Battery management method and device, electronic equipment, storage medium and program product

Abstract

The invention relates to a battery management method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: in the use process of a battery, carrying out electrochemical impedance spectroscopy test on the battery for N times to obtain N groups of test data; wherein N is a natural number greater than 1; calculating an equivalent impedance value of the battery based on each group of test data, and obtaining N equivalent impedance values of the equivalent impedance value; inputting the N equivalent impedance values and a standard equivalent impedance value into a Bayesian inference model, and obtaining posterior probability distribution output by the Bayesian inference model; and determining the health state of the battery according to the posterior probability distribution. By adopting the method, the more accurate battery health state can be obtained, so that the effectiveness of battery health management is improved.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a battery management method and device, electronic equipment, storage medium and program product. Background Art

[0002] Batteries may pose safety risks after long-term use. To ensure safe use of batteries, battery health management is required.

[0003] Traditional health management solutions, such as making battery charge and discharge curves and measuring the internal resistance of batteries, cannot capture early changes in batteries. For example, in the early stage of battery changes, changes have begun to appear inside the battery, but the battery has not yet manifested itself externally. In this case, traditional health management solutions cannot be used to learn about changes inside the battery, and therefore cannot capture early changes in the battery.

[0004] How to know the early changes of the battery to obtain a more accurate battery health status and thus improve the effectiveness of battery health management remains an urgent issue to be solved. Summary of the invention

[0005] Based on this, it is necessary to provide a method for detecting early changes in the battery to obtain a more accurate battery health status, thereby improving the effectiveness of battery health management, in response to the above technical problems.

[0006] In a first aspect, the present application provides a battery management method, comprising:

[0007] During the use of the battery, the battery is subjected to N electrochemical impedance spectroscopy tests to obtain N sets of test data; wherein N is a natural number greater than 1;

[0008] Calculating the equivalent impedance value of the battery based on each group of the test data to obtain N equivalent impedance values ​​of the equivalent impedance value;

[0009] Inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model;

[0010] The health state of the battery is determined according to the posterior probability distribution.

[0011] In one embodiment, determining the health state of the battery according to the posterior probability distribution includes:

[0012] Sampling the posterior probability distribution, and determining an expected value of the sampled sample based on the sampled sample;

[0013] When the expected value is greater than a preset equivalent impedance value, and / or a difference between the expected value and the preset equivalent impedance value is greater than a preset difference, it is determined that the health state of the battery is abnormal.

[0014] In one embodiment, determining the health state of the battery according to the posterior probability distribution includes:

[0015] Sampling the posterior probability distribution, and determining a variance value of the sampled samples according to the sampled samples;

[0016] When the variance value is greater than a preset variance value, it is determined that the health state of the battery is abnormal.

[0017] In one embodiment, determining the health state of the battery according to the posterior probability distribution includes:

[0018] Determine a variation interval of the equivalent impedance value according to the posterior probability distribution;

[0019] When the variation interval exceeds a preset confidence interval of the posterior probability distribution, the health state of the battery is determined to be abnormal.

[0020] In one embodiment, the test data includes one or more of the following: ohmic resistance, charge transfer resistance, double layer capacitance and Warburg impedance of the battery; and the calculation of the equivalent impedance value of the battery based on each set of the test data includes:

[0021] Inputting each set of test data into an estimation formula of an equivalent impedance value to obtain an equivalent impedance value;

[0022] The estimation formula includes: ;

[0023] in, represents the ohmic resistance of the battery, represents the charge transfer resistance, represents the double layer capacitance, represents the Warburg impedance, represents the imaginary part, Represents the angular frequency.

[0024] In one embodiment, the method further comprises:

[0025] Based on the N equivalent impedance values, a change trend graph of the equivalent impedance value during battery use is generated;

[0026] The change trend graph is displayed.

[0027] In a second aspect, the present application also provides a battery management device, including:

[0028] An acquisition module is used to perform N electrochemical impedance spectroscopy tests on the battery during use of the battery to obtain N groups of test data; wherein N is a natural number greater than 1;

[0029] The acquisition module is further used to calculate the equivalent impedance value of the battery based on each group of test data to obtain N equivalent impedance values ​​of the equivalent impedance value;

[0030] A processing module, used for inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model;

[0031] The processing module is further used to determine the health state of the battery according to the posterior probability distribution.

