Battery performance detection system, method, single battery and electric vehicle

Abstract

This application provides a battery performance testing system, method, single-cell battery, and electric vehicle. It relates to the field of battery technology. The battery performance testing system is configured to perform the following steps: determining the current collector performance prediction model corresponding to the battery under test based on the target battery identifier; correcting the original battery parameters according to the parameter rules of the current collector performance prediction model to obtain the target battery parameters; inputting the target battery parameters into the current collector performance prediction model to obtain the data curve between the predicted current and predicted temperature rise of the battery under test output by the current collector performance prediction model; and determining the current collector performance of the battery under test based on the data curve. This solution, by establishing a current collector performance evaluation model capable of predicting current collector performance data, can quickly obtain the data curve between the predicted current and predicted temperature rise based on a small number of battery parameters, thereby rapidly predicting the current collector performance based on the data curve and improving the prediction efficiency of current collector performance.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a battery performance testing system, method, single cell battery, and electric vehicle. Background Technology

[0002] Battery safety plays a decisive role in the performance of devices using batteries. In battery design, the current collector, as the carrier of electrode materials, undertakes the critical function of current transmission. The performance of the current collector, such as its overcurrent performance, directly affects battery safety.

[0003] In related technologies, batteries are subjected to repeated and extensive testing, and the current collector performance of the battery is determined based on the test data.

[0004] However, repeating a large number of tests results in a long evaluation cycle for current collector performance, which is inefficient. Summary of the Invention

[0005] This application provides a battery performance testing system, method, single-cell battery, and electric vehicle to improve the efficiency of battery performance testing.

[0006] In a first aspect, embodiments of this application provide a battery performance testing system for predicting the current collector performance of a battery. The battery performance testing system is configured to perform the following steps: acquiring a target battery identifier and original battery parameters for the battery under test, the original battery parameters including structural parameters of the battery under test and characteristic parameters of the current collector in the battery under test; determining a current collector performance prediction model corresponding to the battery under test based on the target battery identifier, the current collector performance prediction model being used to characterize the mapping relationship between battery parameters and predicted performance data; correcting the original battery parameters according to the parameter rules of the current collector performance prediction model to obtain target battery parameters; inputting the target battery parameters into the current collector performance prediction model to obtain a data curve output by the current collector performance prediction model between the predicted current and the predicted temperature rise of the battery under test; and determining the current collector performance of the battery under test based on the data curve.

[0007] Secondly, embodiments of this application provide a battery performance testing method, comprising: acquiring a target battery identifier and original battery parameters of a battery under test, wherein the original battery parameters include structural parameters of the battery under test and characteristic parameters of the current collector in the battery under test; determining a current collector performance prediction model corresponding to the battery under test based on the target battery identifier, wherein the current collector performance prediction model is used to characterize the mapping relationship between battery parameters and predicted performance data; correcting the original battery parameters according to the parameter rules of the current collector performance prediction model to obtain target battery parameters; inputting the target battery parameters into the current collector performance prediction model to obtain a data curve between the predicted current and the predicted temperature rise of the battery under test output by the current collector performance prediction model; and determining the current collector performance of the battery under test based on the data curve.

[0008] Thirdly, embodiments of this application provide a single-cell battery, wherein the current collector performance of the single-cell battery is greater than a preset performance value; the current collector performance is determined after testing the single-cell battery according to the battery performance testing system described in any one of the first aspects.

[0009] Fourthly, embodiments of this application provide a battery pack comprising at least two individual cells as described in the third aspect, wherein each individual cell is electrically connected to the other.

[0010] Fifthly, embodiments of this application provide a battery pack, including a housing and at least two battery packs as described in the fourth aspect, each battery pack being disposed within the housing and electrically connected to each other.

[0011] Sixthly, embodiments of this application provide an electric vehicle that includes at least the battery pack described in the fifth aspect.

[0012] In a seventh aspect, embodiments of this application provide an electrical device that includes at least a single battery cell as described in the third aspect.

[0013] Eighthly, this application provides a battery performance testing device, comprising: an acquisition module for acquiring a target battery identifier and original battery parameters of a battery under test, the original battery parameters including structural parameters of the battery under test and characteristic parameters of the current collector in the battery under test; a query module for determining a current collector performance prediction model corresponding to the battery under test based on the target battery identifier, the current collector performance prediction model being used to characterize the mapping relationship between battery parameters and predicted performance data; a correction module for correcting the original battery parameters according to the parameter rules of the current collector performance prediction model to obtain target battery parameters; a prediction module for inputting the target battery parameters into the current collector performance prediction model to obtain a data curve between the predicted current and predicted temperature rise of the battery under test output by the current collector performance prediction model; and an analysis module for determining the current collector performance of the battery under test based on the data curve.

[0014] Ninthly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0015] The memory stores computer-executed instructions;

[0016] The processor executes computer execution instructions stored in the memory, causing the processor to perform the implementation method described in the second aspect above.

[0017] In a tenth aspect, embodiments of this application provide a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the embodiments of the second aspect above.

[0018] Eleventhly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the implementation methods described in the second aspect above.

[0019] This application provides a battery performance testing system, method, single-cell battery, and electric vehicle. The above solution, by establishing a current collector performance evaluation model capable of predicting current collector performance data, can quickly obtain the data curve between predicted current and predicted temperature rise based on a small number of battery parameters. This allows for rapid prediction of current collector performance based on the data curve, improving battery performance testing efficiency. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] Figure 1 This is a schematic diagram illustrating an application scenario of a battery performance testing method provided in an embodiment of this application.

[0022] Figure 2 A schematic flowchart of a battery performance testing method provided in an embodiment of this application;

[0023] Figure 3 A flowchart illustrating another battery performance testing method provided in this application embodiment;

[0024] Figure 4 A schematic diagram of the current collector temperature rise provided in an embodiment of this application;

[0025] Figure 5 A schematic diagram of the fitted data curve provided in the embodiments of this application;

[0026] Figure 6 This is a schematic diagram of the structure of a battery performance testing device provided in an embodiment of this application;

[0027] Figure 7 This is a schematic diagram of another battery performance testing device provided in an embodiment of this application;

[0028] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0029] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0031] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0032] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the display interface provided in the embodiments of this application is merely an example, and the display interface may include more or less content.

