A battery short circuit detection method and device, electronic equipment and storage medium

Abstract

The application provides a battery short circuit detection method and device, electronic equipment and storage medium. The method comprises distinguishing the diffusion coefficient of lithium ions of a battery when the battery is working to obtain a first lithium ion diffusion coefficient; determining whether a short circuit fault exists in the battery based on a lithium ion diffusion coefficient theoretical value and the first lithium ion diffusion coefficient, wherein the lithium ion diffusion coefficient theoretical value is obtained by calculation based on battery data. In the embodiment of the application, whether the lithium battery is short-circuited is determined by the lithium ion diffusion coefficient affecting the growth of lithium dendrites, and the lithium ion diffusion coefficient theoretical value is determined by the equation corresponding to the time Sand' stime from the start to the potential jump. According to the size relationship between the real-time diffusion coefficient and the lithium ion diffusion coefficient theoretical value, real-time diagnosis of the short circuit in the lithium battery is realized. In the above manner, whether a fault exists in the battery can be accurately detected, and the detection has high detection and universality.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, and in particular to a method, apparatus, electronic device, and storage medium for detecting battery short circuits. Background Technology

[0002] Due to their high energy density, large capacity, and long cycle life, lithium batteries are now widely used in power grid systems for peak shaving and valley filling, as well as in electric vehicles. With the increasing prevalence of lithium batteries, their safety issues, such as thermal runaway caused by internal short circuits, have become a growing concern. Therefore, effectively, quickly, and accurately detecting internal short circuits is crucial for the safe use of batteries.

[0003] As batteries age, especially in the nonlinear region (i.e., the region where capacity decays to 80%), the aging mechanism is lithium plating. The diffusion coefficient of lithium ions in the electrolyte decreases with aging, resulting in non-uniform lithium deposition on the negative electrode surface, inducing dendrite growth, and thus causing internal short circuits in the battery.

[0004] Current methods for detecting internal short circuits in batteries primarily rely on abnormal electrical signals, such as sudden drops in current, voltage, internal resistance, SOC, and temperature. These parameters are compared to preset thresholds to determine if an internal short circuit exists. However, since these preset thresholds are manually set, and changes in some parameters may not necessarily cause internal battery malfunctions, this method is inaccurate in detecting internal battery faults and has low detection accuracy and general applicability. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, apparatus, electronic device and storage medium for detecting battery short circuits, in order to solve the problems of existing technologies that cannot accurately detect whether there is a fault inside the battery and have low universality.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] A first aspect of this invention discloses a method for detecting a battery short circuit, the method comprising:

[0008] When the battery is working, the diffusion coefficient of lithium ions in the battery is identified to obtain the first lithium ion diffusion coefficient;

[0009] The presence of a short circuit fault in the battery is determined based on the theoretical value of the lithium-ion diffusion coefficient and the first lithium-ion diffusion coefficient. The theoretical value of the lithium-ion diffusion coefficient is calculated based on battery data.

[0010] Optionally, the theoretical value of the lithium-ion diffusion coefficient is calculated based on battery data, including:

[0011] When the battery is working, acquire the battery data of the battery;

[0012] The battery data is processed to obtain the theoretical value of the lithium-ion diffusion coefficient.

[0013] Optionally, the step of calculating and processing the battery data to obtain the theoretical value of the lithium-ion diffusion coefficient includes:

[0014] The ratio of operating voltage to electrode area is determined based on the operating current density in the battery data;

[0015] The migration number of anions in the lithium salt in the electrolyte is determined based on the migration number of lithium ions in the battery data.

[0016] based on Determine the theoretical value of the lithium-ion diffusion coefficient Dapp, where Z C C is the charge number of the cation, C0 is the initial electrolyte salt concentration, F is the Faraday constant, and t is the initial electrolyte salt concentration. a I is the migration number of the anion in the lithium salt in the electrolyte, A is the working voltage, A is the electrode area, and L is the distance between the two electrodes; the charge number of the cation, the distance between the two electrodes, and the initial electrolyte salt concentration are all part of the battery data.

[0017] Optionally, the step of identifying the diffusion coefficient of lithium ions in the battery to obtain a first lithium ion diffusion coefficient includes:

[0018] A simplified electrochemical model was constructed based on the first sample data;

[0019] The initial electrochemical model is simplified to obtain the target electrochemical model;

[0020] The system of differential equations was determined using the target electrochemical model;

[0021] The diffusion coefficient of lithium ions in the battery is identified by combining the aforementioned differential equations and particle swarm optimization algorithm, and the first lithium ion diffusion coefficient is obtained.

