Battery consistency detection method, electronic equipment and storage medium

Abstract

The invention discloses a battery consistency detection method, electronic equipment and a storage medium, and relates to the field of battery test and management systems. The battery consistency detection method comprises the steps of obtaining a voltage time function of a to-be-detected battery in a process of calibrating a function between a state of charge and an open-circuit voltage of the to-be-detected battery; the voltage time function represents the corresponding relation of the voltage along with the time change of the calibration process; fitting the voltage time function of the to-be-detected battery based on an equivalent circuit model to obtain kinetic parameters of the to-be-detected battery; and performing consistency detection on the to-be-detected battery by adopting statistical analysis according to the kinetic parameters of the to-be-detected battery. Consistency detection is carried out on the to-be-detected battery through statistical analysis, subjectivity and limitation caused by judgment only depending on a threshold value or a range are avoided, and systematicness and operability of consistency detection are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of battery testing and management systems, in particular to a battery consistency detection method, an electronic device and a storage medium. BACKGROUND

[0002] With the development of energy storage systems, electric vehicles and portable electronic devices, the performance and reliability of batteries as core components become particularly important. Battery management systems (BMS) rely on accurate kinetic parameters (such as internal resistance, capacity, etc.) and state of charge (SOC) - open circuit voltage (OCV) functions to ensure accuracy and efficiency.

[0003] However, traditional testing methods are time-consuming and complex, making it difficult to meet the needs of large-scale testing and dynamic application scenarios.

[0004] In the battery production process, the same batch of batteries may have individual differences in internal kinetic parameters (such as internal resistance, capacitance, etc.) due to fluctuations in raw materials, process deviations, differences in packaging technology, etc. If the parameter consistency is insufficient, it will directly affect the performance and safety of the battery pack in electric vehicles, energy storage systems and other applications.

[0005] Currently, traditional detection methods mainly rely on performance testing of single batteries (such as capacity, internal resistance, etc.), but have the following defects:

[0006] 1. Lack of systematic statistical standards: only relying on single sample test results, it is impossible to detect the consistency of the entire batch of batteries from a statistical point of view.

[0007] 2. Limitations of sampling: in actual production, only a part of the samples (such as 16) can be sampled, and the traditional method is difficult to infer the characteristics of the entire batch from limited data, which is easy to cause misjudgment or missed detection.

[0008] 3. Non-quantitative evaluation: existing technologies mostly use threshold comparison or simple range analysis, lack of quantitative scoring mechanism, and cannot accurately guide production process optimization. SUMMARY

[0009] To solve the above problems, the present application discloses a battery consistency detection method, which detects the consistency of the battery to be detected through statistical analysis, avoids the subjectivity and limitations of relying only on threshold or range judgment, and improves the systematization and operability of consistency detection.

[0010] The first aspect of the present application provides a battery consistency detection method, comprising:

[0011] In the process of calibrating the function between the state of charge and the open circuit voltage of the battery to be detected, a voltage-time function of the battery to be detected is obtained; the voltage-time function represents the corresponding relationship between the voltage and the time of the calibration process;

[0012] The voltage-time function of the battery to be detected is fitted based on an equivalent circuit model to obtain the kinetic parameters of the battery to be detected;

[0013] According to the kinetic parameters of the battery to be detected, statistical analysis is used to detect the consistency of the battery to be detected.

[0014] In an optional embodiment, the function between the state of charge and the open circuit voltage of the battery to be detected is calibrated, including:

[0015] Applying current pulses of different rates and directions to the battery to be tested to obtain the corresponding open circuit voltages of the battery to be tested at different states of charge;

[0016] Based on the corresponding open circuit voltages of the battery to be tested at different states of charge, a function between the state of charge and the open circuit voltage of the battery to be tested is established;

[0017] The function between the state of charge and the open circuit voltage represents the corresponding relationship between the state of charge and the open circuit voltage of the battery to be detected.

[0018] In an optional embodiment, the corresponding open circuit voltages of different states of charge are discrete data points, and the function between the state of charge and the open circuit voltage of the battery to be tested is established, including:

[0019] The discrete data points are converted into a continuous distribution function between the state of charge and the open circuit voltage based on an interpolation method or a multi-order polynomial fitting method.