[0033] It should be noted that the battery performance testing system, method, single cell, and electric vehicle of this application can be used in the field of battery technology, or in any field other than batteries. The application fields of the battery performance testing system, method, single cell, and electric vehicle of this application are not limited.

[0034] Figure 1 This is a schematic diagram illustrating an application scenario of a battery performance testing method provided in this application embodiment. An example is given based on the illustrated scenario: performance testing is performed on the current collector in the battery to obtain the current collector performance, which can be used to evaluate the battery performance.

[0035] For example, in battery design, the current collector, as the carrier of electrode materials, plays a crucial role in current transmission. With the iterative upgrade of battery technology, composite current collectors (such as composite copper foil) are gradually replacing traditional metal foil current collectors due to their advantages such as lightweight and high safety. However, the reduction in their thickness leads to a reduction in the current-carrying cross-sectional area, which directly affects the battery's current-carrying capacity during high-rate charging and discharging.

[0036] Among them, the overcurrent capacity indicates the amount of current that the current collector can carry. The stronger the overcurrent capacity, the more current the current collector can charge and discharge, and the stronger and safer the battery performance.

[0037] In practical applications, the problem of thinning of composite current collectors is particularly prominent in square stacked cells, because they have many electrode layers and complex dimensions. The evaluation of current carrying capacity needs to be combined with electrode structure parameters (such as number of layers, width, length, etc.) and current collector material characteristics (such as copper plating thickness, resistivity, etc.).

[0038] In related technologies, the current collector performance, i.e. the current collector overcurrent capability, of a battery is evaluated by repeating a large number of tests (such as overcurrent temperature rise tests).

[0039] However, this method requires a separate test process designed for each battery model, which is time-consuming, labor-intensive, and costly.

[0040] The battery performance testing method provided in this application aims to solve the above-mentioned technical problems in related technologies.

[0041] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0042] Figure 2 This is a flowchart illustrating a battery performance testing method provided in an embodiment of this application. The method includes the following steps:

[0043] S201. Obtain the target battery identifier and original battery parameters of the battery under test. The original battery parameters include the structural parameters of the battery under test and the characteristic parameters of the current collector in the battery under test.

[0044] As an example, the entity executing this embodiment can be a battery performance testing system. The battery performance testing system includes a data acquisition unit and a controller.

[0045] Among them, the current collector performance refers to the current collector's flow capacity.

[0046] The system collects the target battery identifier and raw battery parameters using a data acquisition device.

[0047] For example, the battery identifier serves as a unique identifier for the battery under test, and is pre-entered into the battery production system and associated with the battery's core information. This core information includes, but is not limited to, battery model, production batch, manufacturing process, cell specifications, and current collector model, to avoid confusion between models of different battery types and batches.

[0048] Optional structural parameters may include battery casing dimensions, cell thickness, number of electrode layers, electrode dimensions, separator thickness, electrolyte filling amount, and battery packaging method. Structural parameters directly affect the internal heat conduction efficiency and current distribution of the battery, thereby affecting the working state of the current collector.

[0049] Optional characteristic parameters may include the material type of the current collector (e.g., copper foil, aluminum foil, composite current collector, etc.), material thickness, resistivity, thermal conductivity, specific heat capacity, density, surface area, surface roughness, etc. These parameters are the core factors that determine the current collector's heat generation, heat dissipation, and flow capacity.

[0050] S202. Based on the target battery identifier, determine the current collector performance prediction model corresponding to the battery under test. The current collector performance prediction model is used to characterize the mapping relationship between battery parameters and predicted performance data.

[0051] Specifically, battery performance testing is performed by the controller based on the target battery identifier and the original battery parameters.

[0052] For example, a database of current collector performance prediction models is pre-established. This database stores current collector performance prediction models corresponding to different models, batches, and specifications of batteries. Each model is derived and constructed based on physical principles.

[0053] For example, the current collector performance prediction model is used to pre-establish a clear mapping relationship between battery parameters and current collector predicted performance data (mainly predicted current and predicted temperature rise). Based on the input battery parameters, it can accurately output the predicted performance data of the current collector under different operating conditions, providing a data foundation for subsequent predictions.

[0054] With the help of scenario examples, it can be seen that the performance data of the battery is directly related to the battery parameters, and the current collector performance prediction model quantifies this relationship.

[0055] S203. Based on the parameter rules of the current collector performance prediction model, the original battery parameters are corrected to obtain the target battery parameters.

[0056] For example, the original battery parameters may have problems such as data deviation, non-standard format, redundant data, and missing key parameters. If they are directly input into the prediction model, they will seriously affect the accuracy of the prediction results. Therefore, the original battery parameters are specifically corrected to ensure that the parameters input into the model fully comply with the model's parameter rules.

[0057] For example, the parameter rules are pre-defined for the current collector performance prediction model, specifying the format, numerical range, unit standard, and required parameters of the input parameters.

[0058] Optionally, parameter correction includes, but is not limited to, at least one of the following: Parameter format calibration, unifying the units and data formats of the original parameters to meet the standards required by the model; Deviation calibration, correcting any deviations in the original parameters by combining them with standard parameters from the same batch or model of battery under test, eliminating the impact of measurement errors; Redundancy and missing data handling, removing redundant data from the original parameters that are irrelevant to the current collector performance prediction, and appropriately supplementing any missing key parameters to ensure that the corrected target battery parameters are complete, standardized, and accurate, fully meeting the input requirements of the current collector performance prediction model.

[0059] S204. Input the target battery parameters into the current collector performance prediction model to obtain the data curve between the predicted current and the predicted temperature rise of the battery under test output by the current collector performance prediction model.

[0060] For example, the modified target battery parameters are input one by one into the current collector performance prediction model according to the preset parameter input order. The model will perform a series of calculations based on the built-in mapping relationship derived from physical principles and the input target battery parameters to simulate the working state of the current collector under different operating conditions, and finally output the data curve between the predicted current and the predicted temperature rise of the current collector of the battery under test.

[0061] Optionally, the horizontal axis of the data curve represents the predicted current value, and the vertical axis represents the corresponding predicted temperature rise value. The curve can intuitively and comprehensively show the temperature rise variation law of the current collector under different current loads, such as the rate of temperature rise change when the current increases, and the stability of temperature rise in different current ranges.