[0022] Optionally, the construction of the simplified electrochemical model based on the first sample data includes:

[0023] Obtain the first sample data;

[0024] The first sample data is used for training. When the error between the prediction result and the measured data is determined to be within a preset range, the initial electrochemical model that has been trained is obtained.

[0025] Optionally, the step of identifying the diffusion coefficient of lithium ions in the battery based on the combination of the differential equations and the particle swarm optimization algorithm to obtain the first lithium ion diffusion coefficient includes:

[0026] The simulation model of the differential equation system is simulated using the first parameter in the battery data that is related to the diffusion coefficient of the lithium ions, and the simulation curve of each first parameter is obtained.

[0027] When the matching degree between the simulation curve and the corresponding experimental curve of each first parameter is determined to be greater than or equal to a first threshold, the first lithium-ion diffusion coefficient is calculated based on the differential equation system and the first parameter in the simulation curve. The experimental curve refers to the curve corresponding to the first parameter that is pre-plotted under the same battery capacity.

[0028] Optional, also includes:

[0029] When it is determined that the matching degree between the simulation curve with the first parameter and the corresponding experimental curve is less than the first threshold, the particle swarm algorithm is used to adjust the differential equation system to obtain the adjusted differential equation system.

[0030] Optionally, the step of identifying the diffusion coefficient of lithium ions in the battery to obtain a first lithium ion diffusion coefficient includes:

[0031] Obtain the battery data of the battery;

[0032] The battery data is input into the target prediction model, and the battery data is processed based on the target prediction model to output the first lithium-ion diffusion coefficient. The target prediction model is generated by training based on the second sample data.

[0033] Optionally, the process of training and generating a target prediction model based on the second sample data includes:

[0034] Obtain historical battery data and corresponding historical lithium-ion diffusion coefficients for each battery within a historical time period.

[0035] The historical battery data and the corresponding historical lithium-ion diffusion coefficient are used as the second sample data.

[0036] The second sample data is divided into a training set and a validation set;

[0037] The general machine learning model is trained based on the training set to obtain the initial prediction model;

[0038] The initial prediction model is optimized based on the validation set to obtain the trained target prediction model.

[0039] Optionally, determining whether a short-circuit fault exists in the battery based on the theoretical value of the lithium-ion diffusion coefficient and the first lithium-ion diffusion coefficient includes:

[0040] Determine whether the first lithium-ion diffusion coefficient is less than the theoretical value of the lithium-ion diffusion coefficient;

[0041] If it is determined that the first lithium-ion diffusion coefficient is less than the theoretical value of the lithium-ion diffusion coefficient, it is determined that a short circuit fault exists in the battery.

[0042] If the first lithium-ion diffusion coefficient is greater than or equal to the theoretical value of the lithium-ion diffusion coefficient, it is determined that there is no short-circuit fault in the battery.

[0043] A second aspect of the present invention discloses a battery short-circuit detection device, the method comprising:

[0044] The processing unit is used to identify the diffusion coefficient of lithium ions in the battery when the battery is working, and obtain the first lithium ion diffusion coefficient.

[0045] The judgment unit is used to determine whether there is a short circuit fault in the battery based on the theoretical value of the lithium-ion diffusion coefficient and the first lithium-ion diffusion coefficient, wherein the theoretical value of the lithium-ion diffusion coefficient is calculated based on battery data.

[0046] A third aspect of the present invention discloses an electronic device for running a program, wherein the program executes a battery short circuit detection method as shown in the first aspect of the present invention.

[0047] A fourth aspect of the present invention discloses a storage medium comprising a stored program, wherein, when the program is executed, the device on which the storage medium is located executes a battery short circuit detection method as shown in the first aspect of the present invention.

