Pulse current prediction method and device of lithium ion battery and computer equipment

Abstract

The application provides a pulse current prediction method and device of a lithium ion battery and a computer device. The method comprises: obtaining pulse curve data generated by discharging the lithium ion battery under at least three preset pulse current limits, and obtaining variation trend information of each pulse curve data; constructing an initial pulse curve prediction model based on the variation trend information; wherein the initial pulse curve prediction model contains unknown target model parameters; analyzing each pulse curve data through the initial pulse curve prediction model to determine the parameter values of the target model parameters, and obtaining a target pulse curve prediction model with known values; and performing discharge pulse prediction on the lithium ion battery through the target pulse curve prediction model to obtain the maximum pulse current generated by the lithium ion battery after discharging under a preset pulse time limit. The method can improve the pulse current test efficiency of the lithium ion battery and realize accurate current prediction of the lithium ion battery under different pulse times.

Description

Technical Field

[0001] This application relates to the field of lithium-ion battery technology, specifically to a method, apparatus, and computer device for predicting the pulse current of a lithium-ion battery. Background Technology

[0002] With the promotion of new energy vehicles, power lithium-ion batteries have entered a stage of rapid development. Among them, the pulse charge and discharge capability of the battery directly determines the performance of the whole vehicle under different operating conditions. Therefore, it is essential to predict the pulse charge and discharge capability of lithium batteries and the magnitude of the pulse discharge current.

[0003] Currently, existing technologies for predicting the pulse charge and discharge capabilities and pulse discharge current of lithium batteries typically employ methods such as testing power performance at various points under different conditions or calibrating the pulse model by establishing an electrochemical and solid-state thermal coupling model of the battery cell. Although these methods can obtain the desired results, they are complex and require a large number of basic parameters, which still consumes a lot of time and testing resources and cannot meet the needs of lithium-ion battery performance prediction.

[0004] Therefore, existing lithium battery pulse current prediction technologies suffer from low testing efficiency. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, and computer equipment for predicting the pulse current of lithium-ion batteries to address the above-mentioned technical problems. This method can accurately predict the maximum current of lithium-ion batteries under different pulse times by analyzing the relationship between the pulse voltage, pulse current, and pulse time of the lithium-ion battery. This effectively improves the efficiency of lithium-ion battery pulse current testing without requiring point-by-point testing or a large number of basic parameters.

[0006] In a first aspect, this application provides a method for predicting the pulse current of a lithium-ion battery, comprising:

[0007] Acquire pulse curve data generated by the discharge of a lithium-ion battery under at least three preset pulse current limits, and obtain the trend information of each pulse curve data;

[0008] Based on the information on various changing trends, an initial pulse curve prediction model is constructed; wherein, the initial pulse curve prediction model contains target model parameters with unknown numerical values;

[0009] By analyzing the pulse curve data through the initial pulse curve prediction model, the parameter values ​​of the target model are determined, and the target pulse curve prediction model with known values ​​is obtained.

[0010] By using the target pulse curve prediction model, the discharge pulse of the lithium-ion battery is predicted, and the maximum pulse current that the lithium-ion battery will generate after discharge under the preset pulse time limit is obtained.

[0011] In some embodiments of this application, pulse curve data generated by the discharge of a lithium-ion battery under at least three preset pulse current limits are obtained, and the trend information of each pulse curve data is obtained. This includes: performing a discharge pulse test on the lithium-ion battery based on preset test temperature, charge state, and cutoff voltage to obtain test data on the voltage change of the lithium-ion battery over time after discharge from the initial voltage to the cutoff voltage under each preset pulse current limit, thereby obtaining pulse curve data; obtaining the voltage drop rate based on each pulse curve data; and analyzing each voltage drop rate to obtain trend information. The trend information includes a first trend of rapid short-term drop, a second trend of slow drop, and a third trend of rapid drop at the end of the discharge.

[0012] In some embodiments of this application, the trend information includes a first trend, a second trend, and a third trend. Based on each trend information, an initial pulse curve prediction model is constructed, including: constructing a first prediction model containing target model parameters and a first power function based on the first and second trends; wherein the base of the first power function is less than or equal to the first pulse time at the minimum voltage change rate; the minimum voltage change rate is determined based on the second derivative of the pulse curve data; constructing a second prediction model containing target model parameters, a second power function, and an exponential function based on the third trend; wherein the base of the second power function is greater than the second pulse time at the minimum voltage change rate, and the exponent of the exponential function is determined based on the difference between the second pulse time and the minimum voltage change rate; using the first and second prediction models as the initial pulse curve prediction models.

[0013] In some embodiments of this application, the initial pulse curve prediction model includes a first prediction model and a second prediction model. The initial pulse curve prediction model is used to analyze each pulse curve data to determine the parameter values ​​of the target model parameters, resulting in a target pulse curve prediction model with known values. This includes: performing second-order differentiation on each pulse curve data to obtain the voltage value corresponding to the derivative result being zero, which is taken as the target voltage value; obtaining the mean of each target voltage value to obtain the curve segment voltage value; using the first prediction model to fit and analyze each pulse curve data greater than or equal to the curve segment voltage value to determine the parameter values ​​of the target model parameters included in the first prediction model; using the second prediction model to fit and analyze each pulse curve data less than the curve segment voltage value to determine the parameter values ​​of the target model parameters included in the second prediction model; and using the initial pulse curve prediction model with known values ​​as the target pulse curve prediction model.

[0014] In some embodiments of this application, the target model parameters include first model parameters, second model parameters, and third model parameters. A first prediction model is used to fit and analyze pulse curve data greater than or equal to the segmented voltage value to determine the parameter values ​​of the target model parameters included in the first prediction model. This includes: using the first prediction model to fit and analyze pulse curve data greater than or equal to the segmented voltage value to obtain parameter values ​​of the first model parameters, second model parameters, and third model parameters associated with each preset pulse current; performing exponential function fitting processing on the parameter values ​​of each first model parameter and the pulse current to obtain a first function expression for the first model parameter; performing mean calculation processing on the parameter values ​​of each second model parameter to obtain a second parameter value for the second model parameter; and performing linear fitting processing on the parameter values ​​of each third model parameter and the pulse current to obtain a third function expression for the third model parameter; and using the first function expression, the second parameter value, and the third function expression as the parameter values ​​of the target model parameters included in the first prediction model.

