Lithium battery thermal runaway early warning system and method based on multi-source parameter monitoring

Abstract

The invention belongs to the technical field of battery monitoring, and discloses a lithium battery thermal runaway early warning system and method based on multi-source parameter monitoring. The method comprises the following steps: collecting multi-source battery parameters in real time, and marking the multi-source battery parameters as real-time multi-source battery parameters; the real-time multi-source battery parameters are processed, and battery characteristic parameters are extracted; performing fusion analysis on the real-time multi-source battery parameters and the extracted battery characteristic parameters, and predicting the thermal runaway probability of the lithium battery; judging whether to generate an evaluation instruction according to the thermal runaway probability; if the evaluation instruction is generated, evaluating the thermal runaway type of the lithium battery and starting active prevention and control measures; according to the invention, high-precision and timely monitoring and early warning of lithium battery thermal runaway accidents are realized, the thermal runaway risk is reduced, and the safety and reliability of lithium battery operation are effectively improved, so that the life safety of new energy automobile users is protected.

Description

Technical Field

[0001] The present invention relates to the technical field of battery monitoring, and more specifically, to a lithium battery thermal runaway early warning system and method based on multi-source parameter monitoring. Background Art

[0002] In recent years, with the rapid development of new energy vehicles, lithium batteries, as their core power source, have been widely used due to their advantages such as high power density and high energy density. However, lithium batteries also have certain safety hazards at high energy density, and battery thermal runaway is one of their important safety risks. Thermal runaway refers to the uncontrollable and continuous increase in the internal temperature of the battery due to a series of exothermic chemical reactions, which eventually leads to the complete loss of function of the battery and even causes a fire and explosion. Once thermal runaway occurs, it may trigger serious chain reactions and dangerous consequences, posing a threat to the life safety of new energy vehicle users. Traditional battery thermal runaway early warning methods often have problems such as single monitoring parameters and insufficient early warning sensitivity, making it difficult to fully reflect the occurrence mechanism of thermal runaway.

[0003] Of course, there are also lithium battery thermal runaway warning methods with multi-source monitoring parameters. For example, the patent with publication number CN118151017A discloses a lithium battery thermal runaway warning method and medium; the method includes: collecting relevant parameter data of the lithium battery, the relevant parameter data including ambient temperature, current, surface temperature and voltage; inputting the surface temperature and voltage into a pre-trained surface temperature and voltage prediction model, and outputting the predicted surface temperature and voltage; inputting the predicted surface temperature and voltage into a pre-trained three-level thermal runaway warning model to predict the internal temperature of the lithium battery, and obtaining the corresponding level of thermal runaway warning according to the predicted internal temperature of the lithium battery; this invention has the advantages of accurate warning, reduced model complexity and improved calculation speed.

[0004] However, the above technology mainly predicts the internal temperature of lithium batteries through surface temperature and voltage to achieve thermal runaway early warning, ignoring other parameters that affect thermal runaway, such as gas emissions, electrolyte concentration, etc., and is unable to fully capture the complex dynamics inside lithium batteries; and only through the internal temperature of lithium batteries, it is impossible to accurately assess the type of thermal runaway, making it difficult to provide a basis for subsequent maintenance personnel to trace the root cause.

[0005] In view of this, the present invention proposes a lithium battery thermal runaway early warning system and method based on multi-source parameter monitoring to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned purpose, the present invention provides the following technical solution: a lithium battery thermal runaway early warning method based on multi-source parameter monitoring, comprising:

[0007] Collect multi-source battery parameters in real time and mark them as real-time multi-source battery parameters;

[0008] Process real-time multi-source battery parameters and extract battery characteristic parameters;

[0009] Fusion analysis of real-time multi-source battery parameters and extracted battery characteristic parameters to predict the thermal runaway probability of lithium batteries;

[0010] According to the probability of thermal runaway, determine whether to generate an evaluation instruction; if an evaluation instruction is generated, evaluate the thermal runaway type of the lithium battery;

[0011] If an assessment instruction is generated, active prevention and control measures will be initiated.

[0012] Further, the battery characteristic parameter is the change rate of each parameter in the real-time multi-source battery parameter;

[0013] Methods for extracting battery characteristic parameters include:

[0014] Obtain historical multi-source battery parameters, which are the multi-source battery parameters collected last time; subtract the corresponding parameters in the historical multi-source battery parameters from each parameter in the real-time multi-source battery parameters to obtain the change of each parameter; divide the change of each parameter by the collection interval to obtain the change rate of each parameter in the real-time multi-source battery parameters, and use it as the battery characteristic parameter.

[0015] Furthermore, the method for obtaining the collection interval includes:

[0016] Obtain the historical thermal runaway probability and the historical probability change rate; the historical thermal runaway probability is the thermal runaway probability predicted after the last multi-source battery parameter collection, and the historical probability change rate is the difference in thermal runaway probabilities predicted after the last two multi-source battery parameter collections, divided by the value obtained by the corresponding collection interval when the multi-source battery parameters were collected last time;

[0017] Preset an initial collection interval; the time interval between the first collection of multi-source battery parameters and the second collection of multi-source battery parameters is the initial collection interval; perform weighted summation of historical thermal runaway probabilities and historical probability change rates to calculate an adjustment coefficient; multiply the adjustment coefficient by the initial collection interval to obtain the collection interval.

[0018] Furthermore, the method for predicting the thermal runaway probability of a lithium battery includes:

[0019] The real-time multi-source battery parameters and battery characteristic parameters are used as analysis data, and the analysis data are input into the trained probability prediction model to predict the thermal runaway probability of the lithium battery;

[0020] The training process of the probabilistic prediction model includes:

[0021] G groups of analysis data are collected in advance, and corresponding thermal runaway probabilities are set for the g groups of analysis data, where g is an integer greater than 1;

[0022] The analysis data and the corresponding thermal runaway probability are converted into a corresponding set of feature vectors; each set of feature vectors is used as the input of a probability prediction model, and the probability prediction model takes a set of predicted thermal runaway probabilities corresponding to each set of analysis data as output, and takes the actual thermal runaway probability corresponding to each set of analysis data as a prediction target, and the actual thermal runaway probability is the pre-collected thermal runaway probability corresponding to the analysis data; minimizing the sum of prediction errors of all analysis data is used as a training target; the probability prediction model is trained until the sum of prediction errors reaches convergence and the training is stopped; the probability prediction model is a deep neural network model.

