A method and system for detecting thermal runaway failure of a power battery based on multi-source parameters

Abstract

The application discloses a kind of power battery thermal runaway fault detection method and system based on multi-source parameters, belong to battery management technical field, including: to the positive pole of cell's feed point input high-frequency signal from signal generator generation, and in positive pole connection coaxial line, negative pole is grounded to form ground loop;The dendrite reflection signal collected is analyzed, and quantitative correlation model is established, according to quantitative correlation model analysis dendrite growth and preliminary early warning;Collect the electrochemical parameters of cell and auxiliary parameters, and cross validation with the preliminary early warning of dendrite;Establish dynamic risk and grading response mechanism, divide risk into different grades, set corresponding response measures.In the implementation process of the technical scheme of the application, by combining dendrite growth prediction and electrochemical parameter analysis, the internal state of the power battery is accurately monitored, the timeliness and accuracy of the thermal runaway early warning are effectively improved, and the safe and stable operation of the battery system is ensured.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of battery management, in particular to a power battery thermal runaway fault detection method and system based on multiple source parameters. BACKGROUND

[0002] With the development of new energy vehicles, the safety of power batteries is increasingly valued by vehicle manufacturers and OEMs. Currently, power batteries are usually lithium ion batteries. Lithium batteries are prone to thermal runaway when overcharged, short-circuited or physically damaged, which may cause fire or even explosion.

[0003] In the prior art, various solutions are adopted to address the thermal runaway problem of lithium ion batteries. For example, high-nickel positive electrodes and silicon-carbon negative electrodes are used in combination with low-impedance electrolyte to improve the stability of lithium ion batteries. The use of a tab-free design reduces internal resistance. The BMS (Battery Management System) integrates various parameters of lithium ion batteries to achieve thermal runaway early warning and reduce the risk of thermal runaway of lithium ion batteries in various environments. Currently, BMS has become the mainstream solution for vehicle manufacturers, enabling full life cycle data management and thermal runaway detection services.

[0004] However, in actual applications, BMS technology still has some problems. For example, when lithium ion batteries are overcharged, lithium dendrites will form on the negative electrode. These lithium dendrites can pierce the battery separator, causing internal short circuits. The existing BMS technology can only analyze whether the battery is in a normal state through electrical parameters, but it is difficult to accurately determine whether dendrites have appeared inside the battery, which is a chronic and irreversible damage. This leads to a lag in thermal runaway early warning, making it impossible to capture early hidden dangers in a timely manner, thereby affecting overall safety performance.

[0005] Therefore, it is necessary to provide a power battery thermal runaway fault detection method and system based on multiple source parameters to solve the above problems.

[0006] It should be noted that the above information disclosed in the background section is only used to understand the background of the present application and therefore, it can contain information that does not constitute prior art. SUMMARY

[0007] Based on the above problems in the prior art, the present application aims to provide a power battery thermal runaway fault detection method and system based on multiple source parameters, which combines the growth prediction of dendrites inside the battery cell with the prediction of traditional electrochemical parameters to achieve thermal runaway risk detection of power batteries.

[0008] The technical solution adopted by the present application to solve its technical problems is: a power battery thermal runaway fault detection method based on multiple source parameters, comprising:

[0009] The high-frequency signal generated from the signal generator is input to the positive electrode feed point of the battery cell, and a coaxial line is connected to the positive electrode and the negative electrode is grounded to form a grounding loop. After the high-frequency signal propagates directionally in the electrolyte, the dendrite reflection signal is collected through the coaxial line.

[0010] The collected dendrite reflection signal is analyzed, and a quantitative correlation model between a specific frequency band and dendrite growth is established. The growth of the dendrite is analyzed according to the quantitative correlation model and a preliminary warning is given.

[0011] The electrochemical parameters of the battery cell and the auxiliary parameters are collected and cross-verified with the preliminary warning of the dendrite, wherein the electrochemical parameters include voltage, current, temperature and internal resistance, and the auxiliary parameters include gas content.

