Prediction model acquisition method and device, direct current internal resistance prediction method and storage medium

Abstract

The invention relates to the technical field of battery management, and discloses a prediction model obtaining method and device, a direct current internal resistance prediction method and a storage medium, and the prediction model obtaining method comprises the steps: obtaining the direct current internal resistance test data of a battery in at least two cycle periods under different temperatures and different charge states; determining an initial direct-current internal resistance value and an actually measured direct-current internal resistance value of each cycle period based on the test data, and determining an actually measured direct-current internal resistance growth rate corresponding to each cycle period based on the initial direct-current internal resistance value and the actually measured direct-current internal resistance value of each cycle period; and based on the actually measured DC internal resistance growth rate, in combination with a parameter identification method, determining a target prediction model of the DC internal resistance growth rate changing along with the cycle period number, the temperature and the state of charge. Through the target prediction model, accurate prediction of the direct current internal resistance of the battery can be realized.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a method and apparatus for obtaining a prediction model, a method for predicting DC internal resistance, and a storage medium. Background Technology

[0002] In lithium-ion battery performance evaluation, DC internal resistance (DCIR) is a key parameter reflecting its power characteristics and aging state, significantly affected by factors such as temperature, state of charge (SOC), and cycle count. Current technologies often predict DCIR changes by constructing multidimensional fitting models. However, these methods all rely on the battery's state of health (SOH) as an input parameter, which typically requires complete charge-discharge tests or complex algorithms. In actual cycle testing, it is difficult to obtain this information in real-time and accurately, thus limiting the model's application. Furthermore, DCIR exhibits a non-linear growth trend with each cycle, and existing models lack sufficient extrapolation capabilities for predicting conditions not yet experienced, failing to meet the requirements for accurate modeling of long-term aging behavior. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method, apparatus, DC internal resistance prediction method, and storage medium for obtaining a prediction model. This enables accurate prediction of the DC internal resistance of a battery.

[0004] In a first aspect, embodiments of this application provide a method for obtaining a battery DC internal resistance growth rate prediction model, including: Acquire DC internal resistance test data of the battery at at least two cycles, under different temperatures and different states of charge; Based on the test data, the initial DC internal resistance value and the measured DC internal resistance value for each cycle are determined, and based on the initial DC internal resistance value and the measured DC internal resistance value for each cycle, the measured DC internal resistance growth rate corresponding to each cycle is determined. Based on the measured DC internal resistance growth rate, and combined with the parameter identification method, a target prediction model is determined to predict the DC internal resistance growth rate as a function of cycle number, temperature, and state of charge.

[0005] In an optional implementation, the target prediction model is constructed based on a first function, a second function, and a third function; The first function is used to represent the effect of temperature on the battery aging rate; The second function is used to represent the effect of the state of charge on the change of DC internal resistance; The third function is used to represent the effect of increasing the number of cycle periods.

[0006] In an optional implementation, the determination of the target prediction model includes: Initialize the function parameters of the first function, the second function, and the third function; Based on the parameter identification method, the function parameters in the first function, the second function and the third function are iteratively updated according to the error between the predicted DC internal resistance growth rate output by the current prediction model and the measured DC internal resistance growth rate. When the error meets the convergence condition, the current prediction model is used as the target prediction model.

[0007] In an optional implementation, The first function is an exponential function based on the Arrhenius equation. The first function takes absolute temperature as the input variable and outputs a function value representing the effect of temperature on the battery aging rate. The second function is a polynomial function. The second function takes the state of charge as the input variable and outputs a function value that represents the effect of the state of charge on the change of DC internal resistance. The function value output by the second function in the low charge state and the high charge state is higher than the function value corresponding to the intermediate charge interval. The third function is a power function, which takes the number of cycles as input variable and outputs a function value representing the cumulative effect of the cycles.

[0008] In an optional implementation, determining the initial DC internal resistance value and the measured DC internal resistance value for each cycle based on the test data, and determining the measured DC internal resistance growth rate for each cycle based on the initial DC internal resistance value and the measured DC internal resistance value for each cycle, includes: The arithmetic mean of the DC internal resistance values ​​measured in the previous preset number of cyclic tests is used as the initial DC internal resistance value. The measured DC internal resistance value under each cycle is compared with the initial DC internal resistance value to obtain the measured DC internal resistance growth rate of the corresponding cycle.