[0020] In an optional embodiment, the method further includes:

[0021] After adjusting the state of charge each time, a threshold time is set to eliminate polarization effect and record the actual open circuit voltage corresponding to the current state of charge; and the fitted open circuit voltage corresponding to the current state of charge in the function between the state of charge and the open circuit voltage;

[0022] The voltage difference between the actual open circuit voltage corresponding to the current state of charge and the fitted open circuit voltage is obtained;

[0023] If the voltage difference is greater than a difference threshold value, the fitting function parameters are adjusted or the function between the state of charge and the open circuit voltage is modified.

[0024] In an optional embodiment, the different rates include 0.5C, 1C and 1.5C; the directions include charging and discharging, and only discharging pulses are used when the battery is at 100% state of charge, and only charging pulses are used when the battery is at 0% state of charge.

[0025] In an optional embodiment, the equivalent circuit model includes a first-order RC model and / or a second-order RC model; the voltage-time function includes an ohmic voltage drop stage and a relaxation stage; the voltage-time function of the battery under test is fitted based on the equivalent circuit model to obtain the dynamic parameters of the battery under test, including:

[0026] The internal resistance of the battery under test is calculated based on the ohmic voltage drop stage.

[0027] The charge transfer impedance and electrochemical capacitance of the battery under test are obtained by analyzing the voltage-time function during the relaxation stage using the least squares method.

[0028] In an optional embodiment, statistical analysis is used to perform consistency testing on the battery under test based on its kinetic parameters, including:

[0029] Determine the mean and standard deviation of the dynamic parameters, and construct the consistency interval of the dynamic parameters based on the mean and standard deviation;

[0030] The proportion of each dynamic parameter in the corresponding consistency interval is determined separately, and the proportion of each dynamic parameter is obtained by weighted averaging of the proportions of each dynamic parameter.

[0031] When the proportion of a single parameter is not less than the threshold for the proportion of a single parameter and / or the proportion of multiple parameters is not less than the threshold for the proportion of multiple parameters, the battery under test is determined to meet the consistency requirement.

[0032] In an optional embodiment, the method further includes:

[0033] Perform a normality test on the kinetic parameters of the battery under test;

[0034] Obtain the mean and standard deviation of the dynamic parameters, including:

[0035] Given that the kinetic parameters meet the normality test, obtain the mean and standard deviation of the kinetic parameters.

[0036] A second aspect of this application provides an electronic device comprising: a memory and a processor coupled to each other, the processor being configured to execute program instructions stored in the memory to implement the aforementioned battery consistency detection method.

[0037] A third aspect of this application provides a computer-readable storage medium storing program data that can be executed by a processor to implement the aforementioned battery consistency detection method.

[0038] Compared with the prior art, this application has at least one of the following beneficial effects:

[0039] 1. By using statistical analysis to perform consistency testing on the batteries under test, the subjectivity and limitations of relying solely on thresholds or range judgments are avoided, thus improving the systematicness and operability of consistency testing.

[0040] 2. By obtaining the battery's voltage-time function during the calibration of the relationship between the state of charge and open-circuit voltage function, and fitting it based on the equivalent circuit model, dynamic parameters such as ohmic internal resistance, charge transfer impedance, and electrochemical capacitance are obtained, thereby improving the scientific rigor and accuracy of battery consistency testing.

[0041] 3. The relationship between the state of charge and the open-circuit voltage is calibrated by combining current pulse excitation with short-term rest, and the voltage-time function is obtained synchronously without the need for a complete charge-discharge cycle or long-term rest.

[0042] 4. By extracting dynamic parameters through model fitting, the detection time of a single cell is shortened and the testing efficiency is improved. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] in:

[0045] Figure 1 A flowchart illustrating an embodiment of the battery consistency detection method provided in this application;

[0046] Figure 2 A schematic diagram of an embodiment of the first-order RC and second-order RC equivalent circuits provided in this application;

[0047] Figure 3 This is a schematic diagram of an embodiment of the voltage-time curve provided in this application;

[0048] Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of this application;