[0015] In some embodiments of this application, the target model parameters further include fourth and fifth model parameters. The second prediction model is used to fit and analyze pulse curve data smaller than the segmented voltage value to determine the parameter values ​​of the target model parameters included in the second prediction model. This includes: using the second prediction model to fit and analyze pulse curve data smaller than the segmented voltage value to obtain parameter values ​​of the fourth and fifth model parameters associated with each preset pulse current; performing exponential function fitting on the parameter values ​​of each fourth model parameter and the preset pulse current to obtain a fourth function expression for the fourth model parameter; and performing linear fitting on the parameter values ​​of each fifth model parameter and the preset pulse current to obtain a fifth function expression for the fifth model parameter; and using the first function expression, the second parameter value, the third function expression, the fourth function expression, and the fifth function expression as the parameter values ​​of the target model parameters included in the second prediction model.

[0016] In some embodiments of this application, after determining the parameter values ​​of the target model parameters included in the first prediction model, the method further includes: determining the pulse time corresponding to the target voltage value in each pulse curve data as the time of minimum voltage change rate; extracting the constant parameter values ​​in the first function expression and the third function expression; determining the time function expression of the time of minimum voltage change rate based on the curve segment voltage values, the constant parameter values, and the second parameter values; wherein, the time function expression is used to combine the target pulse curve prediction model to predict the discharge pulse of the lithium-ion battery.

[0017] Secondly, this application provides a pulse current prediction device for a lithium-ion battery, comprising:

[0018] The data acquisition module is used to acquire pulse curve data generated by the discharge of a lithium-ion battery under at least three preset pulse current limits, and to obtain the trend information of each pulse curve data.

[0019] The model building module is used to construct an initial pulse curve prediction model based on various trend information; the initial pulse curve prediction model contains target model parameters with unknown values.

[0020] The curve analysis module is used to analyze the data of each pulse curve through the initial pulse curve prediction model in order to determine the parameter values ​​of the target model and obtain the target pulse curve prediction model with known values.

[0021] The current prediction module is used to predict the discharge pulse of the lithium-ion battery using a target pulse curve prediction model, and to obtain the maximum pulse current that the lithium-ion battery will generate after discharge under a preset pulse time limit.

[0022] Thirdly, this application also provides a computer device, comprising:

[0023] One or more processors;

[0024] The memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the pulse current prediction method for the lithium-ion battery described above.

[0025] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the above-described method for predicting the pulse current of a lithium-ion battery.

[0026] Fifthly, embodiments of this application provide a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method provided in the first aspect described above.

[0027] The aforementioned method, apparatus, and computer equipment for predicting the pulse current of lithium-ion batteries allow the server to acquire pulse curve data generated by the lithium-ion battery under at least three preset pulse current limits. This data provides information on the changing trends of each pulse curve. Based on these trends, an initial pulse curve prediction model is constructed. This model then analyzes the pulse curve data to obtain a target pulse curve prediction model with known values. Finally, by using this target pulse curve prediction model, the maximum pulse current that the lithium-ion battery will generate after discharge under preset pulse time limits can be predicted. Therefore, this application, by analyzing the relationship between the pulse voltage, pulse current, and pulse time of a lithium-ion battery, establishes a target pulse curve prediction model that accurately describes this relationship. This model enables precise prediction of the pulse current of the lithium-ion battery at different pulse times, eliminating the need for complex point-by-point testing methods and effectively improving the efficiency of lithium battery pulse current testing. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is an application scenario diagram of the pulse current prediction method for lithium-ion batteries provided in the embodiments of this application;

[0030] Figure 2 This is a schematic flowchart of the pulse current prediction method for lithium-ion batteries provided in the embodiments of this application;

[0031] Figure 3 This is a pulse curve diagram of pulse voltage and pulse time provided in the embodiments of this application;

[0032] Figure 4 This is a fitting graph showing the relationship between the first model parameters and the pulse current provided in the embodiments of this application;

[0033] Figure 5 This is a fitting graph showing the relationship between the third model parameters and the pulse current provided in the embodiments of this application;

[0034] Figure 6 This is a fitting graph showing the relationship between the fourth model parameters and the pulse current provided in the embodiments of this application;

[0035] Figure 7 This is a fitting graph showing the relationship between the fifth model parameters and the pulse current provided in the embodiments of this application;

[0036] Figure 8 This is a schematic diagram illustrating the verification effect of the target pulse curve prediction model provided in the embodiments of this application;

[0037] Figure 9 This is a schematic diagram of the structure of the pulse current prediction device for lithium-ion batteries provided in the embodiments of this application;

[0038] Figure 10 This is a schematic diagram of the structure of the computer device in the embodiments of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0041] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0042] In this embodiment of the application, the pulse current prediction method for lithium-ion batteries provided in this embodiment can be applied to, for example... Figure 1The illustrated pulse current prediction system for a lithium-ion battery includes a terminal 102 and a server 104. The terminal 102 can be a device that includes both receiving and transmitting hardware, i.e., a device with receiving and transmitting hardware capable of performing bidirectional communication over a bidirectional communication link. Such a device can include cellular or other communication devices with single-line displays, multi-line displays, or no multi-line displays. Specifically, the terminal 102 can be a desktop terminal or a mobile terminal; it can also be a mobile phone, tablet computer, or laptop computer. The terminal 102 can even be a discharge device, including but not limited to various battery-powered electronic devices. The server 104 can be a standalone server or a server network or server cluster, including but not limited to computers, network hosts, single network servers, edge servers, multiple network server sets, or cloud servers composed of multiple servers. The cloud server consists of a large number of computers or network servers based on cloud computing. Furthermore, the terminal 102 and server 104 establish a communication connection through a network, which can be any of a wide area network (WAN), local area network (LAN), or metropolitan area network (MAN).

[0043] Those skilled in the art will understand that Figure 1 The application environment shown is merely one applicable scenario for the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include more than one. Figure 1 The number of devices shown may be more or less. For example, Figure 1 Only one server is shown. It is understood that the pulse current prediction system for this lithium-ion battery may also include one or more other devices, which are not specifically limited here. Additionally, the pulse current prediction system for this lithium-ion battery may also include a memory for storing data, such as pulse curve data.