[0032] In a third aspect, the present application further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] During the use of the battery, the battery is subjected to N electrochemical impedance spectroscopy tests to obtain N sets of test data; wherein N is a natural number greater than 1;

[0034] Calculating the equivalent impedance value of the battery based on each group of the test data to obtain N equivalent impedance values ​​of the equivalent impedance value;

[0035] Inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model;

[0036] The health state of the battery is determined according to the posterior probability distribution.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0038] During the use of the battery, the battery is subjected to N electrochemical impedance spectroscopy tests to obtain N sets of test data; wherein N is a natural number greater than 1;

[0039] Calculating the equivalent impedance value of the battery based on each group of the test data to obtain N equivalent impedance values ​​of the equivalent impedance value;

[0040] Inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model;

[0041] The health state of the battery is determined according to the posterior probability distribution.

[0042] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0043] During the use of the battery, the battery is subjected to N electrochemical impedance spectroscopy tests to obtain N sets of test data; wherein N is a natural number greater than 1;

[0044] Calculating the equivalent impedance value of the battery based on each group of the test data to obtain N equivalent impedance values ​​of the equivalent impedance value;

[0045] Inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model;

[0046] The health state of the battery is determined according to the posterior probability distribution.

[0047] The above-mentioned battery management method, device, electronic device, computer-readable storage medium and computer program product perform N electrochemical impedance spectroscopy tests on the battery during the use of the battery to obtain N groups of test data, and determine the health status of the battery based on the test data. It is possible to monitor the health status of the battery during the use of the battery. Most traditional health management methods are based on analysis and prediction based on the historical data of the battery, and lack the ability to dynamically update in real time. This lag makes it impossible for them to accurately predict the future state of the battery and possible failures, which may cause irreversible damage to the battery if it is not maintained in time. The method provided in this embodiment monitors the health status of the battery in real time during the use of the battery, can capture early changes in the battery to obtain a more accurate battery health status, and monitor the early degradation information of the battery in advance before the battery fails, thereby improving the effectiveness of battery health management.

[0048] Moreover, as a non-destructive electrochemical analysis technology, EIS has great potential in evaluating the internal state of the battery. By applying AC signals of different frequencies and measuring the impedance response of the battery, EIS can provide detailed information about the charge transfer, diffusion and other processes inside the battery. By analyzing this information, the health status of the battery can be more accurately evaluated. This embodiment uses the EIS test method to obtain the test data of the battery, which can improve the accuracy and reliability of the battery test.

[0049] The Bayesian inference model is a probabilistic model that not only allows the use of prior knowledge and current measurement data to dynamically update the equivalent impedance value of the battery health state, but also avoids uncertainty and noise in the EIS test data and gives more reliable results. Therefore, using the Bayesian inference model to infer the battery health state can obtain a more accurate battery health state, thereby improving the effectiveness of battery health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0051] FIG1 is a diagram of an application environment of a battery management method according to an embodiment;

[0052] FIG2 is a schematic flow chart of a battery management method according to an embodiment;

[0053] FIG3 is another schematic flow chart of a battery management method according to an embodiment;

[0054] FIG4 is another schematic flow chart of a battery management method in one embodiment;

[0055] FIG5 is another schematic flow chart of a battery management method according to an embodiment;

[0056] FIG6 is a schematic diagram of a variation trend diagram in one embodiment;

[0057] FIG7 is a structural block diagram of a battery management device in one embodiment;

[0058] Figure 8 FIG. 4 is a diagram showing the internal structure of an electronic device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The battery management method provided in the embodiment of the present application can be applied in the application environment as shown in Figure 1. Among them, the electronic device includes a test device 102 and a processing device 104, and the test device 102 and the processing device 104 communicate. The test device is used to test the battery, and the processing device is used to perform calculations based on the test data. The processing device 104 can be a server. The server can be a local server or a cloud server. The processing device 104 can be an independent physical server, or it can be a server cluster or distributed system composed of multiple physical servers, or it can be a cloud server that provides cloud computing services. A data storage system can be set in the processing device 104, and the data storage system can store data that the processing device 104 needs to process.

[0061] In an exemplary embodiment, as shown in FIG2 , a battery management method is provided, which is described by taking the method applied to the electronic device in FIG1 as an example, and includes the following steps 201 to 204. Among them:

[0062] S201, during the use of the battery, perform N electrochemical impedance spectroscopy tests on the battery to obtain N groups of test data.

[0063] Wherein, N is a natural number greater than 1. The value of N depends on the actual test requirements of the battery.

[0064] Among them, the electrochemical impedance spectroscopy test, also known as the EIS test, is the full name of Electrochemical Impedance Spectroscopy. The EIS test is based on the charge transfer and mass transfer process in the electrochemical reaction, and analyzes the characteristics of the electrochemical reaction by measuring the impedance of the electrode surface.

[0065] A set of test data may include one or more of the battery's ohmic resistance, charge transfer resistance, double-layer capacitance and Warburg impedance.