[0009] In an optional implementation, before determining the initial DC internal resistance value and the measured DC internal resistance value for each cycle based on the test data, the method further includes: Anomalies in the test data are detected and removed; wherein, when the change in DC internal resistance between two adjacent cycles exceeds a preset threshold, the current data point is determined to be an anomaly and the anomaly is removed.

[0010] Secondly, embodiments of this application provide a method for predicting the DC internal resistance of a battery, which determines the initial DC internal resistance value of the battery based on DC internal resistance test data of the battery in at least two cycle periods at different temperatures and under different states of charge. Input the target operating condition parameters into the target prediction model described in any of the foregoing embodiments to obtain the predicted DC internal resistance growth rate of the battery under the target operating condition; Based on the initial DC internal resistance value and the predicted DC internal resistance growth rate, the predicted DC internal resistance value of the battery under the target operating condition is determined.

[0011] In an optional implementation, the target operating condition includes at least one of the following: an unexperienced number of cycles, an untested temperature condition, or an unverified state of charge range.

[0012] Thirdly, embodiments of this application provide a device for obtaining a battery DC internal resistance growth rate prediction model, comprising: The acquisition module is used to acquire DC internal resistance test data of the battery at different temperatures and different states of charge during at least two cycle periods. The calculation module is used to determine the initial DC internal resistance value and the measured DC internal resistance value for each cycle based on the test data, and to determine the measured DC internal resistance growth rate corresponding to each cycle based on the initial DC internal resistance value and the measured DC internal resistance value for each cycle. The determination module is used to determine a target prediction model for the change of DC internal resistance growth rate with the number of cycle periods, temperature and state of charge, based on the measured DC internal resistance growth rate and combined with the parameter identification method.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed on a processor, performs the methods described above.

[0014] The embodiments of this application have the following beneficial effects: This application constructs a DC internal resistance growth rate prediction model based on the coupled effects of cycle number, temperature, and state of charge, thereby enabling the prediction of changes in battery DC internal resistance with cycle number, temperature, and state of charge. Furthermore, based on the obtained target prediction model, the DC internal resistance of the battery under target operating conditions can be predicted more accurately. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This paper illustrates a first flowchart of a method for obtaining a battery DC internal resistance growth rate prediction model according to an embodiment of this application. Figure 2This paper illustrates a second flowchart of the battery DC internal resistance growth rate prediction model acquisition method according to an embodiment of this application. Figure 3 A schematic flowchart of a battery DC internal resistance prediction method according to an embodiment of this application is shown. Figure 4 The diagram shows the output curve and the measured curve of an embodiment of this application; Figure 5 A schematic diagram of a battery DC internal resistance growth rate prediction model acquisition device according to an embodiment of this application is shown. Detailed Implementation

[0017] The technical solutions in 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.

[0018] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0020] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0021] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0022] The following examples illustrate the method for obtaining the DC internal resistance prediction model of this battery.

[0023] Figure 1 A schematic flowchart illustrating a method for obtaining a battery DC internal resistance growth rate prediction model according to an embodiment of this application is shown. Exemplarily, the method for obtaining a battery DC internal resistance growth rate prediction model includes steps S100-S300: Step S100: Obtain DC internal resistance test data of the battery at at least two cycle periods, under different temperatures and different states of charge.

[0024] In this embodiment, the target battery first needs to undergo multi-condition cyclic aging tests to collect data on its DC internal resistance changes under different operating conditions. For example, when testing the target battery, multiple test temperature points are set in the ambient temperature control chamber, such as 25°C, 45°C, and 60°C. At the same time, under each temperature condition, the battery is controlled to undergo charge-discharge cycles in different state of charge (SOC) ranges. The SOC test points can be 80%, 90%, and 100% to cover the battery's operating scenarios in the high-energy storage region.

[0025] During the test, methods such as the HPPC pulse method or the single high-current discharge method can be used. After each complete cycle, a current pulse of specified amplitude and duration is applied, and the voltage response curve is recorded. Then, the DC internal resistance value for the current cycle number is calculated according to Ohm's law. In addition, to ensure data representativeness, at least two cycles of testing should be completed for each combination of operating conditions, such as more than five cycles, to form a raw test dataset containing time information.

[0026] Understandably, setting at least two cycle times in the steps ensures that the obtained test data has sufficient data points to identify the growth rate trend, avoiding model distortion due to insufficient samples. Furthermore, combining tests with different temperatures and states of charge helps to cover the impact of complex and variable operating conditions in actual use.

