Method, system and device for battery thermal runaway expansion force signature identification and early warning

Abstract

A method, system and device for identification and early warning of thermal runaway expansion force signatures in batteries is provided. [Solution] The method includes the steps of presetting a threshold value and an initial expansion force value based on the change characteristics of the expansion force during the charging and discharging process of the battery at different current rates; charging and discharging the battery under test at the predefined current rate or leaving it stationary to obtain the current expansion force value of the battery under test; determining the magnitude relationship between the current expansion force value of the battery under test and the threshold value; if the current expansion force value is greater than or equal to the threshold, issuing a level 1 early warning and proceeding to the next step; if not, returning to the previous step; calculating each expansion force peak and the time corresponding to each expansion force peak based on the current expansion force value and initial expansion force value of the battery under test; presetting different early warning levels and determining the early warning level corresponding to the battery under test; and issuing different early warnings according to the early warning level.

Description

[Technical Field]

[0001] The present specification relates to the technical field of batteries, and in particular to methods, systems and devices for identification and early warning of thermal runaway expansion force signatures in batteries.

[0002] [Incorporated by reference] This application claims priority from Chinese Application No. 202411474210.4, filed on October 22, 2024, the entire contents of which are incorporated herein by reference. [Background technology]

[0003] In recent years, the number of fire accidents caused by new energy vehicles has been increasing, which is one of the biggest obstacles to the commercialization of electric vehicles. The internal structure of lithium-ion batteries is an extremely complex electrochemical system, and the normal charging and discharging processes and thermal runaway processes both involve the interaction of various processes such as electricity, heat, chemistry, mechanics, and gas generation kinetics.

[0004] Current common methods for early warning of thermal runaway in lithium-ion batteries mainly rely on voltage, temperature, internal resistance, released gas, smoke, etc. However, these methods are unable to provide timely responses and early warnings for thermal runaway inside the battery.

[0005] When thermal runaway occurs, the expansion force signal appears earlier than traditional early warning signals such as voltage and temperature, and changes as the thermal runaway progresses. Therefore, it is desirable to provide a method, system, and device for identifying and early warning the thermal runaway expansion force characteristics of a battery, which can help timely and accurately determine the thermal runaway state and provide timely early warning. Summary of the Invention [Means for solving the problem]

[0006] To solve the above technical problems, the embodiments of the present specification provide a method, system, and device for identifying and early warning the thermal runaway expansion force characteristics of a battery, which provides an alarm signal as early as possible before the battery clearly experiences thermal runaway, provides early warning regarding the degree of harm to the battery, and provides enough time for active heat dissipation at the battery system level, active gas release, and personnel evacuation, thereby improving the safety level of electric vehicle applications.

[0007] A method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to one or more embodiments of the present specification includes the steps of: (i) setting a threshold value and an initial expansion force value in advance based on the change characteristics of the expansion force during the charging and discharging process of the battery at different current rates; (ii) charging and discharging the battery under test at a preset current rate or leaving it stationary to obtain the current expansion force value of the battery under test; (iii) determining whether the current expansion force value of the battery under test is greater than or equal to the threshold value, and issuing a level 1 early warning and proceeding to step S4; otherwise, proceeding to step S2; (iv) calculating each expansion force peak and the corresponding time of each expansion force peak using a peak detection algorithm based on the current expansion force value and the initial expansion force value of the battery under test, where the expansion force peaks include a first expansion force peak, a second expansion force peak, and a third expansion force peak; and (v) setting different early warning levels and determining the early warning level corresponding to the battery under test based on each expansion force peak and the corresponding time of each expansion force peak. Furthermore, the step of calculating each expansion force peak using the peak detection algorithm includes step S41 of obtaining the expansion force values ​​k seconds before and k seconds after the current expansion force value, where k is a constant other than 0; step S42 of determining the magnitude relationship between the current expansion force value and the expansion force values ​​k seconds before and k seconds after, and if the current expansion force value is greater than or equal to the expansion force value k seconds before and greater than or equal to the expansion force value k seconds after, determining that the current expansion force value is an expansion force peak; otherwise, determining that the current expansion force value is not an expansion force peak; and step S43 of terminating the calculation process if the current expansion force value is less than the initial expansion force value, and proceeding to step S41 if the current expansion force value is greater than or equal to the initial expansion force value.The step of presetting different early warning levels and determining the early warning level for the battery under test based on each expansion force peak and the corresponding time for each expansion force peak includes the steps of presetting a first preset value and a second preset value, issuing a Level 2 early warning when the first expansion force peak is detected, issuing a Level 3 early warning when the difference between the time corresponding to the first expansion force peak and the time corresponding to the second expansion force peak is equal to or greater than the first preset value but less than the second preset value, issuing a Level 4 early warning when the difference between the time corresponding to the first expansion force peak and the time corresponding to the second expansion force peak is less than the first preset value, and issuing a Level 5 early warning when the third expansion force peak is detected. The step of acquiring the current expansion force value for the battery under test includes installing a pressure sensor in the battery under test to acquire the current expansion force value. Furthermore, k has a positive correlation with the real-time SOC of the battery under test.