[0048] Based on the above embodiments of the present invention, a method, apparatus, electronic device, and storage medium for detecting battery short circuits are provided. The method includes, when the battery is working, identifying the diffusion coefficient of lithium ions in the battery to obtain a first lithium ion diffusion coefficient; determining whether a short circuit fault exists in the battery based on the theoretical value of the lithium ion diffusion coefficient and the first lithium ion diffusion coefficient, wherein the theoretical value of the lithium ion diffusion coefficient is calculated based on battery data. In the embodiments of the present invention, whether there is a short circuit inside the lithium battery is determined by the lithium ion diffusion coefficient that affects the growth of lithium dendrites, and the theoretical value of the lithium ion diffusion coefficient is determined by the equation corresponding to the Sand's time from the start to the occurrence of a potential transition; by identifying the change of the diffusion coefficient online, and based on the relationship between the real-time diffusion coefficient and the theoretical value of the lithium ion diffusion coefficient, real-time diagnosis of short circuits inside the lithium battery is achieved. Since the fundamental cause of short circuits inside the lithium battery is the lithium plating reaction, and an important manifestation of the formation of lithium dendrites after the lithium plating reaction is the change of the diffusion coefficient, the above method can accurately detect whether there is a fault inside the battery, and has high detection accuracy and universality. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 This is a schematic flowchart illustrating a battery fault detection method according to an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the internal structure of a battery according to an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram illustrating the online identification process in an embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram illustrating the battery fault detection and judgment process according to an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of the structure of a battery fault detection device according to an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] It should be noted that the descriptions involving "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0058] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0059] The proposed internal short-circuit diagnosis method is based on the principle of internal short-circuit formation to determine whether a short circuit has occurred. It is applicable to lithium batteries with different cathode materials, capacities, and sizes. The method determines whether an internal short circuit exists by analyzing the lithium-ion diffusion coefficient, which affects lithium dendrite growth. The theoretical value of the lithium-ion diffusion coefficient is determined using the equation corresponding to the Sand's time (the time from the start of the potential transition), eliminating the need for manual setting. Real-time diagnosis of internal short circuits is achieved by online identification of changes in the diffusion coefficient. This approach can accurately detect whether a fault exists inside the battery.

[0060] See Figure 1 The above is a flowchart illustrating a battery fault detection method according to an embodiment of the present invention. The method includes:

[0061] Step S101: When the battery is working, the diffusion coefficient of lithium ions in the battery is identified to obtain the first lithium ion diffusion coefficient;

[0062] The battery can be a lithium battery, which consists of a positive electrode, a negative electrode, and an electrolyte, such as... Figure 2 As shown.

[0063] It should be noted that there are multiple ways to implement the specific step S101, which involves identifying the diffusion coefficient of lithium ions in the battery when the battery is working, and obtaining the first lithium ion diffusion coefficient.

[0064] The first implementation method, such as Figure 3 As shown, it includes the following steps:

[0065] Step S301: Construct a simplified electrochemical model based on the first sample data;

[0066] It should be noted that the specific implementation process of step S301 includes the following steps:

[0067] Step S11: Construct a simplified electrochemical model based on the first sample data.

[0068] It should be noted that the specific implementation of step S11 includes:

[0069] Step S21: Obtain the first sample data;

[0070] Step S22: Train the first sample data, and when the error between the prediction result and the measured data is within a preset range, determine the initial electrochemical model that has been trained.

[0071] In the specific implementation of steps S21 and S22, battery data generated during the charging and discharging of multiple batteries over a period of time is acquired and used as the first sample data; the initial electrochemical model is trained using the first sample data, and the battery data is predicted based on the trained initial electrochemical model to obtain the prediction result; when the error between the prediction result and the measured data is determined to be within a preset range, the initial electrochemical model trained at this time is determined; when the error between the prediction result and the measured data is determined to be outside the preset range, the initial electrochemical model is retrained.

[0072] It should be noted that the measured data was obtained by testing the battery under actual conditions.

[0073] Battery data should include at least the operating voltage, the migration number of anions in the lithium salt in the electrolyte, the electrode area, the charge number of cations, temperature, pressure, and the initial electrolyte salt concentration.

[0074] Data were obtained from the battery's state of charge (SOC) for multiple operating voltages, the migration number of anions in the lithium salt in the electrolyte, the electrode area, the charge number of cations, temperature, pressure, and the initial electrolyte salt concentration.

[0075] Step S302: Simplify the initial electrochemical model to obtain the target electrochemical model.

[0076] Electrochemical models establish a set of electrochemical kinetic and transport equations based on the internal mechanisms of the battery, which can more accurately describe the battery's operation and health status. However, electrochemical models, i.e., P2D models, have a high degree of coupling between their governing equations and involve a large computational burden. Therefore, P2D models need to be reasonably simplified before they can be used in actual battery management. In the specific implementation step S302, based on the reaction mechanisms of the battery's positive and negative electrodes and the actual operating conditions of the battery, only the differential equations for the lithium-ion diffusion coefficient and temperature, pressure, and liquid electrolyte concentration are retained in the electrochemical model to simplify it.

[0077] Step S303: Determine the system of differential equations using the target electrochemical model.

[0078] In the specific implementation step S303, the differential equation set related to the diffusion coefficient of lithium ions in the target electrochemical model is obtained. That is, the differential equation set describing the coupling relationship between electric field temperature, pressure, liquid electrolyte concentration and the diffusion coefficient of lithium ions is obtained.

[0079] It should be noted that the system of differential equations also contains fixed parameters, such as the charge radius, which has a fixed value. These fixed parameters are set experimentally by technical personnel.