[0012] A dynamic risk and grading response mechanism is established. Based on historical data and real-time monitoring results, the risk is divided into different grades, and corresponding response measures are set for different grades.

[0013] In the implementation process of the technical solution of the present application, by combining dendrite growth prediction and electrochemical parameter analysis, accurate monitoring of the internal state of the power battery is realized, the timeliness and accuracy of the thermal runaway warning are effectively improved, and the safe and stable operation of the battery system is ensured.

[0014] Further, the end of the coaxial line on the positive electrode is connected in series with a matching resistor, the resistance value of the matching resistor is ohm, and the matching resistor is used to obtain the best matching of the signal during transmission, thereby improving the collection quality of the signal.

[0015] Further, when performing high-frequency signal sweep, a self-triggering sweep mechanism needs to be established, which has a fixed starting point and a dynamic scanning power, wherein the fixed starting point is the frequency point when the internal impedance of the battery cell reaches the minimum value, and the dynamic scanning power is adjusted according to the actual state of the battery.

[0016] Further, the quantitative correlation model is established by a specific frequency band and dendrite growth, wherein the specific frequency is the center frequency 2.4GHz of the high-frequency signal generated by the signal generator.

[0017] Further, the analysis of the growth of the dendrite according to the quantitative correlation model and the preliminary warning further includes: judging the position of the surface dendrite, the coverage area of the dendrite and the growth rate according to the amplitude change, phase jump and time domain change of the reflection signal; quantifying the risk value of the battery cell according to the growth rate and coverage area of the dendrite, and setting a risk threshold value, when the quantified risk value exceeds the risk threshold value, issuing a growth warning of the dendrite.

[0018] Further, the coverage area of the dendrite is determined by establishing a mapping model of the reflection coefficient and the coverage area, the mapping model being a proportional model, wherein the independent variable is the reflection coefficient and the dependent variable is the coverage area, and the parameters of the mapping model are fitted by experimental data or historical data to calculate the coefficient of the proportional model.

[0019] Further, the position information of the dendrite is calculated using a time domain reflection method, taking the connection position of the signal generator and the battery as the starting point and initial time, obtaining the time difference of the high-frequency signal from the transmission point to the dendrite reflection point and back, and the propagation speed of the signal, thereby calculating the distance between the dendrite and the starting point.

[0020] Further, the risk value quantification of the battery specifically includes: establishing a risk value quantification relationship, which considers the dendrite growth rate, coverage area and internal resistance change of the battery, the risk value quantification relationship being a linear weighted model, and the different parameters are normalized first, mapping each parameter to the range of 0 to 1, and then the comprehensive risk value is obtained by weighted summation, wherein the normalization processing of the dendrite growth rate, coverage area and internal resistance change of the battery adopts the maximum value standardization method, each parameter is in a positive proportional relationship with the risk value, and the weight distribution is performed respectively, and the weight coefficient is determined according to the actual application scene and experimental data.

[0021] Further, the hierarchical response mechanism includes at least three risk levels, the first risk level being determined by the dendrite growth rate exceeding the threshold value and the temperature rising sharply, the second risk level being the frequent voltage fluctuation and the abnormal increase of internal resistance, and the third risk level being the abnormal increase of gas content and the continuous increase of gas concentration near the battery pressure relief valve.

[0022] A power battery thermal runaway fault detection system based on multiple source parameters, the system comprising:

[0023] A high-frequency signal input module for inputting high-frequency signals generated from a signal generator to the positive electrode feed point of the battery, and forming a grounding loop by connecting the positive electrode to a coaxial line and grounding the negative electrode, the high-frequency signals being directionally propagated in the electrolyte and then collecting the dendrite reflection signals through the coaxial line;

[0024] A reflection signal analysis module for analyzing the collected dendrite reflection signals and establishing a quantitative correlation model between a specific frequency band and the growth of the dendrite, analyzing the growth of the dendrite and performing preliminary warning according to the quantitative correlation model;

[0025] A cross-validation module for collecting electrochemical parameters and auxiliary parameters of the battery and cross-verifying the preliminary warning of the dendrite, wherein the electrochemical parameters include voltage, current, temperature and internal resistance, and the auxiliary parameter is gas content;

[0026] The dynamic risk and hierarchical response module is used for establishing a dynamic risk and hierarchical response mechanism, combining historical data and real-time monitoring results, dividing the risk into different levels, and setting corresponding response measures for different levels.