[0008] A system for identifying and early warning of thermal runaway expansion force characteristics of a battery according to one or more embodiments of the present disclosure includes a pre-setting module, a calculation module, and an early warning module. The presetting module is configured to perform the steps of presetting a threshold and an initial expansion force value based on the change characteristics of the expansion force during the charging and discharging process of the battery at different current rates; the calculation module is connected to the presetting module and configured to perform the steps of charging, discharging, or leaving the battery to be measured at a preset current rate to obtain the current expansion force value of the battery to be measured; determining whether the current expansion force value of the battery is greater than or equal to the threshold; issuing a level 1 early warning and proceeding to the early warning module if the current expansion force value is greater than or equal to the threshold; and repeating the calculation in the calculation module; the early warning module is connected to the calculation module and configured to perform the steps of using a peak detection algorithm to calculate each expansion force peak and the corresponding time for each expansion force peak based on the current expansion force value and the initial expansion force value of the battery to be measured, where the expansion force peaks include a first expansion force peak, a second expansion force peak, and a third expansion force peak; and presetting different early warning levels and determining the early warning level for the battery to be measured based on each expansion force peak and the corresponding time for each expansion force peak.

[0009] An electronic device according to one or more embodiments of the present disclosure includes a processor and a memory, and the processor executes steps of a method for identifying and early warning of thermal runaway expansion force characteristics of a battery by calling a program or instructions stored in the memory.

[0010] The embodiments of the present specification include at least the following technical effects.

[0011] In the examples of this specification, a peak detection algorithm for expansion force peaks is proposed for the characteristic of multiple jet flame ejections that exist during the thermal runaway process of a battery, and for the characteristic of multiple expansion force peaks associated with this characteristic.The algorithm then classifies the thermal runaway state into levels based on whether or not the peaks appear, the time interval between their appearances, and the number of times they appear, and provides early warnings.Each level indicates the risk of a thermal runaway state, accurately responds to the progress inside the battery, helps personnel accurately understand the battery state, reduces decision-making errors caused by misjudgments, reduces losses, and improves the safety level of electric vehicle applications.

[0012] The present specification is further illustrated by exemplary embodiments, which are not limiting and will be described in detail with reference to the drawings, in which like numerals represent like structures. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a flowchart of a method for identifying and early warning of thermal runaway expansion force signatures of batteries according to an embodiment of the present disclosure. [Figure 2] FIG. 1 is a schematic diagram of the expansion force peaks and corresponding times identified by a peak detection algorithm during the thermal runaway process at SOC=110% for a 51 Ah square hard-shell ternary / graphite battery according to an example herein. [Figure 3] FIG. 1 is a schematic diagram of the expansion force peaks and corresponding times identified by a peak detection algorithm during the thermal runaway process at SOC=100% for a 51 Ah square hard-shell ternary / graphite battery according to an example herein. [Figure 4] FIG. 1 is a schematic diagram of the expansion force peaks and corresponding times identified by a peak detection algorithm during the thermal runaway process at SOC=50% for a 51 Ah square hard-shell ternary / graphite battery according to an example herein. [Figure 5]FIG. 1 is a schematic diagram of the expansion force peaks and corresponding times identified by a peak detection algorithm during the thermal runaway process at SOC=25% for a 51 Ah square hard-shell ternary / graphite battery according to an example herein. [Figure 6] FIG. 1 is a schematic diagram of the expansion force peaks and corresponding times identified by a peak detection algorithm during the thermal runaway process at SOC=0% for a 51 Ah square hard-shell ternary / graphite battery according to an example herein. DETAILED DESCRIPTION OF THE INVENTION

[0014] In order to more clearly explain the technical means of the embodiments of the present specification, the drawings necessary for the description of the embodiments will be briefly described below. Obviously, the drawings described below are only a part of the examples or embodiments of the present specification, and those skilled in the art can apply the present specification to other similar scenarios based on these drawings without any creative effort. Unless otherwise clear from the context or described otherwise, the same symbols in the drawings represent the same structures or operations.

[0015] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are ways of distinguishing between various levels of assemblies, elements, components, parts, or structures. However, other terms may be used in place of the above terms if they achieve the same purpose.