[0044] It should be noted that, Figure 1 The schematic diagram of the pulse current prediction system for lithium-ion batteries shown is merely an example. The pulse current prediction system and scenario for lithium-ion batteries described in this embodiment are intended to more clearly illustrate the technical solutions of this embodiment and do not constitute a limitation on the technical solutions provided by this embodiment. As those skilled in the art will know, with the evolution of the pulse current prediction system for lithium-ion batteries and the emergence of new business scenarios, the technical solutions provided by this embodiment are also applicable to similar technical problems.

[0045] See Figure 2 This application provides a method for predicting the pulse current of a lithium-ion battery. This embodiment mainly applies this method to the above-mentioned... Figure 1Taking server 104 as an example, the method includes steps S201 to S204, as follows:

[0046] S201, acquire pulse curve data generated by the discharge of the lithium-ion battery under at least three preset pulse current limits, and obtain the change trend information of each pulse curve data.

[0047] Lithium-ion batteries are a type of rechargeable battery that primarily relies on the movement of lithium ions between the positive and negative electrodes to function.

[0048] In this context, a periodically repeating current or voltage pulse is called a pulse current. The preset pulse current in this embodiment can be any preset current value used to measure the performance of a lithium-ion battery. For example, the preset pulse current can be "215A", "229.5A", "243A", "256.5A", "270A", etc. It should be noted that the above five preset pulse currents are values ​​proposed as the basis for measurement in this embodiment. It is not excluded that other pulse current values ​​may be used for performance measurement in other embodiments. It is only necessary to clarify that the number of preset pulse currents selected as the basis for measurement is not less than three. That is to say, the preset pulse current can be expressed as "I". n "n is a positive integer ≥ 3".

[0049] In specific implementation, to improve the efficiency of lithium-ion battery pulse current testing, server 104 can analyze the relationship between the pulse voltage (U), pulse current (I), and pulse time (T) of the lithium-ion battery to accurately predict the maximum current of the lithium-ion battery under different pulse times. Prior to this, it is necessary to obtain the pulse curve data generated by the lithium-ion battery discharging under various preset pulse current limits to determine the relationship between pulse voltage (V) and pulse time (t) under different pulse currents, thus obtaining... Figure 3 The data shown is the pulse curve data reflecting the pulse voltage (V) and pulse time (t).

[0050] Specifically, the pulse curve data can be obtained by placing the lithium-ion battery in a discharge device, such as... Figure 1 The terminal 102 shown here can be the discharge device. Then, the ambient temperature and charge state in the discharge device are adjusted as fixed parameters in the lithium-ion battery discharge process. Finally, the lithium-ion battery is controlled to complete one discharge pulse operation according to the preset pulse current one by one, so that the pulse curve data generated by the lithium-ion battery under each preset pulse current limit can be obtained.

[0051] Of course, pulse curve data can also be obtained without relying on discharge equipment, i.e. Figure 1If terminal 102 is not a discharge device, then server 104 can obtain the required pulse curve data in one of the following ways: 1. In a normal network structure, server 104 receives pulse curve data from terminal 102 or other cloud devices with established network connections; 2. In a pre-built blockchain network, server 104 can synchronously obtain pulse curve data from other terminal nodes or server nodes. This blockchain network can be a public chain, a private chain, etc.; 3. In a pre-built tree structure, server 104 can request pulse curve data from an upper-level server or poll pulse curve data from a lower-level server.

[0052] Furthermore, server 104 obtains such as Figure 3 After obtaining the pulse curve data, the trend of all curves in the pulse curve data over time can be analyzed to obtain the trend information of each pulse curve data. The trend analysis steps involved in this embodiment will be described in detail below.

[0053] In one embodiment, this step includes: performing a discharge pulse test on the lithium-ion battery based on a preset test temperature, state of charge, and cutoff voltage to obtain test data on the voltage change over time after the lithium-ion battery discharges from the initial voltage to the cutoff voltage under each preset pulse current limit, thereby obtaining pulse curve data; obtaining the voltage drop rate based on each pulse curve data; and analyzing each voltage drop rate to obtain trend information; wherein the trend information includes a first trend of rapid short-term drop, a second trend of slow drop, and a third trend of rapid drop at the end.

[0054] The test temperature refers to the ambient temperature set during the prediction of the pulse current of the lithium-ion battery. For example, the test temperature can be preset to "25°C", but it is not limited to this value.

[0055] Among them, the state of charge (SOC) refers to the state of charge (SOC) set in the pulse current prediction process of a lithium-ion battery. It represents the ratio between the remaining capacity of the battery and the total usable capacity after the battery has been used or stored for a long period of time. It is usually expressed as a percentage. For example, the SOC can be preset to "50%", but it is not limited to this value.

[0056] The cutoff voltage refers to the lowest operating voltage value at which the battery voltage drops to the point where it is no longer advisable to continue discharging when the battery is discharging. For example, the cutoff voltage can be preset to "2.7V", but it is not limited to this value.

[0057] In specific implementation, in order to obtain the trend information of the pulse curve data, the server 104 first needs to obtain the pulse curve data. As can be seen from the above embodiments, the primary condition for obtaining pulse curve data proposed in this application is to keep the test temperature and the charge state of the lithium-ion battery constant. Then, the battery is controlled to discharge from its initial voltage to a preset cutoff voltage under the limit of each preset pulse current, and the obtained data is the pulse curve data.

[0058] For example, taking a ternary lithium-ion battery as an example, the preset test temperature is "25℃", the state of charge is "50%", and the cutoff voltage is "2.7V". The ternary lithium-ion battery is controlled to undergo discharge pulse tests sequentially according to preset pulse currents of "215A", "229.5A", "243A", "256.5A", "270A", and "297A", ​​and the results can be obtained as follows: Figure 3 The pulse curve data shown.