[0027] Step S200: Determine the initial DC internal resistance value and the measured DC internal resistance value for each cycle based on the test data, and determine the measured DC internal resistance growth rate for each cycle based on the initial DC internal resistance value and the measured DC internal resistance value for each cycle.

[0028] Before step S200, the method also includes: detecting and removing outliers in the test data.

[0029] As an example, due to factors such as contact resistance fluctuations, temperature control system delays, voltage sampling noise, or human interference during actual testing, the DC internal resistance values ​​measured in some cyclic tests may deviate significantly from the normal trend. If these anomalies are not identified and eliminated, these abnormal data will seriously affect the subsequent parameter identification process, leading to model distortion. Therefore, to ensure the accuracy and reliability of subsequent modeling, before executing step S200, it is necessary to first perform anomaly detection and removal processing on the original test data.

[0030] This embodiment employs an anomaly detection method based on comparing the change in DC internal resistance between adjacent cycles. Specifically, it iterates through continuous test data under the same operating conditions, calculates the absolute change in DC internal resistance of the current cycle relative to the previous cycle, and when this change exceeds a preset threshold, the current data point is determined to be an anomaly and is excluded.

[0031] For example, set the preset threshold to 1.5. (Adjustable according to battery type and testing accuracy), if the DC internal resistance value measured in a certain cycle changes from 86.2 in the previous cycle... The mutation was 90.1 Its change amounted to 3.9. If a data point's value exceeds a preset threshold, it is considered to have measurement error or atypical aging behavior, and is therefore marked and removed from the dataset. For removed data points, interpolation or skipping the cycle can be used to ensure good consistency and trend continuity of the remaining data.

[0032] After the anomaly detection and removal are completed, step S200 is executed to determine the initial DC internal resistance value and the measured DC internal resistance growth rate corresponding to each cycle.

[0033] The initial DC internal resistance value represents the battery's baseline performance level in a healthy state and serves as the basis for subsequent normalization processing. In this embodiment, the arithmetic mean of the DC internal resistance values ​​measured in the first preset number of cycle tests is used as the initial DC internal resistance value. The preset number of cycles is an integer greater than or equal to 2 and less than the total number of cycles. For example, the measurement results from the first 3 cycles can be averaged. For instance, if the DC internal resistance measured in the first three cycles is 80.1... 80.3 and 79.8 The initial DC internal resistance value is .

[0034] After determining the initial DC internal resistance value, the measured DC internal resistance growth rate for each effective cycle is calculated based on this initial DC internal resistance value. Specifically, the measured DC internal resistance value measured in each cycle is compared with the aforementioned initial DC internal resistance value, and its growth rate is quantified by the relative rate of change, which can be expressed by the formula... express, This represents the initial DC internal resistance value. This represents the measured DC internal resistance value obtained during the cyclic period. This represents the measured rate of increase of the DC internal resistance. For example, the DC internal resistance was measured to be 92.5 at the 50th cycle. The initial DC internal resistance is 80.07. Then the measured DC internal resistance growth rate is: This represents an increase of approximately 15.53%. It can be understood that determining the measured DC internal resistance growth rate is essentially a normalization operation, which eliminates the influence of initial differences between individual batteries.

[0035] Similarly, by performing this calculation on each of the retained valid cycle periods, a set of growth rate data reflecting the gradual degradation of the battery with the number of cycles can be obtained. This growth rate data not only includes the evolution trend over time, but also correlates with the temperature and state of charge information at the corresponding test time.

[0036] Step S300: Based on the measured DC internal resistance growth rate and combined with the parameter identification method, determine the target prediction model for the DC internal resistance growth rate as a function of cycle number, temperature and state of charge.

[0037] The target prediction model is a composite function structure consisting of the multiplication of the first function, the second function, and the third function.

[0038] The first function represents the effect of temperature on the battery aging rate. Since high temperatures accelerate side reactions such as electrolyte decomposition and SEI film thickening, leading to a rapid increase in internal resistance, this embodiment designs the first function as an exponential function based on the Arrhenius equation. This function takes absolute temperature as the input variable and outputs a function value representing the effect of temperature on the battery aging rate, specifically reflecting the increase in internal resistance caused by the accelerated electrochemical side reactions due to increased temperature.