[0016] As used herein and in the claims, unless the context clearly dictates otherwise, terms such as "a," "one," "one kind," and / or "the" do not specifically refer to the singular but may include the plural. In general, the terms "comprise" and "containing" merely indicate the inclusion of explicitly identified steps and elements, and these steps and elements are not an exclusive listing, and a method or device may include other steps or elements.

[0017] Flowcharts are used herein to describe operations performed by systems according to embodiments of the present invention. It should be understood that the preceding and following operations are not necessarily performed in exact order. Instead, steps may be performed in reverse order or simultaneously. Also, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0018] Thermal runaway is an abnormal phenomenon that occurs in batteries under certain conditions, involving a sudden rise in temperature inside the battery, acceleration of chemical reactions, and the generation and accumulation of gases. These phenomena interact to form a vicious cycle that can cause the battery to fail and eventually explode.

[0019] During thermal runaway, the temperature and pressure inside the battery rise rapidly, causing thermal expansion of the battery materials and electrolyte and the generation and accumulation of gas. These phenomena together generate significant expansion forces, and if the expansion forces exceed the resistance of the battery explosion-proof valve or housing, they may burst and cause the sudden release of gas. The sudden release of gas may form a jet flame, posing a serious threat to the battery and the surrounding environment.

[0020] Research has shown that when external heating of a battery triggers thermal runaway, gas is generated inside the battery, causing the positive and negative electrodes to expand, resulting in an increase in the battery's expansion force. When the battery's expansion force is smaller than the opening pressure of the battery's relief valve, the expansion force continues to increase and eventually reaches a threshold. Typically, whether the expansion force reaches this threshold can be used to detect whether a battery is experiencing thermal runaway more quickly than by detecting changes in the battery's voltage and temperature. When the battery's internal pressure is greater than the opening pressure of the battery's relief valve, the explosion-proof valve opens, causing the first expansion force peak. Subsequently, multiple peaks appear in the expansion force as multiple jet flames occur in the battery. Therefore, using the threshold and the first expansion force peak provides a highly accurate thermal runaway early warning, while using multiple peaks to provide a highly accurate risk early warning.

[0021] Before a battery experiences thermal runaway, the expansion force exhibits abnormal characteristics, and therefore, by monitoring the peak of the expansion force signal, the risk of the battery experiencing thermal runaway can be determined.

[0022] FIG. 1 is an exemplary flowchart of a method for identifying and early warning of thermal runaway expansion force signatures in batteries according to some embodiments herein.

[0023] 1 is a flowchart of a method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to an embodiment of the present disclosure. In some embodiments, as shown in FIG. 1, the method includes the following steps:

[0024] S1, preset a threshold value and an initial expansion force value based on the change characteristics of the expansion force during the charging and discharging process of the battery at different current rates.

[0025] The expansion force value is the force value of the volume expansion of the battery due to chemical reactions and material changes inside the battery during the charge and discharge process of the battery.

[0026] The initial expansion force value is the expansion force value of the battery in a normal state. In some embodiments, the initial expansion force value can be preset and obtained based on prior experience.

[0027] The variation characteristic is the variation characteristic of the expansion force during the charging and discharging process of the battery at different current rates. In some embodiments, the variation characteristic includes a maximum value, a minimum value, an average value, etc. of the expansion force value. The expansion force variation characteristic of the battery can be obtained by charging and discharging the battery at different current rates.

[0028] The threshold value is a critical value of the expansion force that is preset during the charge and discharge process of the battery. For example, the threshold value may be 4000 N. When the current expansion force value reaches the threshold value, there is a high possibility that thermal runaway will occur inside the battery, resulting in the risk of jet flames being emitted.

[0029] In some embodiments, the threshold may be preset based on prior experience, for example, the threshold may be 4000N.

[0030] In some embodiments, the processor may preset a threshold value and an initial expansion force value based on the change characteristics, for example, by determining an average expansion force value during the charging and discharging process of the battery as the initial expansion force value and determining a maximum value of the expansion force value during the charging and discharging process of the battery as the threshold value.

[0031] In some embodiments, the processor may construct a battery feature vector based on the change feature, the operating environment data, and the battery status data, and determine a threshold value using the vector database based on the battery feature vector.

[0032] The operating environment data may include the environment temperature, the environment humidity, the environment pressure, and the like.

[0033] The battery status data may include battery temperature, charge / discharge rate, battery voltage, current, internal resistance, and usage time.

[0034] S2: Charge / discharge the battery under test at a preset current rate or leave it at rest, and obtain the current expansion force value of the battery under test.

[0035] In some embodiments, the preset current rate for charging and discharging the battery under test can be determined according to actual application scenarios and needs. For example, the preset current rate may be the charging and discharging current recommended in the battery's specifications.

[0036] The current expansion force value can be obtained in various ways, for example, by one or more monitoring devices such as a laser displacement sensor, a strain gauge, etc.

