A battery cell fault early warning method and system based on dynamic impedance fitting

Abstract

The application provides a battery cell fault early warning method and system based on dynamic impedance fitting, impedance of a target battery is tested to obtain an initial time impedance value, and the target battery is charged; the target battery is recorded for impedance at each time point by an excitation current of a preset frequency to obtain real-time impedance observation values; an exponential decay model is constructed, and the real-time impedance observation values at each time point are fitted and predicted for impedance to obtain real-time impedance prediction values; residual fitting and deviation analysis are performed on the real-time impedance observation values and the real-time impedance prediction values to obtain battery abnormal change monitoring data, and an early warning is given when a fault occurs in a battery cell of the target battery. The application solves the problem that the prior art cannot detect battery impedance changes in real time during battery charging and discharging, cannot timely warn of battery faults, and leads to poor safety of battery cells. The application can timely feed back impedance abnormal change time and give an early warning, and improve the safety of battery cells.

Description

Technical Field

[0001] This invention relates to the field of battery cell safety testing, and in particular to a battery cell fault early warning method and system based on dynamic impedance fitting. Background Technology

[0002] Lithium batteries are currently widely used in various fields. However, overcharging, overheating, and battery inconsistency can cause malfunctions that seriously affect the safety and stability of energy storage systems. Therefore, it is necessary to provide timely feedback and early warning of abnormal battery conditions.

[0003] Currently, existing technologies mainly employ static impedance measurement, which measures the impedance characteristics of a battery under static (i.e., non-changing or DC) conditions. Typically, changes in voltage and temperature lag behind internal faults. By the time a significant change in external parameters is detected, a serious fault may have already occurred. Therefore, existing methods suffer from insufficient real-time performance during charging and discharging, making it difficult to quickly respond to changes in the battery's internal state and to provide timely warnings of battery faults, resulting in poor battery cell safety. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a cell fault early warning method and system based on dynamic impedance fitting. This method enables real-time dynamic detection of battery cell impedance changes during the charging process, timely feedback of abnormal impedance changes, and early warning, thereby improving battery cell safety.

[0005] To achieve the above objectives, embodiments of the present invention provide a cell fault early warning method based on dynamic impedance fitting, comprising:

[0006] An impedance test is performed on the target battery to obtain the initial impedance value, and the target battery is then charged.

[0007] The impedance of the target battery is recorded at various time points by using an excitation current of a preset frequency to obtain real-time impedance observation values.

[0008] An exponential decay model is constructed and impedance fitting prediction is performed on the real-time impedance observations at each time point to obtain the real-time impedance prediction values.

[0009] Residual fitting and deviation analysis are performed on the real-time impedance observations and the real-time impedance predictions to obtain battery abnormal change monitoring data.

[0010] Based on monitoring data of abnormal changes in the battery, an early warning is issued when a target battery cell malfunctions.

[0011] Furthermore, the step of performing an impedance test on the target battery to obtain the initial impedance value and charging the target battery includes:

[0012] The battery under test is fully discharged to obtain the target battery;

[0013] The impedance value between the positive and negative electrodes of the target battery is tested to obtain the impedance value at the initial moment;

[0014] The target battery is charged using a constant current based on a preset constant current.

[0015] Furthermore, the step of recording the impedance of the target battery at various time points using an excitation current of a preset frequency to obtain real-time impedance observations includes:

[0016] During the charging process, based on a preset time interval, the target battery is subjected to impedance recording using an excitation current of the preset frequency to obtain a first real-time impedance change curve.

[0017] Based on the first real-time impedance change curve, the real-time impedance observation value is obtained.

[0018] Furthermore, the construction of the exponential decay model and the impedance fitting prediction of the real-time impedance observations at each time point to obtain the real-time impedance prediction value includes:

[0019] An exponential decay model is constructed based on the initial impedance value and a preset attenuation constant.

[0020] During the charging process, the real-time impedance observations at each time point are fitted by the exponential decay model to obtain the second real-time impedance change curve.

[0021] Based on the second real-time impedance change curve, the real-time impedance prediction value is obtained.