[0066] The battery usage process can be understood as the battery charging process or the battery discharging process.

[0067] The battery may be a lithium battery or other types of batteries, which is not limited in this embodiment.

[0068] During the use of the battery, performing electrochemical impedance spectroscopy tests on the battery N times can be understood as performing electrochemical impedance spectroscopy tests on the battery N times continuously during the use of the battery. The continuous electrochemical impedance spectroscopy tests N times can be understood as performing electrochemical impedance spectroscopy tests N times at a preset interval.

[0069] The interval length can be fixed, for example, an electrochemical impedance spectroscopy test is performed every 5 minutes, and N electrochemical impedance spectroscopy tests are performed continuously. The interval length can also be non-fixed, for example, the interval length is set to be longer in the early stage of battery use, and the interval length is set to be shorter in the middle or late stage of battery use. In the early stage of battery use (when the battery is not easily damaged), the battery test is performed at a longer interval length to save resources, and in the middle or late stage of battery use, the battery test is performed at a shorter interval length to improve the timeliness and accuracy of battery status monitoring.

[0070] S202, calculating the equivalent impedance value of the battery based on each group of the test data, and obtaining N equivalent impedance values ​​of the equivalent impedance value.

[0071] By calculating the equivalent impedance value of the battery based on a set of the test data, an equivalent impedance value of the equivalent impedance value can be obtained. By estimating the equivalent impedance value of the battery based on each set of the test data in N sets of the test data, N equivalent impedance values ​​of the equivalent impedance value can be obtained.

[0072] In a possible implementation, an equivalent circuit model of a battery can be established based on a set of the test data, and the equivalent impedance value of the battery can be understood as the impedance value of the equivalent circuit model. The equivalent circuit model is composed of components such as resistance, capacitance and inductance, and reflects the charge transfer and ion diffusion process inside the battery. A set of the test data is used to reflect the resistance, capacitance and impedance in the equivalent circuit model, and a set of the test data can be used as a parameter of the equivalent circuit model.

[0073] In one example, by performing EIS testing on lithium batteries, electrochemical response data in the frequency range from 10 megahertz (mHz) to 100 thousand hertz (kHz) were obtained. These data can be used to construct an equivalent circuit model. The equivalent circuit model is specifically composed of the following components:

[0074] Ohm resistance : Reflects the conductive loss between the electrode and the electrolyte. The value can be , which is a typical value measured under normal operating conditions.

[0075] Charge transfer resistance :Reflects the electrode interface reaction kinetics, and the initial measurement value can be set to .

[0076] Double layer capacitor :Describes the capacitance effect of the electrode interface. The initial value can be set to .

[0077] In a possible implementation, when determining the N equivalent impedance values ​​of the equivalent impedance value based on a set of the test data, the N equivalent impedance values ​​of the equivalent impedance value are determined according to a preset calculation rule and a set of the test data. The preset calculation rule can be set according to the equivalent circuit model.

[0078] S203, inputting the N equivalent impedance values ​​and the standard equivalent impedance value into the Bayesian inference model, and obtaining the posterior probability distribution output by the Bayesian inference model.

[0079] The standard equivalent impedance value can be understood as an assumed equivalent impedance value or a hypothetical equivalent impedance value, and the standard equivalent impedance value can be used as prior knowledge in the Bayesian inference model.

[0080] The Bayesian inference model is a statistical inference method based on Bayes' theorem, which uses prior knowledge and new evidence (i.e., data) to update the probability of the hypothesis, thereby obtaining the posterior probability. The Bayesian inference model is used to calculate the probability of another related event occurring under certain known conditions. The standard equivalent impedance value can also be understood as an assumed equivalent impedance value or an assumed equivalent impedance value, which can be used as prior knowledge in the Bayesian inference model. In the Bayesian inference model, an equivalent impedance value can be used as new evidence.

[0081] In this embodiment, the basic form of Bayesian inference is:

[0082]

[0083] in, represents a priori knowledge, such as the standard equivalent impedance value described above. Indicates the equivalent impedance value. Given a set of test data back, The posterior probability of occurrence. is the likelihood function, which means that given back Probability of occurrence. is the prior probability, which means the probability of The assumed distribution of . is the marginal distribution of a set of test data, which is all possible causes The sum of the probabilities of the causes occurring.

[0084] When N equivalent impedance values ​​and the standard equivalent impedance value are input into the Bayesian inference model, the equivalent impedance value may be input into the Bayesian inference model, and each equivalent impedance value may be input into the Bayesian inference model in turn. After an equivalent impedance value is input into the Bayesian inference model, a posterior probability may be obtained correspondingly. The posterior probability may be understood as an estimated equivalent impedance value.

