Method for diagnosing and distinguishing micro short circuit and early thermal runaway of power battery based on Simulink simulation

Abstract

The invention relates to a power battery micro short circuit and early thermal runaway diagnosis distinguishing method based on Simulink simulation, and belongs to the field of power battery fault detection. The method comprises the following steps: simulating battery behaviors based on Simulink to obtain a simulation voltage curve, and extracting fault diagnosis characteristics in discharge voltage signal characteristics from current discharge data of a plurality of groups of cyclic discharge data of the simulation voltage curve; carrying out DBSCAN-based clustering analysis on the extracted fault diagnosis features, and determining a fault battery; for a fault battery, fault classification features are extracted according to historical charging data in a simulation voltage curve of the fault battery so as to distinguish micro short circuit and thermal runaway of the fault battery. According to the method, the fault feature combination is established to carefully capture the tiny change in the battery discharging process, and a feature capable of accurately distinguishing the micro short circuit and the thermal runaway in the early stage of the fault is provided, so that the 100% diagnosis accuracy of the micro short circuit and the thermal runaway of the battery is realized, and the safety early warning of the thermal runaway fault in advance for 12 hours is realized.

Description

Technical Field

[0001] The present invention belongs to the field of power battery fault detection and relates to a method for distinguishing between power battery micro-short circuit and early thermal runaway diagnosis based on Simulink simulation. Background Art

[0002] In the battery management system (BMS), early warning of battery thermal runaway has always been a key issue in ensuring the safety of electric vehicles and energy storage systems. During the use of power batteries, especially under high temperature, overcharging, external impact and other conditions, serious faults such as thermal runaway may occur, causing the internal temperature to rise sharply and cause combustion or even explosion, threatening personal safety and the integrity of the equipment. Therefore, early and accurate identification of battery faults, differentiation between thermal runaway faults and micro-short circuit faults, and taking effective countermeasures in the early stage of faults are crucial to improving the safety and reliability of battery systems.

[0003] Existing thermal runaway early warning methods usually rely on a single or a small number of features, such as the battery's surface temperature, voltage change rate, gas release, etc., but these methods often fail to fully capture the complex failure mechanisms inside the battery, which can easily lead to misjudgments and missed judgments, especially in complex environments and large load fluctuations. In addition, these methods are difficult to distinguish between micro-short circuit faults and thermal runaway faults, resulting in a lack of effective targeted response measures for the system. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method for distinguishing between power battery micro-short circuit and early thermal runaway diagnosis based on Simulink simulation.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation, the method comprising the following steps:

[0007] S1. Simulate the battery behavior based on Simulink to obtain a simulated voltage curve, which includes several groups of cycle discharge data and several groups of cycle charge data;

[0008] S2, extracting fault diagnosis features in discharge voltage signal features from current discharge data of several groups of cyclic discharge data of the simulated voltage curve;

[0009] S3, performing DBSCAN-based cluster analysis on the extracted fault diagnosis features to determine the faulty battery;

[0010] S4. For the faulty battery, extract fault classification features based on the historical charging data in its simulated voltage curve;

[0011] S5. Distinguish between micro-short circuit and thermal runaway of the faulty battery based on the fault classification characteristics.

[0012] Further, in step S1, a Simulink simulation experiment is used to simulate the battery failure behavior under different working conditions to obtain the simulated voltage curves of several groups of batteries, which includes the following steps:

[0013] S11. Establish a second-order equivalent circuit model of a single lithium-ion battery;

[0014] S12, establishing a thermal model of a single lithium-ion battery;

[0015] S13, setting the resistance value of the faulty battery short-circuit resistor to control the battery fault type;

[0016] S14, connect several batteries in series to form a battery pack, and adjust the short-circuit resistance R S The resistance value of the battery pack is controlled to simulate the fault condition, and then several groups of thermal runaway battery data, micro-short circuit battery data and normal battery data are obtained.

[0017] Further, in step S11, the second-order equivalent circuit model includes an open circuit voltage OCV, an ohmic internal resistance R0, polarization resistors R1 and R2, polarization capacitors C1 and C2, and a short circuit resistor R S ,in,

[0018] Output current source at battery cell Current Source The voltage is sequentially connected through the parallel combination of polarization resistor R2 and polarization capacitor C2, the parallel combination of polarization resistor R1 and polarization capacitor C1, the ohmic internal resistance R0 and the open circuit voltage OCV, and the short circuit resistance R S Connected in parallel to both ends of a series combination of a parallel combination of a polarization resistor R2 and a polarization capacitor C2, a parallel combination of a polarization resistor R1 and a polarization capacitor C1, an ohmic internal resistance R0, and an open circuit voltage OCV;

[0019] The pulse voltage curve of the second-order equivalent circuit model of the single lithium-ion battery is obtained through the C / 2 rate GITT discharge test in the long calibration program test, and the parameters are identified through the pulse voltage curve;

[0020] In the pulse voltage curve, the voltage first drops due to battery discharge, then due to the polarization effect, the voltage will rise and finally level off. Select several pulse points A, B, C, D, and E in the curve, where point A is the starting point, point C is the lowest point of the drop, point E is the end point, point B is the point between points A and C, and point D is the point between points C and E.