[0022] Furthermore, residual fitting and deviation analysis are performed on the real-time impedance observations and the real-time impedance predictions to obtain battery anomaly monitoring data, including:

[0023] The impedance residual is obtained by subtracting the real-time impedance observation value from the real-time impedance prediction value.

[0024] The impedance residuals are fitted by minimizing the sum of squared residuals to obtain the fitted residuals;

[0025] By analyzing the residual distribution and magnitude of the fitted residuals, monitoring data on abnormal battery changes are obtained.

[0026] Furthermore, the provision of issuing an early warning based on abnormal battery change monitoring data when a target battery cell malfunctions includes:

[0027] Based on the battery abnormal change monitoring data, the absolute value of the impedance residual at each time point is calculated to obtain the absolute value of the impedance residual.

[0028] If the absolute value of the impedance residual is greater than the fitted residual, it is determined that the target battery cell has failed and an early warning is issued.

[0029] If the distribution of the impedance residual does not meet the requirements of the fitted residual distribution, the target battery cell is determined to be faulty and an early warning is issued.

[0030] This invention also provides a cell fault early warning system based on dynamic impedance fitting, including: an initial impedance testing module, an impedance observation module, an impedance prediction module, an abnormal change analysis module, and an early warning module;

[0031] The initial impedance test module is used to perform impedance testing on the target battery, obtain the initial impedance value, and charge the target battery.

[0032] The impedance observation module is used to record the impedance of the target battery at various time points using an excitation current of a preset frequency, so as to obtain real-time impedance observation values.

[0033] The impedance prediction module is used to construct an exponential decay model and perform impedance fitting prediction on the real-time impedance observations at various time points to obtain real-time impedance prediction values.

[0034] The abnormal change analysis module is used to perform residual fitting and deviation analysis on the real-time impedance observation value and the real-time impedance prediction value to obtain battery abnormal change monitoring data.

[0035] The early warning module is used to issue an early warning when a target battery cell malfunctions, based on monitoring data of abnormal changes in the battery.

[0036] Furthermore, the impedance prediction module is used to construct an exponential decay model and perform impedance fitting prediction on the real-time impedance observations at various time points to obtain real-time impedance prediction values, including:

[0037] Model building unit, impedance prediction unit, and impedance value acquisition unit;

[0038] The model building unit is used to construct an exponential decay model based on the initial impedance value and a preset attenuation constant.

[0039] The impedance prediction unit is used to fit the real-time impedance observation values ​​at each time point during the charging process using the exponential decay model to obtain a second real-time impedance change curve.

[0040] The impedance value acquisition unit is used to acquire the real-time impedance prediction value based on the second real-time impedance change curve.

[0041] Furthermore, the abnormal change analysis module is used to perform residual fitting and deviation analysis on the real-time impedance observation value and the real-time impedance prediction value to obtain battery abnormal change monitoring data, including:

[0042] Residual calculation unit, residual fitting unit, and anomaly analysis unit;

[0043] The residual calculation unit is used to obtain the impedance residual by subtracting the real-time impedance observation value and the real-time impedance prediction value;

[0044] The residual fitting unit is used to fit the impedance residual by minimizing the sum of squared residuals to obtain the fitted residual;

[0045] The anomaly analysis unit is used to analyze the residual distribution and residual magnitude of the fitted residuals to obtain battery anomaly change monitoring data.

[0046] Furthermore, the early warning module is used to issue an early warning when a target battery cell malfunctions based on abnormal battery change monitoring data, including:

[0047] Residual absolute value calculation unit, first early warning unit, and second early warning unit;

[0048] The residual absolute value calculation unit is used to calculate the absolute value of the impedance residual at each time point based on the battery abnormal change monitoring data, and obtain the absolute value of the impedance residual.

[0049] The first early warning unit is used to determine that the target battery cell has failed and issue an early warning if the absolute value of the impedance residual is greater than the fitted residual.

[0050] The second early warning unit is used to determine that the target battery cell has failed and issue an early warning if the distribution of the impedance residual does not meet the requirements of the fitted residual distribution.