[0023] Furthermore, the method for determining whether to generate an evaluation instruction includes:

[0024] Preset a probability threshold and compare the probability of thermal runaway with the probability threshold;

[0025] If the probability of thermal runaway is greater than or equal to the probability threshold, an evaluation instruction is generated;

[0026] If the probability of thermal runaway is less than the probability threshold, an instruction to be evaluated is generated;

[0027] If an instruction to be evaluated is generated, the historical probability change rate used in each calculation of the collection interval is obtained and marked as the historical change rate; the thermal runaway probability predicted this time is marked as the current probability, the current probability is subtracted from the historical thermal runaway probability, and then divided by the collection interval to obtain the current change rate; each historical change rate and current change rate are sorted according to the order of the corresponding calculation time, and a sorting table is generated; the first n values ​​in the sorting table are obtained in reverse order and marked as analysis change rates, where n is an integer greater than 1; according to the positive order of the sorting table, each analysis change rate is subtracted from the next analysis change rate to obtain n-1 change rate differences; a difference threshold is preset, each change rate difference is compared with the difference threshold, and the number of change rate differences whose values ​​are greater than the difference threshold is counted, and marked as the number of increases; the number of increases is compared with For comparison, if the increase is greater than or equal to Generate an evaluation instruction; if the increase is less than No evaluation instructions are generated.

[0028] Furthermore, the method for evaluating the thermal runaway type of a lithium battery includes:

[0029] A preset type database includes a parameter set corresponding to each thermal runaway type of a lithium battery, the parameter set includes multi-source battery parameters and battery characteristic parameters, wherein one thermal runaway type corresponds to m parameter sets, where m is an integer greater than 1;

[0030] Calculate the similarity between each parameter in the analysis data and the corresponding parameter in each parameter set in the type database respectively; add the similarities corresponding to each parameter set in turn to obtain the sum of the similarities of each parameter set; sort each similarity sum from large to small, and obtain the thermal runaway type corresponding to the parameter set corresponding to the front similarity sum.

[0031] Further, the multi-source battery parameters include physical data and chemical data; the physical data include temperature, voltage and current; the temperature is the temperature of the area where the physical reaction occurs inside the lithium battery cell; the chemical data include gas concentration, gas pressure and electrolyte concentration; the gas concentration includes hydrogen concentration and carbon dioxide concentration; the gas pressure is the total pressure formed by the gas inside the lithium battery; the electrolyte concentration is the concentration of lithium salt in the electrolyte;

[0032] The active prevention and control measures include module isolation, monomer isolation and gas isolation;

[0033] The module isolation method is: the lithium battery is divided into multiple independent modules, and a heat insulation board is constructed between each module using heat insulation materials. When an evaluation instruction is generated, the heat insulation board is turned on by an electronic switch;

[0034] The method of isolating the single cell is as follows: an isolation device is provided between each battery cell inside the lithium battery, and when an evaluation instruction is generated, the connection between each battery cell is cut off by the isolation device;

[0035] The method of gas isolation is: a gas valve is set inside the lithium battery, and when an evaluation instruction is generated, the hot gas inside the lithium battery is discharged from the lithium battery by starting the gas valve.

[0036] Furthermore, the method further includes re-evaluating the thermal runaway type of the lithium battery;

[0037] Methods for re-evaluating the thermal runaway type of lithium batteries include:

[0038] Sort each similarity sum from large to small, retain the similarity sums in the first half of all similarity sums in positive order, and mark the parameter set corresponding to the retained similarity sums as the analysis set; represent the analysis data as the analysis matrix C, and calculate the covariance matrix of each analysis set; calculate the Mahalanobis distance between the analysis data and each group of analysis sets based on the covariance matrix, and mark them as similarity distances; sort each similarity distance from small to large, and obtain the thermal runaway type corresponding to the parameter set corresponding to the similarity distance with the smallest value.

[0039] Furthermore, the method of calculating the covariance matrix of each analysis set includes:

[0040] Add the same data in the analysis set corresponding to each thermal runaway type in sequence and divide by Obtain the mean data corresponding to each thermal runaway type and represent it as a 1×14 matrix, marked as B; subtract the corresponding parameter in the mean data from each parameter in each analysis set corresponding to each thermal runaway type to obtain the standardized parameter corresponding to each analysis set; The standardized parameters corresponding to the analysis set are expressed as a The matrix is ​​labeled A i , A i is the matrix of the i-th analysis set,

[0041] The expression of the covariance matrix ∑i is:

[0042] In the formula, ∑ i is the covariance matrix of the ith analysis set, A i T is the matrix A i The transpose of

[0043] The expression of Mahalanobis distance is:

[0044] In the formula, E i (C) is the Mahalanobis distance between the analysis matrix C and the matrix corresponding to the i-th analysis set, is the inverse matrix of the covariance matrix of the ith analysis set.

[0045] The lithium battery thermal runaway early warning system based on multi-source parameter monitoring implements the lithium battery thermal runaway early warning method based on multi-source parameter monitoring, including:

[0046] A parameter acquisition module, used for real-time acquisition of multi-source battery parameters and marking them as real-time multi-source battery parameters;

[0047] A parameter processing module is used to process real-time multi-source battery parameters and extract battery characteristic parameters;

[0048] Fusion analysis module, used to perform fusion analysis on real-time multi-source battery parameters and extracted battery characteristic parameters to predict the probability of thermal runaway of lithium batteries;

[0049] A type assessment module is used to determine whether to generate an assessment instruction based on the probability of thermal runaway; if an assessment instruction is generated, the thermal runaway type of the lithium battery is assessed;

[0050] The active prevention and control module is used to initiate active prevention and control measures if an evaluation instruction is generated.