[0062] Based on the above implementation methods, compared to repeated large-scale testing, the current collector performance prediction model can quickly predict a large amount of data, thereby effectively improving the prediction efficiency of current collector performance.

[0063] S205. Based on the data curve, determine the current collector performance of the battery under test.

[0064] For example, the evaluation index type of the current collector can be determined, such as current carrying capacity, heat dissipation performance, and operational stability. The current carrying capacity is reflected by the maximum predicted current value in the data curve. The larger the maximum predicted current value, the stronger the current carrying capacity of the current collector. The heat dissipation performance is reflected by the slope of the curve. Within the same current variation range, the smaller the temperature rise change (the smaller the curve slope), the higher the heat dissipation efficiency of the current collector. The operational stability is reflected by the smoothness of the curve. The smoother the curve, the more stable the temperature rise response of the current collector under different current loads, without abnormal fluctuations.

[0065] For example, based on the evaluation index type of the current collector, multi-dimensional analysis is performed in conjunction with data curves to obtain the current collector performance.

[0066] The battery performance testing method provided in this application involves obtaining the target battery identifier and original battery parameters of the battery under test. The original battery parameters include the structural parameters of the battery under test and the characteristic parameters of the current collector in the battery under test. Based on the target battery identifier, a current collector performance prediction model is determined for the battery under test. This model characterizes the mapping relationship between battery parameters and predicted performance data. The original battery parameters are corrected according to the parameter rules of the current collector performance prediction model to obtain the target battery parameters. The target battery parameters are then input into the current collector performance prediction model to obtain a data curve between the predicted current and predicted temperature rise of the battery under test, output by the model. Based on the data curve, the current collector performance of the battery under test is determined. This approach, by establishing a current collector performance evaluation model capable of predicting current collector performance data, can quickly obtain the data curve between predicted current and predicted temperature rise based on a small number of battery parameters, thereby rapidly predicting current collector performance and improving battery performance testing efficiency.

[0067] Based on any of the above embodiments, the following, in conjunction with Figure 3 The detailed process of battery performance testing is explained.

[0068] Figure 3 This is a schematic flowchart of another battery performance testing method provided in an embodiment of this application. Figure 3 As shown, the method includes:

[0069] S301. Obtain the target battery identifier and original battery parameters of the battery under test. The original battery parameters include the structural parameters of the battery under test and the characteristic parameters of the current collector in the battery under test.

[0070] It should be noted that the execution process of S301 is the same as that of S201, and will not be repeated here.

[0071] S302. Determine the mapping relationship between battery identification and prediction model.

[0072] For example, a prediction model is pre-built for each battery corresponding to a different battery identifier, and then a mapping relationship is established between the battery identifier and the prediction model.

[0073] For example, the mapping relationship is one-to-one, indicating which prediction model each battery identifier corresponds to.

[0074] One feasible implementation method is to construct a current collector performance prediction model by: obtaining multiple historical battery parameters of the same type of battery as the battery under test; determining the current collector heat generation power formula of the same type of battery based on the Joule heating effect; inputting the multiple historical battery parameters into the current collector heat generation power formula to obtain a general temperature rise and current formula; and determining a current collector performance prediction model suitable for the battery under test based on the general temperature rise and current formula.

[0075] For example, the battery under test and other batteries of the same type are of the same type. For instance, both are ternary lithium-ion batteries, both are cylindrical batteries, and both use composite copper foil current collectors. Batteries of the same type have the same heat generation principle; the difference lies in the parameters that cause specific differences in heat generation.

[0076] For example, the general temperature rise and current formula is adapted to multiple batteries of one battery type. By specifically adapting the general temperature rise and current formula, a current collector performance prediction model adapted to each battery type can be obtained.

[0077] For example, based on the Joule heating effect of the current collector inside the battery during operation, and combined with physical principles, a formula for the heat generation power of the current collector in the same type of battery is derived. The formula for the heat generation power of the current collector is used to characterize the quantitative relationship between the heat generation of the current collector and the operating current, resistance, and operating time.

[0078] Optionally, the Joule heating formula can be expressed as follows:

[0079]

[0080] Where Q represents the heat generated by the battery, and I represents the battery current. The value represents the internal resistance of the battery, and t represents the battery's operating time.

[0081] During the full charge and discharge process of a battery, the battery capacity is the product of the current and the operating time. The formula can be transformed to obtain:

[0082]

[0083] in, Indicates battery capacity.

[0084] Current can be expressed as the product of charge / discharge rate and capacity. The formula can be transformed to obtain:

[0085]

[0086] in, This indicates the charge / discharge rate.

[0087] The internal resistance of a battery can be expressed as the current collector internal resistance and other battery internal resistances. The formula can be transformed to obtain:

[0088]

[0089] in, Indicates other internal resistances, This indicates the internal resistance of the current collector.

[0090] The heat generated by the battery is partly absorbed by the current collector and other materials, and partly released. Transforming the formula yields the formula for the current collector's heat generation power:

[0091]

[0092] in, This indicates the amount of heat absorbed by the current collector. This indicates the energy absorbed and released by other materials in the battery.

[0093] Based on the relationship between temperature rise, heat, and specific heat capacity, the formula is transformed to obtain:

[0094]

[0095] in, The current collector temperature rise is represented by C, the specific heat capacity of the battery material is represented by C, and the mass of the battery material is represented by m.

[0096] Further formula transformation yields:

[0097]

[0098] Where n represents the number of battery electrode layers; L represents the conductor length, i.e., the length of the electrode in the battery, in mm; H represents the conductor length, i.e., the thickness of the current collector metal layer, in μm; B represents the conductor width, i.e., the width of the electrode in the battery, in mm; ρ represents the conductor resistivity, in Ω·m; and p represents the conductor density, in Ω·m. .

[0099] Taking composite copper foil current collectors as an example, the internal resistance of the current collector accounts for 23.86% of the battery's internal resistance. After formula conversion, the general formula for temperature rise and current is obtained:

[0100]

[0101] Where a represents b represents .

[0102] Below, in conjunction with Figure 4 The temperature rise of the current collector during flow is explained.