[0051] Beneficial effects:

[0052] By using an exponential decay model to predict battery impedance during charging, a data foundation is provided for fitting the dynamic changes in battery impedance data. Recording the real-time battery impedance using an excitation current at a preset frequency can effectively reduce high-frequency noise interference. Furthermore, the exponential decay model can more accurately capture the impedance change trend over time, ensuring the real-time nature and accuracy of the measurement data. In addition, dynamic fitting and deviation analysis are used to obtain abnormal impedance change monitoring data, which helps to detect abnormal impedance change trends in real time, monitor the battery health status in real time, and finally provide timely warnings of abnormal changes, providing early warnings of potential risks and improving the safety of battery cells. Attached Figure Description

[0053] Figure 1 A flowchart illustrating the steps of a cell fault early warning method based on dynamic impedance fitting, provided in a certain embodiment of the present invention;

[0054] Figure 2 A schematic diagram of the first real-time impedance change curve of a cell fault early warning method based on dynamic impedance fitting provided in a certain embodiment of the present invention;

[0055] Figure 3 A schematic diagram of the fitting residual change curve of a cell fault early warning method based on dynamic impedance fitting provided in a certain embodiment of the present invention;

[0056] Figure 4 A schematic diagram of the module structure of a cell fault early warning system based on dynamic impedance fitting provided in a certain embodiment of the present invention;

[0057] Figure 5 A schematic diagram of the impedance prediction module structure of a cell fault early warning system based on dynamic impedance fitting, provided in a certain embodiment of the present invention;

[0058] Figure 6 A schematic diagram of the abnormal change analysis module structure of a cell fault early warning system based on dynamic impedance fitting provided in a certain embodiment of the present invention;

[0059] Figure 7 This is a schematic diagram of the early warning module structure of a cell fault early warning system based on dynamic impedance fitting, provided in a certain embodiment of the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Example 1

[0062] See Figure 1 , Figure 1 This is a flowchart illustrating the steps of a cell fault early warning method based on dynamic impedance fitting, provided in a certain embodiment of the present invention. Figure 1 As shown in the figure, this invention proposes a cell fault early warning method based on dynamic impedance fitting, including steps 101 to 105, each step of which is as follows:

[0063] Step 101: Perform an impedance test on the target battery to obtain the initial impedance value and charge the target battery.

[0064] As an example of this embodiment, the battery under test is fully discharged to obtain the target battery; the impedance value between the positive and negative terminals of the target battery is tested to obtain the initial impedance value; and the target battery is charged with a constant current based on a preset constant current. In a specific implementation, the battery under test is fully discharged and left to stand for 4 hours to eliminate interference from residual charge on subsequent tests. The acquisition contacts of the impedance measuring device are connected to the positive and negative terminals of the target battery to detect the impedance value of the target battery. The impedance value obtained from the detection of the target battery is the initial impedance value Z0; then, a constant current of 1C (equivalent to a preset constant current) is set to charge the target battery.

[0065] Step 102: The target battery is recorded at various time points using an excitation current of a preset frequency to obtain real-time impedance observation values.

[0066] As an example of this embodiment, during the charging process, the target battery is impedance recorded by an excitation current of the preset frequency based on a preset time interval to obtain a first real-time impedance change curve; based on the first real-time impedance change curve, a real-time impedance observation value is obtained.

[0067] In one specific implementation method, during the charging process, the impedance of the battery under a 150Hz excitation current (equivalent to a preset time interval) is recorded in real time at fixed intervals (equivalent to a preset frequency excitation current), and the impedance change curve of the battery (equivalent to a first real-time impedance change curve) is obtained in real time, thus acquiring the real-time impedance observation value Z. i ,join Figure 2 , Figure 2 This is a schematic diagram of the first real-time impedance change curve of a cell fault early warning method based on dynamic impedance fitting, provided in a certain embodiment of the present invention; as shown. Figure 2 As shown, the battery impedance is 0.09519Ω at the start of charging. Between 0.1 and 0.7 SOC, the impedance changes from 0.09324Ω to 0.09117Ω, showing a slow decreasing trend. When the SOC reaches 0.8, the battery is about to be overcharged. After the SOC reaches 0.9, the battery impedance changes from 0.08793Ω to 0.08205Ω, showing a significant and rapid decay trend. In this embodiment, the model fitting effect can be optimized by adjusting the impedance data acquisition frequency. Specifically, 150Hz is selected as the data acquisition frequency to ensure accurate and continuous impedance change data is obtained during battery charging.