[0051] The technical effects and advantages of the lithium battery thermal runaway early warning system and method based on multi-source parameter monitoring of the present invention are as follows:

[0052] 1. Through comprehensive collection of multi-dimensional physical and chemical parameters of lithium batteries, the parameter change rate characteristics are proposed, and the parameter collection interval is dynamically adjusted according to the characteristic parameters, which is more in line with the lithium battery state change rate and improves the timeliness of early warning; deep learning technology is used to fully explore the implicit correlation between multi-source battery parameters and accurately predict the probability of heat loss; then the corresponding evaluation instructions are generated to accurately judge and identify the type of thermal runaway, provide a basis for subsequent maintenance personnel to trace the root cause of thermal runaway accidents, and initiate multiple prevention and control measures to form a closed-loop management; achieve high-precision and timely monitoring and early warning of lithium battery thermal runaway accidents, reduce the risk of thermal runaway, and effectively improve the safety and reliability of lithium battery operation, thereby protecting the lives of new energy vehicle users.

[0053] 2. By analyzing the covariance law between different parameters, the inherent linkage law between multi-source battery parameters is grasped; and by calculating the Mahalanobis distance between the analysis data and each analysis set, the parameter correlation degree and similarity distance are reflected at the same time, which enhances the depth and rigor of type judgment; compared with the method of judging the similarity of each parameter separately, it can more comprehensively and microscopically evaluate the internal logical relationship behind multi-source information, effectively improve and standardize the ability to identify the type of thermal runaway accidents, and provide a more accurate basis for subsequent maintenance personnel to trace the root cause, thereby providing better technical guarantee for the safe operation of lithium batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of a lithium battery thermal runaway early warning system based on multi-source parameter monitoring according to Example 1 of the present invention;

[0055] Figure 2 This is a schematic diagram of a lithium battery thermal runaway early warning system based on multi-source parameter monitoring according to Embodiment 2 of the present invention;

[0056] Figure 3 This is a flow chart of a lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to Example 3 of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] See also Figure 1As shown, the lithium battery thermal runaway warning system based on multi-source parameter monitoring described in this embodiment includes a parameter acquisition module, a parameter processing module, a fusion analysis module, a type assessment module and an active prevention and control module; each module is connected by wired and / or wireless means to realize data transmission between modules;

[0060] The parameter acquisition module is used to acquire multi-source battery parameters in real time and mark them as real-time multi-source battery parameters.

[0061] Multi-source battery parameters include physical and chemical data;

[0062] Physical data includes temperature, voltage and current; the temperature is the temperature of the area where the physical reaction occurs inside the lithium battery cell; the temperature is obtained by the temperature sensor built into the lithium battery; the voltage is obtained by the voltage sensor installed at the positive and negative terminals of the battery cell shell in the lithium battery; the current is obtained by the current sensor installed at the positive and negative connecting wires of the battery cell shell in the lithium battery.

[0063] Chemical data include gas concentration, gas pressure, and electrolyte concentration;

[0064] Gas concentrations include hydrogen concentration and carbon dioxide concentration; gas pressure is the total pressure formed by the gas inside the lithium battery; electrolyte concentration is the concentration of lithium salt (such as LiPF6, LiBF4, etc.) in the electrolyte; gas concentration is obtained by a MEMS gas sensor installed between the lithium battery casing and the cover; gas pressure is obtained by a pressure sensor (such as a piezoelectric sensor or a strain gauge) installed on the lithium battery casing close to the gas cavity; electrolyte concentration is obtained using an electrochemical sensor installed on the lithium battery electrode plate.

[0065] It should be noted that temperature rise may be an early signal of thermal runaway. High temperature will accelerate the chemical reaction inside the lithium battery, leading to further temperature rise and thermal runaway. Voltage changes may reflect abnormal state of the lithium battery, such as overcharging, over-discharging or short circuit, which may cause the risk of thermal runaway. Excessive current may cause the lithium battery to overheat, increasing the risk of thermal runaway. The production of hydrogen and carbon dioxide and the concentration of the electrolyte are related to the chemical reaction inside the lithium battery. When the concentration of hydrogen and carbon dioxide increases and the concentration of the electrolyte changes, it means that the chemical reaction rate inside the lithium battery has intensified, which may cause the lithium battery to overheat and cause thermal runaway. The increase in gas pressure will cause the lithium battery casing to rupture or leak, which may cause thermal runaway of the lithium battery.

[0066] The parameter processing module is used to process real-time multi-source battery parameters and extract battery characteristic parameters.

[0067] The battery characteristic parameter is the change rate of each parameter in the real-time multi-source battery parameters.

[0068] Methods for extracting battery characteristic parameters include:

[0069] Obtain historical multi-source battery parameters, which are the multi-source battery parameters collected last time; subtract the corresponding parameters in the historical multi-source battery parameters from each parameter in the real-time multi-source battery parameters to obtain the change of each parameter; divide the change of each parameter by the collection interval to obtain the change rate of each parameter in the real-time multi-source battery parameters, and use it as the battery characteristic parameter.

[0070] Methods for obtaining the collection interval include:

[0071] Obtain the historical thermal runaway probability and the historical probability change rate; the historical thermal runaway probability is the thermal runaway probability predicted after the last multi-source battery parameter collection, and the historical probability change rate is the difference in thermal runaway probabilities predicted after the first two multi-source battery parameter collections, divided by the value obtained by the corresponding collection interval when the multi-source battery parameters were collected last time; illustratively, a total of three multi-source battery parameter collections are performed, and the real-time multi-source battery parameters are the multi-source battery parameters collected for the third time. The thermal runaway probability predicted after the second multi-source battery parameter collection is subtracted from the thermal runaway probability predicted after the first multi-source battery parameter collection to obtain the probability difference; obtain the collection interval corresponding to the second multi-source battery parameter collection, and mark it as the secondary interval, that is, the time interval between the first and second multi-source battery parameter collections; divide the probability difference by the secondary interval to obtain the historical probability change rate;

[0072] The initial collection interval is preset, and the initial collection interval is preset by those skilled in the art according to the rate of change of the multi-source battery parameters under normal operation of the lithium battery; the time interval between the first collection of the multi-source battery parameters and the second collection of the multi-source battery parameters is the initial collection interval; the historical thermal runaway probability and the historical probability change rate are weighted and summed to calculate the adjustment coefficient; the adjustment coefficient is multiplied by the initial collection interval to obtain the collection interval; the expression of the adjustment coefficient is: Where TJ is the adjustment coefficient, LG is the historical thermal runaway probability, LB is the historical probability change rate, and ω1 and ω2 are both preset weight coefficients.