[0103] Figure 4 This is a schematic diagram illustrating the current collector temperature rise in an embodiment of this application. Figure 4 As shown, for different current collectors, the overall trend of temperature rise relative to current is similar, with the temperature rise increasing as the current increases. However, the specific value of temperature rise relative to current differs for each current collector. By establishing universal temperature rise and current formulas for different types of current collectors, the performance prediction of the battery corresponding to each current collector can be accurately performed.

[0104] In this feasible implementation, a general formula for temperature rise and current is established based on objective physical laws, which can be repeatedly used to predict the performance of current collectors, thereby improving the efficiency of battery performance testing.

[0105] One feasible approach to adapting the general temperature rise and current formula may include: obtaining multiple sample battery parameters from sample batteries of the same batch or model as the battery under test; and modifying the general temperature rise and current formula based on the multiple sample battery parameters to obtain a current collector performance prediction model.

[0106] For example, sample batteries from the same batch or model as the battery under test are highly consistent with the battery under test in terms of materials, processes, and specifications. Therefore, the parameters of multiple sample batteries can accurately reflect the battery parameters of the battery under test. Before the battery under test is manufactured, or while the battery under test is being used in electric vehicles or electrical equipment, a current collector performance prediction model corresponding to the battery under test is constructed.

[0107] For example, the collected sample battery parameters are substituted into the general temperature rise and current formula. Based on the actual values ​​of the sample battery parameters, the coefficients and parameter terms in the formula are specifically modified to obtain the current collector performance prediction model. The current collector performance prediction model retains the physical laws of the general formula while adapting to the actual characteristics of the battery under test.

[0108] In this feasible implementation, parameter correction is based on a general formula, eliminating the need to repeatedly derive the formula structure for a single type of battery. This reduces the complexity of building the current collector performance prediction model, thereby improving the efficiency of battery performance testing.

[0109] S303. Determine the current collector performance prediction model based on the target battery identifier and mapping relationship.

[0110] For example, by using the target battery identifier as a conditional query mapping relationship, a current collector performance prediction model corresponding to the target battery identifier and adapted to the battery under test can be obtained.

[0111] Optionally, the mapping relationship can be stored in a database, and the database can be queried using the battery identifier as a query condition to obtain the current collector performance prediction model.

[0112] Based on the above implementation methods, the current collector performance prediction model can be quickly determined through the query method, thereby improving the efficiency of battery performance testing.

[0113] S304. Determine the model identifier for the current collector performance prediction model.

[0114] For example, the model identifier is a unique identifier assigned to the current collector performance prediction model after it is constructed, in order to distinguish different models.

[0115] Optionally, the model identifier is stored in the attribute information of the current collector performance prediction model, and the model identifier is determined by parsing the attribute information.

[0116] S305. Based on the model identifier, find the corresponding parameter rule from the rule base.

[0117] For example, the rule base pre-stores parameter rules corresponding to multiple models, and stores them according to the one-to-one correspondence between model identifiers and parameter rules.

[0118] For example, a query can be performed in the rule base using the model identifier as the query condition to obtain the corresponding parameter rules.

[0119] S306. According to the parameter rules, the original battery parameters are corrected to obtain the target battery parameters.

[0120] For example, the modified parameters are used to obtain target battery parameters that meet the input requirements of the current collector performance prediction model, are correctly formatted, and have reasonable values.

[0121] One feasible implementation method is to perform parameter correction by: supplementing the original battery parameters with supplementary battery parameters by interpolation or supplementary testing based on the number of parameters; and correcting the accuracy of the supplementary battery parameters based on the parameter accuracy to obtain the target battery parameters.

[0122] The parameter rules include the number of parameters and the precision of parameters.

[0123] For example, the number of parameters is the complete set of battery parameters required by the current collector performance prediction model, clearly defining which types of parameters and which ranges of parameters are necessary and indispensable for the model to run.

[0124] For example, parameter precision refers to the numerical precision requirements of each input parameter in the current collector performance prediction model, including the number of significant digits, the number of decimal places, and the range of values.

[0125] For example, the original battery parameters are checked based on the number of parameters. If there are missing or insufficient original battery parameters, the parameters are supplemented by interpolation or supplementary testing.

[0126] Interpolation is suitable for scenarios where known parameter relationships exist. It fills in missing parameter values ​​by analyzing the numerical patterns of existing parameters and performing mathematical interpolation calculations. Supplementary testing is suitable for scenarios where effective parameters cannot be obtained through interpolation. It obtains missing parameter data by conducting targeted tests on sample batteries, ultimately yielding complete supplementary battery parameters.

[0127] For example, based on a complete set of parameters, the supplementary battery parameters are corrected for accuracy according to the model's required precision. Specific operations include rounding the parameter values, removing significant figures, and verifying the numerical range to ensure that the precision of each parameter fully meets the model's input requirements. The final result is a target battery parameter set that is complete, meets the required precision, and can be directly input into the model.

[0128] In this feasible implementation, parameter completion is quickly achieved by combining interpolation with test supplementation, reducing the time cost of parameter preprocessing and improving the efficiency of current collector performance prediction. Standardizing parameter accuracy avoids calculation deviations caused by inconsistent accuracy, thereby improving the accuracy of battery performance testing.

[0129] S307. Determine the temperature rise threshold corresponding to the battery under test based on the target battery identifier.

[0130] For example, the temperature rise threshold is the maximum temperature rise limit for the current collector of the battery under test to operate safely and stably. It is the core constraint for the model to carry out performance prediction. Exceeding this threshold will lead to an increase in the resistivity of the current collector, a decrease in heat dissipation performance, and even safety hazards such as battery thermal runaway.

[0131] For example, the temperature rise threshold corresponds one-to-one with the target battery identifier. The model, production batch, current collector material, battery structure, and application scenario of the battery under test corresponding to the target battery identifier are fixed. Based on multiple pieces of information, a unique temperature rise threshold adapted to the characteristics of each battery identifier can be determined, avoiding the problem of unreasonable constraints caused by a general threshold.

[0132] S308. Input the target battery parameters and temperature rise threshold into the current collector performance prediction model, so as to predict the performance data within the temperature rise threshold range through the current collector performance prediction model, and obtain the predicted current and the corresponding predicted temperature rise. The predicted temperature rise is less than or equal to the temperature rise threshold.