[0021] The open circuit voltage OCV reflects the terminal voltage when the battery reaches a balanced state after being stationary for a period of time, which is expressed as:

[0022] OCV=V E

[0023] Among them, V E is the voltage at pulse segment point E;

[0024] The ohmic internal resistance R0 mainly reflects the transient response, which is identified by extracting the voltage of the AB segment and the CD segment of the pulse voltage, where:

[0025]

[0026] Among them, V A ,V B ,V C ,V D are the voltages at pulse segment points A, B, C, and D, respectively, and I is the test current of GITT;

[0027] Polarization resistors R1 and R2, polarization capacitors C1 and C2 are polarization parameters that affect the voltage response of the battery at the CE point after a pulse discharge. This voltage response is regarded as a zero input voltage response:

[0028]

[0029] Where V(t) represents the battery voltage, V oc Indicates open circuit voltage;

[0030] A second-order exponential fitting is performed on the voltage of this segment, and the fitting formula is:

[0031]

[0032] The fitting function is used to fit the voltage curve, and the coefficients obtained after fitting are used to represent the open circuit voltage OCV, polarization resistances R1 and R2, and polarization capacitances C1 and C2 parameters of the battery;

[0033] The open circuit voltage OCV, polarization resistors R1 and R2, and polarization capacitors C1 and C2 all take into account the influence of aging factors, and the ohmic internal resistance R0 additionally takes into account the influence of temperature.

[0034] Further, in step S12, the thermal model takes into account the internal resistance heat generation and the environmental heat dissipation, and its expression is:

[0035]

[0036] Where m is the battery mass, C p is the battery thermal capacity, represents the derivative of battery temperature with respect to time, U trepresents the battery voltage, T represents the battery temperature, T ∞ It represents the temperature at which the battery stabilizes after standing still, i.e., room temperature, h is the heat exchange coefficient, and A is the surface area of ​​the battery.

[0037] Further, in step S13, the resistance setting of the faulty battery short-circuit resistor includes maximum value calculation, micro short-circuit resistance analysis, moderate short-circuit resistance analysis, severe short-circuit resistance analysis, and thermal runaway short-circuit resistance analysis, wherein the resistance of the short-circuit resistor is determined according to the following formula based on the analysis results:

[0038] R S =R S0 e -f(t)

[0039] Among them, R S0 is the initial resistance of the fault resistor when the thermal runaway occurs. -f(t) Represents a function that varies with time.

[0040] Further, in step S2, the fault diagnosis features in the discharge voltage signal features are extracted from the cyclic discharge data, including the time integral value, the autocorrelation coefficient and the discrete wavelet transform approximation coefficient energy within the voltage interval, wherein:

[0041] The time integral feature in the voltage range refers to the integral value of the voltage curve in the voltage range of 3V to 4V, which is used to measure the cumulative voltage situation in the voltage range. The extraction process is as follows:

[0042] Extract the voltage data points in the 3V-4V interval from the discharge voltage curve V(t), obtain the reconstructed voltage curve V1(t), and then integrate the time:

[0043]

[0044] Wherein, t represents the sampling point, sampling is performed every 1 second, t1 represents the total number of sampling points of the reconstructed voltage curve, and V1(t) represents the voltage value of the tth sampling point in the reconstructed voltage curve;

[0045] The autocorrelation coefficient feature is used to capture the lag relationship or periodicity of the signal, and its expression is:

[0046]

[0047] in, represents the mean value of the voltage signal, N represents the total number of sampling points of the voltage signal, t represents the t-th sampling point, and sampling is performed every 1 second; V(t), V(t+1) represent the voltages of the t-th and t+1-th sampling points respectively;

[0048] The extracted discrete wavelet transform approximate coefficient energy feature DWT-CA E is represented by:

[0049]

[0050] The extraction process is:

[0051] The input discrete voltage signal V={V1,V2,...,V n}Perform discrete wavelet transform decomposition:

[0052]

[0053] Among them, C A [k] is the kth approximation coefficient, C D [k] kth detail coefficient, φ j,k (t) is the scaling function, ψ j,k (t) is the wavelet function, j is the scale level, and k is the position index of the signal in time;

[0054] Calculate the kth approximation coefficient and detail coefficient:

[0055]

[0056] Among them, the approximation coefficient C A Represents the overall trend or stable part of the signal, detail coefficient C D represents the detail change or noise of the signal, and N represents the total number of sampling points of the voltage signal.

[0057] Further, in step S3, the time-integrated feature VIT is subjected to spatial clustering DBSCAN algorithm using density-based noise. 3-4 , autocorrelation coefficient feature ACF, discrete wavelet transform approximate coefficient energy feature DWT-C A E is classified and the faulty battery detection result is finally obtained:

[0058] First, the mean and variance of the three features of all battery data are calculated and normalized;

[0059] Then, the minimum number of samples and the domain radius were set in the DBSCAN algorithm to perform cluster analysis on the normalized features;

[0060] Through DBSCAN clustering, the characteristics of different batteries are automatically divided into normal battery clusters and outliers, and the outliers are faulty batteries.

[0061] Further, in step S4, the second-order maximum charging voltage difference characteristic The extraction process is:

[0062] Calculate the maximum charging voltage:

[0063]

[0064] Calculate the first-order maximum charging voltage difference:

[0065]

[0066] Calculate the second-order maximum charging voltage difference:

[0067]

[0068] Further, in step S5, according to the second-order maximum charging voltage difference value characteristic The logic for distinguishing the fault types of faulty batteries is:

[0069] The second-order maximum charging voltage difference value of the micro-short-circuited battery is stable;

[0070] The second-order maximum charging voltage differential value of the thermal runaway battery changes with the changing rate of the short-circuit resistance. The faster the short-circuit resistance decreases, the smaller the second-order maximum charging voltage differential value of the thermal runaway battery.

