Thermal runaway warning threshold calculation method, device and thermal runaway warning method based on fuzzy logic

Abstract

The present invention discloses a method and device for calculating a thermal runaway warning threshold and a thermal runaway warning method based on fuzzy logic. The calculation method includes representing the parameters in the set of parameters to be optimized of the fuzzy membership function by antibodies in the initial population, and using an immune clone selection algorithm to iteratively optimize the initial population to obtain the global optimal solution of the parameter set; determining the optimal fuzzy membership function based on the global optimal solution of the parameter set; mapping different environmental temperatures and charging rates into different fuzzy intervals through the optimal fuzzy membership function, outputting the warning thresholds under different combinations of environmental temperatures and charging rates, and constructing the corresponding relationship between different combination conditions and warning thresholds; solving the problem of the migration of thermal runaway warning thresholds under different environmental temperatures and charging rates.

Description

Technical Field

[0001] The present invention relates to the technical field of battery safety, and particularly relates to a method and device for calculating a thermal runaway warning threshold and a thermal runaway warning method based on fuzzy logic. Background Art

[0002] In recent years, the energy storage field has shown a rapid growth trend, promoting the upgrade and expansion of the entire green energy system. Many clean technologies, especially lithium-ion battery technology, are rapidly making breakthroughs. However, the frequent occurrence of battery fires and explosions has also brought severe challenges to the healthy development of the new energy industry. The safety issue of lithium batteries has become the main bottleneck in the industry's development. The most serious safety issue of lithium batteries is thermal runaway, and the causes of thermal runaway can be attributed to electrical abuse, thermal abuse, and mechanical abuse. These improper usage behaviors will cause internal short circuits in the battery, resulting in a sharp rise in temperature, which in turn triggers a series of side reactions and ultimately leads to thermal runaway. To ensure safety, it is required to issue a warning at least five minutes before thermal runaway occurs, providing sufficient time for personnel evacuation and escape.

[0003] Up to now, there have been many methods for warning thermal runaway of lithium-ion batteries. Most of them are based on the method of fusing multiple signals. The commonly fused signals include temperature, voltage, gas and other signals. For example, warning is given based on temperature or the rising rate of temperature, and an alarm is issued when the signal reaches the corresponding threshold. In addition, existing research has also confirmed that a large amount of gas is released during the thermal runaway of lithium batteries, and the earliest gas to appear is usually H2. Therefore, there is also a method of using gas signals for warning based on this characteristic. For example, in the related technology, the Chinese patent application document with publication number CN118472446A discloses a thermal runaway safety warning method for lithium batteries. This scheme determines the grade range of environmental data based on a preset threshold range grade and determines the warning level based on the threshold. However, in this scheme, the threshold is set as static, ignoring the fact that the safety performance of lithium batteries varies greatly under different environmental temperatures and charging rates, and the threshold cannot be generalized. The Chinese patent application document with publication number CN117810576A discloses a comprehensive warning method for thermal runaway of a lithium-ion battery energy storage power station. In this scheme, first, the position of the battery in the charge and discharge interval is determined based on temperature, and then the warning level is judged based on preset temperature, voltage, and gas thresholds. However, this scheme also ignores the fact that the safety performance of lithium batteries varies greatly under different environmental temperatures and charging rates, and a static warning cannot be completed with a preset fixed threshold.

[0004] In addition, a method for predicting the probability of battery thermal runaway is disclosed in the Chinese patent application document with publication number CN116093497A. In this solution, the battery temperature and the position of battery thermal runaway are used as fuzzy inputs to fuzzify and output the probability of battery thermal runaway, realizing the prediction of the occurrence probability of thermal runaway. In non-patent literature, "An Immune Clonal Selection Algorithm Based on Fuzzy Sets, Computer Technology and Development, Vol. 17, No. 12, Zhang Kui, etc." proposed an immune clonal selection algorithm based on fuzzy set theory, which introduced the concepts of fuzzy sets and membership degrees, adopted a dynamic intelligent optimization strategy, effectively improved the characteristics of detectors, and enhanced the adaptability of detectors in complex network environments. However, this literature mainly focuses on optimizing the rule weights or parameter distributions of existing fuzzy systems, and the goal is usually to enhance the prediction accuracy or classification ability of the system, but it does not focus on the shape of the membership function. Summary of the Invention

[0005] The technical problem to be solved by the present invention is the migration problem of the thermal runaway warning threshold under different environmental temperatures and charging rates.

[0006] The present invention solves the above technical problems by the following technical means:

[0007] In a first aspect, a method for calculating the thermal runaway warning threshold is proposed, and the method includes:

[0008] Represent the parameters in the set of parameters to be optimized of the fuzzy membership function by antibodies in the initial population, and use the immune clonal selection algorithm to iteratively optimize the initial population to obtain the global optimal solution of the parameter set;

[0009] Determine the optimal fuzzy membership function based on the global optimal solution of the parameter set;

[0010] Map different environmental temperatures and charging rates into different fuzzy intervals through the optimal fuzzy membership function, output the warning threshold under different combinations of environmental temperatures and charging rates, and construct the corresponding relationship between different combination conditions and the warning threshold.

[0011] Further, the step of representing the parameters in the set of parameters to be optimized of the fuzzy membership function by antibodies in the initial population, and using the immune clonal selection algorithm to iteratively optimize the initial population to obtain the global optimal solution of the parameter set includes:

[0012] S11. Randomly generate an initial population, and each antibody in the initial population represents a parameter of the fuzzy membership function;

[0013] S12. Evaluate each antibody using the fitness function, and determine whether the evaluation result meets the termination condition. If not, execute step S13; if so, execute step S16;

[0014] S13. Select high-quality antibodies based on the evaluation results of the antibodies, and perform cloning operations on the selected antibodies to obtain the cloned antibodies;

[0015] S14. Perform mutation operations on the cloned antibodies, evaluate the mutated antibodies using the fitness function, and compete with the antibodies in the initial population. Retain the current optimal antibody for the next generation to form the next-generation population;

[0016] S15. Re-execute steps S12 - S14 for the next-generation population;

[0017] S16. Output the global optimal solution of the parameter set of the fuzzy membership function.