[0080] Step S304: Based on the combination of the differential equations and the particle swarm algorithm, the diffusion coefficient of lithium ions in the battery is identified to obtain the first lithium ion diffusion coefficient.

[0081] It should be noted that the specific implementation step S304, based on the combination of the aforementioned differential equations and particle swarm optimization algorithm, identifies the diffusion coefficient of lithium ions in the battery to obtain the first lithium ion diffusion coefficient, including the following steps:

[0082] Step S31: Use the first parameter in the battery data that is related to the diffusion coefficient of the lithium ions to perform simulation calculations on the simulation model of the differential equation system, and obtain the simulation curve of each first parameter.

[0083] In the specific implementation step S31, a simulation model of the differential equation system is established. Based on each first parameter, Matlab is used to perform simulation calculations on the simulation model of the differential equation system to obtain the simulation curve corresponding to each first parameter.

[0084] It should be noted that the simulation curve refers to the trajectory of the first parameter in the system of differential equations within a preset time period.

[0085] Step S32: Determine whether the matching degree between the simulation curve of each first parameter and the corresponding experimental curve is greater than or equal to the first threshold. If it is greater than or equal to the first threshold, proceed to step S33; if it is less than the first threshold, proceed to step S34.

[0086] In step S33, the experimental curve refers to a pre-plotted curve corresponding to the first parameter under the same battery charge.

[0087] It should be noted that the technicians pre-constructed the correspondence between the battery's state of charge (SOC) and the experimental curves corresponding to each first parameter based on the experiments.

[0088] In the specific implementation of step S32, the experimental curve corresponding to the current battery power SOC is obtained. For each first parameter, its corresponding simulation curve is compared with its corresponding experimental curve to determine whether the simulation curve and the experimental curve match, that is, the matching degree is higher than or equal to the first threshold. If it is higher than or equal to the first threshold, step S33 is executed; if it is lower than the first threshold, step S34 is executed.

[0089] It should be noted that the first threshold is set by technicians based on the actual situation, for example, it can be set to 95%.

[0090] Step S33: Calculate the first lithium-ion diffusion coefficient based on the first parameter in the differential equation set and the simulation curve.

[0091] In the specific implementation step S33, the mean value of the first parameter in the simulation curve is calculated, and the mean value of the first parameter is substituted into the differential equation system for calculation to obtain the first lithium ion diffusion coefficient.

[0092] Step S34: Adjust the differential equation system using the particle swarm optimization algorithm to obtain the adjusted differential equation system, and return to execute step S31.

[0093] It should be noted that the fixed parameters in the differential equation system are adjusted using the particle swarm optimization algorithm to obtain the adjusted differential equation system, and then the process returns to step S31, which uses the first parameter in the battery data related to the diffusion coefficient of the lithium ions to perform simulation calculations on the simulation model of the adjusted differential equation system.

[0094] The second implementation method includes the following steps:

[0095] Step S41: Obtain the battery data of the battery.

[0096] In the specific implementation step S41, the current battery level is determined and the current battery data is obtained.

[0097] Step S42: Input the battery data into the target prediction model, process the battery data based on the target prediction model, and output the first lithium-ion diffusion coefficient.

[0098] The target prediction model is generated by training based on the second sample data.

[0099] It should be noted that the process of training and generating a target prediction model based on the second sample data includes the following steps:

[0100] Step S51: Obtain historical battery data and corresponding historical lithium-ion diffusion coefficient for each battery within the historical time period.

[0101] It should be noted that the historical time period is preset by technical personnel, for example, it can be set to the past six months.

[0102] Step S52: Use the historical battery data and the corresponding historical lithium-ion diffusion coefficient as the second sample data;

[0103] In the specific implementation of steps S51 and S52, battery data such as historical temperature, pressure, and liquid electrolyte concentration of each battery within a historical time period, as well as its corresponding lithium-ion diffusion coefficient, are obtained and used as the second sample data.

[0104] Step S53: Divide the second sample data into a training set and a validation set;

[0105] In the specific implementation of step S5, the second sample data is divided according to a preset ratio to obtain a training set and a validation set divided according to the preset ratio.

[0106] It should be noted that the preset ratio is set in advance by technicians based on experience or experiments, for example, it can be set to the past six months.

[0107] Step S54: Train a general machine learning model based on the training set to obtain an initial prediction model;

[0108] Step S55: Optimize the initial prediction model based on the validation set to obtain the trained target prediction model.

[0109] In the specific implementation of steps S54 and S55, the network parameters of a general machine learning model are determined, and the network parameters are trained based on the training set; an initial prediction model is constructed using the trained network parameters; the battery data in the validation set is processed by the initial prediction model, and a prediction result is output; when the prediction result is determined to be the same as the historical lithium-ion diffusion coefficient in the validation set, the initial prediction model trained at this time is determined to be the target prediction model; when the prediction result is determined to be different from the historical lithium-ion diffusion coefficient in the validation set, the network parameters are trained repeatedly until the prediction result is the same as the historical lithium-ion diffusion coefficient in the validation set.