[0071] Therefore, a threshold is set. When the second-order maximum charging voltage difference value is less than the threshold, the battery is judged to be a thermal runaway battery fault, otherwise it is a micro-short circuit battery fault.

[0072] The beneficial effects of the present invention are:

[0073] The present invention constructs a more comprehensive fault feature combination by introducing three important features: 3-4V voltage curve time integral (VTI34), autocorrelation coefficient (ACF) and wavelet transform low frequency energy (DWT-CAE), which can carefully capture the slight changes in the battery discharge process; at the same time, based on the evolution characteristics of micro-short circuit and early thermal runaway faults, a feature is creatively proposed that can accurately distinguish micro-short circuit from thermal runaway in the early stage of fault, effectively improving the sensitivity and accuracy of fault detection, achieving 100% diagnostic accuracy of battery micro-short circuit and thermal runaway faults, and safety warning of thermal runaway faults 12 hours in advance.

[0074] The present invention uses the density-based spatial clustering of noise applications (DBSCAN) algorithm to cluster the features, which effectively diagnoses abnormal batteries and significantly improves the robustness and accuracy of fault identification. The DBSCAN algorithm can process data sets containing noise and automatically determine the number of clusters, making fault detection more reliable.

[0075] Based on the historical charging voltage data, the present invention extracts the second-order maximum charging voltage difference value, and successfully distinguishes between micro short circuit faults and thermal runaway faults by setting the second-order difference threshold (-0.003V). This method utilizes the different performance of the short-circuit resistance change characteristics in the two faults, realizes high-precision judgment of the fault type, and improves the pertinence and practicality of the early warning system.

[0076] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0078] Figure 1 It is a simplified flow chart of the method for distinguishing between micro-short circuit and early thermal runaway diagnosis of a power battery based on Simulink simulation of the present invention;

[0079] Figure 2 It is a schematic diagram of the second-order equivalent circuit model of a single lithium-ion battery;

[0080] Figure 3 It is the pulse voltage curve of the second-order equivalent circuit model;

[0081] Figure 4 Schematic diagram of voltage curve for GITT test;

[0082] Figure 5 Schematic diagram of the experimental temperature curve of 0.5C constant current discharge condition at 25°;

[0083] Figure 6 Schematic diagram of battery temperature change process;

[0084] Figure 7 It is a schematic diagram of the relationship between short-circuit resistance and time;

[0085] Figure 8 A schematic diagram of a battery pack with multiple batteries connected in series;

[0086] Fig. 9 This is the voltage diagram obtained by Simulink simulation;

[0087] Fig.10 This is the temperature diagram obtained by Simulink simulation;

[0088] Fig.11 is the time-integrated feature VIT3-4 Schematic diagram of the characteristic value distribution of ;

[0089] Fig.12 It is a schematic diagram of the eigenvalue distribution of the autocorrelation coefficient feature ACF;

[0090] Fig.13 DWT-C is the discrete wavelet transform approximate coefficient energy A Schematic diagram of the eigenvalue distribution of E;

[0091] Fig.14 is a schematic diagram of fault diagnosis results;

[0092] Fig.15 is the second-order maximum charging voltage difference characteristic Distribution diagram;

[0093] Fig.16 This is a schematic diagram of the results of distinguishing micro-short circuit and thermal runaway faults;

[0094] Fig.17 It is a detailed flow chart of the method for distinguishing between power battery micro-short circuit and early thermal runaway diagnosis based on Simulink simulation of the present invention. DETAILED DESCRIPTION

[0095] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0096] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0097] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0098] See also Figure 1 to Figure 17 , which is a method for distinguishing between micro-short circuit and early thermal runaway diagnosis of power batteries based on Simulink simulation.

[0099] Example

[0100] This embodiment provides a specific implementation process of a method for distinguishing between micro short circuit and early thermal runaway diagnosis of a power battery based on Simulink simulation. Figure 1 As shown, it includes the following steps:

[0101] S1. Simulate the battery behavior based on Simulink to obtain a simulated voltage curve, which includes several groups of cycle discharge data and several groups of cycle charge data;

[0102] S2, extracting fault diagnosis features in discharge voltage signal features from current discharge data of several groups of cyclic discharge data of the simulated voltage curve;

[0103] S3, performing DBSCAN-based cluster analysis on the extracted fault diagnosis features to determine the faulty battery;

[0104] S4. For the faulty battery, extract fault classification features based on the historical charging data in its simulated voltage curve;

[0105] S5. Distinguish between micro-short circuit and thermal runaway of the faulty battery based on the fault classification characteristics.

[0106] In step S1 of this embodiment, a Simulink simulation experiment is used to simulate the battery failure behavior under different working conditions to obtain the simulated voltage curves of several groups of batteries, which specifically includes the following steps:

[0107] S11. Establish a second-order equivalent circuit model of a single lithium-ion battery; Figure 2 As shown in the figure, in this model, the open circuit voltage OCV, the ohmic internal resistance R0, the polarization resistances R1 and R2, the polarization capacitances C1 and C2, and the short circuit resistance R S, where the output current source at the battery cell is Current Source The voltage is sequentially connected through the parallel combination of polarization resistor R2 and polarization capacitor C2, the parallel combination of polarization resistor R1 and polarization capacitor C1, the ohmic internal resistance R0 and the open circuit voltage OCV, and the short circuit resistance R S The two ends are connected in parallel to the parallel combination of the polarization resistor R2 and the polarization capacitor C2, the parallel combination of the polarization resistor R1 and the polarization capacitor C1, the ohmic internal resistance R0 and the series combination of the open circuit voltage OCV.