[0018] Further, the formula of the fitness function is expressed as:

[0019]

[0020]

[0021] In the formula, respectively represent the th strain turning point and the th voltage turning point predicted by the fuzzy logic system; respectively represent the th strain turning point and the th voltage turning point measured experimentally; represents the fitness of antibody ; represents the population size; and respectively represent the difference between the predicted strain turning point and the actual strain turning point, and the difference between the predicted voltage turning point and the actual voltage turning point.

[0022] Further, the method further includes:

[0023] When performing cloning operations on the selected antibodies, the cloning quantity is proportional to the fitness of the selected antibodies.

[0024] Further, the method further includes:

[0025] When performing mutation operations on the cloned antibodies, introduce random perturbations as follows:

[0026]

[0027] In the formula, represents the th decision variable of the th antibody after mutation; represents the The th decision variable of an antibody; Indicates multiplication; Indicates a standard normal distribution random number; Indicates that the mutation intensity is usually inversely proportional to the antibody fitness, , Indicates the th antibody fitness value, Indicates the maximum fitness value in the current population, which is used for normalization to make the mutation amplitude relatively uniform.

[0028] Furthermore, the formula for the termination condition is expressed as:

[0029]

[0030] In the formula, Indicates the next-generation population; Indicates selecting the antibody with the optimal fitness from the union of the original population and the cloned population to form a new generation of population; Indicates the initial population; Indicates the union; Indicates the population after clone mutation.

[0031] Furthermore, mapping different environmental temperatures and charging rates into different fuzzy intervals through the optimal fuzzy membership function, and outputting the warning thresholds under different combinations of environmental temperatures and charging rates, and constructing the corresponding relationship between different combination conditions and warning thresholds, includes:

[0032] Conducting overcharge thermal runaway experiments on lithium batteries under different combinations of environmental temperatures and charging rates, and continuously recording the experimental parameters, where the experimental parameters include voltage change signals, battery surface temperature change signals, strain change signals, and gas change signals;

[0033] Mapping the environmental temperature and charging rate in each combination condition into different fuzzy intervals through the optimal fuzzy membership function, and obtaining the warning thresholds under different combinations of environmental temperatures and charging rates according to the fuzzy rules established based on the experimental data.

[0034] Furthermore, the method further includes:

[0035] Normalizing the environmental temperature and charging rate in each combination condition and then mapping them into different fuzzy intervals through the optimal fuzzy membership function.

[0036] Furthermore, the warning thresholds include voltage turning points and strain turning points.

[0037] Furthermore, the types of the fuzzy membership function include trigonometric functions and trapezoidal functions.

[0038] In a second aspect, the present invention provides a thermal runaway warning threshold calculation device, which includes:

[0039] A parameter iterative optimization module, configured to represent the parameters in the set of parameters to be optimized of the fuzzy membership function by antibodies in the initial population, and iteratively optimize the initial population using an immune clonal selection algorithm to obtain the global optimal solution of the parameter set;

[0040] An optimal fuzzy membership function determination module, configured to determine an optimal fuzzy membership function based on the global optimal solution of the parameter set;

[0041] A warning threshold migration module, configured to map different environmental temperatures and charging rates into different fuzzy intervals through the optimal fuzzy membership function, output warning thresholds under different combinations of environmental temperatures and charging rates, and construct a correspondence between different combination conditions and warning thresholds.

[0042] In a third aspect, the present invention provides a thermal runaway warning method based on fuzzy logic, which includes:

[0043] Real-time collection of the state parameters of the lithium battery under a certain working condition;

[0044] Comparing the state parameters with the warning threshold corresponding to the current working condition to perform thermal runaway warning;

[0045] Wherein, the warning threshold is the warning threshold calculated based on the above-mentioned thermal runaway warning threshold calculation method and corresponding to the environmental temperature and charging rate under the current working condition.

[0046] Further, the state parameters include voltage signals and strain signals, the warning thresholds include voltage turning points and strain turning points, and the comparing the state parameters with the warning threshold corresponding to the current working condition to perform thermal runaway warning includes:

[0047] Comparing the voltage signal and the voltage turning point with the strain signal and the strain turning point respectively;

[0048] When the voltage signal is greater than the voltage turning point and the strain signal is greater than the strain turning point, a first-level thermal runaway warning is performed.

[0049] Further, the state parameters further include gas signals, and the method further includes:

[0050] When the strain descent rate is greater than a set first threshold and the gas concentration rise rate is greater than a set second threshold, a second-level thermal runaway warning is performed.

[0051] Further, the state parameter further includes a battery surface temperature signal, and the method further includes:

[0052] When the voltage drop rate is greater than a set third threshold and the battery surface temperature rise rate is greater than a set fourth threshold, a third-level thermal runaway warning is issued.

[0053] The advantages of the present invention are as follows:

[0054] (1) The present invention first uses an immune optimization algorithm to optimize the parameters of the fuzzy membership function to obtain the optimal fuzzy membership function, and then uses the optimal fuzzy membership function to construct the correspondence between different combinations of environmental temperature and charging rate and the warning threshold, obtaining a dynamic threshold that changes dynamically with different environmental temperatures and charging rates instead of the static threshold set in the related art, solving the problem of the migration of the thermal runaway warning threshold under different environmental temperatures and charging rates.

[0055] (2) The optimal fuzzy membership function optimized by the immune optimization algorithm of the present invention can effectively reflect the influence of environmental temperature and charging rate on the thermal runaway risk, ensuring the accuracy of the warning threshold calculation while also improving the accuracy and response speed of the membership function to the system.

[0056] (3) When the present invention performs thermal runaway warning on a lithium battery, it compares the real-time collected lithium battery state parameters with the warning threshold to achieve thermal runaway warning, and the warning threshold is the voltage turning point and strain turning point calculated by using the fuzzy membership function according to the environmental temperature and charging rate under the current working condition. Therefore, the warning threshold adopted in the present invention is not a static threshold, but a dynamic threshold that changes dynamically with different environmental temperatures and charging rates, solving the problem of the migration of the thermal runaway warning threshold under different environmental temperatures and charging rates; therefore, by selecting a warning threshold suitable for the environmental temperature and charging rate under the current working condition to perform thermal runaway warning, the accuracy of thermal runaway warning can be greatly improved.