[0110] Step S102: Determine whether there is a short circuit fault in the battery based on the theoretical value of the lithium-ion diffusion coefficient and the first lithium-ion diffusion coefficient. The theoretical value of the lithium-ion diffusion coefficient is calculated based on battery data.

[0111] It should be noted that calculating the theoretical value of the lithium-ion diffusion coefficient based on the battery data includes the following steps:

[0112] When the battery is working, lithium ions are embedded into the negative or positive electrode material through a reduction reaction, thereby consuming the lithium ions in the electrolyte in contact with the electrode interface. When the working current density J exceeds the diffusion limit of lithium ions, or when the lithium ion diffusion coefficient Dapp decreases to a preset threshold after battery aging, the lithium ion concentration in the electrolyte in contact with the electrode interface will drop to zero. After this, the deposition of lithium ions on the electrode surface will no longer be uniform, but will exhibit a dendritic structure. The moment when the lithium ion concentration at the electrode and electrolyte interface becomes zero is called "the time from the start to the occurrence of the potential transition, Sand'stime", which is calculated as shown in formula (1):

[0113]

[0114] Among them, Z C t is the charge number of the cation (i.e., the charge number of a lithium ion as a cation); C0 is the initial electrolyte salt concentration, and F is the Faraday constant. a =1-t Li t represents the transport number of the anion in the lithium salt within the electrolyte. Li =0.38 is the lithium-ion transport number; J is the operating current density. I is the operating voltage, A is the electrode area, and Dapp is the lithium-ion diffusion coefficient.

[0115] In t Sand The diffusion distance within the time is the distance L between the two electrodes, which can be expressed as shown in formula (2).

[0116] Formula (2):

[0117]

[0118] During the aging process of lithium batteries, the electrolyte and lithium ions in the battery are consumed, leading to a gradual decrease in the diffusion coefficient of lithium ions, meaning a shorter distance they can travel per unit time. After this, the diffusion coefficient of lithium ions gradually decreases. Sand The diffusion distance within the electrode will be less than the distance between the positive and negative electrodes. The rate of lithium ion transfer cannot compensate for the rate of lithium ion consumption. The deposition of lithium ions on the positive and negative electrode surfaces will no longer be uniform, but will be deposited on the electrode surfaces in the form of lithium dendrites. Substituting equation (2) into equation (1), we can obtain the diffusion coefficient of lithium ions when lithium dendrite growth occurs, as shown in equation (3).

[0119] Formula (3):

[0120]

[0121] Step S61: When the battery is working, acquire the battery data of the battery;

[0122] In the specific implementation of step S61, when the battery is working, that is, during the charging and discharging process, battery data is acquired, including the charge number of cations, the initial electrolyte salt concentration, the migration number of anions in the lithium salt in the electrolyte, the current density, and the operating voltage, etc.

[0123] Step S62: Calculate and process the battery data to obtain the theoretical value of the lithium-ion diffusion coefficient.

[0124] In the specific implementation step S62, the charge number of the cation, the initial electrolyte salt concentration, the migration number of the anion in the lithium salt in the electrolyte, the current density, and the working voltage are substituted into the formula (3) shown above for calculation to obtain the theoretical value of the lithium ion diffusion coefficient Dapp.

[0125] It should be noted that the specific implementation step S102 determines whether a short circuit fault exists in the battery based on the theoretical value of the lithium-ion diffusion coefficient and the first lithium-ion diffusion coefficient, such as... Figure 4 As shown, it includes the following steps:

[0126] Step S401: Determine whether the first lithium-ion diffusion coefficient is less than the theoretical value of the lithium-ion diffusion coefficient. If it is less, proceed to step S402. If it is greater than or equal to, proceed to step S403.

[0127] In the specific implementation of step S401, the theoretical value of the lithium-ion diffusion coefficient is compared with the size of the first lithium-ion diffusion coefficient. If it is determined that the first lithium-ion diffusion coefficient is less than the theoretical value of the lithium-ion diffusion coefficient, then step S402 is executed. If the first lithium-ion diffusion coefficient is greater than or equal to the theoretical value of the lithium-ion diffusion coefficient, then step S403 is executed.

[0128] Step S402: Determine that a short circuit fault exists in the battery.

[0129] Step S403: Determine that there is no short circuit fault in the battery.

