Parallel battery pack short circuit fault diagnosis method and device based on capacity increment curve

Abstract

The application discloses a parallel battery pack short-circuit fault diagnosis method and device based on a capacity increment curve, which comprises the following steps: constructing a first-order RC equivalent circuit model; acquiring an open-circuit voltage, an ohmic internal resistance and polarization parameters; constructing a single battery simulation model; acquiring optimal time domain screening of the polarization parameters; constructing a parallel battery pack short-circuit fault simulation model; selecting characteristic curves for short-circuit fault analysis; acquiring characteristic peaks and charging rates for short-circuit fault analysis; and performing fault diagnosis and fusion fault diagnosis based on the characteristic peaks. A rough judgment of a short-circuit resistance value is realized through n Ⅰ peaks of IC curves in the middle stage of a charging process, and a more accurate quantitative diagnosis of the short-circuit resistance value is realized through n Ⅱ peaks of IC curves in the later stage of the charging process, so that rapid and high-precision fault diagnosis is realized. The fault diagnosis is performed through different characteristic peaks of IC curves, and a complete charging voltage curve is not needed; meanwhile, the fault diagnosis of n Ⅰ peaks and n Ⅱ peaks is more flexible and adaptable in the charging and discharging state uncertainty under real vehicle working conditions.

Description

Technical Field

[0001] This invention relates to a method and apparatus for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves, and belongs to the field of power battery technology. Background Technology

[0002] To meet the range and maximum power requirements of electric vehicles, battery packs are typically composed of hundreds or even thousands of individual cells connected in series and parallel. A failure in even one cell within the pack will affect the normal operation of the entire pack. Therefore, rapid and efficient battery fault diagnosis methods are crucial for ensuring vehicle safety and reliability.

[0003] Current research on battery short-circuit fault diagnosis focuses on individual cells and series-connected battery packs. Existing technologies include internal short-circuit fault tests and fault identification based on the battery's electrical and thermal characteristics. However, considering the high risk, time-consuming nature, and non-reproducibility of battery fault tests, current research increasingly favors model-based methods for short-circuit fault diagnosis. In individual cell fault analysis, researchers use equivalent circuit models and estimated short-circuit currents and model terminal voltages to extract fault characteristics, thus achieving short-circuit fault diagnosis.

[0004] In research on fault diagnosis of parallel battery packs, considering their unique structure, current research often treats the entire parallel battery pack as a whole for fault diagnosis. Some scholars have trained artificial neural network—backpropagation neural network—models using experimental datasets, employing voltage information to estimate the leakage current of short-circuit faulty batteries. To achieve fault diagnosis of individual cells within a parallel battery pack, some scholars have explored the characteristics of parallel batteries under different conditions. Studies have found that parallel battery packs with individual cell aging during charging exhibit higher charging voltages than normal parallel battery packs.

[0005] In summary, current research on fault diagnosis of parallel battery packs often treats them as a whole for fault diagnosis due to the difficulty in measuring the current in parallel branches. Some scholars also focus on studying the impact of battery differences on the characteristics of parallel batteries under specific conditions. However, research on the characteristics and short-circuit fault diagnosis of parallel battery packs focuses more on specific parallel battery packs, lacking comparative analysis of short-circuit faults in parallel battery packs under different operating conditions, and failing to explore the impact of the number of parallel individual cells and the charging and discharging current of the parallel battery pack on fault diagnosis. In other words, it has not truly achieved rapid and accurate diagnosis of short-circuit faults in battery packs with different numbers of parallel batteries under different operating conditions. In addition, existing fault diagnosis methods are often based on a certain characteristic quantity, and the charging and discharging state under actual vehicle operating conditions is uncertain, reducing the adaptability of fault diagnosis methods. Summary of the Invention

[0006] Purpose of the invention: To address the shortcomings of existing technologies, this invention provides a method and apparatus for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves. By constructing a simulation model of a single battery cell, a short-circuit fault simulation model of the parallel battery pack is built based on this model. The degree of short-circuit fault is quantitatively diagnosed by the characteristic peaks of the IC curve, and the characteristic peaks are fused for fault diagnosis, thereby achieving rapid and accurate diagnosis of short-circuit faults in battery packs with different numbers of parallel batteries under different operating conditions.

[0007] Technical solution: A short-circuit fault diagnosis method for parallel battery packs based on capacity increment curves, comprising the following steps:

[0008] S1: Construct a first-order RC equivalent circuit model;

[0009] S2: Perform a hybrid pulse test on the battery to obtain the model parameters of the first-order RC equivalent circuit model constructed in S1, including open-circuit voltage, ohmic internal resistance, and polarization parameters;

[0010] S3: Construct a simulation model of a single battery cell;

[0011] S4: Optimal time domain selection for polarization parameter acquisition: Polarization parameters include polarization internal resistance and polarization capacitance. By inputting the polarization parameters obtained in different time domains into the single cell simulation model, the difference between the simulated voltage and the measured voltage in different time domains is compared. The time domain with the smallest difference is selected as the optimal time domain, and the corresponding polarization parameters are the optimal polarization parameters.

[0012] S5: Construct a simulation model for short-circuit fault of parallel battery pack: Based on the simulation model of a single battery cell obtained in S3 and the model parameters obtained in S2 and S4, construct a simulation model for short-circuit fault of parallel battery pack and verify it.

[0013] S6: Select the characteristic curve for short-circuit fault analysis: Based on the short-circuit fault simulation model of the parallel battery pack built in S5, perform short-circuit fault characteristic analysis on the charging voltage curve and IC curve respectively, compare the analysis results of the two, and select the IC curve for short-circuit fault characteristic analysis.

[0014] S7: Based on the IC curves selected in S6, perform IC curve analysis on batteries with different short-circuit resistance values ​​under different charging rates and IC curve analysis on battery packs with different numbers of parallel batteries under different short-circuit resistance values, and obtain the influence of charging rate and number of parallel batteries on the characteristic peak value of IC curve of parallel battery packs under short-circuit fault.

[0015] S8: Based on the influence of the charging rate and the number of parallel batteries obtained in S7 on the characteristic peak value of the IC curve of the parallel battery pack under short circuit fault, the characteristic peak and charging rate for short circuit fault analysis are obtained by statistical analysis of the actual vehicle operating conditions, including the actual charging rate and the actual charging voltage range.

[0016] S9: Perform fault diagnosis and fusion fault diagnosis on the characteristic peaks obtained in S8, and finally obtain the short circuit fault diagnosis method for parallel battery packs.