[0059] Further analysis, such as Figure 3 The pulse curve data shown indicates that the voltage change curve of the ternary lithium-ion battery over time can be described as three stages: (1) In the initial stage, the battery voltage drops rapidly. The higher the discharge rate, the faster the voltage drops. The drop response is at the millisecond level, caused by the ohmic internal resistance of the battery, showing the first trend of rapid short-term drop; (2) The battery voltage enters a stage of slow change, called the plateau region of the battery. The lower the discharge rate, the longer the plateau region lasts, the higher the plateau voltage, and the slower the voltage drops, showing the second trend of slow drop; (3) When the battery is almost discharged, the battery voltage begins to drop sharply until it reaches the discharge cutoff voltage. The higher the current, the faster the drop, showing the third trend of rapid drop at the end.

[0060] Of course, the above-mentioned division of the trend of change is only at the level of physical phenomena. The actual division can be analyzed by calculating the voltage drop rate. That is, curve fitting can be performed on each pulse curve data, and then the second derivative of the fitting result can be processed to obtain the voltage drop rate. Then, the voltage drop rate of each curve can be comprehensively analyzed, and the voltage change rate threshold used to divide the trend of change can be determined. Finally, the inflection point of the voltage drop rate can be analyzed by combining the threshold, so as to determine the trend information of all pulse curve data.

[0061] S202, Based on the information of various changing trends, an initial pulse curve prediction model is constructed; wherein, the initial pulse curve prediction model contains target model parameters with unknown values.

[0062] The target model parameters refer to model parameters whose values ​​are unknown, but whose values ​​or expressions need to be determined by analyzing pulse curve data. For example, the target model parameters include, but are not limited to, at least one of the following: "a, b, c, d, e".

[0063] In specific implementation, to improve the efficiency of lithium-ion battery pulse current testing, server 104 can analyze a small amount of pulse curve data to first construct a pulse curve prediction model that illustrates the relationship between pulse voltage (U), pulse current (I), and pulse time (T). Then, the model is debugged to ensure its parameters are highly matched to the current testing scenario, thus achieving accurate prediction of the battery pulse current. This saves on the investment in basic test data, simplifies the testing process, and improves pulse current testing efficiency. Furthermore, it ensures reliable output results based on specific parameter relationships, ultimately improving the accuracy of pulse current prediction. The model construction steps involved in this embodiment will be described in detail below.

[0064] In one embodiment, the trend information includes a first trend, a second trend, and a third trend. This step includes: constructing a first prediction model based on the first and second trends, comprising target model parameters and a first power function; wherein the base of the first power function is a first pulse time less than or equal to the moment of minimum voltage change rate; the moment of minimum voltage change rate is determined based on the second derivative of the pulse curve data; constructing a second prediction model based on the third trend, comprising target model parameters, a second power function, and an exponential function; wherein the base of the second power function is a second pulse time greater than the moment of minimum voltage change rate, and the exponent of the exponential function is determined based on the difference between the second pulse time and the moment of minimum voltage change rate; and using the first and second prediction models as the initial pulse curve prediction model.

[0065] Among them, a power function is a function with the base as the independent variable, the power as the dependent variable, and the exponent as a constant, "y = x". α (α is a rational number). The moment of minimum voltage change rate can be represented as "t0", which can be obtained by calculating the second derivative of each pulse curve data.

[0066] In specific implementation, based on the analysis of the above embodiments, the voltage change curve of the ternary lithium-ion battery over time can be described as three stages: (1) In the initial stage, the battery terminal voltage drops rapidly. The higher the discharge rate, the faster the voltage drops. The drop response is at the millisecond level, caused by the ohmic internal resistance of the battery. Therefore, the parameter "c" can be used to represent the voltage drop result in this stage. "c" is linearly related to the current. (2) The battery voltage enters a stage of slow change, called the plateau region of the battery. The lower the discharge rate, the longer the plateau region lasts, the higher the plateau voltage, and the slower the voltage drop. Therefore, this stage can be represented by the empirical formula "a*t". b“b” indicates that “b” is usually a constant value and “a” is related to the current; (3) When the battery is almost discharged, the battery voltage begins to drop sharply until it reaches the discharge cutoff voltage. The larger the current, the faster the drop. According to the curve shape, add an exponential function form of the formula “d*exp(e*(t-t0))” in the last stage, and then explore the relationship between each parameter “d”, “e” and the current.

[0067] Specifically, server 104 can construct a first prediction model "V = c + a*t" that includes target model parameters and a first power function based on the first and second trends of change. b ; t≤t0". Simultaneously, server 104 can also construct a second prediction model "V=c+a*t" based on the third trend, which includes the target model parameters, the second power function, and the exponential function. b +d*exp(e*(t-t0));t>t0.

[0068] Thus, the initial pulse curve prediction model can be expressed as:

[0069] V = c + a * t b ; t≤t0

[0070] V = c + a * t b +d*exp(e*(t-t0)); t>t0

[0071] S203, through the initial pulse curve prediction model, analyze each pulse curve data to determine the parameter values ​​of the target model, and obtain the target pulse curve prediction model with known values.

[0072] In the specific implementation, after the server 104 constructs an initial pulse curve prediction model by analyzing the changing trend information of the pulse curve data, it can use the model to analyze the pulse curve data obtained in the previous steps, and analyze which values ​​or expressions can be used to express each pulse curve data of the target model parameters in the model, thereby determining the parameter values ​​of the target model parameters and thus obtaining the target pulse curve prediction model.

[0073] In one embodiment, the initial pulse curve prediction model includes a first prediction model and a second prediction model. This step includes: performing second-order differentiation on each pulse curve data to obtain the voltage value corresponding to the derivative result being zero, which is taken as the target voltage value; obtaining the mean of each target voltage value to obtain the curve segment voltage value; using the first prediction model, fitting and analyzing each pulse curve data that is greater than or equal to the curve segment voltage value to determine the parameter values ​​of the target model parameters included in the first prediction model; using the second prediction model, fitting and analyzing each pulse curve data that is less than the curve segment voltage value to determine the parameter values ​​of the target model parameters included in the second prediction model; and using the initial pulse curve prediction model with known values ​​as the target pulse curve prediction model.

[0074] In specific implementation, to determine the parameter values ​​of the target model, server 104 can first smooth the acquired pulse curve data, then perform linear fitting on the smoothed curves to obtain the corresponding curve expressions, and then perform second-order differentiation to calculate the voltage change rate of the pulse curve at each time point. The pulse time with zero voltage change rate (corresponding to the horizontal axis) is then taken as the moment of minimum voltage change rate "t0" mentioned above, and the pulse voltage with zero voltage change rate (corresponding to the vertical axis) is taken as the target voltage value "V". t0 ".