[0049] Figure 5 This is a schematic diagram of the structure of an embodiment of the computer-readable storage medium of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are only for explaining this application and not for limiting it. Furthermore, it should be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all structures. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0051] The terms "first," "second," etc., used in this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0052] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0053] Existing battery testing methods are time-consuming and complex, making them unsuitable for large-scale testing and dynamic application scenarios. To address these issues, this application provides a battery consistency testing method that reduces overall testing time by simultaneously testing kinetic parameters and the SOC-OCV function. Furthermore, by using statistical analysis to perform consistency testing on the batteries under test, it avoids the subjectivity and limitations of relying solely on thresholds or range judgments, thus improving the systematic nature and operability of consistency testing. Figure 1 As shown, Figure 1 A flowchart illustrating an embodiment of the battery consistency testing method provided in this application includes:

[0054] During the calibration process of the function relating the state of charge (SOC) to the open-circuit voltage of the battery under test, the voltage-time function of the battery under test is obtained; the voltage-time function characterizes the relationship between voltage and time changes during the calibration process.

[0055] The battery under test is fully charged to obtain the initial state of charge (SOC); the SOC of the battery under test is adjusted based on the initial battery capacity; the battery under test is fully charged at room temperature, which ensures that the battery reaches its maximum capacity state, thereby accurately setting the initial SOC to 100%; this is achieved through constant current-constant voltage charging, that is, constant current charging is used when the battery voltage has not reached the set value, and when the voltage approaches the set value, it switches to constant voltage mode until the current gradually decreases to a preset threshold.

[0056] The open-circuit voltage corresponding to the initial state of charge (SOC) and the corresponding open-circuit voltage (OCV) after each SOC adjustment are obtained. The initial SOC is 100%. The SOC of the battery is adjusted to a predetermined value by constant current charging and discharging, and the corresponding OCV is obtained under different SOC states. This application does not limit the size of the predetermined value. For example, in this embodiment, the SOC points include 80%, 60%, 40%, 20%, and 0%. In other embodiments, the size of the predetermined value can be taken separately.

[0057] Apply current pulses of different rates and directions to the battery under test, and obtain the open-circuit voltage of the battery under test under different states of charge; that is, apply current pulses of different rates and directions to the SOC after each adjustment and record the voltage response. Fit the voltage-time curve of each stage based on the equivalent circuit model and calculate the parameter information in the equivalent circuit.

[0058] By measuring the current pulse response at different rates and directions, detailed dynamic characteristics of the battery under various operating conditions can be obtained, thereby constructing a more accurate equivalent circuit model. This helps improve the accuracy of state of charge (SOH) estimation in the battery management system. Different rates include 0.5C, 1C, and 1.5C; directions include charging and discharging. Only discharge pulses are used when the battery is at 100% SOC, and only charging pulses are used when the battery is at 0% SOC.

[0059] Based on the open-circuit voltage of the battery under test at different states of charge, a function relating the state of charge and the open-circuit voltage of the battery under test is established; wherein, the function relating the state of charge and the open-circuit voltage represents the correspondence between the state of charge and the open-circuit voltage of the battery under test.

[0060] A State of Charge (SOC)-OV (OCV) function is established based on open-circuit voltage. The SOC-OCV function represents the correspondence between the battery's state of charge and its open-circuit voltage. Based on each adjusted SOC state and its corresponding OCV value, a discrete set of SOC-OCV data points is constructed to directly reflect the open-circuit voltage characteristics of the battery under different states of charge, based on actual measured data. An accurate SOC-OCV function helps improve the working efficiency of the battery management system (BMS), ensures that the system can operate in the best condition, and extends battery life.

[0061] The open-circuit voltages corresponding to different states of charge are discrete data points. A function relating the state of charge and open-circuit voltage of the battery under test is established, including:

[0062] Discrete data points are converted into a function of continuously distributed state of charge and open-circuit voltage based on interpolation or multi-order polynomial fitting methods.

[0063] Each adjustment of SOC results in a corresponding discrete SOC-OCV data point. The discrete SOC-OCV data points are then converted into a continuously distributed SOC-OCV function based on interpolation or multi-order polynomial fitting. The discrete data points are converted into a continuous function using methods such as linear interpolation, spline interpolation, or multi-order polynomial fitting.

[0064] Interpolation methods, such as linear interpolation and cubic spline interpolation, are suitable for situations where it is desirable to maintain a smooth transition between data points and there is no need to make too many assumptions about the shape of the curve.