[0039] The first function can be represented as: ,in, This represents the activation energy coefficient (unit: J / mol). It is the gas constant (8.314 J / (mol·K)). The absolute temperature is expressed in Kelvin (K). This first function exhibits a typical exponential decay characteristic; its output value increases with increasing temperature, reflecting the trend that higher temperatures lead to faster aging rates. In practical applications, As one of the parameters to be identified, it was jointly calibrated in the subsequent optimization process.

[0040] The second function represents the effect of state of charge (SOC) on the change in DC internal resistance. When the battery operates at extreme SOC levels (close to 0% or 100%), interfacial stress intensifies, the risk of lithium deposition increases, and irreversible damage is easily triggered, thus accelerating the increase in internal resistance. Therefore, this embodiment uses a polynomial function to simulate this nonlinear modulation effect. Specifically, this function takes SOC as the input variable and outputs a function value representing the effect of SOC on the change in DC internal resistance. Furthermore, the function values ​​output by the second function at low and high SOCs are higher than the function values ​​corresponding to the intermediate SOC range. The second function can be a quadratic or higher-order polynomial.

[0041] The second function can be expressed as: ;in, , , All are undetermined coefficients. You can input in decimal form (e.g., 80% is represented as 0.8). By setting the parameters appropriately, this second function can operate at low SOC (e.g., ...). 20%) and high SOC (such as The output value in the 90% region is significantly higher than that in the intermediate SOC range (such as 60%-80%), thus forming a U-shaped response characteristic, which effectively captures the nonlinear enhancement effect of the state of charge on the change of internal resistance.

[0042] The third function is used to represent the effect of increasing cycle number. Since the aging process of a battery is not linearly cumulative, but often exhibits a slow initial growth followed by accelerated growth, this embodiment introduces a power function as the third function. This function takes the number of cycle times as the input variable and outputs a function value representing the cumulative effect of cycles, indicating that the battery's DC internal resistance exhibits a nonlinear cumulative growth trend as the number of cycles increases.

[0043] The third function can be expressed as: Where t is the number of cycles and n is the growth factor. When n When the value is 1, the third function exhibits superlinear growth characteristics, which can better characterize the behavior of the battery deteriorating faster after long-term use.

[0044] Based on the first, second, and third functions, the overall structure of the target prediction model can be obtained, i.e. ;in, This is the pre-factor constant, used to adjust the overall amplitude; This represents the predicted DC internal resistance growth rate output by the model. It can be understood that this model structure can comprehensively reflect the coupled effects of three key factors—cycle count, temperature, and state of charge—on battery aging.

[0045] In some implementations, such as Figure 2 As shown, the determination of the target prediction model includes steps S310-S330: Step S310: Initialize the function parameters of the first function, the second function, and the third function.

[0046] Step S320: Based on the parameter identification method, according to the error between the predicted DC internal resistance growth rate output by the current prediction model and the measured DC internal resistance growth rate, the function parameters in the first function, the second function and the third function are iteratively updated.

[0047] Step S330: When the error meets the convergence condition, the current prediction model is used as the target prediction model.

[0048] As an example, before starting iterative optimization, reasonable initial value ranges must be set for all parameters to be identified. These parameters include, but are not limited to, the activation energy coefficient in the first function. Polynomial coefficients in the second function , , The initial values ​​of each parameter to be identified can be determined empirically as the starting point for the optimization algorithm, including the growth factor n in the third function and the global scaling factor A.

[0049] This embodiment employs a combination of nonlinear least squares and differential evolution algorithm for parameter identification. In each iteration, the predicted DC internal resistance growth rate output by the model is calculated using the current parameter combination. And compared with the measured values ​​under the corresponding working conditions obtained in step S200. By comparing the two and calculating the fitting error between them, this embodiment can aim to minimize the root mean square error (RMSE), i.e. Where N is the total number of valid data points.

[0050] In this embodiment, the differential evolution algorithm continuously adjusts the values ​​of various parameters to generate new candidate solutions, and continuously evaluates their error magnitude. After each iteration, the better solution is retained, gradually approaching the globally optimal parameter set. It can be understood that the model structure remains unchanged throughout the optimization process, with only the parameters being dynamically updated.

[0051] Furthermore, in this embodiment, convergence conditions can be preset, such as the change in RMSE being less than 0.001 or the absolute error being less than 0.02 in three consecutive iterations. Once the convergence condition is met, the iteration stops, and the parameter configuration at this point is fixed to form the final target prediction model.