[0017] This invention obtains model parameters corresponding to the constructed first-order RC equivalent circuit model through hybrid power pulse testing, including open-circuit voltage, ohmic internal resistance, and polarization parameters. Based on these parameters, a single-cell battery simulation model is constructed. The polarization parameters are then screened to obtain the optimal polarization parameters that maximize the accuracy of the single-cell battery simulation model. Specifically, polarization parameters obtained in different time domains are input into the simulation model, and the difference between the simulated voltage and the measured voltage is compared. The time domain with the smallest difference is the optimal time domain, and the corresponding polarization parameters are the optimal polarization parameters. Based on the single-cell battery simulation model, a short-circuit fault simulation model for a parallel battery pack is constructed. IC curve analysis is performed on batteries with different short-circuit resistance values ​​at different charging rates and on battery packs with different numbers of parallel batteries at different short-circuit resistance values, based on the parallel battery pack short-circuit fault fault simulation model. Furthermore, by statistically analyzing actual operating conditions, including charging rate and charging voltage ranges, characteristic peaks and charging rates are obtained for short-circuit fault analysis. Finally, fault diagnosis and fusion fault diagnosis are performed based on the characteristic peaks of the IC curve, so as to realize the rapid and accurate diagnosis of short circuit faults in battery packs with different numbers of parallel batteries under different operating conditions, and improve the adaptability of fault diagnosis methods.

[0018] In the preferred embodiment, the specific steps of S1 are as follows:

[0019] Based on Kirchhoff's laws, the state-space equations of the first-order RC equivalent circuit model are as follows:

[0020]

[0021] In the formula, R0 represents the internal resistance in ohms, R1 is the polarization resistance, C1 represents the polarization capacitance, I is the load current, and U represents the battery terminal voltage. OCV U1 represents the open-circuit voltage, and U1 represents the polarization voltage, which is the voltage across R1C1.

[0022] In the preferred embodiment, the specific steps of S2 are as follows:

[0023] The hybrid pulse test specifically includes: subjecting batteries at different states of charge (SOC) to charge and discharge pulse excitation and resting treatment, thereby obtaining battery characteristic parameters at different SOCs;

[0024] Obtaining open-circuit voltage: During the constant current discharge process of the battery, the battery SOC will decrease. After that, the battery is left to stand for a long time so that the open-circuit voltage can reach a stable state, thereby obtaining the corresponding data of different SOC and open-circuit voltage.

[0025] Obtaining the ohmic internal resistance: The ohmic internal resistance is obtained based on the current abrupt change phase. This current abrupt change phase occurs during a hybrid power pulse test when the battery is suddenly subjected to a discharge pulse, causing the voltage to rapidly change from U... A Descending to U B The formula for calculating the ohmic internal resistance R0 at this time is:

[0026]

[0027] In the formula, R0 is the ohmic impedance, U A Let U be the voltage at point A. B The voltage at point B;

[0028] Obtaining polarization parameters: In hybrid pulse testing, there are zero-state response and zero-input response stages that can be used to obtain polarization parameters. Due to the polarization characteristics of the battery, the terminal voltage changes from U... B Slowly descending to U C This is the zero-state response stage of the RC circuit;

[0029] By performing time-domain analysis on the circuit, the functional relationship between the terminal voltage U and time t during this stage can be obtained as follows:

[0030] U = U OCV -IR0-IR1[1-exp(-t / t)] (3)

[0031] In the formula, t is the time constant (t = R1C1).

[0032] In a preferred embodiment, the specific steps of S4 are as follows:

[0033] Different time domains in the discharge stage of hybrid pulse tests at different temperatures were selected for the selection of time domains for obtaining polarization parameters. Simulation model results were analyzed under different operating conditions and compared with actual results. The time domain with the smallest difference in polarization parameter extraction was selected as the optimal time domain. The corresponding polarization parameters at this time are the optimal polarization parameters, thereby constructing a more accurate simulation model of the equivalent circuit of a single battery cell.

[0034] In the preferred embodiment, the specific steps of S5 are as follows:

[0035] Construction of a short-circuit fault simulation model for a parallel battery pack: Based on the established single-cell battery simulation model, two single-cell battery models are connected in parallel according to the physical connection method of the circuit to build a parallel battery pack simulation model; then, a short-circuit resistor is directly added to the parallel battery pack simulation model to realize the short-circuit fault setting. At the same time, a current sensor is added at the connection of the short-circuit resistor to directly obtain the leakage current of the short-circuit fault, thus completing the construction of the short-circuit fault model for the parallel battery pack.

[0036] Simulation model verification of short circuit fault in parallel battery pack: Simulation and experimental results of the parallel battery pack terminal voltage under different short circuit resistance values ​​were compared at the same temperature and under the same discharge and charging conditions. The difference between the two results was used to determine whether the accuracy of the model meets the requirements of battery pack short circuit fault diagnosis. The smaller the difference, the higher the accuracy of the simulation model. The simulation model with the highest accuracy was selected for battery pack short circuit fault diagnosis.

[0037] In the preferred embodiment, the specific steps of S6 are as follows:

[0038] Charging voltage curve analysis: As the charging process continues, the battery voltage gradually increases. Compared to the normal charging time t1 for a single cell, a battery with a short-circuit fault requires a longer time t2 to reach the charging cutoff voltage. Δt 12 The time difference between charging a normal single cell and a short-circuit faulty cell to the cutoff voltage can be used to calculate the total leakage current during the entire charging process. However, in actual applications, due to the differences between single cells, it is difficult for a normal single cell to be fully charged. Therefore, it is difficult to compare the time when directly using the charging voltage curve for fault diagnosis.

[0039] IC curve analysis: The IC curve, i.e. the rate of change of charge-voltage (dQ / dV-V) curve, is obtained by taking the first derivative of the charge-voltage (QV) curve during the battery charging process. A fixed voltage range ΔV is used to replace dV, and ΔQ is used to replace dQ. ΔQ is the charge change under the corresponding ΔV range. When ΔV = 1mV, it is represented as ΔQ / dV = dQ / dV.

[0040] The IC curve has n distinct characteristic peaks, which are divided into the following groups based on their order of appearance during charging: (n Ⅰ peak, n Ⅱ peak, n Ⅲ peak, n Ⅳ peak, n i (peak...), select the characteristic peak n with the highest peak value. i Peak; comparison shows that normal single cells and short-circuit faulty single cells have different peak values ​​in n. Ⅰ peak and n Ⅱ There are significant differences in the peak values.

[0041] In the preferred embodiment, the specific steps of S7 are as follows:

[0042] Analysis of IC curves for different short-circuit resistance values ​​under different charging rates: Considering that the charging mode of the battery pack is divided into slow charging and pulse charging during actual charging, and the charging current is different, the IC curve characteristics of a single cell connected in parallel under different short-circuit resistance values ​​under different charging rates are analyzed based on the short-circuit fault simulation model of parallel battery pack. Finally, the low-rate charging condition is used for the study of battery short-circuit fault.

[0043] IC curve analysis of battery packs with different numbers of parallel batteries and different short-circuit resistance values: Considering that the battery pack of a real vehicle is composed of multiple individual batteries connected in parallel, the influence of the number of parallel individual batteries on the IC curve characteristics is analyzed. Based on the simulation model of short-circuit fault of parallel battery pack, the IC curve of a normal battery pack with m individual batteries connected in parallel and the IC curve of a battery pack with different degrees of short-circuit fault are analyzed under constant current charging conditions.