[0073] It should be noted that the specific value of the weight coefficient in the formula can be set according to actual conditions. The weight coefficient reflects the influence of the historical thermal runaway probability and the historical probability change rate on the collection interval. Technical personnel in this field can preset the corresponding weight coefficient according to the actual influence of the thermal runaway probability and the historical probability change rate on the collection interval, so as to accurately obtain the collection interval of parameters under different states of the lithium battery.

[0074] It should be understood that the greater the historical thermal runaway probability and the historical probability change rate, the faster the internal state of the lithium battery deteriorates and the faster the multi-source battery parameters change. Therefore, the corresponding collection interval should be smaller to timely grasp the dynamic changes in the internal state of the lithium battery and provide more accurate thermal runaway warnings, and vice versa.

[0075] The fusion analysis module is used to perform fusion analysis on real-time multi-source battery parameters and extracted battery characteristic parameters to predict the probability of thermal runaway of lithium batteries.

[0076] Methods for predicting the probability of thermal runaway of lithium batteries include:

[0077] The real-time multi-source battery parameters and battery characteristic parameters are used as analysis data, and the analysis data are input into the trained probability prediction model to predict the thermal runaway probability of the lithium battery.

[0078] The specific training process of the probability prediction model includes:

[0079] G groups of analysis data are collected in advance, and corresponding thermal runaway probabilities are set for the g groups of analysis data, where g is an integer greater than 1; the thermal runaway probabilities corresponding to the analysis data are collected by a technician in the field during the historical lithium battery thermal runaway analysis process, and the g groups of analysis data are collected for experiments. The lithium battery is continuously operated for multiple times under the conditions of each group of analysis data, and whether thermal runaway will occur during each operation is detected, and the corresponding thermal runaway probability is determined according to the experimental results; the corresponding thermal runaway probabilities are set for the g groups of analysis data in sequence;

[0080] The analysis data and the corresponding thermal runaway probability are converted into a corresponding set of feature vectors; each set of feature vectors is used as the input of the probability prediction model, and the probability prediction model takes a set of predicted thermal runaway probabilities corresponding to each set of analysis data as output, and takes the actual thermal runaway probability corresponding to each set of analysis data as the prediction target, and the actual thermal runaway probability is the pre-collected thermal runaway probability corresponding to the analysis data; the training target is to minimize the sum of the prediction errors of all analysis data; wherein, the calculation formula of the prediction error is η K =(β K -ε K ) 2 , where η K is the prediction error, K is the group number of the eigenvector corresponding to the analyzed data, β K is the predicted thermal runaway probability corresponding to the Kth group of analysis data, ε K is the actual thermal runaway probability corresponding to the Kth group of analysis data; the probability prediction model is trained until the sum of the prediction errors reaches convergence and the training is stopped.

[0081] The above-mentioned probability prediction model is specifically a deep neural network model; it includes an input layer, a hidden layer and an output layer; each hidden layer includes multiple neurons, each neuron is connected to the neurons in the next layer, and the connection contains weights, which determine the importance and influence of data transmission in the neural network; an activation function is applied to each neuron between the hidden layer and the output layer, and the activation function reflects nonlinearity, allowing the network to learn more complex patterns and features.

[0082] The type assessment module is used to determine whether to generate an assessment instruction based on the probability of thermal runaway; if an assessment instruction is generated, the thermal runaway type of the lithium battery is assessed.

[0083] Methods for determining whether to generate an evaluation instruction include:

[0084] Preset probability threshold, the probability threshold is preset by those skilled in the art according to the actual situation of thermal runaway of the lithium battery;

[0085] Comparing the probability of thermal runaway with a probability threshold;

[0086] If the probability of thermal runaway is greater than or equal to the probability threshold, an assessment instruction is generated; this indicates that the probability of thermal runaway of the lithium battery is high, and it is necessary to promptly assess the type of thermal runaway and initiate active prevention and control measures to prevent the lithium battery from further heating up and causing thermal runaway;

[0087] If the probability of thermal runaway is less than the probability threshold, an instruction to be evaluated is generated; this indicates that the probability of thermal runaway of the lithium battery is low, but further analysis is needed. If the rate of change of the probability of thermal runaway is large for many times, it still indicates that the risk of thermal runaway of the lithium battery is increased.

[0088] If an instruction to be evaluated is generated, the historical probability change rate used in each calculation of the collection interval is obtained and marked as the historical change rate; the thermal runaway probability predicted this time is marked as the current probability, the current probability is subtracted from the historical thermal runaway probability, and then divided by the collection interval to obtain the current change rate; each historical change rate and the current change rate are sorted according to the order of the corresponding calculation time, and a sorting table is generated; the first n values ​​in the sorting table are obtained in reverse order and marked as the analysis change rate, where n is an integer greater than 1, and the specific value of n is determined by a person skilled in the art. The technicians set it according to the actual situation; according to the positive order of the sorting table, each analysis change rate is subtracted from the next analysis change rate to obtain n-1 change rate differences; for example, the sorting table has 8, 7, 5, and 4 in reverse order; then the change rate difference is 8-7=1, 7-5=2, 5-4=1, and there are 3 change rate differences in total; a difference threshold is preset, each change rate difference is compared with the difference threshold, and the number of change rate differences with a value greater than the difference threshold is counted and marked as the number of increases; the number of increases is compared with For comparison, if the increase is greater than or equal to Generate an evaluation instruction; if the increase is less than No evaluation instruction is generated; the difference threshold is preset by those skilled in the art according to actual conditions.