[0130] In this embodiment of the invention, the presence of a short circuit inside the lithium battery is determined by the lithium-ion diffusion coefficient, which affects the growth of lithium dendrites. The theoretical value of the lithium-ion diffusion coefficient is determined by the equation corresponding to the Sand's time from the start of the potential transition, without the need for manual setting. By identifying changes in the diffusion coefficient online, that is, based on the relationship between the real-time diffusion coefficient and the theoretical value of the lithium-ion diffusion coefficient, real-time diagnosis of short circuits inside the lithium battery is achieved. Since the fundamental cause of short circuits inside the lithium battery is the lithium plating reaction, and an important manifestation of the formation of lithium dendrites after the lithium plating reaction is the change in the diffusion coefficient, the above method can accurately detect whether there is a fault inside the battery, and has high detection accuracy and universality.

[0131] Based on the battery short circuit detection method shown in the above embodiments of the present invention, correspondingly, the present invention provides a schematic diagram of the structure of a battery short circuit detection device, as shown below. Figure 5 As shown, the device includes:

[0132] The processing unit 501 is used to identify the diffusion coefficient of lithium ions in the battery when the battery is working, and obtain a first lithium ion diffusion coefficient.

[0133] The judgment unit 502 is used to determine whether there is a short circuit fault in the battery based on the theoretical value of the lithium ion diffusion coefficient and the first lithium ion diffusion coefficient. The theoretical value of the lithium ion diffusion coefficient is calculated based on battery data.

[0134] It should be noted that the specific principles and execution processes of each unit in the battery short circuit detection device disclosed in the above embodiments of this application are the same as those of the battery short circuit detection method shown in the above embodiments of this application. Please refer to the corresponding parts of the battery short circuit detection method disclosed in the above embodiments of this application, and they will not be repeated here.

[0135] In this embodiment of the invention, the presence of a short circuit inside the lithium battery is determined by the lithium-ion diffusion coefficient, which affects the growth of lithium dendrites. The theoretical value of the lithium-ion diffusion coefficient is determined by the equation corresponding to the Sand's time from the start of the potential transition, without the need for manual setting. By identifying changes in the diffusion coefficient online, that is, by comparing the real-time diffusion coefficient with the theoretical value of the lithium-ion diffusion coefficient, real-time diagnosis of short circuits inside the lithium battery is achieved. Since the root cause of short circuits inside the lithium battery is the lithium plating reaction, and a significant manifestation of lithium dendrite formation after lithium plating is the change in the diffusion coefficient, the above method can accurately detect whether there is a fault inside the battery.

[0136] Optionally, the judgment unit 502, which calculates the theoretical value of the lithium-ion diffusion coefficient based on battery data, in the battery short-circuit detection device shown in the above embodiments of the present invention, is specifically used for:

[0137] When the battery is working, acquire the battery data of the battery;

[0138] The battery data is processed to obtain the theoretical value of the lithium-ion diffusion coefficient.

[0139] The step of calculating and processing the battery data to obtain the theoretical value of the lithium-ion diffusion coefficient includes:

[0140] The ratio of operating voltage to electrode area is determined based on the operating current density in the battery data;

[0141] The migration number of anions in the lithium salt in the electrolyte is determined based on the migration number of lithium ions in the battery data.

[0142] based on Determine the theoretical value of the lithium-ion diffusion coefficient Dapp, where Z C t is the charge number of the cation, C0 is the initial electrolyte salt concentration, and F is the Faraday constant. a I is the migration number of the anion in the lithium salt in the electrolyte, A is the working voltage, A is the electrode area, and L is the distance between the two electrodes; the charge number of the cation and the initial electrolyte salt concentration are both part of the battery data.

[0143] Optionally, based on the battery short-circuit detection device shown in the above embodiments of the present invention, the processing unit 501 is specifically used for:

[0144] A simplified electrochemical model was constructed based on the first sample data;

[0145] The initial electrochemical model is simplified to obtain the target electrochemical model;

[0146] The system of differential equations was determined using the target electrochemical model;

[0147] The diffusion coefficient of lithium ions in the battery is identified by combining the aforementioned differential equations and particle swarm optimization algorithm, and the first lithium ion diffusion coefficient is obtained.

[0148] Optionally, in the battery short-circuit detection device shown in the above embodiments of the present invention, the processing unit 501 for constructing a simplified electrochemical model based on the first sample data is specifically used for:

[0149] Obtain the first sample data;

[0150] The first sample data is used for training. When the error between the prediction result and the measured data is determined to be within a preset range, the initial electrochemical model that has been trained is obtained.