[0027] The application has the beneficial effects that the power battery thermal runaway fault detection method and system based on multiple source parameters provided by the application realizes accurate monitoring of the internal state of the power battery by combining dendrite growth prediction and electrochemical parameter analysis, effectively improves the timeliness and accuracy of thermal runaway early warning, ensures the safe and stable operation of the battery system, and can accurately calculate the actual position of the dendrite, facilitating detection and maintenance of the power battery.

[0028] In addition to the purposes, features and advantages described above, the application has other purposes, features and advantages. The application will be further described in detail below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0029] The drawings accompanying the specification of the application form part of the specification and serve to further illustrate the illustrative embodiments of the application and to explain the application without imposing undue limitation on the application. In the drawings:

[0030] Figure 1 It is a whole schematic diagram of a power battery thermal runaway fault detection method based on multiple source parameters in the application;

[0031] Figure 2 It is a schematic diagram of the connection of matching resistors;

[0032] Figure 3 It is a schematic diagram of the module composition of a power battery thermal runaway fault detection system based on multiple source parameters in the application. DETAILED DESCRIPTION

[0033] It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict. The technical solutions in the embodiments of the application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0034] In order for those skilled in the art to better understand the application scheme, the technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the application.

[0035] Embodiment one: as Figure 1As shown, the present application provides a multi-source parameter-based power battery thermal runaway fault detection method, which is suitable for lithium ion battery application scenarios such as electric vehicles and energy storage power stations, and the method comprises:

[0036] S110, input the high-frequency signal generated from the signal generator to the positive electrode feed point of the battery cell, and form a grounding loop by connecting the positive electrode with a coaxial line and grounding the negative electrode. After the high-frequency signal propagates directionally in the electrolyte, the dendrite reflection signal is collected through the coaxial line;

[0037] Dendrites are crystals formed on the negative electrode surface during the charging process of lithium ion batteries. These crystals may pierce the separator and cause short circuits, leading to safety hazards, and are a common cause of lithium ion battery thermal runaway. During the cyclic charging and discharging process of lithium ion batteries, specific frequency acoustic waves or impedance change signals are generated in the early stage of dendrite growth. By inputting a high-frequency signal into the battery cell, the growth of dendrites can be monitored through the extracted specific frequency acoustic waves or impedance change signals, and timely warnings can be made.

[0038] In this embodiment, the signal generator has a chip model of ADF4350 or other chips that can output high-frequency signals meeting the requirements. A coaxial line is connected to the positive electrode synchronously. During the directional propagation of the high-frequency signal, part of the reflected signal returns along the coaxial line connected to the positive electrode, and part of the transmitted signal generates a reference level through the negative electrode grounding loop. The reflected signal along the coaxial line is due to the presence of dendrites in the battery. The impedance change, capacitance, inductance, and other characteristics of dendrites will cause signal reflection.

[0039] During the dendrite analysis of lithium ion batteries, only the reflected signal of the positive electrode needs to be collected. Therefore, a coaxial line needs to be connected to the positive electrode, and a signal collection device needs to be connected to the coaxial line to collect the reflected signal. In this embodiment, the model of the collection device is not limited and any device with high sampling rate and high precision can be used, such as a data collection card. The collected reflected signal is transmitted to a processing unit for analysis. The processing unit can be an embedded processor in a vehicle-mounted system. Therefore, the collection device and the processing unit have a data exchange channel, which is a high-speed Ethernet interface or a high-speed CAN interface. Through this interface, the collection device can transmit reflected signal data to the embedded processor in real time.