[0068] Step 103: Construct an exponential decay model and perform impedance fitting prediction on the real-time impedance observations at each time point to obtain the real-time impedance prediction value.

[0069] As an example of this embodiment, an exponential decay model is constructed based on the initial impedance value and a preset attenuation constant; during the charging process, the real-time impedance observation values ​​at each time point are fitted by the exponential decay model to obtain a second real-time impedance change curve; based on the second real-time impedance change curve, the real-time impedance prediction value is obtained.

[0070] One possible implementation method involves determining the initial impedance value Z0 and the attenuation constant λ to construct an exponential decay model. The specific calculation formula is as follows:

[0071] Z(t) = Z0·e -λt

[0072] In the formula, Z(t) represents the impedance value at time t, Z0 represents the impedance value at the initial moment, and λ represents the attenuation constant, which controls the attenuation rate of the impedance over time.

[0073] After constructing the exponential decay model, for the data at each observation time point (t) i Z i (Equivalent to the real-time impedance observations at various time points), the impedance value predicted by fitting the exponential decay model is calculated using the following formula:

[0074]

[0075] In the formula This is the real-time impedance prediction value.

[0076] After fitting and predicting the data at each observation time point using an exponential decay model, a battery impedance change prediction curve (equivalent to a second real-time impedance change curve) is constructed. The real-time impedance prediction value can be reflected in the battery impedance change prediction curve. The exponential decay model can more accurately reflect the real-time trend of impedance changes, thereby enabling more accurate fault prediction.

[0077] Step 104: Perform residual fitting and deviation analysis on the real-time impedance observation value and the real-time impedance prediction value to obtain battery abnormal change monitoring data;

[0078] As an example of this embodiment, the impedance residual is obtained by subtracting the real-time impedance observation value and the real-time impedance prediction value; the impedance residual is fitted by minimizing the sum of squares of the residuals to obtain the fitted residual; the residual distribution and residual magnitude of the fitted residual are analyzed to obtain battery abnormal change monitoring data.

[0079] One possible implementation involves real-time fitting of the impedance curve residual at 150Hz from the start of charging to the current moment during the charging process, i.e., the real-time impedance observation value Z. i Compared with real-time impedance prediction value The difference is calculated using the following formula:

[0080]

[0081] In this embodiment, the residual ε i The residual is defined as the difference between the observed impedance value and the model predicted impedance value. The observed real-time impedance value can be obtained from the battery impedance change curve (equivalent to the first real-time impedance change curve), and the predicted real-time impedance value can be obtained from the battery impedance change prediction curve (equivalent to the second real-time impedance change curve). The magnitude of the residual reflects the accuracy of the model prediction.

[0082] The real-time impedance observation value Z was obtained through calculation. i Compared with real-time impedance prediction value After calculating the residuals, the model parameters are estimated by minimizing the sum of squared residuals. The specific calculation formula is as follows:

[0083]

[0084] In the formula, n is the total number of observed data points. Under normal circumstances, the residuals should be randomly distributed around zero and have a small variance.

[0085] The anomaly detection mechanism based on the residual analysis described above helps to detect the rapid decrease in impedance before overcharging in real time. Furthermore, by minimizing the sum of squared residuals, impedance anomalies can be effectively detected. For a more detailed explanation, see [link to relevant documentation]. Figure 3 , Figure 3 A schematic diagram of the fitting residual change curve of a cell fault early warning method based on dynamic impedance fitting provided in a certain embodiment of the present invention; as shown. Figure 3 As shown, at the start of charging, the impedance is 3.963Ω, changing gradually, with a fitting residual of 0, indicating a small level of change. As time progresses, when charging is nearly halfway complete (1500s), the impedance is 3.687Ω, with a fitting residual of 0.043Ω. When the time reaches 2500s, the battery is about to be overcharged, with an impedance of 3.568Ω and a fitting residual of 0.061Ω. In the following 300s, the fitting residual rapidly increases from 0.062Ω to 0.113Ω. By using the fitting residual of the dynamic impedance index to perform deviation analysis on the real-time recorded impedance data, it is possible to determine in real time whether the battery impedance curve shows a rapid decay trend. This allows for the acquisition of battery abnormal change monitoring data, which can record rapid changes in impedance values ​​or fitting residual values.