[0085] After N equivalent impedance values ​​are sequentially input into the Bayesian inference model, N posterior probabilities can be obtained correspondingly, and the N posterior probabilities constitute a posterior probability distribution.

[0086] During the use of the battery, the retrieved equivalent impedance value is input into the Bayesian inference model in real time, and the estimated equivalent impedance value can be dynamically updated.

[0087] S204: Determine the health status of the battery according to the posterior probability distribution.

[0088] According to the posterior probability distribution, the change of the battery equivalent impedance value can be determined, and the health state of the battery can be determined according to the change of the battery equivalent impedance value. For example, when the fluctuation of the battery equivalent impedance value is large or the deviation from the preset equivalent impedance value is large, the health state of the battery is determined to be abnormal. When the fluctuation of the battery equivalent impedance value is small and the deviation from the preset equivalent impedance value is small, the health state of the battery is determined to be normal.

[0089] In summary, in the above-mentioned battery management method, during the use of the battery, the battery is subjected to N EIS tests to obtain N groups of test data, and the health status of the battery is determined based on the test data. It is possible to monitor the health status of the battery during the use of the battery. Most traditional health management methods are based on analysis and prediction based on the historical data of the battery, and lack the ability to dynamically update in real time. This lag makes it impossible for them to accurately predict the future state of the battery and possible failures, which may cause irreversible damage to the battery if it is not maintained in time. The method provided in this embodiment monitors the health status of the battery in real time during the use of the battery, and can capture early changes in the battery to obtain a more accurate battery health status, and monitor the early degradation information of the battery in advance before the battery fails, thereby improving the effectiveness of battery health management.

[0090] Moreover, as a non-destructive electrochemical analysis technology, EIS has great potential in evaluating the internal state of the battery. By applying AC signals of different frequencies and measuring the impedance response of the battery, EIS can provide detailed information about the charge transfer, diffusion and other processes inside the battery. By analyzing this information, the health status of the battery can be more accurately evaluated. This embodiment uses the EIS test method to obtain the test data of the battery, which can improve the accuracy and reliability of the battery test.

[0091] The Bayesian inference model is a probabilistic model that not only allows the use of prior knowledge and current measurement data to dynamically update the equivalent impedance value of the battery health state, but also avoids uncertainty and noise in the EIS test data and gives more reliable results. Therefore, using the Bayesian inference model to infer the battery health state can obtain a more accurate battery health state, thereby improving the effectiveness of battery health management.

[0092] Referring to FIG. 3 , in an exemplary embodiment, S204 includes:

[0093] S301, sampling the posterior probability distribution, and determining the expected value of the sampled sample according to the sampled sample.

[0094] By the posterior distribution Random sampling of , generating parameter sample sequence For this sampling sample.

[0095] Determine the expected value of the sampled sample based on the parameter sample sequence Expected value It can be understood as an average value.

[0096] The Markov chain Monte Carlo method is used to dynamically sample the posterior probability distribution and continuously estimate the parameters in the equivalent circuit model, wherein the parameters in the equivalent circuit model include one or more of the following: the battery's ohmic resistance, charge transfer resistance, double layer capacitance, and Warburg impedance. represents the ohmic resistance of the battery, represents the charge transfer resistance, represents the double layer capacitance, stands for Warburg impedance.

[0097] In an example, setting the initialization parameters , By using the posterior distribution Random sampling of , generating parameter sample sequence is the sample. Determine the expected value of the sample. The expected value of increases over time.

[0098] Assume that new observations are obtained during battery operation and that the charge transfer resistance From 0.15 Increase to , which indicates that some abnormality may have occurred at the battery interface (such as aging, obstructed ion transmission, etc.).

[0099] S302: When the expected value is greater than a preset equivalent impedance value, and / or a difference between the expected value and the preset equivalent impedance value is greater than a preset difference, determining that the health status of the battery is abnormal.

[0100] The preset equivalent impedance value can be set according to actual needs, and can be an equivalent impedance value when the battery is in a normal health state. The preset equivalent impedance value can be understood as a reference equivalent impedance value.

[0101] The preset difference value may also be set according to actual needs, which is not limited in this embodiment. The preset difference value is, for example, 5% of the preset equivalent impedance value.

[0102] In a possible implementation, in order to improve the accuracy of battery health management, when the expected value is greater than the preset equivalent impedance value, the battery health state is determined to be abnormal. Correspondingly, when the expected value is less than or equal to the preset equivalent impedance value, the battery health state is determined to be normal.