[0044] In the preferred embodiment, the specific steps of S9 are as follows:

[0045] Based on n Ⅰ Peak fault diagnosis: Simulations were performed on a battery pack with m individual cells connected in parallel, with short-circuit resistances set at intervals. During the simulation, the charging current was the charging rate obtained from the actual charging test at S8. The IC curve was calculated based on the simulated charging data. The IC curves under different short-circuit resistance values ​​were analyzed. Ⅰ Peak-to-peak values ​​are statistically analyzed, and a function relating characteristic peak values ​​to short-circuit resistance is constructed. At least two sets of short-circuit fault conditions with varying short-circuit resistances are used to verify the constructed function. The IC curves n obtained under these short-circuit fault conditions are then compared. Ⅰ The peak value is substituted into the fault diagnosis function constructed above to solve for the short-circuit resistance value, and the solved short-circuit resistance value is compared with the set short-circuit resistance value.

[0046] Based on n Ⅱ Peak fault diagnosis: Simulations were performed on a battery pack with m individual cells connected in parallel, with short-circuit resistances set at intervals. During the simulation, the charging current was the charging rate obtained from the actual charging test at S8. The IC curve was calculated based on the simulated charging data. The IC curves under different short-circuit resistance values ​​were analyzed. Ⅱ Peak-to-peak values ​​are statistically analyzed, and a function relating characteristic peak values ​​to short-circuit resistance is constructed. At least two sets of short-circuit fault conditions with varying short-circuit resistances are used to verify the constructed function. The IC curves n obtained under these short-circuit fault conditions are then compared. Ⅱ The peak value is substituted into the fault diagnosis function constructed above to solve for the short-circuit resistance value, and the solved short-circuit resistance value is compared with the set short-circuit resistance value.

[0047] Based on n Ⅰ peak and n Ⅱ Peak fusion fault diagnosis: During actual charging, n Ⅰ The charging current will be larger in the peak voltage range, leading to a larger diagnostic error. Considering this compared to n... Ⅱ Peak, n Ⅰ The earlier the peak appears, the easier it is to detect faults earlier; therefore, considering n... Ⅰ peak and n Ⅱ Peak-based short-circuit fault diagnosis is performed, and the mid-stage of the charging process is assessed using the n-value of the IC curve. ⅠThe peak enables the initial judgment of the short-circuit resistance value, and the later stage of the charging process uses the n-axis of the IC curve. Ⅱ Peak enables accurate quantitative diagnosis of short-circuit resistance.

[0048] An apparatus for implementing a short-circuit fault diagnosis method for parallel battery packs based on capacity increment curves, comprising an offline calibration module and an online estimation module;

[0049] The offline calibration module is used to construct the functional relationship between the characteristic peak value of the IC curve and the short-circuit resistance value in offline conditions, mainly including n Ⅰ Offline calibration function for peak-to-peak value and short-circuit resistance and n Ⅱ Offline calibration function for peak-to-peak value and short-circuit resistance;

[0050] The online estimation module is used to calculate the capacity change rate (dQ / dV-V) based on the actual measured voltage and current values ​​of the parallel battery, and to determine in real time whether the capacity has reached n during the parallel battery charging process. Ⅰ peak or n Ⅱ If any peak value is reached, it is substituted into the corresponding offline function to calculate the short-circuit resistance of the actual parallel battery pack, thereby achieving fault diagnosis.

[0051] Preferably, the offline calibration module is used to acquire the open-circuit voltage, ohmic internal resistance, and polarization parameters of the parallel batteries in actual applications, build a short-circuit fault simulation model of the parallel battery pack, simulate the IC curves under different short-circuit faults with different numbers of parallel batteries, and then use the n values ​​on the IC curves respectively. Ⅰ Peak and n Ⅱ Peak-to-peak value establishes a functional relationship between the characteristic peak value and the short-circuit resistance under a specific number of parallel batteries in actual conditions, thereby realizing n Ⅰ Peak-to-peak value and short-circuit resistance function and n Ⅱ Offline calibration of peak and short-circuit resistance functions;

[0052] The online estimation module is used to obtain the actual measured current and voltage of the parallel battery, calculate its capacity change rate (dQ / dV-V), and determine in real time whether it has reached n during the parallel battery charging process. Ⅰ Peak and n Ⅱ The diagnostic range of the peak, if it reaches n Ⅰ The peak voltage range is then substituted with n. Ⅰ Offline calibration function for peak-to-peak value and short-circuit resistance, based on n Ⅰ Calculate the corresponding short-circuit resistance value based on peak-to-peak value; if n is reached... Ⅱ The peak voltage range is then substituted with n. Ⅱ With the offline calibration function for short-circuit resistance, thus based on n Ⅱ The peak-to-peak value yields the short-circuit resistance.

[0053] Beneficial effects: This invention proposes a fault diagnosis method for parallel batteries that integrates characteristic peaks, and the mid-term of the charging process is achieved through n Ⅰ The peak value allows for a rough assessment of the short-circuit resistance. Later in the charging process, the n-value of the IC curve during low-rate charging is used. Ⅱ Peak analysis enables more accurate quantitative diagnosis of short-circuit resistance, thus achieving rapid and high-precision fault diagnosis. Fault diagnosis through different characteristic peaks of the IC curve does not require a complete charging voltage curve, making it more advantageous for diagnosing battery short-circuit faults; it also incorporates n... Ⅰ peak and n Ⅱ Peak's fault diagnosis is more flexible and adaptable when the charging and discharging status is uncertain under real vehicle operating conditions. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0055] Figure 1 This is a flowchart of the method of the present invention;

[0056] Figure 2 The diagram shows the equivalent circuit model of a first-order RC circuit.

[0057] Figure 3 For hybrid power pulse test cycle step voltage and current changes;

[0058] Figure 4 The simulation results show the model parameters extracted in different time domains at 5℃, and the experimental voltage is compared with the model parameters. (a) and (b) show the simulation results of the model parameters at 0.5C and 1C at 20s, and the experimental voltage is compared with the model parameters. (c) and (d) show the simulation results of the model parameters at 0.5C and 1C at 40s, and the experimental voltage is compared with the model parameters. (e) and (f) show the simulation results of the model parameters at 0.5C and 1C at 60s, and the experimental voltage is compared with the model parameters.

[0059] Figure 5 The simulation results show the model parameters extracted in different time domains at 25℃, and the experimental voltage is compared with the model parameters. (a) and (b) show the simulation results of the model parameters at 0.5C and 1C at 20s, and the experimental voltage is compared with the model parameters. (c) and (d) show the simulation results of the model parameters at 0.5C and 1C at 40s, and the experimental voltage is compared with the model parameters. (e) and (f) show the simulation results of the model parameters at 0.5C and 1C at 60s, and the experimental voltage is compared with the model parameters.

[0060] Figure 6 A simulation model for short-circuit faults in parallel battery packs;

[0061] Figure 7 The simulation and experimental terminal voltages of parallel battery packs under different short-circuit resistance values ​​under 1C discharge conditions are compared; (a) 50Ω; (b) 10Ω; (c) 5Ω; (d) 2.5Ω.