[0086] Step 105: Based on the monitoring data of abnormal changes in the battery, issue an early warning when the target battery cell fails.

[0087] As an example of this embodiment, based on the battery abnormal change monitoring data, the absolute value of the impedance residual at each time point is calculated to obtain the absolute value of the impedance residual; if the absolute value of the impedance residual is greater than the fitted residual, it is determined that the target battery cell has failed and an early warning is issued; if the distribution of the impedance residual does not meet the requirements of the fitted residual distribution, it is determined that the target battery cell has failed and an early warning is issued.

[0088] One possible implementation is to identify data points that do not conform to the exponential decay trend by analyzing the distribution and magnitude of the residuals. For example, if the absolute value of a certain residual (equivalent to the absolute value of the impedance residual, to eliminate the influence of negative values) is significantly greater than other residuals, or if the distribution of the residuals shows a systematic deviation (equivalent to not conforming to the requirements of the fitted residual distribution), this may indicate that the impedance has changed abnormally at that point in time. By combining dynamic impedance measurement with residual analysis, an early warning mechanism can be provided, thereby effectively avoiding thermal runaway caused by overcharging. By cutting off charging in time, the operational safety of the battery can be further improved.

[0089] This invention proposes a cell fault early warning method based on dynamic impedance fitting. By using an exponential decay model to predict battery impedance during charging, it provides a data foundation for fitting dynamic changes in battery impedance data. Recording the real-time battery impedance using an excitation current at a preset frequency effectively reduces high-frequency noise interference. Furthermore, the exponential decay model more accurately captures the impedance change trend over time, ensuring the real-time nature and accuracy of the measurement data. Further, dynamic fitting and deviation analysis are used to obtain abnormal impedance change monitoring data, which helps to detect abnormal impedance change trends in real time, monitor the battery health status, and finally provide timely early warnings of abnormal changes, offering early warnings of potential risks and improving the safety of the battery cell.

[0090] Example 2

[0091] See Figure 4 , Figure 4 This is a schematic diagram of the module structure of a cell fault early warning system based on dynamic impedance fitting, provided in a certain embodiment of the present invention; as shown. Figure 4 As shown, this embodiment of the invention proposes a cell fault early warning system based on dynamic impedance fitting, including: an initial impedance testing module 401, an impedance observation module 402, an impedance prediction module 403, an abnormal change analysis module 404, and an early warning module 405.

[0092] The initial impedance test module 401 is used to perform impedance testing on the target battery, obtain the initial impedance value, and charge the target battery.

[0093] The impedance observation module 402 is used to record the impedance of the target battery at various time points using an excitation current of a preset frequency, so as to obtain real-time impedance observation values.

[0094] The impedance prediction module 403 is used to construct an exponential decay model and perform impedance fitting prediction on the real-time impedance observation values ​​at each time point to obtain real-time impedance prediction values.

[0095] As an example of this embodiment, see Figure 5 , Figure 5 This is a schematic diagram of the impedance prediction module structure of a cell fault early warning system based on dynamic impedance fitting, provided in a certain embodiment of the present invention; as shown below. Figure 5 As shown, the impedance prediction module 403 is used to construct an exponential decay model and predict the impedance of the target battery at various time points to obtain real-time impedance prediction values, including:

[0096] Model building unit 501, impedance prediction unit 502 and impedance value acquisition unit 503;

[0097] The model building unit 501 is used to build an exponential decay model based on the initial impedance value and a preset attenuation constant;

[0098] The impedance prediction unit 502 is used to predict the impedance value of the target battery at each time point during the charging process using the exponential decay model, and obtain a second real-time impedance change curve.

[0099] The impedance value acquisition unit 503 is used to acquire the real-time impedance prediction value based on the second real-time impedance change curve.