[0057] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a schematic flowchart of a method for calculating a thermal runaway warning threshold proposed in an embodiment of the present invention;

[0059] Figure 2 is a schematic flowchart of parameter optimization using an immune clone selection algorithm in an embodiment of the present invention;

[0060] Figure 3 is a schematic diagram of antibody fitness convergence in an embodiment of the present invention;

[0061] Figure 4 is a schematic structural diagram of an experimental device in an embodiment of the present invention;

[0062] Figure 5 is a schematic block diagram of a fuzzy logic system in an embodiment of the present invention;

[0063] Figure 6 is a schematic diagram of fuzzy logic output in an embodiment of the present invention;

[0064] Figure 7 is a schematic diagram of the verification process for calculating the thermal runaway warning threshold in an embodiment of the present invention;

[0065] Figure 8 is a schematic diagram of the verification result of calculating the thermal runaway warning threshold in an embodiment of the present invention;

[0066] Figure 9 is a schematic structural diagram of a device for calculating a thermal runaway warning threshold proposed in an embodiment of the present invention;

[0067] Figure 10 is a schematic flowchart of a method for thermal runaway warning based on fuzzy logic proposed in an embodiment of the present invention;

[0068] Figure 11 is a complete flowchart of the method for thermal runaway warning based on fuzzy logic in an embodiment of the present invention. Detailed implementation manners

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0070] Embodiment 1

[0071] As Figure 1 shown, a method for calculating a thermal runaway warning threshold is proposed in the first embodiment of the present invention, and the method includes the following steps:

[0072] S10. Represent the parameters in the set of parameters to be optimized of the fuzzy membership function by the antibodies in the initial population, and use the immune clonal selection algorithm to iteratively optimize the initial population to obtain the global optimal solution of the parameter set;

[0073] It should be noted that the parameters of the fuzzy membership function include shape parameters, position parameters, width parameters, slope parameters, height parameters, etc. It is understandable that the number and meaning of specific parameters may vary according to different types of membership functions.

[0074] This embodiment focuses on the shape problem of the membership function for the lithium battery thermal runaway warning scenario, and the design problem of the fuzzy system for complex and multi-input variable scenarios, and is particularly suitable for tasks that require high reliability such as the safety performance evaluation of lithium batteries.

[0075] S20. Determine the optimal fuzzy membership function based on the global optimal solution of the parameter set;

[0076] S30. Map different ambient temperatures and charging rates into different fuzzy intervals through the optimal fuzzy membership function, output the warning thresholds under different combinations of ambient temperatures and charging rates, and construct the corresponding relationship between different combination conditions and warning thresholds.

[0077] This embodiment provides a thermal runaway warning method based on fuzzy logic for the existing thermal runaway warning technology that sets static warning thresholds and ignores the differences in the safety performance of lithium batteries under different ambient temperatures and charging rates. First, the immune optimization algorithm is used to optimize the parameters of the fuzzy membership function to obtain the optimal fuzzy membership function, and then the optimal fuzzy membership function is used to construct the corresponding relationship between different combinations of ambient temperatures and charging rates and warning thresholds, obtaining a dynamic threshold that changes dynamically with different ambient temperatures and charging rates instead of the static threshold set in the related technology, and solving the problem of the migration of thermal runaway warning thresholds under different ambient temperatures and charging rates.

[0078] As a further preferred technical solution, as Figure 2 shown, the step S10: Represent the parameters in the parameter set to be optimized of the fuzzy membership function by the antibodies in the initial population, and use the immune clone selection algorithm to iteratively optimize the initial population to obtain the global optimal solution of the parameter set, which specifically includes the following steps:

[0079] S11. Randomly generate an initial population, and each antibody represents a parameter of the fuzzy membership function;

[0080] Specifically, first randomly generate an initial population, and each antibody represents a potential solution of a parameter. Randomly generate the initial population P = { , , …, }, where each antibody represents a solution to the problem, and the population size is N. The value range of the solution is determined by the upper and lower boundaries , according to the definition of the problem:

[0081]

[0082] Among them, D is the dimension of the solution, which is determined by the parameters required by the fuzzy membership function. In the embodiment of the present invention, D is set to 22.

[0083] S12. Evaluate each antibody using the fitness function, and determine whether the evaluation result meets the termination condition. If not, execute step S13; if so, execute step S16.

[0084] As a further preferred technical solution, the formula of the termination condition is expressed as:

[0085]

[0086] In the formula, represents the next generation population; represents selecting the antibody with the optimal fitness from the union of the original population and the cloned population to form a new generation population; represents the initial population; represents the union; represents the population after cloning and mutation.

[0087] S13. Select high-quality antibodies based on the evaluation results of the antibodies, and perform a cloning operation on the selected antibodies to obtain the cloned antibodies.

[0088] It should be noted that in this embodiment, the fitness function is used to evaluate the performance of each antibody, and high-quality antibodies are selected therefrom.

[0089] S14. Perform a mutation operation on the cloned antibodies, evaluate the mutated antibodies using the fitness function, and compete with the antibodies in the initial population. Retain the current optimal antibody to the next generation to form the next generation population.

[0090] It should be noted that the mutated antibodies will be evaluated again through the fitness function and compete with the antibodies in the original population to form the next generation population. In order to retain the optimal solution, the elite retention strategy is usually adopted, that is, directly retaining the current optimal antibody to the next generation. This process will be continuously iterated until the stop condition is reached and finally converges, and the global optimal solution can be obtained. Among them, the fitness result is as Figure 3 shown.

[0091] S15. Re-execute steps S12 to S14 on the next generation population;

[0092] S16. Output the global optimal solution of the parameter set of the fuzzy membership function.

[0093] It should be noted that the immune clonal selection algorithm is an optimization algorithm inspired by the biological immune system. Its core idea comes from the mechanism of antibody-antigen recognition in the immune system. The algorithm simulates the clonal selection process of the immune system and gradually finds the global optimal solution by continuously screening, cloning, and mutating the solutions. In this embodiment, a potential solution representing a parameter of the fuzzy membership function is represented by an antibody in the initial population, and the initial population is iteratively optimized to determine the global optimal solution of the parameter set of the fuzzy membership function.