[0133] For example, the target battery parameters and temperature rise threshold are simultaneously input into the current collector performance prediction model. The current collector performance prediction model, based on the mapping relationship derived from the Joule heating effect, combines the input target battery parameters to simulate the working state of the current collector under different current loads and calculate the predicted temperature rise corresponding to different predicted currents.

[0134] Meanwhile, the current collector performance prediction model uses the temperature rise threshold as a constraint boundary and only makes predictions within the boundary to avoid predicting meaningless data.

[0135] For example, after the model completes the calculation, it outputs a data curve showing the relationship between the predicted current and the predicted temperature rise of the current collector in the battery under test. This curve, with the predicted current on the horizontal axis and the predicted temperature rise on the vertical axis, represents the variation of the predicted current and predicted temperature rise within the temperature rise threshold constraint range. Furthermore, all data points on the curve satisfy the constraint that the predicted temperature rise is less than or equal to the temperature rise threshold.

[0136] One feasible implementation method is to determine the predicted current and predicted temperature rise by substituting the target battery parameters into the coefficient parameters to obtain the calculation expression for temperature rise and current, which includes the temperature rise parameter, current parameter, slope constant, and intercept constant; within the temperature rise threshold range, the predicted current and the corresponding predicted temperature rise are calculated using the calculation expression.

[0137] The current collector performance prediction model includes temperature rise and current formulas, and the temperature rise and current formulas include temperature rise parameters, current parameters, and coefficient parameters.

[0138] For example, the temperature rise and current formula includes multiple parameters, the values ​​of which are unknowns. By substituting the target battery parameters into the coefficient parameters, a computational expression is obtained where only the temperature rise and current parameters are unknowns.

[0139] For example, the computational expression can represent a one-to-one mapping relationship between temperature rise parameters and current parameters. Within the temperature rise threshold range, by inputting different predicted currents into the computational expression, the predicted temperature rise corresponding to each predicted current can be obtained.

[0140] With a specific scenario example, taking a current collector metal layer thickness of 1μm, 92 electrode layers, an electrode width of 101.2mm, an electrode length of 308.5mm, and a battery capacity of 166Ah as an example, the general temperature rise and current formulas are input to obtain the calculation expressions for temperature rise and current. Where 0.1032 is the slope constant coefficient A, and -2.535 is the intercept constant coefficient B.

[0141] In this feasible implementation, the temperature rise and current formulas can be quickly established using a small number of battery parameters, and the predicted current and temperature rise can be quickly calculated, thereby improving the efficiency of battery performance testing.

[0142] S309. Based on the data curve, determine the current collector performance of the battery under test.

[0143] One feasible approach is to determine the current collector performance by: determining the operating temperature range and multiple performance index types corresponding to the battery under test based on the target battery identifier; determining the valid data corresponding to the operating temperature range from the data curves; and analyzing the multiple performance index types and valid data to obtain the current collector performance.

[0144] For example, the operating temperature range is determined based on the usage scenario of the battery under test, and is used to limit the range of valid data extracted. For instance, if the minimum temperature rise corresponding to the operating temperature range is 0℃ and the maximum temperature rise is 35℃, data within the temperature rise range of 0℃-35℃ is extracted from the data curve as valid data.

[0145] For example, taking overcurrent capability as a performance indicator, the current corresponding to the maximum temperature rise in the valid data is taken as the maximum charge and discharge current of the battery under test to evaluate the overcurrent capability of the battery under test.

[0146] Optionally, taking rate capability as an example of a performance indicator type, the battery parameters include rated capacity. The maximum charge / discharge rate is obtained by calculating the product of the rated capacity and the maximum charge / discharge current to evaluate the rate capability of the battery under test.

[0147] For example, taking heat dissipation performance as an example, the average slope of the valid data is calculated to evaluate the heat dissipation performance of the battery under test. The smaller the average slope, the higher the heat dissipation efficiency.

[0148] For example, taking operational stability as an indicator, the smoothness or jitter of the valid data is calculated. The smoother the data and the less jitter, the higher the operational stability of the battery under test.

[0149] Table 1 below shows the current collector performance generated for different batteries under test.

[0150] Table 1

[0151]

[0152] Please refer to the following examples and scenarios. Figure 1 Substituting multiple battery parameters of battery C into the current collector performance prediction model, the data curve formula for battery C is obtained. Furthermore, the maximum charge / discharge current was calculated to be 479A, and the maximum charge / discharge rate was 1.92C.

[0153] Below, in conjunction with Figure 5 Explain the fitted data curve.

[0154] Figure 5 This is a schematic diagram of the fitted data curve provided in an embodiment of this application. Figure 5As shown, the measured data of battery A were fitted to obtain a fitted curve, which conforms to the data curve formula output by the current collector performance prediction model. Furthermore, the fitting error is small. This indicates that the prediction results of the current collector performance prediction model are consistent with the actual state of the battery.

[0155] In this feasible implementation, the battery identification is matched with the corresponding operating temperature range and performance indicators to filter valid data, avoid interference from invalid information, reduce redundant calculation and analysis steps, and thus improve the efficiency of battery performance testing.

[0156] One feasible implementation method for battery performance testing may further include: acquiring real-time battery parameters during the use of the battery under test; correcting the parameters of the current collector performance prediction model based on the real-time battery parameters to obtain an updated current collector performance prediction model; determining the current timestamp; generating an update log based on the current timestamp, real-time battery parameters, and the updated current collector performance prediction model, and storing the update log.

[0157] For example, after predicting the current collector performance of the battery under test at the current moment, the battery under test is continuously predicted during its subsequent use.

[0158] For example, real-time battery parameters are obtained by continuously collecting data on the battery to be predicted.

[0159] Using scenario examples, it is illustrated that factors such as battery aging, changes in ambient temperature and humidity, and fluctuations in charging and discharging conditions can lead to deviations in current collector performance. The current collector performance prediction model is corrected using real-time battery parameters to compensate for these prediction deviations.

[0160] For example, the precise timestamps of model parameter correction operations are recorded. Combined with real-time collected battery parameters, model correction content, and the updated model, a standardized update log is generated. The generated update log is stored in a database, forming a complete chain of model iteration and operation records. This enables full traceability of the model update process, providing complete data support for subsequent model optimization, fault diagnosis, and performance evaluation.