[0062] Figure 8 Comparison of simulated and experimental terminal voltages of parallel battery packs under different short-circuit resistance values ​​under 1C charging conditions; (a) 50Ω; (b) 10Ω; (c) 5Ω; (d) 2.5Ω;

[0063] Figure 9 The charging voltage curves of a single cell under different short-circuit resistance values;

[0064] Figure 10 The capacity increment curves of a single cell under different short-circuit resistance values;

[0065] Figure 11 A comparison of IC curves for batteries with different short-circuit resistance values ​​at different charging rates; (a) 1 / 2C; (b) 1 / 5C; (c) 1 / 8C; (d) 1 / 20C;

[0066] Figure 12 Comparison of IC curves for battery packs with different numbers of parallel cells and different short-circuit resistance values: (a) 1 single cell in parallel; (b) 2 single cells in parallel; (c) 4 single cells in parallel; (d) 8 single cells in parallel.

[0067] Figure 13 Statistics on the frequency of average charging rate;

[0068] Figure 14 Statistics on the frequency of (a) starting SOC and (b) ending SOC;

[0069] Figure 15 The relationship between peak-to-peak value and short-circuit resistance of IC curve III for different parallel battery packs: (a) a battery pack with 1 single cell connected in parallel; (b) a battery pack with 2 single cells connected in parallel; (c) a battery pack with 4 single cells connected in parallel; (d) a battery pack with 8 single cells connected in parallel.

[0070] Figure 16 The error is estimated based on the short-circuit resistance of different parallel battery packs using peak III; where 2p represents a battery pack with 2 individual cells in parallel, 4p represents a battery pack with 4 individual cells in parallel, and 8p represents a battery pack with 8 individual cells in parallel.

[0071] Figure 17The relationship between peak-to-peak value and short-circuit resistance of IC curves IV for different parallel battery packs: (a) a battery pack with 1 single cell connected in parallel; (b) a battery pack with 2 single cells connected in parallel; (c) a battery pack with 4 single cells connected in parallel; (d) a battery pack with 8 single cells connected in parallel.

[0072] Figure 18 The error is estimated based on the short-circuit resistance of different parallel battery packs using the IV peak; where 2p represents a battery pack with 2 individual cells in parallel, 4p represents a battery pack with 4 individual cells in parallel, and 8p represents a battery pack with 8 individual cells in parallel.

[0073] Figure 19 This is a schematic diagram of a short-circuit fault diagnosis device for parallel battery packs based on capacity increment curves. Detailed Implementation

[0074] 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.

[0075] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0076] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0077] like Figure 1 As shown, the method for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves includes the following steps:

[0078] S1: Construct a first-order RC equivalent circuit model;

[0079] like Figure 2 As shown, based on Kirchhoff's laws, the state-space equations of the first-order RC equivalent circuit model are as follows:

[0080]

[0081] In the formula, R0 represents the internal resistance in ohms, R1 is the polarization resistance, C1 represents the polarization capacitance, I is the load current, and U represents the battery terminal voltage. OCV U1 represents the open-circuit voltage, and U1 represents the polarization voltage, which is the voltage across R1C1.

[0082] The basic parameters of the battery in this embodiment are shown in Table 1:

[0083] Table 1. Battery performance parameters

[0084] property Specific specifications Battery Category Ternary lithium-ion Rated capacity 3.0Ah nominal voltage 3.6V Voltage range 3.0～4.2V Maximum charging current Continuous 5.0A, pulse 6.0A Maximum discharge current Below 80℃, 30A; Above 80℃, 15A size 18mm in diameter, 65mm in height

[0085] S2: Perform a hybrid pulse test on the battery to obtain the model parameters of the first-order RC equivalent circuit model constructed in S1, including open-circuit voltage, ohmic internal resistance, and polarization parameters;

[0086] The hybrid power pulse test specifically includes: charging the battery at a 1C constant current until the voltage reaches or exceeds 4.2V, then charging at a 4.2V constant voltage until the current is less than or equal to 0.15A. After letting the battery rest for 5 hours, discharging at a 5C constant current for more than 10 seconds, then letting the battery rest for 40 seconds, then charging at a 5C constant current for more than 10 seconds, then letting the battery rest for 40 seconds, then charging at a 0.5C constant current for 6 minutes, then letting the battery rest for 2 hours, then discharging at a 5C constant current for 10 seconds, and finally letting the battery rest. This cycle is repeated 20 times until the voltage reaches or exceeds 3.0V. Figure 3 As shown, the current and voltage change curves of the pulse stage in the cyclic process of the hybrid pulse test are displayed when the SOC is 0.6.

[0087] Obtaining the open-circuit voltage: In each complete pulse phase of the hybrid power pulse test, there is a 6-minute constant current discharge process at 0.5C, during which the SOC decreases by approximately 5%. Afterward, the battery is allowed to rest for 2 hours to allow the battery terminal voltage to reach a stable state, which can be considered as the open-circuit voltage. The corresponding data of SOC and open-circuit voltage are then obtained, as shown in Table 2.

[0088] Table 2. Open-circuit voltage parameter values

[0089]

[0090]

[0091] Obtaining Ohmic internal resistance: such as Figure 3 As shown, the transition from A to B is characterized by a sudden 10-second discharge pulse excitation, causing the voltage to rapidly drop from UA to UB. This phenomenon is caused by the ohmic internal resistance R0. Based on this stage, the ohmic internal resistance R0 can be identified, and its calculation formula is as follows:

[0092]

[0093] In the formula, R0 is the instantaneous impedance, UA is the voltage at point A, and UB is the voltage at point B. The ohmic internal resistance parameters are shown in Table 3.

[0094] Table 3. Ohmic internal resistance parameters

[0095] SOC R0(Ω) SOC R0(Ω) 0 0.029 55 0.021 5 0.026 60 0.021 10 0.023 65 0.022 15 0.022 70 0.022 20 0.021 75 0.023 25 0.021 80 0.024 30 0.021 85 0.026 35 0.021 90 0.028 40 0.021 95 0.032 45 0.021 100 0.036 50 0.021 - -

[0096] Obtaining polarization parameters: The 10s discharge pulse excitation and the 40s resting stage correspond to the zero-state response stage and the zero-input response stage, respectively. Both can be used for polarization parameter identification. Here, we take the 10s discharge pulse excitation as an example. Due to the polarization characteristics of the battery, the terminal voltage slowly decreases from UB to UC. At this time, it is the zero-state response stage of the RC circuit.

[0097] By performing time-domain analysis on the circuit, the functional relationship between the terminal voltage U and time t during this stage can be obtained as follows:

[0098] U = U OCV -IR0-IR1[1-exp(-t / t)] (3)

[0099] In the formula, t is the time constant (t = R1C1).

[0100] S3: Construct a simulation model of a single battery cell;

[0101] S4: Optimal time domain selection for polarization parameter acquisition: Polarization parameters include polarization internal resistance and polarization capacitance. By inputting the polarization parameters obtained in different time domains into the single cell simulation model, the difference between the simulated voltage and the measured voltage in different time domains is compared. The time domain with the smallest difference is selected as the optimal time domain, and the corresponding polarization parameters are the optimal polarization parameters.