[0108] In this embodiment, if Figure 3 As shown, the pulse voltage curve of the second-order equivalent circuit model of a single lithium-ion battery is obtained through the C / 2 rate GITT discharge test in the long calibration program test (RPT), and the parameters are identified through the pulse voltage curve. In the pulse voltage curve, the voltage first drops due to battery discharge, and then due to the polarization effect, the voltage will rise and finally tend to be flat. Select several pulse points A, B, C, D, and E in the curve, where point A is the starting point, point C is the lowest point of the decline, point E is the end point, point B is the point between points A and C, and point D is the point between C and E;

[0109] The open circuit voltage OCV reflects the terminal voltage when the battery reaches a balanced state after being stationary for a period of time, which is expressed as:

[0110] OCV=V E

[0111] Among them, V E is the voltage at pulse segment point E;

[0112] The ohmic internal resistance R0 mainly reflects the transient response, which is identified by extracting the voltage of the AB segment and the CD segment of the pulse voltage, where:

[0113]

[0114] Among them, V A ,V B ,V C ,V D are the voltages at pulse segment points A, B, C, and D, respectively, and I is the test current of GITT, 2.5A;

[0115] Polarization resistors R1 and R2, polarization capacitors C1 and C2 are polarization parameters that affect the voltage response of the battery at the CE point after a pulse discharge. This voltage response is regarded as a zero input voltage response:

[0116]

[0117] Where V(t) represents the battery voltage, Voc Indicates open circuit voltage;

[0118] A second-order exponential fitting is performed on the voltage of this segment, and the fitting formula is:

[0119]

[0120] This fitting function is similar to the zero input voltage response formula of the battery, so this function is used to fit the voltage curve. The coefficients obtained after fitting are then used to represent the five parameters of the battery: open circuit voltage OCV, polarization resistances R1 and R2, and polarization capacitances C1 and C2.

[0121] In this embodiment, the open circuit voltage OCV, the polarization resistors R1 and R2, and the polarization capacitors C1 and C2 are all affected by the aging factor, and the ohmic internal resistance R0 is additionally affected by the temperature.

[0122] In this embodiment, the maximum open circuit voltage OCV max =4.2V, the maximum value of the ohmic internal resistance R 0max =0.1Ω. In addition, the battery type is LG M50T, LG GBM50T21700 cylindrical battery, the nominal capacity is 4.86Ah, and the nominal voltage is set to 3.63V.

[0123] Experimental conditions: For batteries aged in the 0-100% SOC range, each aging group contains 78 cycles in the entire SOC range (discharge to 2.5V, charge to 4.2V, CV hold to C / 100), charge rate is 0.3C, discharge rate is 1C. Two RPT procedures are used, one after even and one after odd aging groups. Model parameterization is mainly based on GITT test in long RPT. Figure 4 As shown, the specific process of obtaining the pulse segment voltage curve of the second-order equivalent circuit model of a single lithium-ion battery is as follows:

[0124] A) Perform discharge-charge cycles of C / 10 and C / 2 between 2.5V and 4.2V voltage limits.

[0125] B) GITT_25 test: Discharge at 0.5C, including 25 pulses, each pulse consumes 200mAh of power, and the battery is left standing for 1 hour between pulses. The lower cut-off voltage is 2.5V.

[0126] C) GITT_5 test: Discharge at 0.5C, including 5 pulses, each pulse consumes 1000mAh (or 1A, considering unit consistency), and rests for 1 hour between pulses. The lower cut-off voltage is also 2.5V.

[0127] S12. Establish a thermal model for a single lithium-ion battery. The thermal model mainly considers internal resistance heat generation and environmental heat dissipation, and its expression is:

[0128]

[0129] Where m is the battery mass, C p is the battery thermal capacity, represents the derivative of battery temperature with respect to time, U t represents the battery voltage, T represents the battery temperature, T ∞ It represents the temperature of the battery after it is left to stand, that is, the room temperature, h is the heat exchange coefficient, and A is the surface area of ​​the battery. These parameters are fitted based on the experimental temperature curve of the 0.5C constant current discharge condition. Figure 5 The temperature curve of the 0.5C constant current discharge condition at 25° is shown.

[0130] S13, setting the resistance value of the faulty battery short-circuit resistor to control the battery fault type; specifically, the Chinese national standard GB / T31486-2015 requires that the LIB module maintain 85% of its initial capacity after 28 days of open circuit storage. Therefore, the RISC of a normal LIB battery that meets this standard should satisfy the following relationship:

[0131]

[0132] Therefore, the short-circuit resistance of the battery in this embodiment is set as follows: 700Ω (slight short circuit), 70Ω (moderate short circuit), and 7Ω (severe short circuit). In this embodiment, the short-circuit resistance is calculated as follows:

[0133] R S =R S0 e -f(t)

[0134] Among them, R S0 is the initial resistance of the fault resistor when the thermal runaway occurs. -f(t) represents a function that changes with time. In this embodiment, R S0 is 700Ω.

[0135] By capturing H2 gas, the lithium dendrites detected can reach the micron level, which can warn the battery overcharge (triggering thermal runaway) before the dendrites pierce the SEI film. It takes about 1600 seconds for the temperature to start rising to the critical point of thermal runaway (120°C).

[0136] Therefore, it is assumed that the battery temperature of this embodiment takes 2100 seconds from the beginning of the rise (7Ω, battery temperature: 44°C) to the critical point of thermal runaway (0.7Ω, battery temperature: 120°C). Figure 6 shown.