[0100] The abnormal change analysis module 404 is used to perform residual fitting and deviation analysis on the real-time impedance observation value and the real-time impedance prediction value to obtain battery abnormal change monitoring data.

[0101] As an example of this embodiment, see Figure 6 , Figure 6 This is a schematic diagram of the abnormal change analysis module structure of a cell fault early warning system based on dynamic impedance fitting, provided in a certain embodiment of the present invention; as shown below. Figure 6 As shown, the anomaly change analysis module 404 is used to perform residual fitting and deviation analysis on the real-time impedance observation values ​​and the real-time impedance prediction values ​​to obtain battery anomaly change monitoring data, including:

[0102] Residual calculation unit 601, residual fitting unit 602 and anomaly analysis unit 603;

[0103] The residual calculation unit 601 is used to obtain the impedance residual by subtracting the real-time impedance observation value and the real-time impedance prediction value;

[0104] The residual fitting unit 602 is used to fit the impedance residual by minimizing the sum of squared residuals to obtain the fitted residual;

[0105] The anomaly analysis unit 603 is used to analyze the residual distribution and residual size of the fitted residuals to obtain battery anomaly change monitoring data.

[0106] The early warning module 405 is used to issue an early warning when a target battery cell malfunctions based on monitoring data of abnormal changes in the battery.

[0107] As an example of this embodiment, see Figure 7 , Figure 7 This is a schematic diagram of the early warning module structure of a cell fault early warning system based on dynamic impedance fitting, provided in a certain embodiment of the present invention. Figure 7 As shown, the early warning module 405 is used to issue an early warning when a target battery cell malfunctions based on abnormal battery change monitoring data, including:

[0108] Residual absolute value calculation unit 701, first early warning unit 702, and second early warning unit 703;

[0109] The residual absolute value calculation unit 701 is used to calculate the absolute value of the impedance residual at each time point based on the battery abnormal change monitoring data, and obtain the absolute value of the impedance residual.

[0110] The first early warning unit 702 is used to determine that the target battery cell has failed and issue an early warning if the absolute value of the impedance residual is greater than the fitted residual;

[0111] The second early warning unit 703 is used to determine that the target battery cell has failed and issue an early warning if the distribution of the impedance residual does not meet the requirements of the fitted residual distribution.

[0112] This invention proposes a cell fault early warning system based on dynamic impedance fitting. During charging, an initial impedance testing module and an impedance prediction module use an exponential decay model to predict battery impedance, providing a data foundation for dynamic fitting of battery impedance data. An impedance observation module records the real-time battery impedance using an excitation current at a preset frequency, effectively reducing high-frequency noise interference. Furthermore, the exponential decay model used in the impedance prediction module more accurately captures the impedance change trend over time, ensuring the real-time nature and accuracy of the measurement data. An abnormal change analysis module further performs dynamic fitting and deviation analysis to obtain abnormal impedance change monitoring data, helping to detect abnormal impedance trends in real time and monitor battery health. Finally, an early warning module provides timely warnings of abnormal changes, offering early warnings of potential risks and improving the safety of the battery cell.

[0113] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0114] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the described specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0115] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

Claims

1. A cell fault early warning method based on dynamic impedance fitting, characterized in that, include: An impedance test is performed on the target battery to obtain the initial impedance value, and the target battery is then charged. The impedance of the target battery is recorded at various time points by using an excitation current of a preset frequency to obtain real-time impedance observation values. An exponential decay model is constructed and impedance fitting prediction is performed on the real-time impedance observations at various time points to obtain real-time impedance prediction values. This includes: constructing an exponential decay model based on the initial impedance value and a preset decay constant; during the charging process, fitting the real-time impedance observations at various time points using the exponential decay model to obtain a second real-time impedance change curve; and obtaining real-time impedance prediction values ​​based on the second real-time impedance change curve. The residual fitting and deviation analysis are performed on the real-time impedance observation value and the real-time impedance prediction value to obtain battery abnormal change monitoring data, including: obtaining impedance residual by subtracting the real-time impedance observation value and the real-time impedance prediction value; fitting the impedance residual by minimizing the sum of squared residuals to obtain fitting residual; and analyzing the residual distribution and residual magnitude of the fitting residual to obtain battery abnormal change monitoring data. Based on monitoring data of abnormal changes in the battery, an early warning is issued when a target battery cell malfunctions.