[0103] In a possible implementation, in order to save resources, when the difference between the expected value and the preset equivalent impedance value is greater than the preset difference, the health state of the battery is determined to be abnormal. When the expected value is greater than the preset equivalent impedance value, the difference is a positive value. When the expected value is less than the preset equivalent impedance value, the difference is a negative value. The difference is greater than the preset difference, which can be understood as the absolute value of the difference is greater than the preset difference. Correspondingly, when the difference between the expected value and the preset equivalent impedance value is less than or equal to the preset difference, the health state of the battery is determined to be normal.

[0104] In a possible implementation, when the expected value is greater than a preset equivalent impedance value, and the difference between the expected value and the preset equivalent impedance value is greater than the preset difference, it is determined that the health status of the battery is abnormal.

[0105] The method provided in this embodiment samples the posterior probability distribution, then determines the expected value of the sampled sample, and judges the health status of the battery by the expected value. It can avoid the uncertainty and noise in the EIS test data and give more reliable results. Therefore, using the Bayesian inference model to infer the health status of the battery can obtain a more accurate battery health status, thereby improving the effectiveness of battery health management.

[0106] Please refer to FIG. 4 , in one embodiment, S204 includes:

[0107] S401, sampling the posterior probability distribution, and determining the variance value of the sampled samples according to the sampled samples.

[0108] Initialization parameters , by the posterior distribution Random sampling of , generating parameter sample sequence For this sampling sample.

[0109] According to the parameter sample sequence, the expected value of the sample can be determined Expected value It can be understood as the average value. According to the parameter sample sequence, the variance of the sample can also be determined ,variance Used to characterize the degree of deviation.

[0110] S402: When the variance value is greater than a preset variance value, determining that the health state of the battery is abnormal.

[0111] The preset variance value can be set according to actual needs, and can be a variance value determined according to multiple equivalent impedance values ​​when the battery health state is normal. The preset variance value can be understood as a reference variance value.

[0112] When the variance value is greater than the preset variance value, it proves that there are parameters with a large degree of deviation in the parameter sample, and correspondingly, the equivalent impedance value of the battery is not stable. Therefore, when the variance value is greater than the preset variance value, it is determined that the health status of the battery is abnormal.

[0113] The method provided in this embodiment samples the posterior probability distribution, determines the variance value of the sampled samples, and judges the health state of the battery by the variance value. Or, the health state of the battery is judged in combination with the expected value and the variance value to improve the accuracy of the judgment result. The method provided in this embodiment can avoid the uncertainty and noise in the EIS test data and give more reliable results. Therefore, using the Bayesian inference model to infer the health state of the battery can obtain a more accurate battery health state, thereby improving the effectiveness of battery health management.

[0114] Referring to FIG. 5 , in an exemplary embodiment, S204 includes:

[0115] S501, determining a variation range of the equivalent impedance value according to the posterior probability distribution.

[0116] The equivalent impedance value provided in this embodiment may be understood as an estimated equivalent impedance value.

[0117] As described above, the posterior probability distribution includes multiple posterior probabilities, and one posterior probability can be understood as an estimated equivalent impedance value. Therefore, the variation range of the equivalent impedance value can be determined through the posterior probability distribution.

[0118] The variation interval may be understood as a first value range.

[0119] S502: When the variation interval exceeds a preset confidence interval of the posterior probability distribution, determining that the health state of the battery is abnormal.

[0120] The preset confidence interval can be understood as the second value range. The preset confidence interval can be set according to actual needs, for example, it can be 95% of the preset equivalent impedance value to the preset equivalent impedance value.

[0121] When the variation interval exceeds the preset confidence interval, it is proved that there is an equivalent impedance value (estimated equivalent impedance value) beyond the normal range (the preset confidence interval), and the health state of the battery is abnormal.

[0122] The method provided in this embodiment determines the variation interval of the equivalent impedance value through the posterior probability distribution, and judges the health state of the battery according to the variation interval and the preset confidence interval of the equivalent impedance value. The method provided in this embodiment can avoid uncertainty and noise in EIS test data and give more reliable results. Therefore, using the Bayesian inference model to infer the health state of the battery can obtain a more accurate battery health state, thereby improving the effectiveness of battery health management.

[0123] In some embodiments, the test data includes one or more of the following: ohmic resistance, charge transfer resistance, double layer capacitance and Warburg impedance of the battery.

[0124] S202 includes: inputting each set of the test data into an estimation formula of an equivalent impedance value to obtain an equivalent impedance value.

[0125] The estimation formula includes: ;

[0126] in, represents the ohmic resistance of the battery, represents the charge transfer resistance, represents the double layer capacitance, represents the Warburg impedance, represents the imaginary part, Represents the angular frequency.