[0102] The polarization parameters of the model were identified in the first 20s, 40s, and 60s of the 6-minute discharge phase from the hybrid pulse test data at 5℃ and 25℃. Then, the simulation results were analyzed under different operating conditions to study the influence of the polarization parameter identification time domain on the simulation accuracy.

[0103] The simulation and experimental terminal voltage comparison results of the model polarization parameters extracted through different time domains at 5℃ and 25℃ are as follows: Figure 4 and Figure 5As shown in the figure, the comparison results show that extracting the corresponding model parameters using the 40s time domain yields higher accuracy. The polarization parameters extracted using the 40s time domain are shown in Tables 4 and 5:

[0104] Table 4. Polarization internal resistance parameters

[0105] SOC R1(Ω) SOC R1(Ω) 0 0.066 55 0.030 5 0.060 60 0.031 10 0.052 65 0.031 15 0.044 70 0.03 20 0.038 75 0.028 25 0.033 80 0.025 30 0.029 85 0.023 35 0.027 90 0.022 40 0.027 95 0.024 45 0.028 100 0.031 50 0.029 - -

[0106] Table 5. Polarization Capacitance Parameters

[0107]

[0108]

[0109] S5: Construct a simulation model for short-circuit fault of parallel battery pack: Based on the simulation model of a single battery cell obtained in S3 and the model parameters obtained in S2 and S4, construct a simulation model for short-circuit fault of parallel battery pack and verify it.

[0110] Construction of a Simulation Model for Short-Circuit Fault in a Parallel Battery Pack: This embodiment selects Simscape in MATLAB / Simulink to build a simulation model of the equivalent circuit of a single battery cell. Two single-cell battery models are connected in parallel according to the physical connection method of the circuit to construct a parallel battery pack simulation model. Then, a short-circuit resistor is directly added to the parallel battery pack simulation model to realize the short-circuit fault setting. Simultaneously, a current sensor is added at the connection point of the short-circuit resistor to directly obtain the leakage current of the short-circuit fault. The principle is as follows... Figure 6 As shown in the figure, the impedance of the connecting pieces in the actual connection structure is simulated using the resistor module in the Simscape library, namely connecting pieces 1 and 2 in the figure. In addition, current sensors are directly added to the branches of the two parallel batteries to monitor the branch current, corresponding to branch current 1 and branch current 2 respectively. Simultaneously, the SOC of each individual battery and the parallel battery pack is calculated separately, corresponding to SOC1, SOC2, and SOC, respectively. The SOC calculation uses the ampere-hour integration method.

[0111] Simulation model verification of short-circuit fault in parallel battery packs: The simulated terminal voltage and experimental terminal voltage of the parallel battery pack under different short-circuit resistance values ​​were compared at 25℃ under 1C discharge and 1C charge conditions. The results are as follows: Figure 7 and Figure 8 As shown, under 1C discharge conditions, with four short-circuit resistance values, the maximum absolute error is 60mV, corresponding to a maximum relative error of 1.9%. Under 1C charging conditions, with four short-circuit resistance values, the maximum absolute error is approximately 28mV, and the maximum relative error is approximately 0.79%. The charge / discharge results indicate that the accuracy of this model can meet the requirements for subsequent battery pack short-circuit fault diagnosis.

[0112] S6: Select the characteristic curve for short-circuit fault analysis: Based on the short-circuit fault simulation model of the parallel battery pack built in S5, perform short-circuit fault characteristic analysis on the charging voltage curve and IC curve respectively, compare the analysis results of the two, and select the IC curve for short-circuit fault characteristic analysis.

[0113] This embodiment analyzes the charging voltage curve and IC curve of a single cell under normal conditions and 100Ω short-circuit fault under 1 / 8C constant current charging conditions.

[0114] Charging voltage curve analysis: such as Figure 9 As shown, the battery voltage gradually increases as the charging process continues. Compared to the normal charging time t1 for a single cell, a battery with a short-circuit fault requires a longer time t2 to reach the charging cutoff voltage. Δt 12 The time difference between charging a normal single cell and a 100Ω short-circuit faulty cell to the cutoff voltage allows for the calculation of the total leakage current during the entire charging process. However, in practical applications, due to the differences between individual cells, it is difficult for a normal single cell to be fully charged. Therefore, directly using the charging voltage time difference for fault diagnosis presents a challenge in time comparison.

[0115] IC curve analysis: such as Figure 10 The image shows the IC curves of a normal single cell and a 100Ω short-circuit faulty single cell under 1 / 8C charging conditions. The IC curves have four distinct characteristic peaks, which can be divided into four peaks based on their order of appearance during charging: peak I, peak II, peak III, and peak IV. Peaks III and IV have high peak values ​​and their voltage ranges conform to normal charging conditions. Analyzing the IC curves, the entire voltage range can be divided into four peak intervals. Figure 9 As can be seen, peak I has the largest voltage range, while peak II has the smallest voltage range; peaks III and IV have high peak values ​​and their voltage ranges are within the normal charging range. A comparison reveals significant differences in the peak values ​​of peaks III and IV between a normal single-cell battery and a single-cell battery with a 100Ω short-circuit fault.

[0116] S7: Based on the IC curves selected in S6, perform IC curve analysis on batteries with different short-circuit resistance values ​​under different charging rates and IC curve analysis on battery packs with different numbers of parallel batteries under different short-circuit resistance values, and obtain the influence of charging rate and number of parallel batteries on the characteristic peak value of IC curve of parallel battery packs under short-circuit fault.

[0117] Analysis of IC Curves for Different Short-Circuit Resistances at Different Charging Rates: Considering that battery packs undergo various charging modes such as slow charging and pulse charging during actual charging, resulting in different charging currents, the IC curve characteristics of a single battery cell connected in parallel were studied based on a short-circuit fault simulation model of a parallel battery pack at charging rates of 1 / 2C, 1 / 5C, 1 / 8C, and 1 / 20C, under different short-circuit resistance values. Figure 11 As shown, it can be seen that under low-rate charging conditions, the peak-to-peak value difference of the IC curves of normal single cells and single cells with short-circuit faults is more obvious, indicating that low-rate charging conditions are more suitable for the study of battery short-circuit faults.

[0118] IC curve analysis of battery packs with different numbers of parallel cells and different short-circuit resistance values: Considering that the battery pack in a real vehicle is composed of multiple individual cells connected in parallel, the influence of the number of parallel individual cells on the IC curve characteristics is analyzed. Based on the simulation model of short-circuit fault in parallel battery packs, the IC curves of normal battery packs with 1, 2, 4, and 8 individual cells connected in parallel are analyzed under 1 / 20C constant current charging conditions, and the IC curves of batteries with different degrees of short-circuit faults are compared. Figure 12 As shown in the figure, the IC curve results show that as the number of parallel batteries increases, the difference between the peak value of the characteristic peak of a normal battery and the peak value of the characteristic peak of a battery with a short circuit fault becomes smaller and smaller. This means that the more parallel batteries there are, the more difficult it is to diagnose short circuit faults through characteristic peaks.