[0094] As a further preferred technical solution, for each antibody calculate its fitness , in this embodiment, the fitness is the error between the predicted label and the true label, that is, the difference between the strain turning point and the voltage turning point predicted by the fuzzy system and the strain turning point and the voltage turning point obtained by the experiment. The formula of the fitness function is expressed as:

[0095]

[0096]

[0097] In the formula, respectively represent the th strain turning point and the th voltage turning point predicted by the fuzzy logic system; respectively represent the th strain turning point and the th voltage turning point measured by the experiment; represents the fitness of antibody ; represents the population size; , respectively represent the difference between the predicted strain turning point and the actual strain turning point, and the difference between the predicted voltage turning point and the actual voltage turning point.

[0098] As a further preferred technical solution, the method further includes: when performing a cloning operation on the selected antibody, the cloning number is proportional to the fitness of the selected antibody , is the cloning number of

[0099] to ensure that the high-quality solution is further expanded.

[0100]

[0101] In the formula, Indicates the th variable of the th antibody after mutation; Indicates the th decision variable of the th antibody before mutation; Indicates multiplication; Indicates a standard normal distribution random number; Indicates that the mutation intensity is usually inversely proportional to the antibody fitness, , Indicates the th antibody fitness value, Indicates the maximum fitness value in the current population, which is used for normalization to make the mutation amplitude relatively uniform.

[0102] Furthermore, the global optimal solutions of the parameter sets determined by the immune clonal selection algorithm in this embodiment are shown in Tables 1, 2, 3, and 4 as follows:

[0103] Table 1 Specific parameters of the input charging rate

[0104]

[0105] Table 2 Specific parameters of the input environmental temperature

[0106]

[0107] Table 3 Specific parameters of the output strain turning point

[0108]

[0109] Table 4 Specific parameters of the output voltage turning point and charging rate

[0110]

[0111] It should be noted that in a fuzzy control system, selecting an appropriate membership function is crucial for the accuracy and response speed of the system. When selecting a membership function, the following factors generally need to be considered:

[0112] The actual requirements of the system, the shape and quantity of the membership function should match the characteristics of the system. For example, if the system is more sensitive to input changes, a narrower membership function can be selected; otherwise, a wider function can be used. For the research on battery thermal runaway warning, the membership function selected in this study should be able to effectively reflect the influence of temperature and charging rate on the thermal runaway risk.

[0113] Different membership functions vary in computational complexity. For example, trigonometric functions and trapezoidal functions are computationally simple and suitable for real-time systems; while Gaussian functions and Bell functions, although more computationally complex, can provide smoother transitions. In cases where high real-time performance is required, simple membership functions may be a better choice.

[0114] As a further preferred technical solution, in step S30: mapping different ambient temperatures and charging rates into different fuzzy intervals through the optimal fuzzy membership function, outputting warning thresholds under different combinations of ambient temperature and charging rate, and constructing the corresponding relationship between different combination conditions and warning thresholds, including the following steps:

[0115] S31. Conduct overcharge thermal runaway experiments on lithium batteries under different combinations of ambient temperature and charging rate, and continuously record the experimental parameters, where the experimental parameters include voltage change signals, battery surface temperature change signals, strain change signals, and gas change signals.

[0116] In this embodiment, overcharge thermal runaway experiments are conducted using commercial soft-pack lithium iron phosphate batteries as an example, and the specific parameters of the lithium batteries are shown in Table 5:

[0117] Table 5 Parameters of Commercial Soft-Pack Lithium Iron Phosphate Batteries

[0118]

[0119] Specifically, the overcharge thermal runaway experiment is as follows:

[0120] (1) Conduct overcharge thermal runaway experiments at 25°C, 50°C, and 75°C ambient temperatures at charging rates of 0.5C, 1C, and 2C respectively:

[0121] 1-1) When conducting the overcharge thermal runaway experiment, the experimental device should first be initialized. This includes the initialization of the charge and discharge device, explosion-proof device, voltage signal, surface temperature signal, strain signal, and gas signal acquisition equipment. The safety measures of the experimental environment should also be checked simultaneously to ensure the stable operation of the equipment and the accurate acquisition of data during the experiment. Specific equipment is as Figure 4 shown.

[0122] 1-2) Pretreat the battery to make the battery SOC reach 100%. Before the experiment starts, use the constant current and constant voltage charging method to pretreat the battery to ensure that the SOC of the battery reaches 100%. Specifically, first discharge the battery at a constant current of 1C until the battery voltage reaches the cut-off voltage of 2.0V. When the constant current discharge stage ends, rest for 10 minutes and then start constant current and constant voltage charging. First is the constant current charging, charging at a 1C charging rate until the battery reaches the cut-off voltage of 3.65V, and then enter the constant voltage stage until the battery charging current reaches about 0.05C, and the charging process is considered to be over. During the charging process, parameters such as voltage, current and temperature need to be monitored to ensure that the battery is within a safe range during the pretreatment stage, so as to provide consistent initial conditions for the subsequent overcharge thermal runaway experiment.

[0123] 1-3) Set different environmental temperatures and charging rates to conduct overcharge thermal runaway experiments. The experiments will be carried out under three typical environmental temperature conditions: 25°C, 50°C and 75°C, representing normal temperature, high temperature and extreme high temperature environments respectively. These three temperature settings are used to simulate the operating states of the battery under daily use, outdoor high temperature exposure and extreme working conditions.

[0124] The experiments also need to be carried out at different charging rates to simulate the thermal runaway risks of the battery under different charging conditions. The charging rates will be set to 0.5C, 1C and 2C, corresponding to slow charging, standard charging and fast charging respectively. The setting of the charging rate helps to understand the influence of the charging current on the internal reaction rate and thermal runaway tendency of the battery.

[0125] Before the experiment starts, first adjust the laboratory environmental temperature to the set value (25°C, 50°C or 75°C). After the environmental temperature stabilizes, place the battery in the experimental environment and ensure that the temperature between the battery and the environment reaches an equilibrium state, which usually requires a certain standing time (about 30 minutes). Monitor the surface temperature of the battery in real time to ensure that the battery temperature is consistent with the experimental environment temperature.

[0126] The charging process of the battery will be carried out at the set charging rate, using the constant current and constant voltage charging mode (as described in step 1-2). After reaching SOC 100%, continue to carry out overcharge operation at the set rate until the battery triggers thermal runaway.