[0065] Multi-order polynomial fitting: Choosing an appropriate polynomial order can more flexibly fit complex data trends, but care must be taken to avoid overfitting.

[0066] By using interpolation or polynomial fitting, the OCV corresponding to any given SOC value can be derived from a finite number of discrete data points, improving the resolution and accuracy of the SOC-OCV relationship; continuous functions allow queries to be performed on any SOC value, without being limited to the original measurement points.

[0067] Furthermore, the battery consistency detection method also includes: a resting threshold time after each adjustment of the state of charge to eliminate polarization effects and recording the actual open-circuit voltage corresponding to the current state of charge; and the fitted open-circuit voltage corresponding to the current state of charge in the function between the state of charge and the open-circuit voltage.

[0068] After each adjustment of the state of charge (SOC), a settling threshold time is allowed to eliminate the polarization effect, and the actual open-circuit voltage U under that SOC state is recorded. OCVAfter each SOC adjustment, allow the battery to rest for a period of time to ensure that any polarization effects caused by rapid charging and discharging completely disappear. After the threshold time is over, the battery voltage will gradually stabilize and maintain a balanced state. Measure and record the open-circuit voltage at this point. This open-circuit voltage represents the true voltage level under this SOC state without external current influence, i.e., the actual open-circuit voltage U. OCV .

[0069] In this embodiment, the threshold time is 2 hours; in other embodiments, the threshold time for resting can be different, and no limitation is made here.

[0070] The fitted open-circuit voltage is the open-circuit voltage corresponding to the current SOC, which is a function of the previously obtained state of charge and open-circuit voltage.

[0071] Obtain the voltage difference between the actual open-circuit voltage and the fitted open-circuit voltage corresponding to the current state of charge;

[0072] If the voltage difference is greater than the difference threshold, the fitting function parameters or reconstruction function are adjusted to correct the function between the state of charge and the open-circuit voltage.

[0073] The voltage-time function of the battery under test is fitted based on the equivalent circuit model to obtain the dynamic parameters of the battery under test; that is, the dynamic parameters of the battery under test are obtained based on the equivalent circuit.

[0074] During battery production, due to factors such as fluctuations in raw materials, process deviations, and differences in packaging technology, the dynamic parameters of batteries in the same batch may vary individually. Traditional testing methods mainly rely on the performance testing of individual cells, but they have problems such as a lack of systematic statistical standards, limitations of sampling, and non-quantitative evaluation, making it difficult to comprehensively detect the consistency of the entire batch of batteries from a statistical perspective.

[0075] In view of this, this application extracts kinetic parameters through equivalent circuit modeling, including ohmic internal resistance R0, charge transfer impedances R1 and R2, and electrochemical capacitances C1 and C2; and combines normality test, consistency interval construction and quantitative scoring mechanism to solve the problems of insufficient statistical inference and vague evaluation criteria in traditional methods.

[0076] The equivalent circuit model includes a first-order RC model and / or a second-order RC model; the voltage-time function includes an ohmic voltage drop stage and a relaxation stage; based on the equivalent circuit model, the voltage-time function of the battery under test is fitted to obtain the dynamic parameters of the battery under test, including:

[0077] The internal resistance R0 of the battery under test is calculated based on the ohmic voltage drop stage.

[0078] The charge transfer impedances R1 and R2 and the electrochemical capacitances C1 and C2 of the battery under test are obtained by analyzing the voltage-time function during the relaxation stage using the least squares method.

[0079] like Figures 2-3 As shown, Figure 2 This is a schematic diagram of an embodiment of the first-order RC and second-order RC equivalent circuits provided in this application. Figure 3 This is a schematic diagram of an embodiment of the voltage-time curve provided in this application.

[0080] The following is a detailed analysis of the steps for calculating the parameter information of each component in the equivalent circuit based on charge and discharge pulses:

[0081] Figure 3 In the diagram, segments AB and CD represent the battery ohmic voltage drop stage, and segment DE represents the relaxation curve stage.