[0137] The setting parameters of the faulty battery short-circuit resistance are shown in Table 1 below:

[0138] Table 1

[0139]

[0140] In this embodiment, the relationship curve between short-circuit resistance and time is obtained as follows: Figure 7 As shown, R 2 Represents the coefficient of determination, and RMSE represents the root mean square error; and then the thermal runaway short-circuit resistance is set:

[0141]

[0142] Furthermore, in this embodiment, the resistance of the micro short-circuit resistor is set to 600Ω and 500Ω.

[0143] S14, connect several batteries in series to form a battery pack, and adjust the short-circuit resistance R S The resistance value of the battery pack is controlled to simulate the fault condition, and then several groups of thermal runaway battery data, micro short circuit battery data and normal battery data are obtained;

[0144] In this embodiment, if Figure 8 As shown in the figure, the battery pack is composed of 4 single cells connected in series, and 15 groups of data are simulated, including 2 groups of thermal runaway batteries, 2 groups of micro-short-circuit batteries, and 11 groups of normal batteries. In addition, the individual batteries in each group are set to be inconsistent: that is, the SOC is inconsistent. The inconsistency settings are shown in Table 2:

[0145] Table 2

[0146]

[0147] The specific process includes:

[0148] ① A battery pack consisting of 4 single cells connected in series

[0149] ②Working condition setting: 0.8C charging-UDDS discharging, ambient temperature 25℃;

[0150] ③Charge and discharge strategy: When the maximum voltage of the four batteries is 4.2V and the minimum voltage is 2.5V, switch between charge and discharge;

[0151] ④Battery inconsistency setting: The initial SOC of the four batteries is different, and the inconsistency is about 4%;

[0152] ⑤ Fault injection: Connect a resistor in parallel to the target battery and inject a fault at 36,000 seconds.

[0153] Thermal runaway failure (short-circuit resistance changes quickly over time): A 700Ω short-circuit resistor is connected in parallel (a micro-short circuit occurs), and it decreases to a 0.7Ω short circuit over time (the smaller the resistance, the greater the reduction ratio), causing the temperature to rise rapidly, leading to thermal runaway.

[0154] Micro short circuit fault (short circuit resistance changes slowly over time, or remains unchanged): connect a 600Ω / 500Ω short circuit resistor in parallel.

[0155] The simulated voltage curve and temperature diagram of thermal runaway resistor group 1 are shown as follows: Fig. 9 and Fig.10 shown. Fig. 9 The voltage curve changes of the four single cells in the thermal runaway battery pack 1 are shown. The straight rising part is the charging voltage curve of each cycle, the fluctuating and falling part is the UDDS condition discharge voltage curve of each cycle, and the red curve is the voltage curve change of the thermal runaway fault battery. Fig.10 The temperature changes of the four single cells in the thermal runaway battery pack 1 are shown. The red curve is the temperature change of the thermal runaway battery. The temperature rises sharply in the later stage of thermal runaway.

[0156] Since the initial battery voltage is less than 4.2V at the beginning of the simulation, which is quite different from the charge and discharge strategy, that is, the voltage curve is significantly different from the subsequent cycles. In order to eliminate interference, this embodiment uses the discharge data starting around 22,000 seconds as the first cycle discharge voltage curve data, and the subsequent charging curve is the first cycle charging voltage curve data, and extracts the charge and discharge data of 6 cycles backward to conduct early warning research on battery thermal runaway.

[0157] In step S2 of this embodiment, the fault diagnosis features in the discharge voltage signal features extracted from the cyclic discharge data include the time integral feature of the voltage interval, the autocorrelation coefficient, and the discrete wavelet transform approximate coefficient energy feature, wherein the time integral feature of the voltage interval refers to the integral value of the voltage curve in the voltage interval of 3V to 4V, which is used to measure the cumulative voltage situation in the voltage interval, and the extraction process is:

[0158] Extract the voltage data points in the 3V-4V interval from the discharge voltage curve V(t), obtain the reconstructed voltage curve V1(t), and then integrate the time:

[0159]

[0160] Where t represents the sampling point, sampling is performed every 1 second, t1 represents the total number of sampling points of the reconstructed voltage curve, and V1(t) represents the voltage value of the tth sampling point in the reconstructed voltage curve. In the cyclic discharge data curve, the time integral characteristic VIT 3-4 The eigenvalue distribution of Fig.11 shown.

[0161] The autocorrelation coefficient feature is used to capture the lag relationship or periodicity of the signal, and its expression is:

[0162]

[0163] in, represents the mean value of the voltage signal, N represents the total number of sampling points of the voltage signal, t represents the tth sampling point, and sampling is performed every 1 second; V(t), V(t+1) represent the voltages of the tth and t+1th sampling points respectively. In the cyclic discharge data curve, the characteristic value distribution of the autocorrelation coefficient feature ACF is as follows: Fig.12 shown.

[0164] Discrete Wavelet Transform Approximate Coefficient Energy DWT-C A The E feature is extracted as follows:

[0165] Haar wavelet decomposition is performed by discrete wavelet transform (DWT) and decomposed into low-frequency parts (approximate coefficients C A : represents the overall trend or stable part of the signal) and the high-frequency part (detail coefficient C D : represents the detailed changes or noise of the signal) to capture the trend and rapid changes of the signal.