[0127] (2) Collect data such as the surface temperature, voltage, strain, gas concentration, etc. of the battery during the overcharge thermal runaway process

[0128] As Figure 4As shown, during the entire overcharge experiment, the data collection device, including voltage signal, surface temperature signal, strain signal, and gas signal acquisition equipment, will continuously record the changes in battery surface temperature, voltage, strain, gas concentration, etc. as the overcharge progresses. The data for each combination of temperature and charging rate is classified and stored for subsequent feature analysis, such as to specify fuzzy rules and verify whether the designed fuzzy system is reasonable.

[0129] S32. Map the ambient temperature and charging rate in each combination condition into different fuzzy intervals through the optimal fuzzy membership function, and obtain the warning thresholds under different combinations of ambient temperature and charging rate according to the fuzzy rules established based on the experimental data.

[0130] In this embodiment, to adapt to the differences in battery safety performance and the different thermal runaway thresholds under different ambient temperatures and charging rates, the ambient temperature and charging rate are used as the inputs of the optimal fuzzy membership function, and the fuzzy rules established based on the experimental data are mapped into different fuzzy intervals through the fuzzy membership function. At the same time, to adapt to different thermal runaway warning thresholds in different situations, the outputs are the strain turning point and voltage turning point.

[0131] It should be noted that the fuzzy membership function is used to describe the degree of membership of a certain input variable in a fuzzy set, that is, the degree to which the variable belongs to a certain fuzzy set. This kind of membership is not the "all or nothing" binary membership in the traditional set, but is represented by a continuous value between 0 and 1 to represent the degree of membership, thus providing the ability to process fuzzy phenomena. The specific process of fuzzy logic is as Figure 5 shown. It realizes precise control and decision-making in a multivariable system by processing uncertainty and fuzziness. The fuzzy logic system mainly includes the following key steps: fuzzification, fuzzy rule base, fuzzy inference, fuzzy output, and defuzzification.

[0132] Furthermore, the fuzzy rule base contains several fuzzy rules in the form of "if-then" to describe the relationship between input and output variables. These rules are usually expressed in language form, for example:

[0133]

[0134] Among them, is the fuzzy rule, is the set of fuzzy inputs, representing the ambient temperature and charging rate respectively, is the set of fuzzy outputs, representing the strain turning point and voltage turning point respectively, and the fuzzy rules established based on the experimental data are shown in Table 6 and Table 7 as follows:

[0135] Table 6 Fuzzy Rules for Strain Turning Point

[0136]

[0137] Table 7 Fuzzy Rules for Voltage Inflection Points

[0138]

[0139] In a fuzzy control system, fuzzy rules convert the relationship between inputs and outputs into conditional statements in natural language form, thereby achieving the control of complex or uncertain systems. The main functions of fuzzy rules are as follows: establishing the relationship between inputs and outputs. For example, in this system, the relationship between different charging rates and ambient temperatures of lithium iron phosphate batteries and the corresponding strain inflection points and voltage inflection points is established. It should be understood that this embodiment can also adopt the fuzzy control system proposed in this embodiment for other types of lithium batteries such as ternary lithium batteries to establish the relationship between different charging rates and ambient temperatures and the corresponding strain inflection points and voltage inflection points.

[0140] Specifically, LC, MC, HC, LT, MT, and HT represent low, medium, and high charging rates and low, medium, and high ambient temperatures respectively, LS, MS, and HS represent low, medium, and high strain inflection points respectively, and LV, MV, and HV represent low, medium, and high voltage inflection points respectively.

[0141] Furthermore, according to the established fuzzy rules and fuzzy inputs, the corresponding fuzzy outputs, namely strain inflection points and voltage inflection points, are obtained:

[0142] This embodiment is based on the Mamdani system. The Mamdani system is based on fuzzy logic rules and input fuzzy sets. Among them, the "Min" operation is used to determine the membership degree value of the rule antecedent, that is, the minimum value of the membership degrees of all input conditions in the rule is selected as the activation strength of the rule. The "Max" operation is used to aggregate the outputs of multiple rules. If different rules output the same fuzzy set, the maximum value of the membership degrees of these rule outputs is selected.

[0143] After obtaining the corresponding fuzzy output according to the MIN-MAX method, defuzzification is still required. Here, the centroid method is adopted in this embodiment, which is specifically as follows:

[0144]

[0145] Among them, respectively represent the fuzzy membership function of the output variable and the output variable. The outputs are strain inflection points and voltage inflection points. Specifically, as shown in Figure 6 shown, among them, Figure 6 in (a) is the result of the output voltage inflection point, Figure 6In (b) is the output result of the strain turning point. It can be seen that it conforms to the analysis of this embodiment, that is, higher ambient temperature and charging rate will reduce the safety performance of the battery, that is, the threshold decreases.

[0146] As a further preferred technical solution, to verify the effectiveness and generalization of the proposed thermal runaway warning threshold calculation method, this embodiment respectively selects the same battery at a charging rate of 1.5C and an ambient temperature of 50°C, and a battery with a capacity of 17.8AH of the same type at a charging rate of 1C and an ambient temperature of 30°C to conduct an overcharge thermal runaway experiment. Then verify whether the method of the embodiment of the present invention can give an effective warning. The entire verification process is as Figure 7 shown, where Figure 7 In (a) is the calculation process of the strain turning point, Figure 7 In (b) is the calculation process of the voltage turning point:

[0147] (1) In the overcharge thermal runaway experiment under the above conditions, including initializing the experimental equipment, setting the initial SOC of the battery to 100, and conducting an overcharge thermal runaway experiment on the battery at the preset ambient temperature and charging rate, while collecting signals such as surface temperature, voltage, strain, and gas concentration during the thermal runaway process of the battery.

[0148] (2) Perform normalization of the input. First, normalize the input ambient temperature and charging rate. The method proposed in the embodiment of the present invention studies the range under an ambient temperature of 25°C - 75°C and a charging rate of 0.5C - 2C, and the normalization process can be carried out according to the following formula:

[0149]

[0150] where represents the input fuzzy variable, represent the upper and lower limits of the input fuzzy variable respectively, and the specific range has been introduced above. For example, after normalizing a charging rate of 1.5C, the input is 2 / 3.

[0151] (3) Map the normalized input through the fuzzy membership function. Map the input values of the ambient temperature and charging rate to different stages according to the membership function.