[0119] S8: Based on the influence of the charging rate and the number of parallel batteries obtained in S7 on the characteristic peak value of the IC curve of the parallel battery pack under short circuit fault, the characteristic peak and charging rate for short circuit fault analysis are obtained by statistical analysis of the actual vehicle operating conditions, including the actual charging rate and the actual charging voltage range.

[0120] like Figure 13 As shown, analysis of actual charging rate test data revealed that the charging rate was below 0.2C in the vast majority of charging processes, with 609 charging cycles occurring in the 0.05C to 0.1C range. Nearly half of the results showed an average charging rate less than 0.1C. Based on these findings, an IC curve with a charging rate of 0.075C was selected as a compromise for subsequent short-circuit fault diagnosis research.

[0121] like Figure 14As shown, statistical analysis of data from actual charging voltage range tests reveals that the initial SOC range for charging is not clearly defined and is widely distributed, with a relatively high proportion of charging terminations above 95%. Charging began at below 40% SOC 773 times, meaning that more than half of the charging process covered the SOC range from 40% to 98%. The corresponding voltage range for this SOC range is approximately 3.74V to 4.15V, within which peaks III and IV of the IC curve can be largely formed. Therefore, for subsequent short-circuit fault diagnosis of the battery, peaks III and IV of the IC curve will be selected for further study.

[0122] S9: Perform fault diagnosis and fusion fault diagnosis on the characteristic peaks obtained in S8, and finally obtain the short circuit fault diagnosis method for parallel battery packs.

[0123] Fault diagnosis based on peak III: Specifically, battery packs with 1, 2, 4, and 8 cells connected in parallel were simulated under charging conditions, with short-circuit resistances ranging from 100Ω to 1000Ω and spaced 100Ω apart. The charging current during the simulation was 0.075C. The IC curve was obtained based on the simulated charging data, and the peak value of peak III of the IC curve under different short-circuit resistance values ​​was statistically analyzed.

[0124] like Figure 15 The figure shows the relationship between the peak-to-peak value of peak III and the short-circuit resistance of the IC curves for different parallel battery packs. From... Figure 14 It can be seen that as the short-circuit resistance increases, the peak value of IC curve III gradually decreases; the rate of change of the peak value also decreases as the short-circuit resistance increases. A functional relationship between the peak value of IC curve III and the short-circuit resistance is established for different parallel battery packs, as follows: Figure 14 The fitted curve is shown in the figure.

[0125] To verify the accuracy of the established function, short-circuit faults with short-circuit resistance values ​​of 950Ω, 750Ω, 550Ω, 350Ω, and 150Ω were investigated. The peak-to-peak value of the IC curves obtained for each of these short-circuit resistance values ​​was substituted into the constructed function relating peak-to-peak value of ...

[0126] Table 6. Diagnostic results of short-circuit resistance values ​​for different parallel battery packs

[0127]

[0128]

[0129] like Figure 16The figure shows the short-circuit resistance error of parallel batteries with different numbers of parallel cells based on the III-peak diagnostic method. From the diagnostic error results, the diagnostic errors for battery packs with 1, 2, and 4 parallel cells are all less than 1% for these five short-circuit resistance values. For a battery pack with 8 parallel cells, the diagnostic error is approximately 1% for a short-circuit resistance of 150Ω. However, as the short-circuit resistance increases, the diagnostic error also increases, reaching 7% for a short-circuit resistance of 950Ω. The reason for this is that as the short-circuit resistance increases, the peak-to-peak difference of the fault characteristics decreases, increasing the impact of the fitting error.

[0130] Fault diagnosis based on peak IV: Battery packs with 1, 2, 4, and 8 cells connected in parallel were simulated under 0.075C charging conditions with short-circuit resistance values ​​ranging from 100Ω to 1000Ω at 100Ω intervals. IC curves were obtained from the simulated charging data, and the peak values ​​of peak III of the IC curves under different short-circuit resistance values ​​were statistically analyzed.

[0131] like Figure 17 The figure shows the relationship between the peak-to-peak value and short-circuit resistance of IC curve IV for different parallel battery packs. It also compares... Figure 15 and Figure 17 It can be seen that, compared with the peak-to-peak value of III, the peak-to-peak value of IV is larger than that of III, and the difference between the peak-to-peak values ​​of IV is relatively larger among different short-circuit resistance values.

[0132] To verify the short-circuit fault diagnosis function constructed using the IV peak, short-circuit fault cases with short-circuit resistance values ​​of 950Ω, 750Ω, 550Ω, 350Ω, and 150Ω were also investigated. The peak value of the IV peak of the IC curve obtained for each of these short-circuit resistance values ​​was substituted into the constructed function relating the peak value of the IV peak to the short-circuit resistance value to calculate the short-circuit resistance. The specific diagnosis results are shown in Table 7.

[0133] Table 7. Diagnostic results of short-circuit resistance values ​​for different parallel battery packs

[0134]

[0135] like Figure 18 The figure shows the short-circuit resistance error of parallel battery packs with different numbers of parallel batteries, based on IV peak diagnosis. (Comparison) Figure 16 and Figure 18 It can be seen that the error of short-circuit diagnosis through peak IV is relatively more stable.

[0136] Fault Diagnosis Based on the Fusion of Peaks III and IV: A comprehensive comparison of the short-circuit fault diagnosis results from peaks III and IV reveals that when the number of parallel batteries is small, both peaks offer high diagnostic accuracy. However, as the number of parallel batteries increases, especially in battery packs with eight individual cells connected in parallel, the diagnostic error using peak III significantly increases. Considering that the voltage range of peak IV on the IC curve is in the later stages of charging, where the trickle charging current is small, peak IV is considered more suitable for fault diagnosis using a low current rate. In actual charging, the charging current in the voltage range of peak III may be larger, leading to a larger diagnostic error. However, considering that peak III appears earlier than peak IV, it is beneficial for earlier fault detection. Therefore, peaks III and IV can be combined for short-circuit fault diagnosis. In the middle stage of charging, peak III is used to roughly determine the short-circuit resistance, while peak IV is used in the later stages of charging for a more precise quantitative diagnosis of the short-circuit resistance.

[0137] like Figure 19 As shown, the apparatus for implementing a short-circuit fault diagnosis method for parallel battery packs based on capacity increment curves includes an offline calibration module and an online estimation module.

[0138] The offline calibration module is used to construct the functional relationship between the characteristic peak value and the short-circuit resistance value of the IC curve in offline mode. It mainly includes the offline calibration function of the peak value of III and the short-circuit resistance value and the offline calibration function of the peak value of IV and the short-circuit resistance value.

[0139] The online estimation module is used to calculate the capacity change rate (dQ / dV-V) based on the actual measured voltage and current values ​​of the parallel battery. During the charging process of the parallel battery, it determines in real time whether it reaches peak III or peak IV. If a certain peak value is reached, it is substituted into the corresponding offline function to calculate the short-circuit resistance value of the actual parallel battery pack, thereby realizing fault diagnosis.