[0089] Methods for evaluating the type of thermal runaway of lithium batteries include:

[0090] A preset type database includes a parameter set corresponding to each thermal runaway type of a lithium battery, the parameter set includes multi-source battery parameters and battery characteristic parameters, wherein one thermal runaway type corresponds to m parameter sets, and m is an integer greater than 1; the type database is constructed by a technician in this field who collects the multi-source battery parameters and battery characteristic parameters corresponding to each thermal runaway type for multiple times when thermal runaway occurs in lithium batteries in the past; thermal runaway types include thermal runaway during normal operation of lithium batteries (thermal runaway caused by overcharging, over-discharging, short circuit, etc.), thermal runaway caused by mechanical abuse (thermal runaway caused by mechanical damage such as extrusion and puncture of lithium batteries), etc.;

[0091] Calculate the similarity between each parameter in the analysis data and the corresponding parameter in each parameter set in the type database. The similarity calculation method is, for example, Euclidean distance, cosine similarity, Manhattan distance, etc. The similarity calculation method is a prior art and will not be described in detail here. Add the similarities corresponding to each parameter set in turn to obtain the sum of the similarities of each parameter set. Sort each similarity sum from large to small to obtain the thermal runaway type corresponding to the parameter set corresponding to the similarity sum at the front.

[0092] It should be noted that the purpose of evaluating the thermal runaway type of lithium batteries is to understand the cause of thermal runaway accidents: different types of thermal runaway may be caused by different factors, and identifying the type helps to trace the root cause of the accident; optimize battery design: based on the type analysis, it helps technical personnel in this field to improve the structural design of lithium batteries and reduce the possibility of similar accidents happening again; improve accident handling efficiency: clarifying the thermal runaway type helps maintenance personnel to maintain lithium batteries faster, control the scope of thermal runaway in a timely manner, and reduce economic losses and safety hazards.

[0093] The active prevention and control module is used to initiate active prevention and control measures to prevent heat spread if an evaluation instruction is generated.

[0094] Active prevention and control measures include module isolation, monomer isolation and gas isolation;

[0095] The module isolation method is: the lithium battery is divided into multiple independent modules, and heat-resistant and heat-insulating materials are used to build heat insulation boards between each module. When an evaluation instruction is generated, the heat insulation board is turned on through an electronic switch (such as a relay, a circuit breaker, etc.) to block the spread of heat;

[0096] The method of single-cell isolation is: an isolation device, such as a thermal fuse or a solenoid valve, is set between each battery cell inside the lithium battery. When an evaluation instruction is generated, the connection between each battery cell can be cut off through the isolation device to limit heat diffusion;

[0097] The method of gas isolation is: a gas valve is set inside the lithium battery. When an evaluation instruction is generated, the gas valve is activated to quickly discharge the hot gas inside the lithium battery out of the lithium battery to prevent the heat from spreading.

[0098] It should be noted that the purpose of initiating active prevention and control measures is to quickly block the conduction of heat in the lithium battery. Compared with passive heat dissipation, active isolation can cut off the heat conduction path to the maximum extent and prevent the spread of heat. In addition, multiple isolation measures can be reasonably combined to perform superimposed prevention from multiple angles to improve the safety of lithium batteries.

[0099] This embodiment comprehensively collects multi-dimensional physical and chemical parameters of lithium batteries, proposes parameter change rate characteristics, and dynamically adjusts the parameter collection interval according to the characteristic parameters, so as to better fit the lithium battery state change rate and improve the timeliness of early warning. It uses deep learning technology to fully explore the implicit correlation between multi-source battery parameters and accurately predict the probability of heat loss. It then generates corresponding evaluation instructions, accurately judges and identifies the type of thermal runaway, provides a basis for subsequent maintenance personnel to trace the root cause of thermal runaway accidents, and initiates multiple prevention and control measures to form a closed-loop management. It achieves high-precision and timely monitoring and early warning of lithium battery thermal runaway accidents, reduces the risk of thermal runaway, and effectively improves the safety and reliability of lithium battery operation, thereby protecting the lives of new energy vehicle users.

[0100] Example 2

[0101] See also Figure 2 As shown, this embodiment further improves the design on the basis of embodiment 1. In embodiment 1, the thermal runaway type evaluation is achieved by respectively calculating the cosine similarity, Euclidean distance, etc. of each parameter in the analysis data and the corresponding parameter in each parameter set in the type database; however, this method does not fully consider the joint variation form between different parameters; for example, when a short circuit or overcharge occurs in a lithium battery, the temperature rises and the voltage drops; only by independently analyzing the similarity of the temperature and voltage and adding them, the thermal runaway type cannot be effectively evaluated, which leads to the misjudgment of the thermal runaway type; therefore, this embodiment provides a lithium battery thermal runaway early warning system based on multi-source parameter monitoring, and also includes a secondary evaluation module;

[0102] The secondary assessment module is used to re-evaluate the thermal runaway type of lithium batteries.

[0103] Methods for re-evaluating the thermal runaway type of lithium batteries include:

[0104] Sort each similarity sum from large to small, retain the similarity sums in the first half of all similarity sums in positive order, and mark the parameter set corresponding to the retained similarity sums as the analysis set; represent the analysis data as an analysis matrix C, calculate the covariance matrix of each analysis set to reflect the correlation between different parameters in the analysis data; calculate the Mahalanobis distance between the analysis data and each group of analysis sets based on the covariance matrix, and mark them as similarity distances; sort each similarity distance from small to large, and obtain the thermal runaway type corresponding to the parameter set corresponding to the similarity distance with the smallest value.

[0105] The method for calculating the covariance matrix for each analysis set includes:

[0106] Add the same data in the analysis set corresponding to each thermal runaway type in sequence and divide by Obtain the mean data corresponding to each thermal runaway type and represent it as a 1×14 matrix, marked as B; subtract the corresponding parameter in the mean data from each parameter in each analysis set corresponding to each thermal runaway type to obtain the standardized parameter corresponding to each analysis set; The standardized parameters corresponding to the analysis set are expressed as a The matrix is ​​labeled A i , A i is the matrix of the i-th analysis set, where 14 is the number of parameters in the standardized parameters.

[0107] The expression of the covariance matrix ∑i is:

[0108] In the formula, ∑ i is the covariance matrix of the ith analysis set, A i T is the matrix A i The transpose of .