[0082] R0 = Ohmic voltage drop / pulse current, or can be solved by linear fitting, as shown in the following formula:

[0083]

[0084] U AB 0.5C U represents the instantaneous voltage jump measured when a 0.5C current pulse is applied to segment AB. CD 0.5C This represents the instantaneous voltage jump measured when a 0.5C current pulse is applied to segment CD. Segment AB represents the charging phase, and segment CD represents the discharging phase. AB 0.5C I represents the pulse current amplitude corresponding to a 0.5C multiplier current pulse applied in segment AB. CD 0.5C This indicates the pulse current amplitude corresponding to the application of a 0.5C multiplier current pulse in segment CD.

[0085] The instantaneous voltage jumps and pulse current amplitudes corresponding to the discharge and charging phases when applying 1C and 1.5C rate current pulses will not be elaborated here.

[0086] In the RC model, R1, R2, C1, and C2 are fitted with functions to fit the zero-response voltage of segment DE, and the coefficients are solved using the least squares method.

[0087]

[0088] Where U(t) is the battery terminal voltage measured at relaxation time t, U is the battery open-circuit voltage, I is the amplitude of the applied pulse current, R0 is the ohmic internal resistance, and R n and C n For the nth (1 or 2)th RC branch, there are the polarization resistance and electrochemical capacitance.

[0089] In this embodiment, the duration of the current pulse is 10s. In other embodiments, the duration of the current pulse may be different, and no limitation is made here.

[0090] The DC resistance (i.e., the internal resistance in ohms) within 10 seconds is solved by fitting the zero-response voltage of segment DE using a function, as shown in the following equation:

[0091] [U AC 0.5C U AC 1C U AC 1.5C ] = R0[I AC 0.5C I AC 1C I AC 1.5C ]

[0092] The synchronous testing method for dynamic parameters and SOC-OCV function also includes repeating the experiment with current pulses of different magnifications and directions to improve the accuracy and reliability of parameter estimation. By repeating the experiment multiple times, the random error in a single experiment can be effectively reduced, making the final dynamic parameters closer to the true values ​​and improving the accuracy of parameter estimation.

[0093] By repeatedly testing the current pulse response at various rates and directions, the complex operating conditions in real-world applications can be better simulated, enhancing the battery management system's (BMS) ability to cope with various situations.

[0094] Based on the kinetic parameters of the battery under test, statistical analysis is used to perform consistency testing on the battery under test, including:

[0095] Determine the mean and standard deviation of the kinetic parameters, and construct the consistency intervals for the kinetic parameters based on the mean and standard deviations; that is, under the premise of normality, determine the sample mean μ and standard deviation σ of each kinetic parameter, and construct the consistency intervals for each kinetic parameter based on the sample mean μ and standard deviation σ.

[0096] I = [μ - Nσ, μ + Nσ]

[0097] Where I is the consistency interval and N is the interval adjustment coefficient, which is used to control the width of the consistency interval. Its value is adjusted according to the actual consistency control requirements.

[0098] In this embodiment, N is set to 3, covering 99.7% of the normally distributed data. This reflects overall consistency while avoiding misjudgments due to oversensitivity. It should be noted that the value of N can be adjusted according to different application scenarios or quality control levels. For example, it can be set to 2 in scenarios with high reliability requirements, and to 2.5 or 4 under more lenient conditions; no limitation is imposed in this regard.

[0099] The proportion of each kinetic parameter within its corresponding consistency interval is determined, as well as the proportion of multiple parameters obtained by weighted averaging of the proportions of each kinetic parameter. The proportion of a single parameter (e.g., charge transfer impedance R1) represents the percentage of kinetic parameters (e.g., the number of kinetic parameters falling within their corresponding consistency intervals) out of the total number of such kinetic parameters. The number of samples Nin falling within the corresponding consistency interval for each kinetic parameter is also counted. The coverage of the normally distributed data is adjusted based on the value of N.

[0100] Single parameter proportion S i =Nin / n×100%; Average the proportions of each parameter with equal weight to obtain the proportion of multiple parameters. Where S R0 S represents the percentage of a single parameter corresponding to the ohmic internal resistance. R1 S represents the percentage of the single parameter corresponding to charge transfer impedance. C1 This represents the percentage of a single parameter corresponding to an electrochemical capacitor.

[0101] When the percentage of a single parameter is not less than the single parameter percentage threshold and / or the percentage of multiple parameters is not less than the multiple parameter percentage threshold, the battery under test is determined to meet the consistency requirement. In this embodiment, the single parameter percentage threshold is 94% and the multiple parameter percentage threshold is 94%. In other embodiments, the single parameter percentage threshold and the multiple parameter percentage threshold can be other values, and no limitation is made in this regard.