[0166] DWT decomposition is achieved by convolving and downsampling the signal. In this embodiment, the input signal is a discrete voltage signal V = {V1, V2, ..., V n},but:

[0167]

[0168] Among them, C A [k] is the kth approximation coefficient, C D [k] kth detail coefficient, φ j,k (t) is the scaling function, ψ j,k (t) is the wavelet function, j is the scale level, and k is the temporal position index of the signal.

[0169] Further, calculate the kth approximation coefficient and detail coefficient:

[0170]

[0171] Among them, the scaling function and wavelet function are expressed as:

[0172]

[0173] The final discrete wavelet transform approximate coefficient energy DWT-C A The calculation formula of E is:

[0174]

[0175] In the cyclic discharge data curve, the discrete wavelet transform approximation coefficient energy DWT-C AThe eigenvalue distribution of E is as follows Fig.13 shown.

[0176] In step S3 of this embodiment, based on the discharge voltage curve of the entire battery in the current cycle, three features are extracted: 3-4V voltage curve versus time integral feature VIT 3-4 , autocorrelation coefficient feature ACF, discrete wavelet transform approximate coefficient energy DWT-C A E. Use the density-based spatial clustering of noise applications (DBSCAN) algorithm to detect faulty batteries. The process of detecting faulty batteries based on the DBSCAN algorithm is as follows:

[0177] First, the mean and variance of the three features of all battery data were calculated and normalized to eliminate the influence of feature dimension. Then, the minimum sample number was set to 5 and the domain radius was set to 0.5 in the DBSCAN algorithm, and cluster analysis was performed on the normalized features.

[0178] Through DBSCAN clustering, the features of different batteries can be automatically divided into normal battery clusters and outliers. Since the features of normal batteries are less different, their feature points are usually concentrated in areas with higher density. DBSCAN can easily identify these points as core points or boundary points to form normal battery clusters. The features of faulty batteries are obviously different from those of normal batteries. Their feature points are usually distributed in areas with lower density and cannot meet the clustering conditions. Therefore, they are identified as outliers by the DBSCAN algorithm. Based on this, outliers are defined as faulty batteries, thereby achieving automatic diagnosis and differentiation of faulty batteries. The final faulty battery detection results are shown below. Fig.14 As shown in the figure, the blue dots represent the normal battery features and the red dots represent the faulty battery features. As can be seen from the figure, the features of the normal battery are clustered together to form a dense cluster, while the features of the faulty battery are identified as outliers and successfully distinguished with an accuracy of up to 100%. This shows the effectiveness and accuracy of the DBSCAN algorithm in faulty battery detection and the superiority and rationality of feature combination selection.

[0179] In step S4 of this embodiment, the second-order maximum charging voltage differential value feature is extracted for the historical charging data of the faulty battery. Its physical meaning is to reflect the change in the maximum voltage change rate during the battery charging process. The short-circuit resistance is a key factor affecting the battery charging rate. The smaller the short-circuit resistance, the slower the battery charging rate and the smaller the maximum voltage during the charging process. Second-order maximum charging voltage differential value feature The extraction process is:

[0180] Calculate the maximum charging voltage:

[0181]

[0182] Calculate the first-order maximum charging voltage difference:

[0183]

[0184] Calculate the second-order maximum charging voltage difference:

[0185]

[0186] In the above process, because the fault is diagnosed based on the current cycle, the historical charging data can only be extracted for 6 cycles. The 6 maximum charging voltages, the latter minus the previous one, V2-V1; V3-V2...V6-V5, can only generate 5 values. Similarly, the second-order difference generates 4 values.

[0187] Extracted second-order maximum charging voltage difference feature Δ 2 V Cmax(n) like Fig.15 As shown in the figure, the purple line segment represents two micro-short-circuited batteries, and the red line segment represents the change in the second-order maximum charging voltage difference value of the two thermal runaway batteries. It can be seen from the figure that the second-order maximum voltage difference value of the micro-short-circuited battery changes almost slowly and is close to 0, while the second-order maximum voltage difference value of the thermal runaway battery does not change in the first few cycles, and drops significantly in the sixth cycle.

[0188] In step S5 of this embodiment, according to the second-order maximum charging voltage difference characteristic The logic for distinguishing the fault types of faulty batteries is as follows:

[0189] For a micro-short-circuit battery, its short-circuit resistance changes slowly over time, causing the charging rate of the micro-short-circuit battery to be slower than that of a normal battery, but the rate of change of the charging rate is relatively small. Therefore, the second-order maximum charging voltage difference value of the micro-short-circuit battery is relatively stable.

[0190] In contrast, the short-circuit resistance of the thermal runaway battery changes faster over time, which makes the charging rate of the thermal runaway battery change faster and the charging rate gradually slows down. Therefore, the second-order maximum charging voltage differential value of the thermal runaway battery will change with the change rate of the short-circuit resistance. The faster the short-circuit resistance decreases, the smaller the second-order maximum charging voltage differential value of the thermal runaway battery.

[0191] Based on the above characteristics, this embodiment selects the second-order maximum charging voltage difference value as the feature to distinguish between micro-short circuit fault and thermal runaway fault, and sets a threshold value, i.e. -0.003V. When the second-order maximum charging voltage difference value is less than this threshold value, the battery is considered to be a thermal runaway battery, and the final fault distinction result is as follows: Fig.16As shown, the diagnosed faulty batteries were further differentiated and accurately divided into thermal runaway batteries and micro-short-circuit batteries. The fault differentiation accuracy was 100%, and the sixth cycle charge and discharge data was 12 hours ahead of the occurrence of thermal runaway, achieving a 12-hour advance thermal runaway warning.