[0109] The expression of Mahalanobis distance is:

[0110] In the formula, E i (C) is the Mahalanobis distance between the analysis matrix C and the matrix corresponding to the i-th analysis set, is the inverse matrix of the covariance matrix of the ith analysis set.

[0111] This embodiment grasps the inherent linkage law between multi-source battery parameters by analyzing the covariance law between different parameters; and reflects the parameter correlation degree and similarity distance by calculating the Mahalanobis distance between the analysis data and each analysis set, thereby enhancing the depth and rigor of type judgment; compared with the method of judging the similarity of each parameter separately, it can more comprehensively and microscopically evaluate the inherent logical relationship behind multi-source information, effectively improve and standardize the ability to identify the type of thermal runaway accidents, and provide a more accurate basis for subsequent maintenance personnel to trace the root cause, thereby providing better technical guarantee for the safe operation of lithium batteries.

[0112] Example 3

[0113] See also Figure 3 As shown, the parts not described in detail in this embodiment refer to the description of embodiment 1 and embodiment 2, and a lithium battery thermal runaway early warning method based on multi-source parameter monitoring is provided, and the method includes:

[0114] Collect multi-source battery parameters in real time and mark them as real-time multi-source battery parameters;

[0115] Process real-time multi-source battery parameters and extract battery characteristic parameters;

[0116] Fusion analysis of real-time multi-source battery parameters and extracted battery characteristic parameters to predict the thermal runaway probability of lithium batteries;

[0117] According to the probability of thermal runaway, determine whether to generate an evaluation instruction; if an evaluation instruction is generated, evaluate the thermal runaway type of the lithium battery;

[0118] If an assessment instruction is generated, active prevention and control measures will be initiated.

[0119] Further, the battery characteristic parameter is the change rate of each parameter in the real-time multi-source battery parameter;

[0120] Methods for extracting battery characteristic parameters include:

[0121] Obtain historical multi-source battery parameters, which are the multi-source battery parameters collected last time; subtract the corresponding parameters in the historical multi-source battery parameters from each parameter in the real-time multi-source battery parameters to obtain the change of each parameter; divide the change of each parameter by the collection interval to obtain the change rate of each parameter in the real-time multi-source battery parameters, and use it as the battery characteristic parameter.

[0122] Furthermore, the method for obtaining the collection interval includes:

[0123] Obtain the historical thermal runaway probability and the historical probability change rate; the historical thermal runaway probability is the thermal runaway probability predicted after the last multi-source battery parameter collection, and the historical probability change rate is the difference in thermal runaway probabilities predicted after the last two multi-source battery parameter collections, divided by the value obtained by the corresponding collection interval when the multi-source battery parameters were collected last time;

[0124] Preset an initial collection interval; the time interval between the first collection of multi-source battery parameters and the second collection of multi-source battery parameters is the initial collection interval; perform weighted summation of historical thermal runaway probabilities and historical probability change rates to calculate an adjustment coefficient; multiply the adjustment coefficient by the initial collection interval to obtain the collection interval.

[0125] Furthermore, the method for predicting the thermal runaway probability of a lithium battery includes:

[0126] The real-time multi-source battery parameters and battery characteristic parameters are used as analysis data, and the analysis data are input into the trained probability prediction model to predict the thermal runaway probability of the lithium battery;

[0127] The training process of the probabilistic prediction model includes:

[0128] G groups of analysis data are collected in advance, and corresponding thermal runaway probabilities are set for the g groups of analysis data, where g is an integer greater than 1;

[0129] The analysis data and the corresponding thermal runaway probability are converted into a corresponding set of feature vectors; each set of feature vectors is used as the input of a probability prediction model, and the probability prediction model takes a set of predicted thermal runaway probabilities corresponding to each set of analysis data as output, and takes the actual thermal runaway probability corresponding to each set of analysis data as a prediction target, and the actual thermal runaway probability is the pre-collected thermal runaway probability corresponding to the analysis data; minimizing the sum of prediction errors of all analysis data is used as a training target; the probability prediction model is trained until the sum of prediction errors reaches convergence and the training is stopped; the probability prediction model is a deep neural network model.

[0130] Furthermore, the method for determining whether to generate an evaluation instruction includes:

[0131] Preset a probability threshold and compare the probability of thermal runaway with the probability threshold;

[0132] If the probability of thermal runaway is greater than or equal to the probability threshold, an evaluation instruction is generated;

[0133] If the probability of thermal runaway is less than the probability threshold, an instruction to be evaluated is generated;

[0134] If an instruction to be evaluated is generated, the historical probability change rate used in each calculation of the collection interval is obtained and marked as the historical change rate; the thermal runaway probability predicted this time is marked as the current probability, the current probability is subtracted from the historical thermal runaway probability, and then divided by the collection interval to obtain the current change rate; each historical change rate and current change rate are sorted according to the order of the corresponding calculation time, and a sorting table is generated; the first n values ​​in the sorting table are obtained in reverse order and marked as analysis change rates, where n is an integer greater than 1; according to the positive order of the sorting table, each analysis change rate is subtracted from the next analysis change rate to obtain n-1 change rate differences; a difference threshold is preset, each change rate difference is compared with the difference threshold, and the number of change rate differences whose values ​​are greater than the difference threshold is counted, and marked as the number of increases; the number of increases is compared with For comparison, if the increase is greater than or equal to Generate an evaluation instruction; if the increase is less than No evaluation instructions are generated.

[0135] Furthermore, the method for evaluating the thermal runaway type of a lithium battery includes:

[0136] A preset type database includes a parameter set corresponding to each thermal runaway type of a lithium battery, the parameter set includes multi-source battery parameters and battery characteristic parameters, wherein one thermal runaway type corresponds to m parameter sets, where m is an integer greater than 1;

[0137] Calculate the similarity between each parameter in the analysis data and the corresponding parameter in each parameter set in the type database respectively; add the similarities corresponding to each parameter set in turn to obtain the sum of the similarities of each parameter set; sort each similarity sum from large to small, and obtain the thermal runaway type corresponding to the parameter set corresponding to the front similarity sum.