[0040] It should be noted that due to different manufacturing tolerances of different battery cells, the standing wave ratio may deteriorate, so a matching resistor needs to be connected in series at the end of the coaxial line on the positive electrode, and the resistance value of the matching resistor is generally about 50 ohms. The purpose is to obtain the best matching of the signal in the transmission process, thereby improving the acquisition quality of the signal, so as to ensure that the collected signal can accurately reflect the growth state of the dendrite. The specific wiring method can be referred to Figure 2 Wherein, other pins of the signal generator (such as enable pin, ground pin, etc.) are not shown, only the output pin is shown, and the wiring of other pins can refer to the chip manual of this model, which will not be described in detail in this embodiment.

[0041] When performing high-frequency signal sweep, a self-triggering sweep mechanism needs to be established, which has a fixed starting point and a dynamic scanning power. The fixed starting point is the frequency point when the internal impedance of the battery cell reaches the minimum value. At this time, the battery itself generates the least heat, and the actual capacity is closest to the theoretical maximum value. The internal impedance of the battery cell can be measured by the existing BMS. The dynamic scanning power is adjusted according to the actual state of the battery. Specifically, the current scanning power is equal to the actual capacity at the time when the internal impedance of the battery cell reaches the minimum value multiplied by a specific adjustment coefficient, so as to ensure that the scanning power is adapted to the state of the battery cell. For example, when the internal impedance of the battery cell reaches the minimum value, the actual capacity of the battery cell at this time is 90% of the rated battery capacity, and the unit is kilowatt-hour. The adjustment coefficient is 0.1. Therefore, the corresponding dynamic scanning power should be 9% of the rated battery capacity, and the unit is milliwatt. Through the self-triggering sweep mechanism, accurate sweep of high-frequency signal can be performed when the battery cell is closest to the actual capacity. Compared with the traditional fixed cycle detection mode, the determination of the dynamic scanning power can select the scanning power according to the running state of the battery cell, thereby avoiding polarization interference.

[0042] S120, analyze the collected dendrite reflection signal, and establish a quantitative correlation model between a specific frequency band and the growth of the dendrite. According to the quantitative correlation model, the growth of the dendrite is analyzed and a preliminary warning is given.

[0043] After the dendrite reflection signal is collected, the growth of the dendrite can be analyzed according to the signal. The growth of the dendrite is fed back to the reflection signal, which is the amplitude change, phase jump and time domain reflection. According to these changes, the dendrite can be quantitatively analyzed. In order to quantitatively analyze the dendrite according to different parameter changes, a quantitative correlation model between a specific frequency band and the growth of the dendrite is established in this embodiment, wherein the specific frequency is the center frequency 2.4 GHz of the high-frequency signal generated by the signal generator. The purpose of selecting the center frequency is to ensure that the frequency band covers the dendrite growth characteristic frequency, enhance the recognition ability of the signal, and reduce other frequency interference. Specifically, according to the quantitative correlation model, the growth of the dendrite is analyzed and a preliminary warning is given, which further includes:

[0044] S220, judging the position of the surface dendrite, the coverage area of the dendrite and the growth rate according to the amplitude variation, phase jump and time domain variation of the reflection signal;

[0045] During the growth of the dendrite, it will cause the internal impedance of the battery to suddenly change, and then the reflection coefficient will increase. At the same time, the conductivity of the generated dendrite will be higher than that of the electrolyte (such as metal lithium dendrite), and then the reflection coefficient will tend to be negative. In either case, the amplitude of the reflection signal will change, so whether the dendrite is generated can be judged by the amplitude variation of the reflection signal. In addition, the amplitude of the reflection signal is approximately linearly related to the coverage rate of the dendrite. Therefore, after obtaining the amplitude variation of the reflection signal, the coverage area of the dendrite can be determined by establishing a mapping model of the reflection coefficient and the coverage area.