[0075] Furthermore, based on the above analysis strategy, by analyzing the pulse curve data corresponding to each preset pulse current one by one, the target voltage value "V" corresponding to different pulse currents can be obtained. t0 Then calculate all target voltage values ​​"V". t0 The average value of "" can be used to obtain the segmented voltage values ​​of the curve for segmented analysis of the pulse curve.

[0076] For example, taking preset pulse currents "215A", "229.5A", "243A", "256.5A", "270A", and "297A" as examples, the target voltage value "V" t0 The calculation results of the segmented voltage values ​​of the curve are shown in Table 1 below:

[0077]

[0078] Furthermore, taking pulse curve data with "V≥3.1744V", the first prediction model "V=c+a*t" is used. b By performing fitting analysis, the values ​​of the target model parameters "c, a, b" under different pulse currents can be obtained; taking the pulse curve data of "V < 3.1744V", the second prediction model "V = c + a * t" is adopted. bBy performing a fitting analysis using "+d*exp(e*(t-t0))", the values ​​of the target model parameters "d" and "e" under different pulse currents can be obtained.

[0079] For example, following the parameter settings in the previous embodiment, taking preset pulse currents "215A", "229.5A", "243A", "256.5A", and "270A" as examples, the parameter values ​​of the target model parameters "a", "b", "c", "d", and "e" under different preset pulse currents are analyzed in Table 2 below:

[0080] Current (A) c a b d e 270 3.36567 -0.0745279 0.647812 -0.0026414 0.8391168 256.5 3.37723 -0.0702998 0.648767 -0.003356 0.721939 243 3.38943 -0.0664814 0.64643 -0.0041683 0.6055752 229.5 3.40159 -0.063444 0.638086 -0.0050732 0.4941909 215 3.41589 -0.0610234 0.621834 -0.0062905 0.3800334

[0081] Therefore, the values ​​of the target model parameters "a, b, c, d, e" are all known, and the target pulse curve prediction model can be obtained. However, analysis of the values ​​in Table 2 shows that the parameter data of some target model parameters differ when the pulse current is different. Therefore, to improve the prediction accuracy of the target pulse curve prediction model, it is necessary to further analyze the values ​​of the target model parameters "a, b, c, d, e" to ensure that the target model parameters remain constant when the pulse current value is different, that is, they will not cause fluctuations in the prediction results. The target model parameter analysis steps involved in this embodiment will be described in detail below.

[0082] In one embodiment, the target model parameters include first model parameters, second model parameters, and third model parameters. The first prediction model is used to fit and analyze pulse curve data greater than or equal to the segmented voltage value to determine the parameter values ​​of the target model parameters included in the first prediction model. This includes: using the first prediction model to fit and analyze pulse curve data greater than or equal to the segmented voltage value to obtain parameter values ​​of the first model parameters, second model parameters, and third model parameters associated with each preset pulse current; performing exponential function fitting on the parameter values ​​of each first model parameter and the preset pulse current to obtain a first function expression for the first model parameter; performing mean calculation on the parameter values ​​of each second model parameter to obtain a second parameter value for the second model parameter; and performing linear fitting on the parameter values ​​of each third model parameter and the preset pulse current to obtain a third function expression for the third model parameter; and using the first function expression, the second parameter value, and the third function expression as the parameter values ​​of the target model parameters included in the first prediction model.

[0083] The first model parameter can be "a", the second model parameter can be "b", and the third model parameter can be "c".

[0084] In the specific implementation, based on the results analysis in Table 2 of the previous embodiment, it can be seen that, except for the target model parameter "b", the parameter values ​​of other target model parameters vary significantly. Therefore, the average value of "b" "0.64" can be taken as its corresponding parameter value. The relationship between the other target model parameters "a" and "c" and the pulse current "I" is as follows:

[0085] a = -exp(a1*I + a2)

[0086] c = c1 * I + c2

[0087] The determination of the relationship between "a" and "c" and the pulse current "I" depends on, for example, Figure 4 , Figure 5 The data analysis results, taking preset pulse currents "215A", "229.5A", "243A", "256.5A", and "270A" as examples, show the relationship between "a" and "c" and the current "I" as follows: Figure 4 and Figure 5 As shown. Since "a" is a number less than zero, an exponential function can be used for analysis and fitting; "b" changes little and has little correlation with "I", so the average of all "b" values ​​can be taken as the value of the second parameter; "c" has a linear relationship with the current "I", so a linear function can be used for analysis and fitting. The specific expressions for the first and third functions are as follows:

[0088] a = -exp(0.00366*I - 3.59263)

[0089] c = -0.00091 * I + 3.61122

[0090] Therefore, the first prediction model "V=c+a*t" b The target model parameters "a", "b", and "c" can all be determined. The target pulse curve prediction model includes: V = -0.00091*I + 3.61122 - exp(0.00366*I - 3.59263)*t 0.64 ; t≤t0, can actually be expressed as:

[0091] V = (c1*I + c2) - exp(a1*I + a2)*t b ; t≤t0

[0092] In one embodiment, the target model parameters further include fourth and fifth model parameters. The second prediction model is used to fit and analyze pulse curve data smaller than the segmented voltage value to determine the parameter values ​​of the target model parameters included in the second prediction model. This includes: using the second prediction model to fit and analyze pulse curve data smaller than the segmented voltage value to obtain parameter values ​​of the fourth and fifth model parameters associated with each preset pulse current; performing exponential function fitting on the parameter values ​​of each fourth model parameter and the preset pulse current to obtain a fourth function expression for the fourth model parameter; and performing linear fitting on the parameter values ​​of each fifth model parameter and the preset pulse current to obtain a fifth function expression for the fifth model parameter; and using the first function expression, the second parameter value, the third function expression, the fourth function expression, and the fifth function expression as the parameter values ​​of the target model parameters included in the second prediction model.

[0093] The fourth model parameter can be "d" and the fifth model parameter can be "e".