[0127] In some embodiments, the battery management method further includes:

[0128] Step 1: Based on the N equivalent impedance values, a trend graph of the change of the equivalent impedance value during the use of the battery is generated.

[0129] When the test data only includes the charge transfer resistance, the equivalent impedance value is calculated based on the charge transfer resistance. Correspondingly, the above formula In , and The value is 0. FIG6 shows a variation trend diagram obtained after testing a lithium battery with a battery capacity of 3500 mAh, and is a variation trend diagram generated based on N equivalent impedance values ​​when the test data only includes the charge transfer resistance.

[0130] Step 2: Display the change trend graph.

[0131] By displaying the changing trend graph, the staff can observe the current status of the battery and prevent battery failure in time.

[0132] Figure 6 shows the charge transfer resistance The corresponding simulation waveform of equivalent impedance value changing with time (change trend graph). The curve in the figure is generated by continuous analysis of test data (i.e. EIS data), which describes the change trend of equivalent impedance value during battery use. Assume that in the early stage of battery use, the charge transfer resistance Changes slowly, keeping However, as the battery ages and is frequently charged and discharged, Start to rise gradually.

[0133] The curve in the figure can be divided into the following stages. The first stage (initial stage): the equivalent impedance value changes slightly. Maintained within the normal range. In the second stage (mid-stage), the characteristic frequency gradually increases and the charge transfer resistance Start increasing to , but it is still within the normal operating range. In the third stage (latter stage), the equivalent impedance value fluctuates violently. Exceeded , at this time, a warning signal is triggered, indicating that the battery has been seriously aged or has potential failure.

[0134] By analyzing the changing trend of characteristic frequency over time, the system can detect potential battery failures in advance and avoid sudden failures.

[0135] In some embodiments, after determining the health status of the battery, the battery management method further includes:

[0136] Step 1: If the battery health status is abnormal, a fault warning is issued.

[0137] Set up a fault warning mechanism, and trigger a fault warning when the battery health status is determined to be abnormal. For example, the charge transfer resistance The normal value is , set its preset confidence interval to , then when monitoring When the value increases to , the system will issue a warning signal.

[0138] Through the above trend chart, the health status of the battery can be monitored in real time, and a fault warning can be issued when the battery performance parameters change significantly. It can not only improve the accuracy of monitoring, but also dynamically update the estimate based on the latest EIS data, thereby helping to extend the battery life and avoid potential failures.

[0139] It should be understood that, although the steps in the flowcharts of the embodiments described above are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the steps or stages in other steps.

[0140] Based on the same inventive concept, the embodiment of the present application also provides a battery management device for implementing the battery management method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more battery management device embodiments provided below can refer to the limitations of the battery management method above, and will not be repeated here.

[0141] In an exemplary embodiment, as shown in FIG. 7 , a battery management device 70 is provided, including: an acquisition module 701 and a processing module 702, wherein:

[0142] The acquisition module 701 is used to perform N electrochemical impedance spectroscopy tests on the battery during use of the battery to obtain N groups of test data; wherein N is a natural number greater than 1.

[0143] The acquisition module 701 is further used to calculate the equivalent impedance value of the battery based on each group of the test data, and obtain N equivalent impedance values ​​of the equivalent impedance value.

[0144] The processing module 702 is used to input the N equivalent impedance values ​​and the standard equivalent impedance value into the Bayesian inference model, and obtain the posterior probability distribution output by the Bayesian inference model.

[0145] The processing module 702 is further configured to determine the health state of the battery according to the posterior probability distribution.

[0146] In some embodiments, the processing module 702 is used to: sample the posterior probability distribution and determine the expected value of the sampled sample based on the sampled sample; when the expected value is greater than a preset equivalent impedance value, and / or the difference between the expected value and the preset equivalent impedance value is greater than a preset difference, determine that the health status of the battery is abnormal.

[0147] In some embodiments, the processing module 702 is used to: sample the posterior probability distribution, determine a variance value of the sampled samples based on the sampled samples; and when the variance value is greater than a preset variance value, determine that the health status of the battery is abnormal.

[0148] In some embodiments, the processing module 702 is used to: determine a variation interval of the equivalent impedance value according to the posterior probability distribution; and determine that the health status of the battery is abnormal when the variation interval exceeds a preset confidence interval of the posterior probability distribution.

[0149] In some embodiments, the test data includes one or more of the following: ohmic resistance, charge transfer resistance, double layer capacitance and Warburg impedance of the battery; the acquisition module 701 is used to: input each set of the test data into an estimation formula of an equivalent impedance value to obtain an equivalent impedance value;

[0150] The estimation formula includes: ;

[0151] in, represents the ohmic resistance of the battery, represents the charge transfer resistance, represents the double layer capacitance, represents the Warburg impedance, represents the imaginary part, Represents the angular frequency.