[0046] Specifically, the mapping model can be a proportional model, where the independent variable is the reflection coefficient and the dependent variable is the coverage area. The parameters of the mapping model are fitted by experimental data or historical data, and the coefficient of the proportional model is calculated, so as to realize accurate estimation of the coverage area of the dendrite. The specific calculation method is not described in detail in this embodiment. After obtaining the coverage area of the dendrite, the growth rate of the dendrite can be calculated according to the change of the coverage area with time, and then the health status of the battery can be evaluated.

[0047] At the same time, the capacitive or inductive property of the dendrite will cause the reflection signal to produce phase shift. When the dendrite is in a capacitive state, it is not completely conductive, which will cause the phase of the reflection signal to advance. When it is in an inductive state, the opposite is true, and the phase will lag. Therefore, by monitoring the phase shift of the reflection signal in real time, the capacitive and inductive states of the dendrite can be effectively identified. When the time-varying characteristic analysis of this change process is performed, the growth rate and position information of the dendrite can be obtained. Specifically, the position information of the dendrite is calculated by time domain reflection method, taking the connection position of the signal generator and the battery as the starting point and the initial time. The time difference t of the high-frequency signal from the transmission point to the reflection point of the dendrite and back is obtained, as well as the propagation speed v of the signal. Therefore, the distance d between the dendrite and the starting point can be calculated, where the propagation speed v is calculated by the relative dielectric constant of the electrolyte and the speed of light. The specific calculation method can refer to the calculation method of the speed of light and the relative dielectric constant in the existing formula. Therefore, considering the transmission and reflection process of the signal, the distance d between the dendrite and the starting point is v*t / 2.

[0048] For example, the signal is injected from the positive terminal surface, and the relative dielectric constant of the electrolyte is 30. The calculated propagation speed v is 5.5*10 7 m / s. If the time difference of the reflection signal is 10 ps, the dendrite depth d is 5.5*10 7 *10*10 -12 / 2=0.275 microns, that is, the dendrite is located about 0.275 microns below the surface of the positive terminal, so as to accurately determine the position of the dendrite;

[0049] It should be noted that the above process is for the analysis of the case where the dendrite is located inside the battery cell, so only the relative dielectric constant of the electrolyte needs to be considered. For dendrites on the surface of the electrode, the propagation of the signal in the electrode also needs to be considered, so it is not described in this embodiment. For the generation of dendrites on the surface of the electrode, an image analysis method can be used. For specific reference, refer to the analysis method for the growth characteristics of the electrode surface dendrites in the prior art.

[0050] In addition, since the selected specific frequency band is 2.4 GHz, the resonance effect of the dendrite will produce a characteristic frequency deviation or secondary reflection peak near this frequency band. According to the visualization simulation calculation in the prior art, the length of the dendrite can be deduced. For specific reference, refer to the Chinese invention patent with publication number CN118553342A.

[0051] S230, according to the growth rate and coverage area of the dendrite, quantifying the risk value of the battery cell, and setting a risk threshold. When the quantified risk value exceeds the risk threshold, a growth warning of the dendrite is issued.

[0052] When the dendrite appears in the battery cell, it does not immediately cause a short circuit or other risks in the early stage, but as the dendrite grows and expands, its coverage area and growth rate will gradually increase, causing changes in the internal resistance of the battery cell, thereby affecting the battery performance, and eventually causing a short circuit or even thermal runaway. However, in the existing method, the growth process of the dendrite is not quantified with the risk of the battery cell, so it is difficult to effectively warn the risk. The risk value quantification of the battery cell specifically includes:

[0053] A risk value quantification relationship is established, which considers the dendrite growth rate, coverage area, and internal resistance change of the battery cell. The risk value quantification relationship is a linear weighted model, and since the dimensions of different parameters are not unified, normalization processing is also needed to map each parameter to the range of 0 to 1, and then the comprehensive risk value is calculated by weighted summation. The normalization processing of the dendrite growth rate, coverage area, and internal resistance change of the battery cell uses the min-max standardization method. Each parameter is in a positive relationship with the risk value, and is respectively assigned a weight. The weight coefficient is determined according to the actual application scenario and experimental data. For example, the weight of the dendrite growth rate is set to 0.5, the weight of the coverage area is 0.3, and the weight of the resistance change is 0.2. Then, the comprehensive risk value is calculated by weighted summation.