[0151] Optionally, based on the battery short-circuit detection device shown in the above embodiments of the present invention, the processing unit 501 that identifies the diffusion coefficient of lithium ions in the battery based on the combination of the differential equations and the particle swarm optimization algorithm to obtain the first lithium ion diffusion coefficient includes:

[0152] The simulation model of the differential equation system is simulated using the first parameter in the battery data that is related to the diffusion coefficient of the lithium ions, and the simulation curve of each first parameter is obtained.

[0153] When the matching degree between the simulation curve and the corresponding experimental curve of each first parameter is determined to be greater than or equal to a first threshold, the first lithium-ion diffusion coefficient is calculated based on the differential equation system and the first parameter in the simulation curve. The experimental curve refers to the curve corresponding to the first parameter that is pre-plotted under the same battery capacity.

[0154] When it is determined that the matching degree between the simulation curve with the first parameter and the corresponding experimental curve is less than the first threshold, the particle swarm algorithm is used to adjust the differential equation system to obtain the adjusted differential equation system.

[0155] Optionally, based on the battery short-circuit detection device shown in the above embodiments of the present invention, the processing unit 501 is specifically used for:

[0156] Obtain the battery data of the battery;

[0157] The battery data is input into the target prediction model, and the battery data is processed based on the target prediction model to output the first lithium-ion diffusion coefficient. The target prediction model is generated by training based on the second sample data.

[0158] The process of training and generating a target prediction model based on the second sample data includes:

[0159] Obtain historical battery data and corresponding historical lithium-ion diffusion coefficients for each battery within a historical time period.

[0160] The historical battery data and the corresponding historical lithium-ion diffusion coefficient are used as the second sample data.

[0161] The second sample data is divided into a training set and a validation set;

[0162] The general machine learning model is trained based on the training set to obtain the initial prediction model;

[0163] The initial prediction model is optimized based on the validation set to obtain the trained target prediction model.

[0164] Optionally, in the battery short-circuit detection device shown in the above embodiments of the present invention, the judgment unit 502 for determining whether a short-circuit fault exists in the battery based on the theoretical value of the lithium-ion diffusion coefficient and the first lithium-ion diffusion coefficient is specifically used for:

[0165] Determine whether the first lithium-ion diffusion coefficient is less than the theoretical value of the lithium-ion diffusion coefficient;

[0166] If it is determined that the first lithium-ion diffusion coefficient is less than the theoretical value of the lithium-ion diffusion coefficient, it is determined that a short circuit fault exists in the battery.

[0167] If the first lithium-ion diffusion coefficient is greater than or equal to the theoretical value of the lithium-ion diffusion coefficient, it is determined that there is no short-circuit fault in the battery.

[0168] This invention also discloses an electronic device for running database stored procedures, wherein running the database stored procedures involves executing the above-described... Figure 1 , Figure 3 and Figure 4 Publicly available methods for detecting battery short circuits.

[0169] This invention also discloses a storage medium, which includes a stored database procedure, wherein, during the execution of the stored database procedure, the device where the storage medium is located is controlled to execute the above-mentioned... Figure 1 , Figure 3 and Figure 4 Publicly available methods for detecting battery short circuits.

[0170] In the context of this disclosure, a storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0171] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0172] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0173] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting a battery short circuit, characterized in that, The method includes: When the battery is working, the diffusion coefficient of lithium ions in the battery is identified to obtain the first lithium ion diffusion coefficient; The presence of a short circuit fault in the battery is determined based on the theoretical value of the lithium-ion diffusion coefficient and the first lithium-ion diffusion coefficient. The theoretical value of the lithium-ion diffusion coefficient is calculated based on battery data. The calculation of the theoretical value of the lithium-ion diffusion coefficient based on battery data includes: acquiring battery data of the battery when the battery is working; and performing calculations on the battery data to obtain the theoretical value of the lithium-ion diffusion coefficient. The calculation and processing of the battery data to obtain the theoretical value of the lithium-ion diffusion coefficient includes: The ratio of operating voltage to electrode area is determined based on the operating current density in the battery data; The migration number of anions in the lithium salt in the electrolyte is determined based on the migration number of lithium ions in the battery data. based on The theoretical value of the lithium-ion diffusion coefficient, Dapp, is determined, where, It is the charge number of the cation. Where F is the initial electrolyte salt concentration, and F is the Faraday constant. I is the migration number of the anion in the lithium salt in the electrolyte, A is the working voltage, A is the electrode area, and L is the distance between the two electrodes; the charge number of the cation, the distance between the two electrodes, and the initial electrolyte salt concentration are all part of the battery data.