[0102] If the proportion of any single parameter is less than the single parameter proportion threshold or the proportion of multiple parameters is less than the multiple parameter proportion threshold, then the consistency of this batch of batteries is determined to be unsatisfactory. The range can be widened and / or the passing score can be lowered by adjusting the value of N to adapt to the needs of different production environments.

[0103] It also includes testing the normality of the kinetic parameters of the battery under test;

[0104] Obtain the mean and standard deviation of the dynamic parameters, including:

[0105] If the kinetic parameters meet the normality test, obtain the mean and standard deviation of the kinetic parameters. The normality test is used to determine whether the kinetic parameters conform to a normal distribution; if the significance level p of the test is greater than the preset threshold, the normality hypothesis is accepted, and the process proceeds to the consistency interval construction step; if p is less than or equal to the preset threshold, the normality hypothesis is rejected, and the data can be logarithmically transformed or marked as requiring further analysis.

[0106] In this embodiment, the preset threshold value is 0.05. In other embodiments, the preset threshold value can be adjusted based on different normality test criteria, and no limitation is made in this regard.

[0107] The steps of battery consistency testing are described in detail below with reference to a specific embodiment:

[0108] Parameter verification: Samples with n>24 were randomly selected, and R0, R1, C1 were obtained by EIS (electrochemical impedance spectroscopy).

[0109] Normality test:

[0110] The Shapiro-Wilk test result for R0 is p = 0.12 (accepting normality);

[0111] Calculated μ R0 =24.6mΩ, σ R0 =1.2mΩ, construct the interval [21.0, 28.2]mΩ, and the value of N is 3;

[0112] 15 samples fall into the interval, S R0 =93.75%.

[0113] Overall rating: S R1 =100%, S C1 =100%, the overall score S = (93.75% + 100% + 100%) / 3 = 97.72% > 94%, the batch is deemed qualified.

[0114] In summary, the battery consistency detection method of this embodiment includes: obtaining the voltage-time function of the battery under test during the calibration process of the functional relationship between the state of charge and open-circuit voltage of the battery under test; the voltage-time function characterizes the corresponding relationship of voltage change with time during the calibration process; fitting the voltage-time function of the battery under test based on an equivalent circuit model to obtain the dynamic parameters of the battery under test; and performing consistency detection on the battery under test using statistical analysis based on the dynamic parameters of the battery under test. By performing consistency detection on the battery under test through statistical analysis, the subjectivity and limitations of relying solely on thresholds or range judgments are avoided, improving the systematic nature and operability of consistency detection.

[0115] In one embodiment, the discharge time and discharge current are recorded during the adjustment of the State of Charge (SOC). The available capacity under the current SOC is obtained based on the integral of the discharge current over the discharge time. Since the discharge current is not completely constant, numerical integration can more accurately reflect the actual discharge situation. The calculated battery capacity is compared with the battery's rated capacity. The SOC adjustment accuracy is verified based on the difference between the initial state of charge and the available capacity under the current SOC. In one embodiment, an error threshold can be set. If the relative error between the calculated battery capacity and the battery's rated capacity is greater than the error threshold, the test parameters are adjusted.

[0116] By directly measuring the discharge current and time, and using numerical integration methods to calculate the battery capacity, more accurate results can be obtained than theoretical estimates. Accurate battery capacity information helps to detect the trend of battery capacity degradation in a timely manner and extend battery life.

[0117] Regarding the above embodiments, this application provides a computer device; please refer to [link / reference]. Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of the present application. The computer device includes a memory and a processor, wherein the memory and the processor are coupled to each other. The memory stores program data, and the processor executes the program data to implement the steps of any embodiment of the battery consistency detection method described above.

[0118] In this embodiment, the processor may also be referred to as a CPU (Central Processing Unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0119] The methods described in the above embodiments can be implemented as computer programs; therefore, this application proposes a computer-readable storage medium. Please refer to [link to relevant documentation]. Figure 5 , Figure 5 This is a schematic diagram of the structure of an embodiment of the computer-readable storage medium of this application. The computer-readable storage medium stores program data that can be executed by a processor to implement the steps of any embodiment of the battery consistency detection method described above.