[0054] When the comprehensive risk value exceeds the preset threshold, the early warning mechanism is automatically triggered, and the early warning information is sent to the monitoring system in real time through the data transmission module, prompting the maintenance personnel to intervene in time, and the intervention measures include but are not limited to replacing the battery cell, adjusting the charging and discharging strategy (for example, dissolving part of the dendrite by small current charging and discharging), or performing deep maintenance, to ensure the safe and stable operation of the battery system;

[0055] S130: Collecting the electrochemical parameters of the battery cell and the auxiliary parameters, and cross-verifying the preliminary warning of the dendrite, wherein the electrochemical parameters include voltage, current, temperature and internal resistance, and the auxiliary parameters are gas content;

[0056] In the foregoing steps, the dendrite growth characteristics of the battery cell are monitored in real time, and early warning can be issued in time in the early stage of dendrite growth, and corresponding measures are taken. The thermal runaway problem of the battery cell is not only caused by dendrite growth, but also caused by external short circuit, overcharge and overdischarge, etc. Therefore, it is necessary to comprehensively analyze the electrochemical parameters and auxiliary parameters, wherein the electrochemical parameters include the fluctuation range of voltage, abnormal change of current, abnormal increase of temperature and significant increase of internal resistance, and the auxiliary parameters such as abnormal increase of gas content, i.e. increase of carbon monoxide or hydrogen concentration near the pressure relief valve of the battery cell. The abnormal changes of these parameters can be used as precursors of thermal runaway;

[0057] Specifically, the electrochemical parameters are combined with the preliminary warning of the dendrite for joint analysis, because the growth of the dendrite is coupled with the abnormal change of the electrochemical parameters, for example, when the voltage fluctuation is intensified, the dendrite growth rate is often accelerated, resulting in increase of internal resistance, increase of temperature and abnormal increase of gas content. Therefore, when analyzing, the linkage effect of various parameters is considered comprehensively, a multi-parameter linkage analysis module can be added to the original monitoring function of the existing BMS (battery management system), which applies machine learning algorithm to analyze the correlation between parameters, and adjusts the risk value in the foregoing process according to the analysis result, for example, when voltage fluctuation and temperature rise occur at the same time, the dendrite growth rate weight is automatically adjusted dynamically. Since voltage fluctuation and temperature rise are highly related to dendrite growth rate, the weight of dendrite growth rate is adjusted to 0.6, the coverage area weight is adjusted to 0.2, and the resistance change weight is adjusted to 0.2, so that the comprehensive risk value can more accurately reflect the actual situation and improve the accuracy of early warning.

[0058] S140: Establishing a dynamic risk and grading response mechanism, combining historical data and real-time monitoring results to divide the risk into different grades, and setting corresponding response measures for different grades.

[0059] The risk of thermal runaway in battery cells is not an isolated event. It requires comprehensive consideration of multiple factors, real-time assessment through dynamic risk models, and triggering corresponding intervention measures according to the risk level. For example, Level 1 risk requires immediate shutdown and inspection, Level 2 risk requires key monitoring and preparation of emergency plans, and Level 3 risk requires strengthening daily inspections to ensure that the system can respond efficiently under different risk levels and ensure the long-term stable operation of the battery system.

[0060] Among them, the dynamic risk mechanism adopts time-series correlation analysis. By judging whether there are time-series correlation changes in different parameters, the risk points are determined. For example, when dendrite growth changes synchronously with the temperature rise and has a time-series correlation, it indicates that the risk of thermal runaway has increased significantly and the risk level needs to be raised immediately.