[0192] The present invention proposes an early warning method for thermal runaway of power batteries based on Simulink simulation experiments. The method combines a more comprehensive combination of fault characteristics and designs a two-step diagnostic process, such as Fig.17 The detailed flow chart of the method for distinguishing between micro short circuit and early thermal runaway of a power battery based on Simulink simulation of the present invention is shown as follows:

[0193] Faulty battery detection: Based on the discharge voltage curve in the current cycle (Cycle6 of the feature graph), three important features are extracted: 3-4V voltage curve time integral (VTI34), autocorrelation coefficient (ACF), and wavelet transform low frequency energy (DWT-CAE). These features can fully reflect the subtle changes in the battery discharge process, and are particularly suitable for capturing early voltage anomalies caused by internal micro-short circuits or thermal runaway. The density-based spatial clustering of noise applications (DBSCAN) algorithm is used to cluster these features, achieving abnormal battery detection with an accuracy rate of up to 100%.

[0194] Fault type distinction: After the faulty battery is detected, the second-order maximum charging voltage differential value is extracted based on its historical charging voltage data, and the fault type is determined by the set second-order differential threshold (-0.003V). Studies have shown that the short-circuit resistance of micro-short-circuit batteries changes little, while the short-circuit resistance of thermal runaway batteries increases significantly during the fault process. This difference leads to a significant difference in the second-order maximum charging voltage differential value between the two - the differential value of the thermal runaway battery will decrease significantly as the short-circuit resistance decreases, while the differential value of the micro-short-circuit battery is smaller and close to 0. Therefore, if the differential value is less than -0.003V, it can be judged as a thermal runaway fault; conversely, if the differential value is greater than -0.003V and is in the micro-short circuit range, it is judged as a micro-short circuit fault.

[0195] The innovation of this method is that it proposes a more comprehensive feature combination (VTI 34 , ACF, DWT-C A E), and improves the robustness and accuracy of fault identification through the DBSCAN algorithm.

[0196] At the same time, based on the short-circuit resistance change characteristics, this method realizes the distinction between micro-short-circuit faults and thermal runaway faults through second-order differential threshold analysis, making the early warning system more accurate and targeted.

[0197] In this embodiment, the discharge voltage data of the 6th cycle, i.e., the data between 140,000 seconds and 165,000 seconds (thermal runaway occurred at 209,100 seconds), and the charging voltage history data of cycles 1 to 6 were used to successfully detect all fault signals (with 100% accuracy), and the micro-short circuit fault and thermal runaway fault were diagnosed and distinguished, achieving a safety warning of thermal runaway fault 12 hours in advance.

[0198] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation, characterized in that: The method comprises the following steps: S1. Simulate the battery behavior based on Simulink to obtain a simulated voltage curve, which includes several groups of cycle discharge data and several groups of cycle charge data; S2, extracting fault diagnosis features in discharge voltage signal features from current discharge data of several groups of cyclic discharge data of the simulated voltage curve; S3, performing DBSCAN-based cluster analysis on the extracted fault diagnosis features to determine the faulty battery; S4. For the faulty battery, extract fault classification features based on the historical charging data in its simulated voltage curve; S5. Distinguish between micro-short circuit and thermal runaway of the faulty battery based on the fault classification characteristics.

2. According to claim 1, a method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation, characterized in that: In step S1, a Simulink simulation experiment is used to simulate the battery failure behavior under different working conditions to obtain the simulated voltage curves of several groups of batteries, which includes the following steps: S11. Establish a second-order equivalent circuit model of a single lithium-ion battery; S12, establishing a thermal model of a single lithium-ion battery; S13, setting the resistance value of the faulty battery short-circuit resistor to control the battery fault type; S14, connect several batteries in series to form a battery pack, and adjust the short-circuit resistance R S The resistance value of the battery pack is controlled to simulate the fault condition, and then several groups of thermal runaway battery data, micro-short circuit battery data and normal battery data are obtained.

3. The method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation according to claim 2 is characterized in that: In step S11, the second-order equivalent circuit model includes an open circuit voltage OCV, an ohmic internal resistance R0, polarization resistors R1 and R2, polarization capacitors C1 and C2, and a short circuit resistor R S ,in, Output current source at battery cell Current Source The voltage is sequentially connected through the parallel combination of polarization resistor R2 and polarization capacitor C2, the parallel combination of polarization resistor R1 and polarization capacitor C1, the ohmic internal resistance R0 and the open circuit voltage OCV, and the short circuit resistance R S Connected in parallel to both ends of a series combination of a parallel combination of a polarization resistor R2 and a polarization capacitor C2, a parallel combination of a polarization resistor R1 and a polarization capacitor C1, an ohmic internal resistance R0, and an open circuit voltage OCV; The pulse voltage curve of the second-order equivalent circuit model of the single lithium-ion battery is obtained through the C / 2 rate GITT discharge test in the long calibration program test, and the parameters are identified through the pulse voltage curve; In the pulse voltage curve, the voltage first drops due to battery discharge, then due to the polarization effect, the voltage will rise and finally level off. Select several pulse points A, B, C, D, and E in the curve, where point A is the starting point, point C is the lowest point of the drop, point E is the end point, point B is the point between points A and C, and point D is the point between points C and E. The open circuit voltage OCV reflects the terminal voltage when the battery reaches a balanced state after standing for a period of time, which is expressed as: OCV=V E Among them, V E is the voltage at pulse segment point E; The ohmic internal resistance R0 mainly reflects the transient response, which is identified by extracting the voltage of the AB segment and the CD segment of the pulse voltage, where: Among them, V A ,V B ,V C ,V D are the voltages at pulse segment points A, B, C, and D, respectively, and I is the test current of GITT; Polarization resistors R1 and R2, polarization capacitors C1 and C2 are polarization parameters that affect the voltage response of the battery at the CE point after a pulse discharge. This voltage response is regarded as a zero input voltage response: Where V(t) represents the battery voltage, V oc Indicates open circuit voltage; A second-order exponential fitting is performed on the voltage of this segment, and the fitting formula is: The fitting function is used to fit the voltage curve, and the coefficients obtained after fitting are used to represent the open circuit voltage OCV, polarization resistances R1 and R2, and polarization capacitances C1 and C2 parameters of the battery; The open circuit voltage OCV, polarization resistors R1 and R2, and polarization capacitors C1 and C2 all take into account the influence of aging factors, and the ohmic internal resistance R0 additionally takes into account the influence of temperature.