[0094] In the specific implementation, based on the results analysis in Table 2 of the previous embodiment, it can be seen that, except for the target model parameter "b", the values ​​of other target model parameters all vary significantly. The relationship between the target model parameters "d" and "e" and the pulse current "I" is as follows:

[0095] d = -exp(d1*I + d2)

[0096] e = e1 * I + e2

[0097] The determination of the relationship between "d, e" and the pulse current "I" depends on Figure 6 , Figure 7 The data analysis results, taking preset pulse currents "215A", "229.5A", "243A", "256.5A", and "270A" as examples, show the relationship between "d" and "e" and the current "I" as follows: Figure 6 and Figure 7 As shown. Since "d" is a number less than zero, an exponential function can be used for analysis and fitting; "e" has a linear relationship with the current "I", so a linear function can be used for analysis and fitting. The specific expressions for the fourth and fifth functions are as follows:

[0098] d = -exp(-0.01568*I - 1.68672)

[0099] e = 0.00836 * I - 1.42212

[0100] Therefore, the second prediction model "V=c+a*t" bThe target model parameters "a, b, c, d, e" in "+d*exp(e*(t-t0))" can all be determined. The target pulse curve prediction model includes: V=-0.00091*I+3.61122-exp(0.00366*I-3.59263)*t 0.64 -exp(-0.01568*I-1.68672)*exp((0.00836*I-1.42212)*(t-t0)); t>t0, can actually be expressed as:

[0101] V = (c1*I + c2) - exp(a1*I + a2)*t b -exp(d1*I+d2)*exp((e1*I+e2)*(t-t0));t>t0

[0102] In one embodiment, after determining the parameter values ​​of the target model parameters included in the first prediction model, the method further includes: determining the pulse time corresponding to the target voltage value in each pulse curve data as the time of minimum voltage change rate; extracting the constant parameter values ​​from the first function expression and the third function expression; and determining the time function expression of the time of minimum voltage change rate based on the curve segment voltage values, constant parameter values, and second parameter values; wherein the time function expression is used to combine the target pulse curve prediction model to predict the discharge pulse of the lithium-ion battery.

[0103] In the specific implementation, the above embodiments have analyzed and determined the parameter values ​​of all target model parameters "a, b, c, d, e". However, the time function expression for the moment of minimum voltage change rate still needs to be further determined. By analyzing the moment of minimum voltage change rate corresponding to each preset pulse current and fitting the data at all times, the following expression can be obtained:

[0104]

[0105] Analysis of the above expressions shows that the time-time function expression is actually determined based on the segmented voltage values ​​of the curve, the value of the second parameter, and the values ​​of the constant parameters in the first and third function expressions. It can be expressed as: And t0≥0.

[0106] S204 uses a target pulse curve prediction model to predict the discharge pulse of a lithium-ion battery, obtaining the maximum pulse current that the lithium-ion battery will generate after discharge under a preset pulse time limit.

[0107] In practical implementation, based on the above analysis, a target pulse curve prediction model for predicting the discharge pulse of lithium-ion batteries can be constructed. That is, the target pulse curve prediction model consists of the following three parts:

[0108] V = (c1*I + c2) - exp(a1*I + a2)*t b ; t≤t0

[0109] V = (c1*I + c2) - exp(a1*I + a2)*t b -exp(d1*I+d2)*exp((e1*I+e2)*(t-t0));t>t0

[0110] And t0≥0

[0111] The model parameters "a1, a2, b, c1, c2, d1, d2, e1, e2" can all be adjusted based on different test temperatures, charge states, and cutoff voltages. The pulse current prediction method provided in this application has actually illustrated the debugging process for preparing for prediction, the purpose of which is to obtain a target pulse curve prediction model for predicting the discharge pulse of a lithium-ion battery. To verify the prediction accuracy of this target pulse curve prediction model, taking a pulse current "I = 297A" as an example, one approach is to analyze the target pulse curve prediction model, resulting in the following... Figure 8 The predicted value is shown by the dashed line; secondly, by recording actual discharges of lithium batteries, the following can be obtained: Figure 8 The measured values ​​are shown by the solid line (the line connecting the diamond-shaped recorded values ​​at different times). Observation shows that the two values ​​highly overlap; therefore, this target pulse curve prediction model can be used to predict the maximum current corresponding to different pulse durations under specified temperature and SOC conditions.

[0112] For example, under the conditions of "25℃" temperature and "50%" SOC, the predicted maximum pulse current for different pulse times is shown in Table 3 below:

[0113] Pulse duration (s) 2.85 5.00 8.00 10.00 15.00 Current (A) 479.3 358.2 296.5 271.8 233.2

[0114] The above results further illustrate that the pulse current prediction method for lithium-ion batteries proposed in the embodiments of this application can not only improve the pulse current testing efficiency of lithium-ion batteries, but also further improve the prediction accuracy of the maximum pulse current of lithium-ion batteries under different pulse times.

[0115] The pulse current prediction method for lithium-ion batteries in the above embodiments involves the server acquiring pulse curve data generated by the lithium-ion battery under at least three preset pulse current limits. This allows the server to obtain the trend information of each pulse curve data. Based on this trend information, an initial pulse curve prediction model is constructed. This model then analyzes the pulse curve data to obtain a target pulse curve prediction model with known values. Finally, by using this target pulse curve prediction model to predict the discharge pulse of the lithium-ion battery, the maximum pulse current that the lithium-ion battery will generate after discharge under a preset pulse time limit can be obtained. Therefore, this application, by analyzing the relationship between the pulse voltage, pulse current, and pulse time of a lithium-ion battery, establishes a target pulse curve prediction model that accurately describes this relationship. This target pulse curve prediction model can then be used to accurately predict the pulse current of the lithium-ion battery under different pulse times, eliminating the need for complex methods such as point-by-point testing to measure the pulse current and effectively improving the pulse current testing efficiency of lithium batteries.

[0116] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0117] To better implement the pulse current prediction method for lithium-ion batteries provided in the embodiments of this application, based on the pulse current prediction method for lithium-ion batteries proposed in the embodiments of this application, the embodiments of this application also provide a pulse current prediction device for lithium-ion batteries, such as... Figure 9 As shown, the pulse current prediction device 900 for the lithium-ion battery includes:

[0118] The data acquisition module 910 is used to acquire pulse curve data generated by the discharge of the lithium-ion battery under at least three preset pulse current limits, and to obtain the change trend information of each pulse curve data.