[0152] The normalized input data is mapped through a fuzzy membership function to convert it into a fuzzy set suitable for fuzzy inference. Specifically, the membership function maps the input values of the ambient temperature and the charging rate to different stages or levels according to the preset membership degree range and membership degree levels. For example, by dividing the ambient temperature into stages such as "low temperature", "medium temperature", and "high temperature", and the charging rate into levels such as "slow", "medium", and "fast", the fuzziness of the input variables can be characterized more precisely. This process helps the fuzzy logic system to more accurately understand and reason about the state of the battery under different temperatures and charging rates, thus supporting the subsequent fuzzy control decision-making process.

[0153] For example, after normalization, the charging rate of 1.5C has an input of 2 / 3, corresponding to a combination of half MC and half HC in the membership function, that is, a combination of half medium speed and half high speed.

[0154] (4) Obtain the fuzzy output through the established fuzzy rules. The specific formula is as follows:

[0155]

[0156] j

[0157] This is the definition of a fuzzy rule R(s), where 、 and represent the fuzzy membership functions, corresponding to the membership degrees of the input variables respectively; is the operation of logical "AND", usually corresponding to the minimum value operation in fuzzy logic; represents the final fuzzy output, represents the union operation, indicating the combination of multiple results; j = 1 to j = 9 indicates that there are 9 different fuzzy rules; A represents the set of input fuzzy variables, represents the activation of the th fuzzy rule, represents the vector of the complement of the set of input fuzzy variables, represents the AND operation, T represents the transpose of the matrix.

[0158] Finally, the strain turning point and voltage turning point corresponding to the output are obtained. The specific detection results are as Figure 8 shown. Figure 8 In (a) of Figure 8Among them, (b) shows the test results of a larger-capacity battery of the same type at a 1C charging rate and an ambient temperature of 30°C. The test results successfully predicted both the voltage turning point and the strain turning point, effectively demonstrating the effectiveness of the method proposed in this embodiment.

[0159] Embodiment 2

[0160] As Figure 9 shown, the second embodiment of the present invention proposes a thermal runaway warning threshold calculation device, which includes:

[0161] A parameter iterative optimization module 10, configured to represent the parameters in the set of parameters to be optimized of the fuzzy membership function using the antibodies in the initial population, and perform iterative optimization on the initial population using an immune clone selection algorithm to obtain the global optimal solution of the parameter set;

[0162] An optimal fuzzy membership function determination module 20, configured to determine the optimal fuzzy membership function based on the global optimal solution of the parameter set;

[0163] A warning threshold migration module 30, configured to map different ambient temperatures and charging rates into different fuzzy intervals through the optimal fuzzy membership function, output the warning thresholds under different combinations of ambient temperature and charging rate, and construct the corresponding relationship between different combination conditions and warning thresholds.

[0164] As a further preferred technical solution, the parameter iterative optimization module 10 is specifically configured to perform the following steps:

[0165] S11. Randomly generate an initial population, where each antibody represents a parameter of the fuzzy membership function;

[0166] S12. Evaluate each antibody using a fitness function, and determine whether the evaluation result meets the termination condition. If not, execute step S13; if so, execute step S16;

[0167] Specifically, the formula of the fitness function is expressed as:

[0168]

[0169]

[0170] In the formula, respectively represent the th strain turning point and the th voltage turning point predicted by the fuzzy logic system; respectively represent the th strain turning point and the th voltage turning point measured experimentally; represents the antibody Fitness; Indicates the population size; , respectively represent the difference between the predicted strain turning point and the actual strain turning point, and the difference between the predicted voltage turning point and the actual voltage turning point.

[0171] S13. Select high-quality antibodies based on the evaluation results of the antibodies, and perform a cloning operation on the selected antibodies to obtain the cloned antibodies;

[0172] Furthermore, when performing the cloning operation on the selected antibodies, the cloning quantity is proportional to the fitness of the selected antibodies.

[0173] S14. Perform a mutation operation on the cloned antibodies, evaluate the mutated antibodies using the fitness function, and compete with the antibodies in the initial population. Retain the current optimal antibody to the next generation to form the next generation population;

[0174] Furthermore, when performing the mutation operation on the cloned antibodies, introduce a random perturbation as follows:

[0175]

[0176] In the formula, represents the th variable of the th mutated antibody; represents the th variable of the th antibody before mutation; represents multiplication; represents a standard normal distribution random number; indicates that the mutation intensity is usually inversely proportional to the antibody fitness, , represents the th antibody fitness value, represents the maximum fitness value in the current population, which is used for normalization to make the mutation amplitude relatively unified.

[0177] S15. Re-execute steps S12 - S14 for the next generation population;

[0178] S16. Output the global optimal solution of the parameter set of the fuzzy membership function.

[0179] As a further preferred technical solution, the early warning threshold migration module 30 specifically includes:

[0180] An experimental unit is used to conduct overcharge thermal runaway experiments on lithium batteries under the combined conditions of different ambient temperatures and charging rates, and continuously record the experimental parameters, where the experimental parameters include voltage change signals, battery surface temperature change signals, strain change signals, and gas change signals;

[0181] A fuzzy logic unit is used to map the ambient temperature and charging rate in each combination condition into different fuzzy intervals through the optimal fuzzy membership function, and obtain the warning thresholds under different combinations of ambient temperature and charging rate according to the fuzzy rules established based on the experimental data, where the warning thresholds include voltage turning points and strain turning points.

[0182] It should be noted that other embodiments or implementation methods of the thermal runaway warning threshold calculation device of the present invention can refer to the above method embodiments, and will not be elaborated here.

[0183] Embodiment 3

[0184] As Figure 10 shown, the third embodiment of the present invention proposes a thermal runaway warning method based on fuzzy logic, and the method includes the following steps:

[0185] S1. Real-time collect the state parameters of the lithium battery under a certain working condition;

[0186] S2. Compare the state parameters with the corresponding warning thresholds under the current working condition to conduct thermal runaway warning;

[0187] Among them, the warning threshold is the warning threshold corresponding to the ambient temperature and charging rate under the current working condition calculated based on the thermal runaway warning threshold calculation method described in the above Embodiment 1.