[0061] The graded response mechanism includes at least three risk levels. The first risk level is determined when the dendrite growth rate exceeds the threshold and the temperature rises sharply. The second risk level is determined when the voltage fluctuates frequently and the internal resistance increases abnormally. The third risk level is determined when the gas content increases abnormally and the gas concentration near the cell pressure relief valve continues to rise. The judgment conditions corresponding to each level can be combined according to the actual situation. For example, the dendrite growth rate and the increase in gas content can be used as the first risk level, the voltage fluctuation and the increase in temperature can be used as the second risk level, and the abnormal internal resistance and the continuous increase in gas concentration can be used as the third risk level. This ensures that the judgment conditions of each level can be flexibly adjusted, reflect the system status in real time, provide accurate early warning and effective control of thermal runaway risk, avoid misjudgment of a single indicator, improve the overall prevention and control efficiency, and thus achieve a multi-dimensional comprehensive risk assessment.

[0062] The intervention measures of the graded response mechanism include, but are not limited to, power outage protection, optimized charging strategy, active thermal treatment (directional cooling, temperature limiting, etc.), power limiting, and pressure relief, which are linked to the dynamic risk level to ensure that effective measures are taken as soon as the risk first appears.

[0063] Example 2: Figure 3 As shown, this application also provides a power battery thermal runaway fault detection system based on multi-source parameters. This system operates the detection method described in Embodiment 1. The system includes:

[0064] The high-frequency signal input module is used to input a high-frequency signal generated by a signal generator to the positive feed point of the battery cell. The positive terminal is connected to a coaxial line, and the negative terminal is grounded to form a grounding loop. After the high-frequency signal propagates directionally in the electrolyte, the dendrite reflection signal is collected through the coaxial line.

[0065] The reflection signal analysis module is used to analyze the collected dendrite reflection signals and establish a quantitative correlation model between specific frequency bands and dendrite growth. Based on the quantitative correlation model, the growth of dendrites is analyzed and a preliminary warning is given.

[0066] A cross-validation module is configured to cross-validate the preliminary warning of the dendrite with electrochemical parameters of the battery cell and auxiliary parameters, wherein the electrochemical parameters include voltage, current, temperature and internal resistance, and the auxiliary parameters include gas content;

[0067] A dynamic risk and hierarchical response module is configured to establish a dynamic risk and hierarchical response mechanism, divide the risk into different levels in combination with historical data and real-time monitoring results, and set corresponding response measures for different levels.

[0068] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application. Various modifications and changes can be made by those skilled in the art based on the spirit and principles of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting thermal runaway failure of a power battery based on multi-source parameters, characterized in that: The application relates to a high-frequency signal detection method for detecting dendrite growth in a lithium-ion battery. The application comprises the following steps: A high-frequency signal generated by a signal generator is input to a positive electrode feed point of an electric core, and a coaxial line is connected to the positive electrode and the negative electrode is grounded to form a grounding loop. After the high-frequency signal is directionally propagated in electrolyte, a dendrite reflection signal is collected through the coaxial line. The collected dendrite reflection signal is analyzed, and a specific frequency band and a quantitative correlation model of dendrite growth are established. The growth of the dendrite is analyzed according to the quantitative correlation model, and a preliminary warning is given. The analysis of the growth of the dendrite according to the quantitative correlation model and the preliminary warning further comprises the following steps: the position of the surface dendrite, the coverage area of the dendrite and the growth rate are determined according to the amplitude change, the phase jump and the time domain change of the reflection signal; the risk value of the electric core is quantified according to the growth rate and the coverage area of the dendrite, and a risk threshold is set. When the quantified risk value exceeds the risk threshold, a growth warning of the dendrite is given. The electrochemical parameters and auxiliary parameters of the electric core are collected and cross-verified with the preliminary warning of the dendrite. The electrochemical parameters include voltage, current, temperature and internal resistance, and the auxiliary parameters are gas content.

2. The method of claim 1, wherein: A dynamic risk and grading response mechanism is established. According to historical data and real-time monitoring results, the risk is divided into different grades, and corresponding response measures are set for different grades.

3. The method of claim 1, wherein: The end of the coaxial line on the positive electrode is connected in series with a matching resistor, the resistance value of the matching resistor is 50 ohms, and the matching resistor is used to obtain the best matching of the signal in the transmission process, thereby improving the collection quality of the signal.