[0052] like Figure 3 As shown, this application also provides a method for predicting the DC internal resistance of a battery, the method comprising steps S10-S30: Step S10: Determine the initial DC internal resistance value of the battery based on the DC internal resistance test data of the battery in at least two cycle periods, at different temperatures and under different states of charge.

[0053] This step is the same as the method for determining the initial DC internal resistance value in the above embodiments, and will not be repeated here.

[0054] Step S20: Input the target operating condition parameters into the target prediction model described above to obtain the predicted DC internal resistance growth rate of the battery under the target operating condition.

[0055] Step S30: Determine the predicted DC internal resistance value of the battery under the target operating condition based on the initial DC internal resistance value and the predicted DC internal resistance growth rate.

[0056] The target operating condition includes at least one of the following: an unexperienced number of cycles, an untested temperature condition, or an unverified state of charge range.

[0057] As an example, when determining the predicted DC internal resistance of a battery under target operating conditions, the required operating conditions are first determined, namely the target number of cycles, the target operating temperature (in K), and the target state of charge.

[0058] By inputting these operating condition data into the aforementioned target prediction model, the corresponding predicted DC internal resistance growth rate can be calculated.

[0059] For example, in a certain application scenario, the initial DC internal resistance of the battery is determined to be 80.17 Ω after step S10. To predict the DC internal resistance of a battery under the conditions of 200 cycles, 55℃ (328.15 K), and 90% SOC, the model calculates a predicted DC internal resistance growth rate of 38.6% using the formula... =80.17 That is, the predicted DC internal resistance of the battery under the target operating conditions is approximately 111.1 mΩ.

[0060] like Figure 4As shown in the figure, using the method of this embodiment, the variation trend of battery DC internal resistance (DCIR) with the number of cycles (0 to approximately 1750 cycles) is demonstrated under the conditions of 45℃ environment, 1C charging / 1C discharging, and 100% SOC. The red scatter dots in the figure represent the measured DCIR values, and the green curve represents the fitting result of the model established in this embodiment. The horizontal axis represents the number of battery cycles, and the vertical axis represents the actual / predicted DC internal resistance at different cycle numbers. It can be seen that the model output curve has good consistency with the measured data points, indicating that the prediction model can accurately reflect the evolution of battery internal resistance during long-term cycling.

[0061] This embodiment constructs a DC internal resistance growth rate prediction model based on the coupled effects of cycle number, temperature, and state of charge, achieving high-precision modeling and extrapolation prediction of battery DCIR variation trends. Compared to existing technologies, it eliminates the need for battery state of health (SOH) parameters, which are difficult to obtain in real time. Model training can be completed using only multi-condition DCIR data available from conventional cycle testing, significantly improving the method's practicality. The prediction model in this embodiment employs a composite structure combining the Arrhenius equation, polynomial functions, and power functions, accurately reflecting multiple physical mechanisms such as temperature-accelerated aging, extreme SOC-induced degradation, and the cumulative effect of cycles. This enables accurate prediction of DC internal resistance values ​​under target operating conditions.

[0062] Figure 5 A schematic diagram of a battery DC internal resistance growth rate prediction model acquisition device according to an embodiment of this application is shown. Exemplarily, the battery DC internal resistance growth rate prediction model acquisition device includes: The acquisition module 100 is used to acquire DC internal resistance test data of the battery at different temperatures and different states of charge during at least two cycle periods.

[0063] The calculation module 200 is used to determine the initial DC internal resistance value and the measured DC internal resistance value for each cycle based on the test data, and to determine the measured DC internal resistance growth rate for each cycle based on the initial DC internal resistance value and the measured DC internal resistance value for each cycle.

[0064] The determination module 300 is used to determine the target prediction model for the change of DC internal resistance growth rate with the number of cycle periods, temperature and state of charge, based on the measured DC internal resistance growth rate and combined with the parameter identification method.

[0065] It is understood that the device in this embodiment corresponds to the battery DC internal resistance growth rate prediction model acquisition method in the above embodiment. The options in the above embodiment are also applicable to this embodiment, so they will not be described again here.

[0066] This application also provides a terminal device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to cause the terminal device to perform the above-described method for obtaining the battery DC internal resistance growth rate prediction model or the above-described method for predicting battery DC internal resistance.