[0140] The offline calibration module is used to identify model parameters of parallel batteries in actual applications, build a short-circuit fault simulation model of its parallel battery pack, simulate IC curves under different short-circuit faults with different numbers of parallel batteries, and then use the peak values ​​of the III peak and IV peak on the IC curve to establish the functional relationship between the characteristic peak value and the short-circuit resistance value under a specific number of parallel batteries, thereby realizing the offline calibration of the function of the peak value of the III peak and the short-circuit resistance value and the function of the peak value of the IV peak and the short-circuit resistance value.

[0141] The online estimation module is used to obtain the current and voltage of a parallel battery based on actual measurements, calculate its capacity change rate (dQ / dV-V), and determine in real time whether it reaches the diagnostic range of peak III and peak IV during the charging process of the parallel battery. If it reaches the voltage range of peak III, it uses the offline calibration function of peak III and short-circuit resistance to calculate the corresponding short-circuit resistance based on the peak value of peak III. If it reaches the voltage range of peak IV, it uses the offline calibration function of peak IV and short-circuit resistance to obtain a more accurate short-circuit resistance based on the peak value of peak IV.

[0142] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0143] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves, characterized in that: Includes the following steps: S1: Construct a first-order RC equivalent circuit model; S2: Perform hybrid pulse test on the battery to obtain the parameters of the first-order RC equivalent circuit model constructed in S1, including open-circuit voltage, ohmic internal resistance, and polarization parameters; S3: Construct a simulation model of a single battery cell; S4: Optimal time domain selection for polarization parameter acquisition: Polarization parameters include polarization internal resistance and polarization capacitance. By inputting the polarization parameters obtained in different time domains into the single cell simulation model, the difference between the simulated voltage and the measured voltage in different time domains is compared. The time domain with the smallest difference is selected as the optimal time domain, and the corresponding polarization parameters are the optimal polarization parameters. S5: Construct a simulation model for short-circuit fault of parallel battery pack: Based on the simulation model of a single battery cell obtained in S3 and the model parameters obtained in S2 and S4, construct a simulation model for short-circuit fault of parallel battery pack and verify it. S6: Select the characteristic curve for short-circuit fault analysis: Based on the short-circuit fault simulation model of the parallel battery pack built in S5, perform short-circuit fault characteristic analysis on the charging voltage curve and IC curve respectively, compare the analysis results of the two, and select the IC curve for short-circuit fault characteristic analysis. The IC curve has n distinct characteristic peaks, which are divided into the following groups based on their order of appearance during charging: (n Ⅰ peak, n Ⅱ peak, n Ⅲ peak, n Ⅳ peak, n i Peaks…), select the characteristic peak n with the highest peak value. i Peak; comparison shows that normal single cells and short-circuit faulty single cells have different peak values ​​at n. Ⅰ peak and n Ⅱ There are significant differences in the peak values; S7: Based on the IC curves selected in S6, perform IC curve analysis on batteries with different short-circuit resistance values ​​under different charging rates and IC curve analysis on battery packs with different numbers of parallel batteries under different short-circuit resistance values, and obtain the influence of charging rate and number of parallel batteries on the characteristic peak value of IC curve of parallel battery packs under short-circuit fault. S8: Based on the influence of the charging rate and the number of parallel batteries obtained from S7 on the characteristic peak value of the IC curve of the parallel battery pack under short-circuit fault, the characteristic peak and charging rate for short-circuit fault analysis are obtained by statistically analyzing the actual operating conditions, including the actual charging rate and the actual charging voltage range. S9: Perform fault diagnosis and fusion fault diagnosis on the characteristic peaks obtained in S8, and finally obtain the short circuit fault diagnosis method for parallel battery packs. The fault diagnosis of the characteristic peaks obtained by S8 includes based on n Ⅰ Peak fault diagnosis and n-based Ⅱ Peak fault diagnosis; The fault diagnosis based on the fusion of feature peaks obtained from S8 is based on n Ⅰ peak and n Ⅱ Peak fusion fault diagnosis: During actual charging, n Ⅰ The charging current will be larger in the peak voltage range, leading to a larger diagnostic error. Considering this compared to n... Ⅱ Peak, n Ⅰ The earlier the peak appears, the easier it is to detect faults earlier; therefore, considering n... Ⅰ peak and n Ⅱ Peak-based short-circuit fault diagnosis is performed, and the mid-stage of the charging process is assessed using the n-value of the IC curve. Ⅰ The peak enables the initial judgment of the short-circuit resistance value, and the later stage of the charging process uses the n-axis of the IC curve. Ⅱ Peak enables accurate quantitative diagnosis of short-circuit resistance.

2. The method for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves according to claim 1, characterized in that: The specific steps of S1 are as follows: Based on Kirchhoff's laws, the state-space equations of the first-order RC equivalent circuit model are as follows: (1) In the formula, R0 represents the internal resistance in ohms, R1 is the polarization resistance, C1 represents the polarization capacitance, I is the load current, and U represents the battery terminal voltage. OCV U1 represents the open-circuit voltage, and U1 represents the polarization voltage, which is the voltage across R1C1.

3. The method for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves according to claim 2, characterized in that: The specific steps of S2 are as follows: The hybrid pulse test specifically includes: subjecting the battery to charge and discharge pulse excitation at different SOCs and then allowing it to rest, thereby obtaining the battery characteristic parameters at different SOCs; Obtaining open-circuit voltage: During the constant current discharge process of the battery, the battery SOC will decrease. After that, the battery is left to stand for a long time so that the open-circuit voltage can reach a stable state, thereby obtaining the corresponding data of different SOC and open-circuit voltage. Obtaining the ohmic internal resistance: The ohmic internal resistance is obtained based on the current abrupt change phase. This current abrupt change phase occurs during a hybrid power pulse test when the battery is suddenly subjected to a discharge pulse, causing the voltage to rapidly change from U... A Descending to U B The formula for calculating the ohmic internal resistance R0 at this time is: (2) In the formula, R0 is the ohmic impedance, U A Let U be the voltage at point A. B The voltage at point B; Obtaining polarization parameters: In hybrid pulse testing, there are zero-state response and zero-input response stages that can be used to obtain polarization parameters. Due to the polarization characteristics of the battery, the terminal voltage changes from U... B Slowly descending to U C This is the zero-state response stage of the RC circuit; By performing time-domain analysis on the circuit, the functional relationship between the terminal voltage U and time t during this stage can be obtained as follows: (3) In the formula, The time constant ( ).

4. The method for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves according to claim 3, characterized in that: The specific steps of S4 are as follows: Different time domains for obtaining polarization parameters were selected during the discharge phase of hybrid pulse tests at different temperatures. Simulation model results were analyzed under different operating conditions and compared with actual results. The time domain with the smallest difference in polarization parameter extraction was selected as the optimal time domain. The corresponding polarization parameters were then the optimal polarization parameters, thus constructing an equivalent circuit simulation model of a single battery cell.