[0188] In this embodiment, when conducting thermal runaway warning for the lithium battery, the real-time collected state parameters of the lithium battery are compared with the warning threshold to achieve thermal runaway warning, and the warning threshold is the voltage turning point and strain turning point calculated by using the fuzzy membership function according to the ambient temperature and charging rate under the current working condition. Therefore, the warning threshold adopted in the present invention is not a static threshold, but a dynamic threshold that changes dynamically with different ambient temperatures and charging rates, solving the problem of the migration of the thermal runaway warning threshold under different ambient temperatures and charging rates; therefore, by selecting a warning threshold suitable for the ambient temperature and charging rate under the current working condition to conduct thermal runaway warning, the accuracy of thermal runaway warning can be greatly improved.

[0189] As a further preferred technical solution, as Figure 11 shown, the state parameters include voltage signals and strain signals, the warning thresholds include voltage turning points and strain turning points, and the comparing the state parameters with the corresponding warning thresholds under the current working condition to conduct thermal runaway warning includes:

[0190] Compare the voltage signal and the voltage turning point with the strain signal and the strain turning point respectively;

[0191] When the voltage signal is greater than the voltage turning point and the strain signal is greater than the strain turning point, a first-level warning of thermal runaway is given.

[0192] As a further preferred technical solution, the state parameter further includes a gas signal, and the method further includes:

[0193] When the strain decrease rate is greater than a set first threshold and the gas concentration increase rate is greater than a set second threshold, a second-level warning of thermal runaway is given.

[0194] As a further preferred technical solution, the state parameter further includes a battery surface temperature signal, and the method further includes:

[0195] When the voltage decrease rate is greater than a set third threshold and the battery surface temperature increase rate is greater than a set fourth threshold, a third-level warning of thermal runaway is given.

[0196] It should be noted that the first threshold, the second threshold, the third threshold, and the fourth threshold in this embodiment are respectively taken as , , , .

[0197] It should be understood that the first threshold, the second threshold, the third threshold, and the fourth threshold in this embodiment are empirical values obtained by those skilled in the art through a large number of experiments for comparing with the strain decrease rate, the gas concentration increase rate, the voltage decrease rate, and the battery surface temperature increase rate respectively.

[0198] It should be noted that in order to explore the characteristics of battery overcharge thermal runaway under different ambient temperatures and charging rates, the following key warning signal characteristics need to be extracted for the experimental parameters collected during the overcharge thermal runaway experiment. These characteristics will provide a reliable basis for the establishment of a battery thermal runaway warning system:

[0199] Maximum voltage drop rate: The maximum voltage drop rate represents the speed at which the battery voltage begins to drop sharply and is a key indicator for evaluating the acceleration of internal electrochemical reactions in the battery. By analyzing the voltage change curve, the peak value of the voltage drop rate and the time point at which it occurs can be determined.

[0200] Maximum temperature rise rate: The maximum temperature rise rate refers to the maximum speed at which the temperature on the surface or inside of the battery rises per unit time. Generally, as the intensity of the internal reaction of the battery increases, the temperature rise rate will increase significantly. When the temperature rise rate reaches a certain level, it indicates that thermal runaway inside the battery has started or is about to occur. This characteristic can help predict the occurrence time of thermal runaway and provide a basis for taking preventive measures in advance.

[0201] Strain turning point: The strain turning point refers to the critical moment when significant deformation occurs in the battery casing or internal structure during overcharging. During the process of overcharging the battery, which causes the generation of internal gas and an increase in pressure, the strain of the casing will show a turning point, marking the change in the battery structure and the potential risk of rupture. Strain sensors can monitor this turning point in real time and, by analyzing its change trend, give an early warning that the battery is about to enter an out-of-control state.

[0202] Voltage turning point: The voltage turning point refers to the key node where a significant change occurs in the battery voltage curve in the early stage of overcharging. Usually, a turning point occurs after the voltage reaches a certain local peak, marking a change in the battery's electrochemical reaction, which may be accompanied by phenomena such as internal overheating and gas generation. The extraction of the voltage turning point helps to identify the reaction stage inside the battery and provides important information for judging the risk of thermal runaway.

[0203] Exhaust SOC: Exhaust SOC refers to the state of charge when the battery starts to emit gas significantly. As the overcharging reaction progresses, the electrolyte inside the battery decomposes to produce gas. Usually, at this time, the SOC of the battery is close to or exceeds 100%. Exhaust SOC can reflect the charge accumulation situation of the battery before thermal runaway. By monitoring the relationship between the gas emission signal and the battery SOC, the critical point at which the battery enters an out-of-control state can be judged more accurately.

[0204] Furthermore, by extracting these characteristics (maximum pressure drop rate, maximum temperature rise rate, strain turning point, voltage turning point, exhaust SOC), the overcharging thermal runaway characteristics of the battery under different temperature and charging rate conditions can be comprehensively captured. The battery thermal runaway can be divided into three different stages:

[0205] The judgment basis for the first stage is that the battery voltage and strain reach the strain and voltage turning points respectively, which indicates that the electrolyte inside the battery has reacted and started to produce gas, corresponding to the strain turning point and voltage turning point of the extracted characteristics.

[0206] The judgment basis for the second stage is that the battery strain drops sharply and the gas concentration rises sharply because the gas accumulated inside the battery continues to increase and the pressure is too high, eventually causing the battery to rupture and exhaust gas, corresponding to the exhaust SOC of the extracted characteristics.

[0207] The judgment basis for the third stage is that the battery voltage drops sharply and the battery surface temperature rises sharply, which indicates that a large-scale internal short circuit has occurred in the battery and thermal runaway is about to occur, corresponding to the maximum voltage drop rate and the maximum temperature rise rate of the extracted features.

[0208] Under different ambient temperatures and charging rates, the voltage and strain turning points of the battery are different and are determined by using the above-mentioned thermal runaway warning threshold calculation method.

[0209] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0210] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0211] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for calculating a thermal runaway warning threshold, characterized in that: include: The parameters in the parameter set to be optimized of the fuzzy membership function are represented by antibodies in the initial population, and the initial population is iteratively optimized using the immune clone selection algorithm to obtain the global optimal solution of the parameter set. The formula of the fitness function used in the immune clone selection algorithm is expressed as follows: In the formula, They represent the first The strain turning point, Voltage turning point; They represent the experimental measurements. The strain turning point, Voltage turning point; Indicates antibody Adaptability; Indicates the population size; , They represent the difference between the predicted strain turning point and the actual strain turning point, and the difference between the predicted voltage turning point and the actual voltage turning point respectively; Determining an optimal fuzzy membership function based on the global optimal solution of the parameter set; Different ambient temperatures and charging rates are mapped to different fuzzy intervals through the optimal fuzzy membership function, warning thresholds under different combinations of ambient temperatures and charging rates are output, and a corresponding relationship between different combinations of conditions and warning thresholds is constructed. The warning thresholds include voltage turning points and strain turning points.