4. The method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation according to claim 3 is characterized in that: In step S12, the thermal model takes into account the internal resistance heat generation and the environmental heat dissipation, and its expression is: Where m is the battery mass, C p is the battery thermal capacity, represents the derivative of battery temperature with respect to time, U t represents the battery voltage, T represents the battery temperature, T ∞ It represents the temperature at which the battery stabilizes after standing still, i.e., room temperature, h is the heat exchange coefficient, and A is the surface area of ​​the battery.

5. The method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation according to claim 4 is characterized in that: In step S13, the resistance setting of the faulty battery short-circuit resistor includes maximum value calculation, micro short-circuit resistance analysis, moderate short-circuit resistance analysis, severe short-circuit resistance analysis, and thermal runaway short-circuit resistance analysis, wherein the resistance of the short-circuit resistor is determined according to the following formula based on the analysis results: R S =R S0 e -f(t) Among them, R S0 is the initial resistance of the fault resistor when the thermal runaway occurs. -f(t) Represents a function that varies with time.

6. The method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation according to claim 2, characterized in that: In step S2, the fault diagnosis features in the discharge voltage signal features are extracted from the cyclic discharge data, including the time integral value, autocorrelation coefficient and discrete wavelet transform approximation coefficient energy in the voltage interval, wherein: The time integral feature in the voltage range refers to the integral value of the voltage curve in the voltage range of 3V to 4V, which is used to measure the cumulative voltage situation in the voltage range. The extraction process is as follows: Extract the voltage data points in the 3V-4V interval from the discharge voltage curve V(t), obtain the reconstructed voltage curve V1(t), and then integrate the time: Wherein, t represents the sampling point, sampling is performed every 1 second, t1 represents the total number of sampling points of the reconstructed voltage curve, and V1(t) represents the voltage value of the tth sampling point in the reconstructed voltage curve; The autocorrelation coefficient feature is used to capture the lag relationship or periodicity of the signal, and its expression is: in, represents the mean value of the voltage signal, N represents the total number of sampling points of the voltage signal, t represents the t-th sampling point, and sampling is performed every 1 second; V(t), V(t+1) represent the voltages of the t-th and t+1-th sampling points respectively; The extracted discrete wavelet transform approximate coefficient energy feature DWT-C A E is represented by: The extraction process is: The input discrete voltage signal V={V1,V2,...,V n }Perform discrete wavelet transform decomposition: Among them, C A [k] is the kth approximation coefficient, C D [k] kth detail coefficient, φ j,k (t) is the scaling function, ψ j,k (t) is the wavelet function, j is the scale level, and k is the position index of the signal in time; Calculate the kth approximation coefficient and detail coefficient: Among them, the approximation coefficient C A Represents the overall trend or stable part of the signal, detail coefficient C D represents the detail change or noise of the signal, and N represents the total number of sampling points of the voltage signal.

7. The method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation according to claim 6, characterized in that: In step 3, the spatial clustering DBSCAN algorithm is applied to the time-integrated feature VIT using density-based noise. 3-4 , autocorrelation coefficient feature ACF, discrete wavelet transform approximate coefficient energy feature DWT-C A E is classified and the faulty battery detection result is finally obtained: First, the mean and variance of the three features of all battery data are calculated and normalized; Then, the minimum number of samples and the domain radius were set in the DBSCAN algorithm to perform cluster analysis on the normalized features; Through DBSCAN clustering, the characteristics of different batteries are automatically divided into normal battery clusters and outliers, and the outliers are faulty batteries.

8. The method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation according to claim 7 is characterized in that: In step 4, the second-order maximum charging voltage difference characteristic The extraction process is: Calculate the maximum charging voltage: Calculate the first-order maximum charging voltage difference: Calculate the second-order maximum charging voltage difference:

9. The method for distinguishing between micro-short circuit and early thermal runaway of a power battery based on Simulink simulation according to claim 8 is characterized in that: In step 5, according to the second-order maximum charging voltage difference characteristic The logic for distinguishing the fault types of faulty batteries is: The second-order maximum charging voltage difference value of the micro-short-circuited battery is stable; The second-order maximum charging voltage differential value of the thermal runaway battery changes with the changing rate of the short-circuit resistance. The faster the short-circuit resistance decreases, the smaller the second-order maximum charging voltage differential value of the thermal runaway battery. Therefore, a threshold is set. When the second-order maximum charging voltage difference value is less than the threshold, the battery is judged to be a thermal runaway battery fault, otherwise it is a micro-short circuit battery fault.

Images