[0067] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0068] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0069] This application also provides a computer-readable storage medium for storing the computer program used in the aforementioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0070] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0071] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0072] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0073] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for obtaining a prediction model for the DC internal resistance growth rate of a battery, characterized in that, include: Acquire DC internal resistance test data of the battery at at least two cycles, under different temperatures and different states of charge; Based on the test data, the initial DC internal resistance value and the measured DC internal resistance value for each cycle are determined, and based on the initial DC internal resistance value and the measured DC internal resistance value for each cycle, the measured DC internal resistance growth rate corresponding to each cycle is determined. Based on the measured DC internal resistance growth rate, and combined with the parameter identification method, a target prediction model is determined to predict the DC internal resistance growth rate as a function of cycle number, temperature, and state of charge.

2. The method for obtaining the battery DC internal resistance growth rate prediction model according to claim 1, characterized in that, The target prediction model is constructed based on the first function, the second function, and the third function; The first function is used to represent the effect of temperature on the battery aging rate; The second function is used to represent the effect of the state of charge on the change of DC internal resistance; The third function is used to represent the effect of increasing the number of cycle periods.

3. The method for obtaining the battery DC internal resistance growth rate prediction model according to claim 2, characterized in that, The determination of the target prediction model includes: Initialize the function parameters of the first function, the second function, and the third function; Based on the parameter identification method, the function parameters in the first function, the second function and the third function are iteratively updated according to the error between the predicted DC internal resistance growth rate output by the current prediction model and the measured DC internal resistance growth rate. When the error meets the convergence condition, the current prediction model is used as the target prediction model.

4. The method for obtaining the battery DC internal resistance growth rate prediction model according to claim 2 or 3, characterized in that, The first function is an exponential function based on the Arrhenius equation. The first function takes absolute temperature as the input variable and outputs a function value representing the effect of temperature on the battery aging rate. The second function is a polynomial function. The second function takes the state of charge as the input variable and outputs a function value that represents the effect of the state of charge on the change of DC internal resistance. The function value output by the second function in the low charge state and the high charge state is higher than the function value corresponding to the intermediate charge interval. The third function is a power function, which takes the number of cycles as input variable and outputs a function value representing the cumulative effect of the cycles.

5. The method for obtaining the battery DC internal resistance growth rate prediction model according to claim 1, characterized in that, The step of determining the initial DC internal resistance value and the measured DC internal resistance value for each cycle based on the test data, and determining the measured DC internal resistance growth rate for each cycle based on the initial DC internal resistance value and the measured DC internal resistance value for each cycle, includes: The arithmetic mean of the DC internal resistance values ​​measured in the previous preset number of cyclic tests is used as the initial DC internal resistance value. The measured DC internal resistance value under each cycle is compared with the initial DC internal resistance value to obtain the measured DC internal resistance growth rate of the corresponding cycle.

6. The method for obtaining the battery DC internal resistance growth rate prediction model according to claim 1, characterized in that, Before determining the initial DC internal resistance value and the measured DC internal resistance value for each cycle based on the test data, the method further includes: Anomalies in the test data are detected and removed; wherein, when the change in DC internal resistance between two adjacent cycles exceeds a preset threshold, the current data point is determined to be an anomaly and the anomaly is removed.

7. A method for predicting the DC internal resistance of a battery, characterized in that, The initial DC internal resistance value of the battery is determined based on the DC internal resistance test data of the battery in at least two cycles, at different temperatures and under different states of charge. Input the target operating condition parameters into the target prediction model according to any one of claims 1-6 to obtain the predicted DC internal resistance growth rate of the battery under the target operating condition; Based on the initial DC internal resistance value and the predicted DC internal resistance growth rate, the predicted DC internal resistance value of the battery under the target operating condition is determined.

8. The method for predicting the DC internal resistance of a battery according to claim 7, characterized in that, The target operating condition includes at least one of the following: an unexperienced number of cycles, an untested temperature condition, or an unverified state of charge range.

9. A device for obtaining a prediction model for the DC internal resistance growth rate of a battery, characterized in that, include: The acquisition module is used to acquire DC internal resistance test data of the battery at different temperatures and different states of charge during at least two cycle periods. The calculation module is used to determine the initial DC internal resistance value and the measured DC internal resistance value for each cycle based on the test data, and to determine the measured DC internal resistance growth rate corresponding to each cycle based on the initial DC internal resistance value and the measured DC internal resistance value for each cycle. The determination module is used to determine a target prediction model for the change of DC internal resistance growth rate with the number of cycle periods, temperature and state of charge, based on the measured DC internal resistance growth rate and combined with the parameter identification method.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed on a processor, implements the method according to any one of claims 1-8.

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