[0119] The model building module 920 is used to build an initial pulse curve prediction model based on various trend information; wherein, the initial pulse curve prediction model contains target model parameters with unknown values;

[0120] The curve analysis module 930 is used to analyze the pulse curve data through the initial pulse curve prediction model to determine the parameter values ​​of the target model and obtain the target pulse curve prediction model with known values.

[0121] The current prediction module 940 is used to predict the discharge pulse of the lithium-ion battery through the target pulse curve prediction model, and obtain the maximum pulse current that the lithium-ion battery will generate after discharge under the preset pulse time limit.

[0122] In one embodiment, the data acquisition module 910 is further configured to perform a discharge pulse test on the lithium-ion battery based on a preset test temperature, state of charge, and cutoff voltage, so as to obtain test data on the voltage change of the lithium-ion battery over time after discharge from the initial voltage to the cutoff voltage under each preset pulse current limit, thereby obtaining pulse curve data; obtain the voltage drop rate based on each pulse curve data; analyze each voltage drop rate to obtain trend information; wherein, the trend information includes a first trend of rapid drop in short time, a second trend of slow drop, and a third trend of rapid drop at the end.

[0123] In one embodiment, the trend information includes a first trend, a second trend, and a third trend. The model building module 920 is further configured to construct a first prediction model based on the first and second trends, comprising target model parameters and a first power function; wherein the base of the first power function is less than or equal to the first pulse time at the minimum voltage change rate; the minimum voltage change rate is determined based on the second derivative of the pulse curve data; and based on the third trend, construct a second prediction model comprising target model parameters, a second power function, and an exponential function; wherein the base of the second power function is greater than the second pulse time at the minimum voltage change rate, and the exponent of the exponential function is determined based on the difference between the second pulse time and the minimum voltage change rate; and use the first and second prediction models as the initial pulse curve prediction models.

[0124] In one embodiment, the initial pulse curve prediction model includes a first prediction model and a second prediction model. The curve analysis module 930 is further used to perform second-order derivative processing on each pulse curve data to obtain the voltage value corresponding to the derivative result being zero, which is used as the target voltage value; obtain the mean of each target voltage value to obtain the curve segment voltage value; use the first prediction model to fit and analyze each pulse curve data that is greater than or equal to the curve segment voltage value to determine the parameter values ​​of the target model parameters included in the first prediction model; use the second prediction model to fit and analyze each pulse curve data that is less than the curve segment voltage value to determine the parameter values ​​of the target model parameters included in the second prediction model; and use the initial pulse curve prediction model with known values ​​as the target pulse curve prediction model.

[0125] In one embodiment, the target model parameters include first model parameters, second model parameters, and third model parameters. The curve analysis module 930 is further configured to use the first prediction model to fit and analyze the pulse curve data of each pulse curve that is greater than or equal to the voltage value of the curve segment, and obtain the parameter values ​​of the first model parameters, second model parameters, and third model parameters associated with each preset pulse current; perform exponential function fitting processing on the parameter values ​​of each first model parameter to obtain the first function expression of the first model parameter; perform mean calculation processing on the parameter values ​​of each second model parameter to obtain the second parameter value of the second model parameter; and perform linear fitting processing on the parameter values ​​of each third model parameter to obtain the third function expression of the third model parameter; and use the first function expression, the second parameter value, and the third function expression as the parameter values ​​of the target model parameters included in the first prediction model.

[0126] In one embodiment, the target model parameters further include fourth and fifth model parameters. The curve analysis module 930 is further configured to use the second prediction model to fit and analyze the pulse curve data of each pulse curve smaller than the curve segment voltage value to obtain the parameter values ​​of the fourth and fifth model parameters associated with each preset pulse current; perform exponential function fitting processing on the parameter values ​​of each fourth model parameter and the preset pulse current to obtain the fourth function expression of the fourth model parameter; and perform linear fitting processing on the parameter values ​​of each fifth model parameter and the preset pulse current to obtain the fifth function expression of the fifth model parameter; and use the first function expression, the second parameter value, the third function expression, the fourth function expression, and the fifth function expression as the parameter values ​​of the target model parameters included in the second prediction model.

[0127] In one embodiment, the curve analysis module 930 is further configured to determine the pulse time corresponding to the target voltage value in each pulse curve data as the time of minimum voltage change rate; extract the constant parameter values ​​from the first function expression and the third function expression; and determine the time function expression of the time of minimum voltage change rate based on the voltage values ​​of the curve segments, the constant parameter values, and the second parameter values; wherein, the time function expression is used to predict the discharge pulse of the lithium-ion battery in conjunction with the target pulse curve prediction model.

[0128] In the above embodiments, by analyzing the relationship between the pulse voltage, pulse current, and pulse time of a lithium-ion battery, a target pulse curve prediction model that can accurately describe the above relationship is established. This target pulse curve prediction model can be used to accurately predict the pulse current of a lithium-ion battery at different pulse times, thus eliminating the need for complex methods such as point-by-point testing to measure the pulse current and effectively improving the pulse current testing efficiency of lithium batteries.

[0129] It should be noted that the specific limitations regarding the pulse current prediction device for lithium-ion batteries can be found in the limitations of the pulse current prediction method for lithium-ion batteries mentioned above, and will not be repeated here. Each module in the aforementioned pulse current prediction device for lithium-ion batteries can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware form or independently of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0130] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The database stores texture coordinate data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a pulse current prediction method for a lithium-ion battery.

[0131] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0132] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0133] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 specification.

[0134] The pulse current prediction method, apparatus, and computer equipment for lithium-ion batteries provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for predicting the pulse current of a lithium-ion battery, characterized in that, include: Based on preset test temperature, charge state and cutoff voltage, a discharge pulse test is performed on the lithium-ion battery to obtain test data on the voltage change over time after the lithium-ion battery discharges from the initial voltage to the cutoff voltage under at least three preset pulse current limits, and to obtain the pulse curve data generated by the discharge. Based on the pulse curve data, the voltage drop rate is obtained, and the voltage drop rate is analyzed to obtain the trend information of the pulse curve data. The trend information includes a first trend of rapid drop in short duration, a second trend of slow drop, and a third trend of rapid drop at the end. Based on the aforementioned trend information, an initial pulse curve prediction model is constructed; wherein, the initial pulse curve prediction model includes target model parameters with unknown values; By analyzing the pulse curve data using the initial pulse curve prediction model, the parameter values ​​of the target model parameters are determined, resulting in a target pulse curve prediction model with known values. The target pulse curve prediction model is used to predict the discharge pulse of the lithium-ion battery, thereby obtaining the maximum pulse current that the lithium-ion battery will generate after discharge under a preset pulse time limit.