5. The method for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves according to claim 4, characterized in that: The specific steps of S5 are as follows: Construction of a short-circuit fault simulation model for a parallel battery pack: Based on the established single-cell battery simulation model, two single-cell battery models are connected in parallel according to the physical connection method of the circuit to build a parallel battery pack simulation model; then, a short-circuit resistor is directly added to the parallel battery pack simulation model to realize the short-circuit fault setting. At the same time, a current sensor is added at the connection of the short-circuit resistor to directly obtain the leakage current of the short-circuit fault, thus completing the construction of the short-circuit fault model for the parallel battery pack. Simulation model verification of short circuit fault in parallel battery pack: Simulation and experimental results of the parallel battery pack terminal voltage under different short circuit resistance values ​​were compared at the same temperature and under the same discharge and charging conditions. The difference between the two results was used to determine whether the accuracy of the model meets the requirements of battery pack short circuit fault diagnosis. The smaller the difference, the higher the accuracy of the simulation model. The simulation model with the highest accuracy was selected for battery pack short circuit fault diagnosis.

6. The method for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves according to claim 5, characterized in that: The specific steps of S6 are as follows: Charging voltage curve analysis: As the charging process continues, the battery voltage gradually increases. Compared to the normal charging time t1 for a single cell, a battery with a short-circuit fault requires a longer time t2 to reach the charging cutoff voltage. Δt 12 The time difference between charging a normal single cell and a short-circuit faulty cell to the cutoff voltage can be used to calculate the total leakage current during the entire charging process. However, in actual applications, due to the differences between single cells, it is difficult for a normal single cell to be fully charged. Therefore, it is difficult to compare the time when directly using the charging voltage curve for fault diagnosis. IC curve analysis: The IC curve, i.e., the rate of change of charge-voltage (dQ / dV - V) curve, is obtained by taking the first derivative of the charge-voltage (Q - V) curve during battery charging. A fixed voltage range ΔV is used to replace dV, and ΔQ is used to replace dQ, where ΔQ represents the change in charge within the corresponding ΔV range. When ΔV = 1 mV, it is characterized as... .

7. The method for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves according to claim 6, characterized in that: The specific steps of S7 are as follows: Analysis of IC curves for different short-circuit resistance values ​​under different charging rates: Considering that the charging mode of the battery pack is divided into slow charging and pulse charging during actual charging, and the charging current is different, the IC curve characteristics of a single cell in parallel under different short-circuit resistance values ​​under different charging rates are analyzed based on the simulation model of short-circuit fault of parallel battery pack. Finally, the low-rate charging condition is used for the diagnosis of battery short-circuit faults. IC curve analysis of battery packs with different numbers of parallel batteries and different short-circuit resistance values: Considering that the battery pack of a real vehicle is composed of multiple individual batteries connected in parallel, the influence of the number of parallel individual batteries on the IC curve characteristics is analyzed. Based on the simulation model of short-circuit fault of parallel battery pack, the IC curve of a normal battery pack with m individual batteries connected in parallel and the IC curve of a battery pack with different degrees of short-circuit fault are analyzed under constant current charging conditions.

8. The method for diagnosing short-circuit faults in parallel battery packs based on capacity increment curves according to claim 7, characterized in that: The specific steps of S9 are as follows: Based on n Ⅰ Peak fault diagnosis: Simulations were performed on a battery pack with m individual cells connected in parallel, with short-circuit resistances set at intervals. During the simulation, the charging current was the charging rate obtained from the actual charging test at S8. The IC curve was calculated based on the simulated charging data. The IC curves under different short-circuit resistance values ​​were analyzed. Ⅰ Peak-to-peak values ​​are statistically analyzed, and a function relating characteristic peak values ​​to short-circuit resistance is constructed. At least two sets of short-circuit fault conditions with varying short-circuit resistances are used to verify the constructed function. The IC curves n obtained under these short-circuit fault conditions are then compared. Ⅰ The peak value is input into the fault diagnosis function constructed above to solve for the short-circuit resistance value, and the solved short-circuit resistance value is compared with the set short-circuit resistance value. Based on n Ⅱ Peak fault diagnosis: Simulations were performed on a battery pack with m individual cells connected in parallel, with short-circuit resistances set at intervals. During the simulation, the charging current was the charging rate obtained from the actual charging test at S8. The IC curve was calculated based on the simulated charging data. The IC curves under different short-circuit resistance values ​​were analyzed. Ⅱ Peak-to-peak values ​​are statistically analyzed, and a function relating characteristic peak values ​​to short-circuit resistance is constructed. At least two sets of short-circuit fault conditions with varying short-circuit resistances are used to verify the constructed function. The IC curves n obtained under these short-circuit fault conditions are then compared. Ⅱ The peak value is input into the fault diagnosis function constructed above to solve for the short-circuit resistance value, and the solved short-circuit resistance value is compared with the set short-circuit resistance value.

9. An apparatus for implementing the parallel battery pack short-circuit fault diagnosis method based on capacity increment curves as described in any one of claims 1-8, characterized in that: Includes an offline calibration module and an online estimation module; The offline calibration module is used to construct the functional relationship between the characteristic peak value of the IC curve and the short-circuit resistance value in offline conditions, mainly including n Ⅰ Offline calibration function for peak-to-peak value and short-circuit resistance and n Ⅱ Offline calibration function for peak-to-peak value and short-circuit resistance; The online estimation module is used to calculate the capacity change rate (dQ / dV - V) of the parallel battery based on the actual measured voltage and current values, and to determine in real time whether the capacity has reached n during the parallel battery charging process. Ⅰ peak or n Ⅱ If any peak value is reached, it is substituted into the corresponding offline function to calculate the short-circuit resistance of the actual parallel battery pack, thereby achieving fault diagnosis.

10. The apparatus for implementing a short-circuit fault diagnosis method for parallel battery packs based on capacity increment curves according to claim 9, characterized in that: The offline calibration module is used to acquire the open-circuit voltage, ohmic internal resistance, and polarization parameters of the parallel batteries in actual applications, build a short-circuit fault simulation model of the parallel battery pack, and simulate the IC curves under different short-circuit faults with different numbers of parallel batteries. Then, the n values ​​on the IC curves are used to... Ⅰ Peak and n Ⅱ Peak-to-peak value establishes a functional relationship between the characteristic peak value and the short-circuit resistance under actual conditions for single parallel, two parallel, four parallel, and eight parallel battery configurations, thereby realizing n Ⅰ Peak-to-peak value and short-circuit resistance function and n Ⅱ Offline calibration of peak and short-circuit resistance functions; The online estimation module is used to obtain the actual measured current and voltage of the parallel battery, calculate its capacity change rate (dQ / dV - V), and determine in real time whether it has reached n during the parallel battery charging process. Ⅰ Peak and n Ⅱ The diagnostic range of the peak, if it reaches n Ⅰ The peak voltage range is then substituted with n. Ⅰ Offline calibration function for peak-to-peak value and short-circuit resistance, based on n Ⅰ Calculate the corresponding short-circuit resistance value based on peak-to-peak value; if n is reached... Ⅱ The peak voltage range is then substituted with n. Ⅱ With the offline calibration function for short-circuit resistance, thus based on n Ⅱ The peak-to-peak value yields the short-circuit resistance.

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