[0161] In this feasible implementation, the current collector performance prediction model is updated in real time to avoid the problem of prediction inaccuracy caused by long-term use, and to ensure that the current collector performance prediction results remain highly accurate throughout the entire battery life cycle.

[0162] One feasible implementation method for testing battery performance may include: screening all batteries to be tested and identifying the batteries whose current collector performance is greater than a preset performance value as target batteries.

[0163] The battery performance testing system connects to multiple batteries under test, traverses each battery under test, and obtains the performance of each current collector for each battery under test.

[0164] The battery performance testing system also includes a filter for screening.

[0165] For example, the battery performance testing system can simultaneously connect to multiple batteries under test, and through a traversal testing method, sequentially complete the current collector performance testing process for each battery under test, and obtain the current collector performance results for each of the batteries under test.

[0166] For example, the filter compares and filters the current collector performance of all batteries under test using a preset performance value as the criterion, and determines the batteries under test whose current collector performance is better than the preset performance value as the target batteries that meet the requirements.

[0167] In this feasible implementation, by simultaneously connecting multiple batteries under test and traversing the detection process, instead of detecting each battery individually, the time required for current collector performance testing of batch batteries is significantly shortened, thereby improving battery performance testing efficiency.

[0168] One feasible implementation method for testing battery performance may further include: storing the performance of each current collector corresponding to all batteries under test; and retrieving the performance of each current collector corresponding to all batteries under test from the memory.

[0169] The battery performance testing system also includes a memory. The memory stores the performance characteristics of each current collector. The filter retrieves the performance characteristics of each current collector from the memory.

[0170] For example, the memory serves as a data storage unit, specifically used to persistently store the current collector performance data obtained by the system after all the batteries under test are tested, and the stored data corresponds one-to-one with each battery under test, so as to achieve accurate data association and traceability.

[0171] For example, the filter retrieves the current collector performance data of all batteries to be tested from the memory through internal data interaction of the battery performance testing system, and then completes the screening of target batteries by using preset performance values ​​as the judgment criteria.

[0172] This feasible implementation method enables the persistent storage of current collector performance data, avoiding data loss due to system anomalies, power outages, or other issues during batch testing, thereby improving the reliability of battery performance testing.

[0173] This application provides a single-cell battery with a current collector performance greater than a preset performance value; the current collector performance is determined after testing the single-cell battery using a battery performance testing system.

[0174] Based on the above implementation methods, by establishing a current collector performance evaluation model that can predict current collector performance data, the data curve between predicted current and predicted temperature rise can be quickly obtained based on a small number of battery parameters. Thus, the current collector performance can be quickly predicted based on the data curve, thereby improving the efficiency of battery performance testing.

[0175] This application provides a battery pack comprising at least two of the above-described individual cells, each of which is electrically connected to the other.

[0176] Based on the above implementation methods, by establishing a current collector performance evaluation model that can predict current collector performance data, the data curve between predicted current and predicted temperature rise can be quickly obtained based on a small number of battery parameters. Thus, the current collector performance can be quickly predicted based on the data curve, thereby improving the efficiency of battery performance testing.

[0177] This application provides a battery pack, including a housing and at least two battery packs as described above, each battery pack being disposed within the housing and electrically connected to each other.

[0178] Based on the above implementation methods, by establishing a current collector performance evaluation model that can predict current collector performance data, the data curve between predicted current and predicted temperature rise can be quickly obtained based on a small number of battery parameters. Thus, the current collector performance can be quickly predicted based on the data curve, thereby improving the efficiency of battery performance testing.

[0179] This application provides an electric vehicle that includes at least the aforementioned battery pack.

[0180] Based on the above implementation methods, by establishing a current collector performance evaluation model that can predict current collector performance data, the data curve between predicted current and predicted temperature rise can be quickly obtained based on a small number of battery parameters. Thus, the current collector performance can be quickly predicted based on the data curve, thereby improving the efficiency of battery performance testing.

[0181] This application provides an electrical device that includes at least the aforementioned single battery cell.

[0182] Based on the above implementation methods, by establishing a current collector performance evaluation model that can predict current collector performance data, the data curve between predicted current and predicted temperature rise can be quickly obtained based on a small number of battery parameters. Thus, the current collector performance can be quickly predicted based on the data curve, thereby improving the efficiency of battery performance testing.

[0183] Figure 6 This is a schematic diagram of a battery performance testing device provided in an embodiment of this application. Figure 6 As shown, the battery performance testing device 60 may include: an acquisition module 61, a query module 62, a correction module 63, a prediction module 64, and an analysis module 65.

[0184] The acquisition module 61 is used to acquire the target battery identifier and original battery parameters of the battery under test. The original battery parameters include the structural parameters of the battery under test and the characteristic parameters of the current collector in the battery under test.

[0185] The query module 62 is used to determine the current collector performance prediction model corresponding to the battery under test based on the target battery identifier. The current collector performance prediction model is used to characterize the mapping relationship between battery parameters and predicted performance data.

[0186] The correction module 63 is used to correct the original battery parameters according to the parameter rules of the current collector performance prediction model to obtain the target battery parameters.

[0187] The prediction module 64 is used to input the target battery parameters into the current collector performance prediction model and obtain the data curve between the predicted current and the predicted temperature rise of the battery under test output by the current collector performance prediction model.

[0188] Analysis module 65 is used to determine the current collector performance of the battery under test based on the data curve.

[0189] Optionally, module 61 can be executed. Figure 2 S201 in the embodiment.

[0190] Optionally, query module 62 can execute... Figure 2 S202 in the embodiment.

[0191] Optionally, the correction module 63 can be executed. Figure 2 S203 in the embodiment.

[0192] Optionally, prediction module 64 can perform... Figure 2 S204 in the embodiment.

[0193] Optionally, analysis module 65 can execute... Figure 2 S205 in the embodiment.

[0194] It should be noted that the battery performance testing device shown in the embodiments of this application can execute the technical solution shown in the above method embodiments, and its implementation principle and beneficial effects are similar, so they will not be described again here.