[0138] Further, the multi-source battery parameters include physical data and chemical data; the physical data include temperature, voltage and current; the temperature is the temperature of the area where the physical reaction occurs inside the lithium battery cell; the chemical data include gas concentration, gas pressure and electrolyte concentration; the gas concentration includes hydrogen concentration and carbon dioxide concentration; the gas pressure is the total pressure formed by the gas inside the lithium battery; the electrolyte concentration is the concentration of lithium salt in the electrolyte;

[0139] The active prevention and control measures include module isolation, monomer isolation and gas isolation;

[0140] The module isolation method is: the lithium battery is divided into multiple independent modules, and a heat insulation board is constructed between each module using heat insulation materials. When an evaluation instruction is generated, the heat insulation board is turned on by an electronic switch;

[0141] The method of isolating the single cell is as follows: an isolation device is provided between each battery cell inside the lithium battery, and when an evaluation instruction is generated, the connection between each battery cell is cut off by the isolation device;

[0142] The method of gas isolation is: a gas valve is set inside the lithium battery, and when an evaluation instruction is generated, the hot gas inside the lithium battery is discharged from the lithium battery by starting the gas valve.

[0143] Furthermore, the method further comprises: re-evaluating the thermal runaway type of the lithium battery;

[0144] Methods for re-evaluating the thermal runaway type of lithium batteries include:

[0145] Sort each similarity sum from large to small, retain the similarity sums in the first half of all similarity sums in positive order, and mark the parameter set corresponding to the retained similarity sums as the analysis set; represent the analysis data as the analysis matrix C, and calculate the covariance matrix of each analysis set; calculate the Mahalanobis distance between the analysis data and each group of analysis sets based on the covariance matrix, and mark them as similarity distances; sort each similarity distance from small to large, and obtain the thermal runaway type corresponding to the parameter set corresponding to the similarity distance with the smallest value.

[0146] Furthermore, the method of calculating the covariance matrix of each analysis set includes:

[0147] Add the same data in the analysis set corresponding to each thermal runaway type in sequence and divide by Obtain the mean data corresponding to each thermal runaway type and represent it as a 1×14 matrix, marked as B; subtract the corresponding parameter in the mean data from each parameter in each analysis set corresponding to each thermal runaway type to obtain the standardized parameter corresponding to each analysis set; The standardized parameters corresponding to the analysis set are expressed as a The matrix is ​​labeled A i , A i That is the matrix of the i-th analysis set,

[0148] The expression of the covariance matrix ∑i is:

[0149] In the formula, ∑ i is the covariance matrix of the ith analysis set, A i T is the matrix A i The transpose of

[0150] The expression of Mahalanobis distance is:

[0151] In the formula, E i (C) is the Mahalanobis distance between the analysis matrix C and the matrix corresponding to the i-th analysis set, is the inverse matrix of the covariance matrix of the ith analysis set.

[0152] Example 4

[0153] The present application also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories store computer-readable codes, and when the computer-readable codes are executed by one or more processors, the lithium battery thermal runaway early warning method based on multi-source parameter monitoring as described above may be executed.

[0154] The method or system according to the implementation mode of the present application can also be implemented with the help of the architecture of the electronic device shown in the present application. The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output, a hard disk, etc. A storage device in the electronic device, such as a ROM or a hard disk, can store the lithium battery thermal runaway early warning method based on multi-source parameter monitoring provided in the present application. Furthermore, the electronic device may also include a user interface. Of course, the architecture shown in the present application is only exemplary. When implementing different devices, one or more components in the electronic device shown in the present application may be omitted according to actual needs.

[0155] Example 5

[0156] One embodiment of the present application discloses a computer-readable storage medium. Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by the processor, the lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0157] In addition, according to the implementation of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, for example: a lithium battery thermal runaway early warning method based on multi-source parameter monitoring. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0158] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0159] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A lithium battery thermal runaway early warning method based on multi-source parameter monitoring, characterized in that: include: Collect multi-source battery parameters in real time and mark them as real-time multi-source battery parameters; Process real-time multi-source battery parameters and extract battery characteristic parameters; Fusion analysis of real-time multi-source battery parameters and extracted battery characteristic parameters to predict the thermal runaway probability of lithium batteries; According to the probability of thermal runaway, determine whether to generate an evaluation instruction; if an evaluation instruction is generated, evaluate the thermal runaway type of the lithium battery; If an assessment instruction is generated, active prevention and control measures will be initiated.

2. The lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to claim 1, characterized in that: The battery characteristic parameter is the change rate of each parameter in the real-time multi-source battery parameter; Methods for extracting battery characteristic parameters include: Obtain historical multi-source battery parameters, which are the multi-source battery parameters collected last time; subtract the corresponding parameters in the historical multi-source battery parameters from each parameter in the real-time multi-source battery parameters to obtain the change of each parameter; divide the change of each parameter by the collection interval to obtain the change rate of each parameter in the real-time multi-source battery parameters, and use it as the battery characteristic parameter.

3. The lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to claim 2 is characterized in that: Methods for obtaining the collection interval include: Obtain the historical thermal runaway probability and the historical probability change rate; the historical thermal runaway probability is the thermal runaway probability predicted after the last multi-source battery parameter collection, and the historical probability change rate is the difference in thermal runaway probabilities predicted after the last two multi-source battery parameter collections, divided by the value obtained by the corresponding collection interval when the multi-source battery parameters were collected last time; Preset an initial collection interval; the time interval between the first collection of multi-source battery parameters and the second collection of multi-source battery parameters is the initial collection interval; perform weighted summation of historical thermal runaway probabilities and historical probability change rates to calculate an adjustment coefficient; multiply the adjustment coefficient by the initial collection interval to obtain the collection interval.

4. The lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to claim 3 is characterized in that: The method for predicting the thermal runaway probability of a lithium battery comprises: The real-time multi-source battery parameters and battery characteristic parameters are used as analysis data, and the analysis data are input into the trained probability prediction model to predict the thermal runaway probability of the lithium battery; The training process of the probabilistic prediction model includes: G groups of analysis data are collected in advance, and corresponding thermal runaway probabilities are set for the g groups of analysis data, where g is an integer greater than 1; The analysis data and the corresponding thermal runaway probability are converted into a corresponding set of feature vectors; each set of feature vectors is used as the input of a probability prediction model, and the probability prediction model takes a set of predicted thermal runaway probabilities corresponding to each set of analysis data as output, and takes the actual thermal runaway probability corresponding to each set of analysis data as a prediction target, and the actual thermal runaway probability is the pre-collected thermal runaway probability corresponding to the analysis data; minimizing the sum of prediction errors of all analysis data is used as a training target; the probability prediction model is trained until the sum of prediction errors reaches convergence and the training is stopped; the probability prediction model is a deep neural network model.

