the plurality of temperature change significances; The ratio of the actual ambient temperature, the actual charging voltage and the actual consistency parameter to the standard ambient temperature, the standard charging voltage and the standard consistency parameter is calculated and multiplied by the preset value as the pixel value of the image data to construct an actual condition map.

5. A lithium battery failure prediction method according to claim 1, characterized in that: Identifying the similarity between the actual condition graph and the standard condition graph and calculating the conditional saliency includes: Collecting a set of sample actual condition graphs and a set of sample standard condition graphs; Using a twin neural network, using the sample actual condition graph set and the sample standard condition graph set, supervise the training of a condition graph similarity identifier until the training converges; Inputting the actual condition graph and the standard condition graph into the converged condition graph similarity identifier, and identifying the output to obtain similarity; The conditional saliency is obtained by subtracting the similarity from 1.

6. A lithium battery fault prediction method according to claim 1, characterized in that: According to the multiple voltage change significances, the multiple current change significances, and the multiple temperature change significances, an overcharge fault prediction of the lithium battery pack is performed to obtain a first overcharge fault coefficient, including: According to the overcharge fault detection data of the same type of lithium battery pack in the historical time, the sample voltage change significance set, the sample current change significance set and the sample temperature change significance set are collected, and the proportion of overcharge faults of the lithium battery pack under different sample voltage change significances, sample current change significances and sample temperature change significances are collected to obtain the sample overcharge fault coefficient set; Using the sample voltage change significance set, the sample current change significance set, the sample temperature change significance set and the sample overcharge fault coefficient set as supervised training data, training an overcharge fault predictor until the training converges; Combining the multiple voltage change significances, the multiple current change significances and the multiple temperature change significances to obtain multiple groups of input data, respectively inputting them into the overcharge fault predictor, and outputting multiple predicted overcharge fault coefficients; An average value of the plurality of predicted overcharge fault coefficients is calculated to obtain a first overcharge fault coefficient.

7. A lithium battery fault prediction method according to claim 1, characterized in that: The first overcharge fault coefficient is corrected and calculated by using the condition significance to obtain a second overcharge fault coefficient, including: Calculate the sum of 1 and the conditional significance as the correction coefficient; The correction coefficient is multiplied by the first overcharge fault coefficient to obtain a second overcharge fault coefficient as a lithium battery fault prediction result.

8. A lithium battery failure prediction system, characterized in that: The steps for implementing a lithium battery fault prediction method according to any one of claims 1 to 7 include: A change significance analysis module is used to collect the voltage, current and temperature of multiple lithium battery cells in the lithium battery pack during the charging process of the lithium battery pack, form multiple voltage sequences, multiple current sequences and multiple temperature sequences, and obtain the standard voltage sequence, standard current sequence and standard temperature sequence for charging the lithium battery cells, and perform change significance analysis to obtain multiple voltage change significances, multiple current change significances and multiple temperature change significances; An actual condition map construction module is used to obtain standard use condition parameters of the lithium battery pack, construct a standard condition map, and collect actual use condition parameters of the current lithium battery pack to construct an actual condition map, wherein the consistency analysis of the lithium battery pack is performed according to the multiple temperature change significances, the consistency condition parameters are obtained, and the actual condition map is constructed; A conditional saliency calculation module, used to identify the similarity between the actual condition graph and the standard condition graph, and calculate the conditional saliency; An overcharge fault prediction module is used to predict the overcharge fault of the lithium battery pack according to the multiple voltage change significances, the multiple current change significances and the multiple temperature change significances, obtain a first overcharge fault coefficient, use the conditional significance to perform correction calculation on the first overcharge fault coefficient, and obtain a second overcharge fault coefficient as a lithium battery fault prediction result.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the steps of a lithium battery fault prediction method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the steps of a lithium battery fault prediction method as described in any one of claims 1 to 7 are implemented.

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