[0152] In some embodiments, the processing module 702 is further used to: generate a change trend graph of the equivalent impedance value during battery use based on the N equivalent impedance values; and display the change trend graph.

[0153] Each module in the above-mentioned battery management device can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory in the electronic device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0154] In an exemplary embodiment, an electronic device is provided. The electronic device may be a server, and its internal structure diagram may be as shown in FIG. Figure 8As shown. The electronic device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store test data, equivalent impedance values, posterior probability distributions, etc. The input / output interface of the electronic device is used to exchange information between the processor and an external device. The communication interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a battery management method is implemented.

[0155] Those skilled in the art will understand that Figure 8 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0156] In an exemplary embodiment, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0157] During the use of the battery, the battery is subjected to N electrochemical impedance spectroscopy tests to obtain N sets of test data; wherein N is a natural number greater than 1;

[0158] Calculating the equivalent impedance value of the battery based on each group of the test data to obtain N equivalent impedance values ​​of the equivalent impedance value;

[0159] Inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model;

[0160] The health state of the battery is determined based on the posterior probability distribution.

[0161] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0162] Sampling the posterior probability distribution, and determining the expected value of the sampled sample based on the sampled sample;

[0163] When the expected value is greater than a preset equivalent impedance value, and / or a difference between the expected value and the preset equivalent impedance value is greater than a preset difference, it is determined that the health state of the battery is abnormal.

[0164] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0165] Sampling the posterior probability distribution, and determining the variance value of the sampled samples according to the sampled samples;

[0166] When the variance value is greater than a preset variance value, it is determined that the health state of the battery is abnormal.

[0167] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0168] Determine a variation range of the equivalent impedance value according to the posterior probability distribution;

[0169] When the variation interval exceeds a preset confidence interval of the posterior probability distribution, the health state of the battery is determined to be abnormal.

[0170] In one embodiment, the test data includes one or more of the following: ohmic resistance, charge transfer resistance, double layer capacitance and Warburg impedance of the battery; when the processor executes the computer program, the following steps are also implemented:

[0171] Input each set of the test data into an estimation formula of an equivalent impedance value to obtain an equivalent impedance value;

[0172] The estimation formula includes: ;

[0173] in, represents the ohmic resistance of the battery, represents the charge transfer resistance, represents the double layer capacitance, represents the Warburg impedance, represents the imaginary part, Represents the angular frequency.

[0174] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0175] Based on the N equivalent impedance values, a change trend graph of the equivalent impedance value during battery use is generated;

[0176] Display the change trend graph.

[0177] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0178] During the use of the battery, the battery is subjected to N electrochemical impedance spectroscopy tests to obtain N sets of test data; wherein N is a natural number greater than 1;

[0179] Calculating the equivalent impedance value of the battery based on each group of the test data to obtain N equivalent impedance values ​​of the equivalent impedance value;

[0180] Inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model;

[0181] The health state of the battery is determined based on the posterior probability distribution.

[0182] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0183] Sampling the posterior probability distribution, and determining the expected value of the sampled sample based on the sampled sample;

[0184] When the expected value is greater than a preset equivalent impedance value, and / or a difference between the expected value and the preset equivalent impedance value is greater than a preset difference, it is determined that the health state of the battery is abnormal.

[0185] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0186] Sampling the posterior probability distribution, and determining the variance value of the sampled samples according to the sampled samples;

[0187] When the variance value is greater than a preset variance value, it is determined that the health state of the battery is abnormal.

[0188] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0189] Determine a variation range of the equivalent impedance value according to the posterior probability distribution;

[0190] When the variation interval exceeds a preset confidence interval of the posterior probability distribution, the health state of the battery is determined to be abnormal.

[0191] In one embodiment, the test data includes one or more of the following: ohmic resistance, charge transfer resistance, double layer capacitance and Warburg impedance of the battery; when the computer program is executed by the processor, the following steps are also implemented:

[0192] Input each set of the test data into an estimation formula of an equivalent impedance value to obtain an equivalent impedance value;

[0193] The estimation formula includes: ;

[0194] in, represents the ohmic resistance of the battery, represents the charge transfer resistance, represents the double layer capacitance, represents the Warburg impedance, represents the imaginary part, Represents the angular frequency.

[0195] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0196] Based on the N equivalent impedance values, a change trend graph of the equivalent impedance value during battery use is generated;

[0197] Display the change trend graph.