4. The method of claim 1, wherein: When the high-frequency signal is swept, a self-triggering sweep mechanism needs to be established. The self-triggering sweep mechanism has a fixed starting point and a dynamic scanning power. The fixed starting point is the frequency point when the internal impedance of the electric core reaches the minimum value, and the dynamic scanning power is adjusted according to the actual state of the battery.

5. The method of claim 1, wherein: The quantitative correlation model is established by a specific frequency band and dendrite growth. The specific frequency is the center frequency 2.4 GHz of the high-frequency signal generated by the signal generator.

6. The multi-source parameter based thermal runaway fault detection method for power batteries according to claim 1, characterized in that: The coverage area of the dendrite is determined by establishing a mapping model of the reflection coefficient and the coverage area. The mapping model is a positive proportional model, the independent variable is the reflection coefficient, and the dependent variable is the coverage area. The mapping model parameters are fitted by experimental data or historical data, and the coefficient of the positive proportional model is calculated. The position information of the dendrite is calculated by using the time domain reflection method. The connection position of the signal generator and the electric core is taken as the starting point and the initial time. The time difference of the high-frequency signal from the transmission point to the dendrite reflection point and back is obtained, and the propagation speed of the signal is obtained, so that the distance between the dendrite and the starting point is calculated.

7. The multi-source parameter based thermal runaway fault detection method of claim 1, wherein: The risk value quantification of the battery cell specifically includes: establishing a risk value quantification relationship considering the dendrite growth rate, coverage area, and internal resistance change of the battery cell, the risk value quantification relationship being a linear weighted model, and first normalizing different parameters to map each parameter to a range of 0 to 1, and then obtaining a comprehensive risk value by weighted summation, wherein the normalization processing of the dendrite growth rate, coverage area, and internal resistance change of the battery cell adopts a maximum value standardization method, each parameter is in a positive proportional relationship with the risk value, and the weight coefficients are determined according to actual application scenarios and experimental data.

8. The multi-source parameter based thermal runaway fault detection method of claim 1, wherein: The hierarchical response mechanism includes at least three risk levels, the first risk level being determined when the dendrite growth rate exceeds a threshold value and the temperature suddenly rises, the second risk level being determined when the voltage fluctuation is frequent and the internal resistance abnormally increases, and the third risk level being determined when the gas content abnormally increases and the gas concentration near the battery cell pressure relief valve continuously rises.

9. A multi-source parameter based thermal runaway failure detection system for power battery, for implementing the detection method according to any one of claims 1 to 8, characterized in that: The system includes: a high-frequency signal input module for inputting a high-frequency signal generated from a signal generator to a positive electrode feed point of the battery cell, and forming a grounding loop by connecting a coaxial line to the positive electrode and grounding the negative electrode, the high-frequency signal being collected through the coaxial line after directional propagation in the electrolyte; a reflected signal analysis module for analyzing the collected dendrite reflected signal, establishing a quantitative correlation model between a specific frequency band and dendrite growth, and analyzing the growth of the dendrite and giving a preliminary warning according to the quantitative correlation model; the further including: judging the position of the surface dendrite, the coverage area and the growth rate of the dendrite according to the amplitude change, phase jump and time domain change of the reflected signal; quantifying the risk value of the battery cell according to the growth rate and coverage area of the dendrite, and setting a risk threshold value, when the quantified risk value exceeds the risk threshold value, giving a growth warning of the dendrite; a cross-validation module for collecting electrochemical parameters and auxiliary parameters of the battery cell, and cross-verifying the preliminary warning of the dendrite, wherein the electrochemical parameters include voltage, current, temperature, and internal resistance, and the auxiliary parameters are gas content; a dynamic risk and hierarchical response module for establishing a dynamic risk and hierarchical response mechanism, dividing the risk into different levels according to historical data and real-time monitoring results, and setting corresponding response measures for different levels.

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