[0195] Based on the above implementation methods, by establishing a current collector performance evaluation model that can predict current collector performance data, the data curve between predicted current and predicted temperature rise can be quickly obtained based on a small number of battery parameters. Thus, the current collector performance can be quickly predicted based on the data curve, thereby improving the efficiency of battery performance testing.

[0196] In one possible implementation, the correction module 63 is specifically used for:

[0197] Determine the model identifier for the current collector performance prediction model;

[0198] Based on the model identifier, the corresponding parameter rule is searched from the rule base;

[0199] Based on the parameter rules, the original battery parameters are corrected to obtain the target battery parameters.

[0200] In one possible implementation, the correction module 63 is specifically used for:

[0201] Based on the number of parameters, the original battery parameters are supplemented by interpolation or supplementary testing to obtain supplementary battery parameters.

[0202] Based on the accuracy of the parameters, the parameters of the supplementary battery are corrected to obtain the parameters of the target battery.

[0203] In one possible implementation, the prediction module 64 is specifically used for:

[0204] Based on the target battery identifier, determine the temperature rise threshold corresponding to the battery under test;

[0205] The target battery parameters and temperature rise threshold are input into the current collector performance prediction model. The current collector performance prediction model is used to predict the performance data within the temperature rise threshold range, and the predicted current and the corresponding predicted temperature rise are obtained. The predicted temperature rise is less than or equal to the temperature rise threshold.

[0206] In one possible implementation, the prediction module 64 is specifically used for:

[0207] Substituting the target battery parameters into the coefficient parameters, we obtain the calculation expressions for temperature rise and current. The calculation expressions include temperature rise parameters, current parameters, slope constants, and intercept constants.

[0208] Within the temperature rise threshold range, the predicted current and the corresponding predicted temperature rise are obtained through calculation using a computational expression.

[0209] In one possible implementation, the analysis module 65 is specifically used for:

[0210] Based on the target battery identification, determine the operating temperature range and multiple performance index types corresponding to the battery under test;

[0211] From the data curves, determine the valid data corresponding to the operating temperature range;

[0212] The current collector performance is obtained by analyzing multiple performance index types and valid data.

[0213] Figure 7 This is a schematic diagram of another battery performance testing device provided in an embodiment of this application. Figure 6 Based on the illustrated embodiments, as Figure 7As shown, the battery performance testing device 60 also includes: an execution module 66, a construction module 67, a storage module 68, a filtering module 69, and a calling module 610.

[0214] Execution module 66 is used for:

[0215] Determine the mapping relationship between battery identifiers and prediction models;

[0216] Based on the target battery identifier and mapping relationship, a current collector performance prediction model is determined.

[0217] Module 67 is used for:

[0218] Obtain multiple historical battery parameters of the same type of battery as the battery under test;

[0219] Based on the Joule heating effect, the formula for the heat generation power of the current collector in the same type of battery is determined.

[0220] By inputting multiple historical battery parameters into the current collector heat generation power formula, a general temperature rise and current formula is obtained.

[0221] Based on the general temperature rise and current formula, a current collector performance prediction model adapted to the battery under test is constructed.

[0222] In one possible implementation, the construction module 67 is specifically used for:

[0223] Obtain parameters from multiple sample batteries of the same batch or model as the battery under test;

[0224] Based on the parameters of multiple sample batteries, the general temperature rise and current formulas are modified to obtain a current collector performance prediction model.

[0225] Storage module 68 is used for:

[0226] Obtain real-time battery parameters of the battery under test during use;

[0227] Based on real-time battery parameters, the current collector performance prediction model is modified to obtain an updated current collector performance prediction model.

[0228] Determine the current timestamp;

[0229] An update log is generated and stored based on the current timestamp, real-time battery parameters, and updated current collector performance prediction model.

[0230] Filtering module 69 is used for:

[0231] All batteries to be tested are screened, and those with current collector performance greater than the preset performance value are identified as target batteries.

[0232] Call module 610, used for

[0233] Retrieve the fluid performance data for each cell under test from memory.

[0234] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 8 As shown, the electronic device includes:

[0235] The electronic device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke logical instructions stored in the memory 292 to execute the methods of the above embodiments.

[0236] Furthermore, the logic instructions in the aforementioned memory 292 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0237] The memory 292, as a non-volatile computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, it implements the methods in the above-described method embodiments.

[0238] The memory 292 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 292 may include high-speed random access memory and may also include non-volatile memory.

[0239] Based on the above implementation methods, by establishing a current collector performance evaluation model that can predict current collector performance data, the data curve between predicted current and predicted temperature rise can be quickly obtained based on a small number of battery parameters. Thus, the current collector performance can be quickly predicted based on the data curve, thereby improving the efficiency of battery performance testing.

[0240] This application provides a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in the foregoing embodiments.

[0241] Based on the above implementation methods, by establishing a current collector performance evaluation model that can predict current collector performance data, the data curve between predicted current and predicted temperature rise can be quickly obtained based on a small number of battery parameters. Thus, the current collector performance can be quickly predicted based on the data curve, thereby improving the efficiency of battery performance testing.

[0242] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in the foregoing embodiments.

[0243] Based on the above implementation methods, by establishing a current collector performance evaluation model that can predict current collector performance data, the data curve between predicted current and predicted temperature rise can be quickly obtained based on a small number of battery parameters. Thus, the current collector performance can be quickly predicted based on the data curve, thereby improving the efficiency of battery performance testing.

[0244] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0245] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps; they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages, which do not necessarily complete at the same time but can be executed at different times. The execution order of these sub-steps or stages is also not necessarily sequential but can be alternated or carried out in turn with other steps or at least some of the sub-steps or stages of other steps.

[0246] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0247] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0248] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. The processor can be any suitable hardware processor, such as CPU, GPU, FPGA, DSP, and ASIC. The storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0249] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0250] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0251] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0252] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A battery performance detection system, characterized by, The battery performance testing system includes a data acquisition unit and a controller. The data acquisition device is used to obtain the target battery identifier and original battery parameters of the battery under test. The original battery parameters include the structural parameters of the battery under test and the characteristic parameters of the current collector in the battery under test. The controller is configured to perform the following steps: Based on the target battery identifier, the current collector performance prediction model corresponding to the battery under test is determined. The current collector performance prediction model is used to characterize the mapping relationship between battery parameters and predicted performance data. Based on the parameter rules of the current collector performance prediction model, the original battery parameters are corrected to obtain the target battery parameters; The target battery parameters are input into the current collector performance prediction model to obtain the data curve between the predicted current and the predicted temperature rise of the battery under test, output by the current collector performance prediction model. Based on the data curve, the current collector performance of the battery under test is determined.