[0120] In this embodiment, the computer-readable storage medium can be 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, or a medium that can store program data. Alternatively, it can be a server that stores the program data. The server can send the stored program data to other devices for execution, or it can run the stored program data itself.

[0121] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A battery consistency detection method, characterized in that, include: During the calibration process of the state of charge and open-circuit voltage of the battery under test, the voltage-time function of the battery under test is obtained; the voltage-time function characterizes the relationship between voltage and time change during the calibration process. The voltage-time function of the battery under test is fitted based on the equivalent circuit model to obtain the dynamic parameters of the battery under test. Based on the kinetic parameters of the battery under test, statistical analysis is used to perform consistency testing on the battery under test.

2. The battery consistency detection method according to claim 1, characterized in that, The function relating the state of charge (SOC) to the open-circuit voltage of the battery under test includes: Apply current pulses of different rates and directions to the battery under test, and obtain the open-circuit voltage of the battery under test under different states of charge; Based on the open-circuit voltage of the battery under test at different states of charge, a function relating the state of charge of the battery under test to the open-circuit voltage is established. The function relating the state of charge to the open-circuit voltage represents the correspondence between the state of charge and the open-circuit voltage of the battery under test.

3. The battery consistency detection method according to claim 2, characterized in that, The open-circuit voltages corresponding to different states of charge are discrete data points, and the function for establishing the relationship between the state of charge and the open-circuit voltage of the battery under test includes: Discrete data points are converted into a function of continuously distributed state of charge and open-circuit voltage based on interpolation or multi-order polynomial fitting methods.

4. The battery consistency detection method according to claim 3, characterized in that, The method further includes: Each time the state of charge is adjusted, a resting threshold time is set to eliminate the polarization effect and the actual open-circuit voltage corresponding to the current state of charge is recorded; and the fitted open-circuit voltage corresponding to the current state of charge is in the function between the state of charge and the open-circuit voltage. Obtain the voltage difference between the actual open-circuit voltage and the fitted open-circuit voltage corresponding to the current state of charge; If the voltage difference is greater than the difference threshold, the fitting function parameters or reconstruction function are adjusted to correct the function between the state of charge and the open-circuit voltage.

5. The battery consistency detection method according to claim 2, characterized in that, The different rates include 0.5C, 1C, and 1.5C; the directions include charging and discharging, with only discharge pulses used when the battery is at 100% state of charge and only charging pulses used when the battery is at 0% state of charge.

6. The battery consistency detection method according to claim 1, characterized in that, The equivalent circuit model includes a first-order RC model and / or a second-order RC model; the voltage-time function includes an ohmic voltage drop stage and a relaxation stage; the fitting of the voltage-time function of the battery under test based on the equivalent circuit model to obtain the dynamic parameters of the battery under test includes: The ohmic internal resistance of the battery under test is calculated based on the ohmic voltage drop stage. The charge transfer impedance and electrochemical capacitance of the battery under test are obtained by analyzing the voltage-time function during the relaxation stage using the least squares method.

7. The battery consistency detection method according to claim 1, characterized in that, The step of performing consistency testing on the battery under test using statistical analysis based on the kinetic parameters of the battery under test includes: Determine the mean and standard deviation of the dynamic parameters, and construct the consistency interval of the dynamic parameters based on the mean and standard deviation; The proportion of each dynamic parameter in the corresponding consistency interval is determined separately, and the proportion of each dynamic parameter is obtained by weighted averaging of the proportions of each dynamic parameter. When the proportion of a single parameter is not less than the threshold for the proportion of a single parameter and / or the proportion of multiple parameters is not less than the threshold for the proportion of multiple parameters, the battery to be tested is determined to meet the consistency requirement.

8. The battery consistency detection method according to claim 7, characterized in that, The method further includes: The normality of the kinetic parameters of the battery under test is tested. The process of obtaining the mean and standard deviation of the dynamic parameters includes: If the kinetic parameters meet the normality test, obtain the mean and standard deviation of the kinetic parameters.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor coupled to each other, the processor being configured to execute program instructions stored in the memory to implement the battery consistency detection method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program data that can be executed by a processor to implement the battery consistency detection method as described in any one of claims 1-8.

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