2. The cell fault early warning method based on dynamic impedance fitting as described in claim 1, characterized in that, The process of performing impedance testing on the target battery to obtain the initial impedance value and then charging the target battery includes: The battery under test is fully discharged to obtain the target battery; The impedance value between the positive and negative electrodes of the target battery is tested to obtain the impedance value at the initial moment; The target battery is charged using a constant current based on a preset constant current.

3. The cell fault early warning method based on dynamic impedance fitting as described in claim 1, characterized in that, The process of recording the impedance of the target battery at various time points using an excitation current of a preset frequency to obtain real-time impedance observations includes: During the charging process, based on a preset time interval, the target battery is subjected to impedance recording using an excitation current of the preset frequency to obtain a first real-time impedance change curve. Based on the first real-time impedance change curve, the real-time impedance observation value is obtained.

4. The cell fault early warning method based on dynamic impedance fitting as described in claim 1, characterized in that, The method of issuing an early warning when a target battery cell malfunctions, based on monitoring data of abnormal battery changes, includes: Based on the battery abnormal change monitoring data, the absolute value of the impedance residual at each time point is calculated to obtain the absolute value of the impedance residual. If the absolute value of the impedance residual is greater than the fitted residual, it is determined that the target battery cell has failed and an early warning is issued. If the distribution of the impedance residual does not meet the requirements of the fitted residual distribution, the target battery cell is determined to be faulty and an early warning is issued.

5. A cell fault early warning system based on dynamic impedance fitting, characterized in that, include: The system includes an initial impedance test module, an impedance observation module, an impedance prediction module, an anomaly analysis module, and an early warning module. The initial impedance test module is used to perform impedance testing on the target battery, obtain the initial impedance value, and charge the target battery. The impedance observation module is used to record the impedance of the target battery at various time points using an excitation current of a preset frequency, so as to obtain real-time impedance observation values. The impedance prediction module is used to construct an exponential decay model and perform impedance fitting prediction on the real-time impedance observations at various time points to obtain real-time impedance prediction values. It includes a model construction unit, an impedance prediction unit, and an impedance value acquisition unit. The model construction unit is used to construct an exponential decay model based on the initial impedance value and a preset decay constant. The impedance prediction unit is used to fit the real-time impedance observations at various time points using the exponential decay model during the charging process to obtain a second real-time impedance change curve. The impedance value acquisition unit is used to acquire the real-time impedance prediction value based on the second real-time impedance change curve. The abnormal change analysis module is used to perform residual fitting and deviation analysis on the real-time impedance observation value and the real-time impedance prediction value to obtain battery abnormal change monitoring data. It includes: a residual calculation unit, a residual fitting unit, and an abnormality analysis unit. The residual calculation unit is used to obtain the impedance residual by subtracting the real-time impedance observation value and the real-time impedance prediction value. The residual fitting unit is used to fit the impedance residual by minimizing the sum of squared residuals to obtain the fitted residual. The abnormality analysis unit is used to analyze the residual distribution and residual magnitude of the fitted residual to obtain battery abnormal change monitoring data. The early warning module is used to issue an early warning when a target battery cell malfunctions, based on monitoring data of abnormal changes in the battery.

6. A cell fault early warning system based on dynamic impedance fitting as described in claim 5, characterized in that, The early warning module is used to issue an early warning when a target battery cell malfunctions based on abnormal battery change monitoring data, including: Residual absolute value calculation unit, first early warning unit, and second early warning unit; The residual absolute value calculation unit is used to calculate the absolute value of the impedance residual at each time point based on the battery abnormal change monitoring data, and obtain the absolute value of the impedance residual. The first early warning unit is used to determine that the target battery cell has failed and issue an early warning if the absolute value of the impedance residual is greater than the fitted residual. The second early warning unit is used to determine that the target battery cell has failed and issue an early warning if the distribution of the impedance residual does not meet the requirements of the fitted residual distribution.

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