2. The method as described in claim 1, characterized in that, The trend information includes a first trend, a second trend, and a third trend. The step of constructing an initial pulse curve prediction model based on each of the trend information includes: Based on the first and second trends of change, a first prediction model is constructed, which includes the target model parameters and a first power function; wherein, the base of the first power function is less than or equal to the first pulse time at the moment of minimum voltage change rate; the moment of minimum voltage change rate is determined based on the second derivative of the pulse curve data; Based on the third trend of change, a second prediction model is constructed, which includes the target model parameters, a second power function, and an exponential function; wherein, the base of the second power function is greater than the second pulse time at the moment of minimum voltage change rate, and the exponent of the exponential function is determined based on the difference between the second pulse time and the moment of minimum voltage change rate; The first prediction model and the second prediction model are used as the prediction model for the initial pulse curve.

3. The method as described in claim 1, characterized in that, The initial pulse curve prediction model includes a first prediction model and a second prediction model. The process of analyzing the pulse curve data using the initial pulse curve prediction model to determine the parameter values ​​of the target model, thereby obtaining a target pulse curve prediction model with known values, includes: The second derivative of each pulse curve data is performed to obtain the voltage value corresponding to the zero derivative result, which is used as the target voltage value. The mean value of each target voltage value is obtained to obtain the segmented voltage values ​​of the curve. By using the first prediction model, the data of each pulse curve that is greater than or equal to the voltage value of the curve segment are fitted and analyzed to determine the parameter values ​​of the target model parameters included in the first prediction model. The second prediction model is used to fit and analyze pulse curve data that are smaller than the voltage value of the curve segment to determine the parameter values ​​of the target model parameters included in the second prediction model. The initial pulse curve prediction model with known values ​​is used as the target pulse curve prediction model.

4. The method as described in claim 3, characterized in that, The target model parameters include first model parameters, second model parameters, and third model parameters. The step of using the first prediction model to fit and analyze pulse curve data greater than or equal to the segmented voltage value to determine the parameter values ​​of the target model parameters included in the first prediction model includes: By using the first prediction model, the data of each pulse curve that is greater than or equal to the voltage value of the curve segment are fitted and analyzed to obtain the parameter values ​​of the first model parameter, the second model parameter and the third model parameter associated with each preset pulse current. The parameter values ​​of each of the first model parameters are subjected to exponential function fitting with a preset pulse current to obtain a first functional expression for the first model parameters; and The mean values ​​of each parameter of the second model are calculated to obtain the second parameter values ​​of the second model; and The parameter values ​​of each of the third model parameters are linearly fitted with the preset pulse current to obtain the third function expression of the third model parameters; The first function expression, the second parameter value, and the third function expression are used as the parameter values ​​of the target model parameters included in the first prediction model.

5. The method as described in claim 4, characterized in that, The target model parameters also include a fourth model parameter and a fifth model parameter. The step of using the second prediction model to fit and analyze pulse curve data smaller than the segmented voltage value to determine the parameter values ​​of the target model parameters included in the second prediction model includes: By using the second prediction model, the data of each pulse curve that is less than the voltage value of the curve segment are fitted and analyzed to obtain the parameter values ​​of the fourth model parameter and the fifth model parameter associated with each preset pulse current; The parameter values ​​of each of the fourth model parameters are subjected to exponential function fitting with a preset pulse current to obtain the fourth function expression of the fourth model parameters; and The parameter values ​​of each of the fifth model parameters are linearly fitted with the preset pulse current to obtain the fifth function expression of the fifth model parameters; The first function expression, the second parameter value, the third function expression, the fourth function expression, and the fifth function expression are used as the parameter values ​​of the target model parameters included in the second prediction model.

6. The method as described in claim 4, characterized in that, After determining the parameter values ​​of the target model parameters included in the first prediction model, the method further includes: Determine the pulse moment corresponding to the target voltage value in each of the pulse curve data as the moment of minimum voltage change rate; Extract the constant parameter values ​​from the first function expression and the third function expression; Based on the segmented voltage values ​​of the curve, the constant parameter values, and the second parameter values, a time function expression is determined for the moment when the voltage change rate is at its minimum; wherein, the time function expression is used to predict the discharge pulse of the lithium-ion battery in conjunction with the target pulse curve prediction model.

7. A pulse current prediction device for a lithium-ion battery, characterized in that, include: The data acquisition module performs discharge pulse tests on the lithium-ion battery based on preset test temperature, charge state, and cutoff voltage. This acquires test data showing the voltage change over time after the lithium-ion battery discharges from the initial voltage to the cutoff voltage under at least three preset pulse current limits. The module obtains discharge pulse curve data, acquires the voltage drop rate based on each pulse curve, analyzes each voltage drop rate, and obtains the trend information of each pulse curve data. The trend information includes a first trend of rapid short-term drop, a second trend of slow drop, and a third trend of rapid drop at the end of the discharge. The model building module is used to build an initial pulse curve prediction model based on the aforementioned trend information; wherein, the initial pulse curve prediction model includes target model parameters with unknown values. The curve analysis module is used to analyze each of the pulse curve data through the initial pulse curve prediction model to determine the parameter values ​​of the target model parameters and obtain a target pulse curve prediction model with known values. The current prediction module is used to predict the discharge pulse of the lithium-ion battery using the target pulse curve prediction model, so as to obtain the maximum pulse current that the lithium-ion battery will generate after discharge under a preset pulse time limit.

8. A computer device, characterized in that, include: One or more processors; Memory; And one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the pulse current prediction method for a lithium-ion battery according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to perform the steps in the pulse current prediction method for a lithium-ion battery according to any one of claims 1 to 6.

Images