2. The thermal runaway warning threshold calculation method according to claim 1, characterized in that: The parameters in the parameter set to be optimized of the fuzzy membership function are represented by antibodies in the initial population, and the initial population is iteratively optimized using an immune clonal selection algorithm to obtain a global optimal solution of the parameter set, including: S11, randomly generate an initial population, each antibody represents a parameter of the fuzzy membership function; S12, using the fitness function to evaluate each antibody, and determine whether the evaluation result meets the termination condition, if not, execute step S13, if yes, execute step S16; S13, selecting high-quality antibodies based on the antibody evaluation results, and cloning the selected antibodies to obtain cloned antibodies; S14, performing a mutation operation on the cloned antibody, evaluating the mutated antibody using the fitness function, and competing with the antibodies in the initial population, retaining the current optimal antibody to the next generation, and forming the next generation population; S15, re-execute steps S12 to S14 for the next generation population; S16. Output the global optimal solution of the parameter set of the fuzzy membership function.

3. The thermal runaway warning threshold calculation method according to claim 2, characterized in that: The method further comprises: When the selected antibody is cloned, the number of clones is proportional to the fitness of the selected antibody.

4. The thermal runaway warning threshold calculation method according to claim 2, characterized in that: The method further comprises: When performing mutation operations on cloned antibodies, random perturbations are introduced as follows: In the formula, Indicates the mutated The first variables; Indicates the number before mutation The first decision variables; Indicates multiplication; represents a standard normal distribution random number; indicates that the strength of mutation is usually inversely proportional to the antibody fitness, , Indicates The fitness value of an antibody, It indicates the maximum fitness value in the current population and is used for normalization to make the variation range relatively uniform.

5. The thermal runaway warning threshold calculation method according to claim 2, characterized in that: The formula of the termination condition is expressed as: In the formula, represents the next generation population; It means that the antibody with the best fitness is selected from the combination of the original population and the clone population to form a new generation population; represents the initial population; represents a union; Represents the population after clonal mutation.

6. The thermal runaway warning threshold calculation method according to claim 1, characterized in that: The mapping of different ambient temperatures and charging rates to different fuzzy intervals through the optimal fuzzy membership function, outputting warning thresholds under different combinations of ambient temperatures and charging rates, and constructing corresponding relationships between different combinations of conditions and warning thresholds include: Conducting an overcharge thermal runaway experiment on a lithium battery under different combinations of ambient temperature and charging rate, and continuously recording experimental parameters, wherein the experimental parameters include a voltage change signal, a battery surface temperature change signal, a strain change signal, and a gas change signal; The ambient temperature and charging rate in each combination condition are mapped to different fuzzy intervals through the optimal fuzzy membership function, and the warning thresholds under different combinations of ambient temperature and charging rate are obtained based on the fuzzy rules established according to the experimental data.

7. The thermal runaway warning threshold calculation method according to claim 6, characterized in that: The method further comprises: The ambient temperature and charging rate in each combination condition are normalized and then mapped to different fuzzy intervals through the optimal fuzzy membership function.

8. The thermal runaway warning threshold calculation method according to any one of claims 1 to 7, characterized in that: The types of the fuzzy membership function include trigonometric function and trapezoidal function.

9. A thermal runaway warning threshold calculation device, characterized in that: The device comprises: The parameter iteration optimization module is used to represent the parameters in the parameter set to be optimized of the fuzzy membership function using antibodies in the initial population, and to iteratively optimize the initial population using the immune clone selection algorithm to obtain the global optimal solution of the parameter set. The formula of the fitness function used in the immune clone selection algorithm is expressed as follows: In the formula, They represent the first The strain turning point, Voltage turning point; They represent the experimental measurements. The strain turning point, Voltage turning point; Indicates antibody Adaptability; Indicates the population size; , They represent the difference between the predicted strain turning point and the actual strain turning point, and the difference between the predicted voltage turning point and the actual voltage turning point respectively; An optimal fuzzy membership function determination module, used to determine an optimal fuzzy membership function based on a global optimal solution of the parameter set; The warning threshold migration module is used to map different ambient temperatures and charging rates to different fuzzy intervals through the optimal fuzzy membership function, output the warning thresholds under different combinations of ambient temperatures and charging rates, and construct the corresponding relationship between different combinations of conditions and the warning thresholds, wherein the warning thresholds include voltage turning points and strain turning points.

10. A thermal runaway early warning method based on fuzzy logic, characterized in that: The method comprises: Real-time collection of lithium battery status parameters under certain working conditions; Comparing the state parameter with the corresponding warning threshold under the current working condition to issue a thermal runaway warning; Among them, the warning threshold is a warning threshold calculated based on the thermal runaway warning threshold calculation method described in any one of claims 1 to 8 and corresponding to the ambient temperature and charging rate under the current operating conditions.

11. The thermal runaway early warning method based on fuzzy logic according to claim 10, characterized in that: The state parameter includes a voltage signal and a strain signal, the warning threshold includes a voltage turning point and a strain turning point, and the state parameter is compared with the corresponding warning threshold under the current working condition to perform thermal runaway warning, including: comparing the voltage signal and the voltage turning point with the strain signal and the strain turning point respectively; When the voltage signal is greater than the voltage turning point and the strain signal is greater than the strain turning point, a first-level thermal runaway warning is performed.

12. The thermal runaway early warning method based on fuzzy logic according to claim 11, characterized in that: The state parameter also includes a gas signal, and the method further includes: When the strain decrease rate is greater than the set first threshold and the gas concentration increase rate is greater than the set second threshold, a second-level thermal runaway warning is issued.

13. The thermal runaway early warning method based on fuzzy logic according to claim 11, characterized in that: The state parameter also includes a battery surface temperature signal, and the method further includes: When the voltage drop rate is greater than the set third threshold and the battery surface temperature rise rate is greater than the set fourth threshold, a third-level thermal runaway warning is issued.

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