2. The method according to claim 1, characterized in that, The process of identifying the diffusion coefficient of lithium ions in the battery to obtain the first lithium ion diffusion coefficient includes: An initial electrochemical model was constructed based on the first sample data; The initial electrochemical model is simplified to obtain the target electrochemical model; The system of differential equations was determined using the target electrochemical model; The diffusion coefficient of lithium ions in the battery is identified by combining the aforementioned differential equations and particle swarm optimization algorithm, and the first lithium ion diffusion coefficient is obtained.

3. The method according to claim 2, characterized in that, The construction of the simplified electrochemical model based on the first sample data includes: Obtain the first sample data; The first sample data is used for training. When the error between the prediction result and the measured data is determined to be within a preset range, the initial electrochemical model that has been trained is obtained.

4. The method according to claim 2, characterized in that, The method of identifying the diffusion coefficient of lithium ions in the battery based on the combination of the differential equations and particle swarm optimization algorithm to obtain the first lithium ion diffusion coefficient includes: The simulation model of the differential equation system is simulated using the first parameter in the battery data that is related to the diffusion coefficient of the lithium ions, and the simulation curve of each first parameter is obtained. When the matching degree between the simulation curve and the corresponding experimental curve of each first parameter is determined to be greater than or equal to a first threshold, the first lithium-ion diffusion coefficient is calculated based on the differential equation system and the first parameter in the simulation curve. The experimental curve refers to the curve corresponding to the first parameter that is pre-plotted under the same battery capacity.

5. The method according to claim 4, characterized in that, Also includes: When it is determined that the matching degree between the simulation curve with the first parameter and the corresponding experimental curve is less than the first threshold, the particle swarm algorithm is used to adjust the differential equation system to obtain the adjusted differential equation system.

6. The method according to claim 1, characterized in that, The process of identifying the diffusion coefficient of lithium ions in the battery to obtain the first lithium ion diffusion coefficient includes: Obtain the battery data of the battery; The battery data is input into the target prediction model, and the battery data is processed based on the target prediction model to output the first lithium-ion diffusion coefficient. The target prediction model is generated by training based on the second sample data.

7. The method according to claim 6, characterized in that, The process of training and generating a target prediction model based on the second sample data includes: Obtain historical battery data and corresponding historical lithium-ion diffusion coefficients for each battery within a historical time period. The historical battery data and the corresponding historical lithium-ion diffusion coefficient are used as the second sample data. The second sample data is divided into a training set and a validation set; The general machine learning model is trained based on the training set to obtain the initial prediction model; The initial prediction model is optimized based on the validation set to obtain the trained target prediction model.

8. The method according to claim 1, characterized in that, The method of determining whether a short circuit fault exists in the battery based on the theoretical value of the lithium-ion diffusion coefficient and the first lithium-ion diffusion coefficient includes: Determine whether the first lithium-ion diffusion coefficient is less than the theoretical value of the lithium-ion diffusion coefficient; If it is determined that the first lithium-ion diffusion coefficient is less than the theoretical value of the lithium-ion diffusion coefficient, it is determined that a short circuit fault exists in the battery. If the first lithium-ion diffusion coefficient is greater than or equal to the theoretical value of the lithium-ion diffusion coefficient, it is determined that there is no short-circuit fault in the battery.

9. A battery short-circuit detection device, characterized in that, The device includes: The processing unit is used to identify the diffusion coefficient of lithium ions in the battery when the battery is working, and obtain the first lithium ion diffusion coefficient. The judgment unit is used to determine whether there is a short circuit fault in the battery based on the theoretical value of the lithium-ion diffusion coefficient and the first lithium-ion diffusion coefficient. The theoretical value of the lithium-ion diffusion coefficient is calculated based on battery data. The calculation of the theoretical value of the lithium-ion diffusion coefficient based on battery data includes: acquiring battery data of the battery when the battery is working; and performing calculations on the battery data to obtain the theoretical value of the lithium-ion diffusion coefficient. The calculation and processing of the battery data to obtain the theoretical value of the lithium-ion diffusion coefficient includes: The ratio of operating voltage to electrode area is determined based on the operating current density in the battery data; The migration number of anions in the lithium salt in the electrolyte is determined based on the migration number of lithium ions in the battery data. based on The theoretical value of the lithium-ion diffusion coefficient, Dapp, is determined, where, It is the charge number of the cation. Where F is the initial electrolyte salt concentration, and F is the Faraday constant. I is the migration number of the anion in the lithium salt in the electrolyte, A is the working voltage, A is the electrode area, and L is the distance between the two electrodes; the charge number of the cation, the distance between the two electrodes, and the initial electrolyte salt concentration are all part of the battery data.

10. An electronic device, characterized in that, The electronic device is used to run a program, wherein the program executes the battery short circuit detection method as described in any one of claims 1-8.

11. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the battery short circuit detection method as described in any one of claims 1-8.

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