[0198] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0199] During the use of the battery, the battery is subjected to N electrochemical impedance spectroscopy tests to obtain N sets of test data; wherein N is a natural number greater than 1;

[0200] Calculating the equivalent impedance value of the battery based on each group of the test data to obtain N equivalent impedance values ​​of the equivalent impedance value;

[0201] Inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model;

[0202] The health state of the battery is determined based on the posterior probability distribution.

[0203] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0204] Sampling the posterior probability distribution, and determining the expected value of the sampled sample based on the sampled sample;

[0205] When the expected value is greater than a preset equivalent impedance value, and / or a difference between the expected value and the preset equivalent impedance value is greater than a preset difference, it is determined that the health state of the battery is abnormal.

[0206] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0207] Sampling the posterior probability distribution, and determining the variance value of the sampled samples according to the sampled samples;

[0208] When the variance value is greater than a preset variance value, it is determined that the health state of the battery is abnormal.

[0209] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0210] Determine a variation range of the equivalent impedance value according to the posterior probability distribution;

[0211] When the variation interval exceeds a preset confidence interval of the posterior probability distribution, the health state of the battery is determined to be abnormal.

[0212] In one embodiment, the test data includes one or more of the following: ohmic resistance, charge transfer resistance, double layer capacitance and Warburg impedance of the battery; when the computer program is executed by the processor, the following steps are also implemented:

[0213] Input each set of the test data into an estimation formula of an equivalent impedance value to obtain an equivalent impedance value;

[0214] The estimation formula includes: ;

[0215] in, represents the ohmic resistance of the battery, represents the charge transfer resistance, represents the double layer capacitance, represents the Warburg impedance, represents the imaginary part, Represents the angular frequency.

[0216] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0217] Based on the N equivalent impedance values, a change trend graph of the equivalent impedance value during battery use is generated;

[0218] Display the change trend graph.

[0219] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0220] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0221] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A battery management method, characterized in that: The method comprises: During the use of the battery, the battery is subjected to N electrochemical impedance spectroscopy tests to obtain N sets of test data; wherein N is a natural number greater than 1; Calculating the equivalent impedance value of the battery based on each group of the test data to obtain N equivalent impedance values ​​of the equivalent impedance value; Inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model; The health state of the battery is determined according to the posterior probability distribution.

2. The method according to claim 1, characterized in that Determining the health status of the battery according to the posterior probability distribution includes: Sampling the posterior probability distribution, and determining an expected value of the sampled sample based on the sampled sample; When the expected value is greater than a preset equivalent impedance value, and / or a difference between the expected value and the preset equivalent impedance value is greater than a preset difference, it is determined that the health state of the battery is abnormal.

3. The method according to claim 1 or 2, characterized in that: Determining the health status of the battery according to the posterior probability distribution includes: Sampling the posterior probability distribution, and determining a variance value of the sampled samples according to the sampled samples; When the variance value is greater than a preset variance value, it is determined that the health state of the battery is abnormal.

4. The method according to claim 1 or 2, characterized in that: Determining the health status of the battery according to the posterior probability distribution includes: Determine a variation interval of the equivalent impedance value according to the posterior probability distribution; When the variation interval exceeds a preset confidence interval of the posterior probability distribution, the health state of the battery is determined to be abnormal.

5. The method according to claim 1 or 2, characterized in that: The test data includes one or more of the following: ohmic resistance, charge transfer resistance, double layer capacitance and Warburg impedance of the battery; the calculation of the equivalent impedance value of the battery based on each set of the test data includes: Inputting each set of test data into an estimation formula of an equivalent impedance value to obtain an equivalent impedance value; The estimation formula includes: ; in, represents the ohmic resistance of the battery, represents the charge transfer resistance, represents the double layer capacitance, represents the Warburg impedance, represents the imaginary part, Represents the angular frequency.

6. The method according to claim 1 or 2, characterized in that: The method further comprises: Based on the N equivalent impedance values, a change trend graph of the equivalent impedance value during battery use is generated; The change trend graph is displayed.

7. A battery management device, characterized in that: include: An acquisition module is used to perform N electrochemical impedance spectroscopy tests on the battery during use of the battery to obtain N groups of test data; wherein N is a natural number greater than 1; The acquisition module is further used to calculate the equivalent impedance value of the battery based on each group of test data to obtain N equivalent impedance values ​​of the equivalent impedance value; A processing module, used for inputting N equivalent impedance values ​​and the standard equivalent impedance value into a Bayesian inference model, and obtaining a posterior probability distribution output by the Bayesian inference model; The processing module is further used to determine the health state of the battery according to the posterior probability distribution.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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