5. The lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to claim 4 is characterized in that: The method for determining whether to generate an evaluation instruction comprises: Preset a probability threshold and compare the probability of thermal runaway with the probability threshold; If the probability of thermal runaway is greater than or equal to the probability threshold, an evaluation instruction is generated; If the probability of thermal runaway is less than the probability threshold, an instruction to be evaluated is generated; If an instruction to be evaluated is generated, the historical probability change rate used in each calculation of the collection interval is obtained and marked as the historical change rate; the thermal runaway probability predicted this time is marked as the current probability, the current probability is subtracted from the historical thermal runaway probability, and then divided by the collection interval to obtain the current change rate; each historical change rate and current change rate are sorted according to the order of the corresponding calculation time, and a sorting table is generated; the first n values ​​in the sorting table are obtained in reverse order and marked as analysis change rates, where n is an integer greater than 1; according to the positive order of the sorting table, each analysis change rate is subtracted from the next analysis change rate to obtain n-1 change rate differences; a difference threshold is preset, each change rate difference is compared with the difference threshold, and the number of change rate differences whose values ​​are greater than the difference threshold is counted, and marked as the number of increases; the number of increases is compared with For comparison, if the increase is greater than or equal to Generate an evaluation instruction; if the increase is less than No evaluation instructions are generated.

6. The lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to claim 5, characterized in that: The method for evaluating the thermal runaway type of a lithium battery comprises: A preset type database includes a parameter set corresponding to each thermal runaway type of a lithium battery, the parameter set includes multi-source battery parameters and battery characteristic parameters, wherein one thermal runaway type corresponds to m parameter sets, where m is an integer greater than 1; Calculate the similarity between each parameter in the analysis data and the corresponding parameter in each parameter set in the type database respectively; add the similarities corresponding to each parameter set in turn to obtain the sum of the similarities of each parameter set; sort each similarity sum from large to small, and obtain the thermal runaway type corresponding to the parameter set corresponding to the front similarity sum.

7. The lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to claim 6, characterized in that: The multi-source battery parameters include physical data and chemical data; the physical data include temperature, voltage and current; the temperature is the temperature of the area where the physical reaction occurs inside the lithium battery cell; the chemical data include gas concentration, gas pressure and electrolyte concentration; the gas concentration includes hydrogen concentration and carbon dioxide concentration; the gas pressure is the total pressure formed by the gas inside the lithium battery; the electrolyte concentration is the concentration of lithium salt in the electrolyte; The active prevention and control measures include module isolation, monomer isolation and gas isolation; The module isolation method is: the lithium battery is divided into multiple independent modules, and a heat insulation board is constructed between each module using heat insulation materials. When an evaluation instruction is generated, the heat insulation board is turned on by an electronic switch; The method of isolating the single cell is as follows: an isolation device is provided between each battery cell inside the lithium battery, and when an evaluation instruction is generated, the connection between each battery cell is cut off by the isolation device; The method of gas isolation is: a gas valve is set inside the lithium battery, and when an evaluation instruction is generated, the hot gas inside the lithium battery is discharged from the lithium battery by starting the gas valve.

8. The lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to claim 7 is characterized in that: The method further includes: re-evaluating the thermal runaway type of the lithium battery; Methods for re-evaluating the thermal runaway type of lithium batteries include: Sort each similarity sum from large to small, retain the similarity sums in the first half of all similarity sums in positive order, and mark the parameter set corresponding to the retained similarity sums as the analysis set; represent the analysis data as the analysis matrix C, and calculate the covariance matrix of each analysis set; calculate the Mahalanobis distance between the analysis data and each group of analysis sets based on the covariance matrix, and mark them as similarity distances; sort each similarity distance from small to large, and obtain the thermal runaway type corresponding to the parameter set corresponding to the similarity distance with the smallest value.

9. The lithium battery thermal runaway early warning method based on multi-source parameter monitoring according to claim 8, characterized in that: The method for calculating the covariance matrix for each analysis set includes: Add the same data in the analysis set corresponding to each thermal runaway type in sequence and divide by Obtain the mean data corresponding to each thermal runaway type and represent it as a 1×14 matrix, marked as B; subtract the corresponding parameter in the mean data from each parameter in each analysis set corresponding to each thermal runaway type to obtain the standardized parameter corresponding to each analysis set; The standardized parameter corresponding to the analysis set is expressed as a The matrix is ​​labeled A i , A i That is the matrix of the i-th analysis set, The expression of the covariance matrix ∑i is: In the formula, ∑ i is the covariance matrix of the ith analysis set, A i T is the matrix A i The transpose of The expression of Mahalanobis distance is: In the formula, E i (C) is the Mahalanobis distance between the analysis matrix C and the matrix corresponding to the i-th analysis set, is the inverse matrix of the covariance matrix of the ith analysis set.

10. A lithium battery thermal runaway early warning system based on multi-source parameter monitoring, implementing a lithium battery thermal runaway early warning method based on multi-source parameter monitoring as claimed in any one of claims 1 to 9, characterized in that: include: A parameter acquisition module, used for real-time acquisition of multi-source battery parameters and marking them as real-time multi-source battery parameters; A parameter processing module is used to process real-time multi-source battery parameters and extract battery characteristic parameters; Fusion analysis module, used to perform fusion analysis on real-time multi-source battery parameters and extracted battery characteristic parameters to predict the probability of thermal runaway of lithium batteries; A type assessment module is used to determine whether to generate an assessment instruction based on the probability of thermal runaway; if an assessment instruction is generated, the thermal runaway type of the lithium battery is assessed; The active prevention and control module is used to initiate active prevention and control measures if an evaluation instruction is generated.

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