2. The system of claim 1, wherein, The step of correcting the original battery parameters according to the parameter rules of the current collector performance prediction model to obtain the target battery parameters specifically includes: Determine the model identifier for the current collector performance prediction model; Based on the model identifier, the corresponding parameter rule is searched from the rule base; The original battery parameters are corrected according to the parameter rules to obtain the target battery parameters.

3. The system of claim 2, wherein, The parameter rules include the number of parameters and the parameter precision; the step of correcting the original battery parameters according to the parameter rules to obtain the target battery parameters specifically includes: Based on the number of parameters, the original battery parameters are supplemented by interpolation or supplementary testing to obtain supplementary battery parameters. Based on the accuracy of the parameters, the supplementary battery parameters are corrected to obtain the target battery parameters.

4. The system according to claim 1, characterized in that, The step of inputting the target battery parameters into the current collector performance prediction model to obtain the data curve between the predicted current and the predicted temperature rise of the battery under test output by the current collector performance prediction model specifically includes: Based on the target battery identifier, determine the temperature rise threshold corresponding to the battery under test; The target battery parameters and the temperature rise threshold are input into the current collector performance prediction model to predict performance data within the temperature rise threshold range, thereby obtaining the predicted current and the corresponding predicted temperature rise, wherein the predicted temperature rise is less than or equal to the temperature rise threshold.

5. The system according to claim 4, characterized in that, The current collector performance prediction model includes temperature rise and current formulas, wherein the temperature rise and current formulas include temperature rise parameters, current parameters, and coefficient parameters; the target battery parameters and the temperature rise threshold are input into the current collector performance prediction model to predict performance data within the temperature rise threshold range, thereby obtaining the predicted current and the corresponding predicted temperature rise, including: Substituting the target battery parameters into the coefficient parameters yields the operational expression for temperature rise and current, which includes temperature rise parameters, current parameters, slope constant, and intercept constant. Within the temperature rise threshold range, the predicted current and the corresponding predicted temperature rise are calculated using the calculation expression.

6. The system according to claim 1, characterized in that, The step of determining the current collector performance of the battery under test based on the data curve specifically includes: Based on the target battery identifier, determine the operating temperature range and multiple performance index types corresponding to the battery under test; From the data curves, determine the valid data corresponding to the operating temperature range; The current collector performance is obtained by analyzing the multiple performance index types and the valid data.

7. The system according to any one of claims 1-6, characterized in that, The step of determining the current collector performance prediction model corresponding to the battery under test based on the target battery identifier specifically includes: Determine the mapping relationship between battery identifiers and prediction models; The current collector performance prediction model is determined based on the target battery identifier and the mapping relationship.

8. The system according to claim 7, characterized in that, The battery performance testing system is also configured to construct the current collector performance prediction model, including the following steps: Obtain multiple historical battery parameters of the same type of battery as the battery under test; Based on the Joule heating effect, the formula for the current collector heat generation power of the same type of battery is determined; By inputting the multiple historical battery parameters into the current collector heat generation power formula, a general temperature rise and current formula is obtained. Based on the general temperature rise and current formula, a current collector performance prediction model adapted to the battery under test is constructed.

9. The system according to claim 8, characterized in that, The step of constructing a current collector performance prediction model adapted to the battery under test based on the general temperature rise and current formula specifically includes: Obtain parameters of multiple sample batteries from the same batch or model as the battery under test; Based on the parameters of the multiple sample batteries, the general temperature rise and current formula is modified to obtain the current collector performance prediction model.

10. The system according to claim 9, characterized in that, The battery performance testing system is also configured to perform the following steps: Obtain real-time battery parameters during the use of the battery under test; Based on the real-time battery parameters, the current collector performance prediction model is modified to obtain an updated current collector performance prediction model. Determine the current timestamp; An update log is generated and stored based on the current timestamp, the real-time battery parameters, and the updated current collector performance prediction model.

11. The system according to claim 1, characterized in that, The battery performance testing system connects to multiple batteries under test, traverses each battery under test, and obtains the performance of each current collector for all batteries under test. The battery performance testing system also includes a filter, which is used for: All batteries to be tested are screened, and those with current collector performance greater than a preset performance value are identified as target batteries.

12. The system according to claim 11, characterized in that, The battery performance testing system further includes a memory, which is used for: Store the fluid performance of each cell for all cells under test; The filter is also used for: Retrieve the fluid performance of each cell corresponding to all the cells under test from the memory.

13. A method for testing battery performance, characterized in that, include: Obtain the target battery identifier and original battery parameters of the battery under test, wherein the original battery parameters include the structural parameters of the battery under test and the characteristic parameters of the current collector in the battery under test; Based on the target battery identifier, the current collector performance prediction model corresponding to the battery under test is determined. The current collector performance prediction model is used to characterize the mapping relationship between battery parameters and predicted performance data. Based on the parameter rules of the current collector performance prediction model, the original battery parameters are corrected to obtain the target battery parameters; The target battery parameters are input into the current collector performance prediction model to obtain the data curve between the predicted current and the predicted temperature rise of the battery under test, output by the current collector performance prediction model. Based on the data curve, the current collector performance of the battery under test is determined.

14. A single-cell battery, characterized in that, The current collector performance of the single cell is greater than the preset performance value; The current collector performance is determined by testing the individual battery using the battery performance testing system according to any one of claims 1-12.

15. A battery pack, characterized in that, It includes at least two individual cells as described in claim 14, each of which is electrically connected to the other.

16. A battery pack, characterized in that, It includes a housing and at least two battery packs as described in claim 15, each of the battery packs being disposed within the housing and electrically connected to each other.

17. An electric vehicle, characterized in that, It includes at least the battery pack as described in claim 16.

18. An electrical appliance, characterized in that, It includes at least the single cell battery as described in claim 14.

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