[0037] In some embodiments, a pressure sensor may be installed in the battery under test to obtain the current expansion force value.

[0038] When the condition of "charging / discharging at a preset current rate or leaving the battery stationary" is satisfied, the actual expansion force of the battery can be directly monitored without specifically limiting the state of the battery, such as SOC.

[0039] In some embodiments of the present specification, the current expansion force of the battery under test can be accurately monitored in real time by obtaining the current expansion force value using a pressure sensor.

[0040] In some embodiments, the processor may determine the expansion collection frequency based on the expansion increase rate.

[0041] The expansion collection frequency is the frequency at which the expansion collection device acquires the expansion force value. The expansion collection device may be a pressure sensor.

[0042] In some embodiments, the expansion collection frequency is positively correlated with the expansion increase rate, e.g., the higher the expansion increase rate, the higher the expansion collection frequency.

[0043] In some embodiments of the present specification, by determining the expansion collection frequency, the expansion force value inside the battery can be monitored timely and efficiently to prevent the occurrence of thermal runaway.

[0044] S3: Determine the magnitude relationship between the current expansion force value of the battery under test and the threshold value. If the current expansion force value is greater than or equal to the threshold value, issue a level 1 early warning and proceed to S4; if not, proceed to S2.

[0045] The early warning may include, but is not limited to, one or more of light early warning, sound early warning, and information early warning (such as sending a short message).

[0046] In some embodiments, the early warning device may be multiple distributed alarm devices or a single integrated alarm device, such as a buzzer, warning light, or other device distributed in multiple locations, or an integrated monitoring early warning system integrated in a monitoring room.

[0047] The early warning may have multiple early warning levels, for example, level 2 early warning, level 3 early warning, etc. See below for further content of this section.

[0048] If the current expansion force value is above the threshold, the battery housing may visibly expand, deforming the battery module connection sheet and potentially causing a local arc discharge. At this time, there is a high probability of thermal runaway occurring, and a Level 1 early warning is issued to warn nearby personnel to evacuate as soon as possible.

[0049] S4, based on the current expansion force value and the initial expansion force value of the battery under test, calculate each expansion force peak and the corresponding time of each expansion force peak using a peak detection algorithm.

[0050] The peak detection algorithm is an algorithm that detects the extreme value of the current expansion force value within a certain time range.

[0051] In some embodiments, the peak detection algorithm may include, but is not limited to, algorithms such as divide and conquer, heapsort, etc.

[0052] In some embodiments, the process of calculating each expansive force peak using a peak detection algorithm is shown in steps S41 to S43 below.

[0053] S41, obtain the expansion force values ​​k seconds before and k seconds after the current expansion force value, where k is a non-zero constant.

[0054] S42: Determine the magnitude relationship between the current inflation force value and the inflation force values ​​for the previous k seconds and the following k seconds. If the current inflation force value is equal to or greater than the inflation force value for the previous k seconds and equal to or greater than the inflation force value for the following k seconds, determine that the current inflation force value is the inflation force peak; otherwise, determine that the current inflation force value is not the inflation force peak. That is, in the following cases, it is determined that the current inflation force value is not the inflation force peak.

[0055] Case 1: The current expansion force value is smaller than the expansion force value of the previous k seconds or smaller than the expansion force value of the next k seconds.

[0056] Case 2: The current expansion force value is smaller than the expansion force value for the previous k seconds and is also smaller than the expansion force value for the next k seconds.

[0057] S43: If the current expansion force value is smaller than the initial expansion force value, the calculation process is terminated; if the current expansion force value is equal to or greater than the initial expansion force value, the process proceeds to S41.

[0058] In the above peak detection algorithm, a time window is set, i.e., a time range of k seconds before and after the real-time inflation value, and the current inflation force value is determined to be the inflation force peak by determining whether it is the maximum within the time window range.

[0059] In some embodiments of the present specification, each expansion force peak is calculated using a peak detection algorithm, which allows for efficient and accurate detection of the expansion force peak, thereby enabling accurate determination of the level of thermal runaway.

[0060] In some embodiments, the magnitude of k depends on the real-time SOC of the battery under test, where SOC refers to the remaining capacity of the battery; the larger the SOC, the smaller the k, i.e., the two are negatively correlated.

[0061] Exemplarily, when 75% < SOC ≤ 110%, set k = 0.5; when 35% < SOC ≤ 75%, set k = 1.5; when 10% < SOC ≤ 35%, set k = 6; when 0% ≤ SOC ≤ 10%, set k = 60. It can be set according to the actual situation, and the description is omitted in this application.

[0062] When the current expansion force value is smaller than the initial expansion force value, in order to indicate that the battery is in a normal state, stop detecting the peak. When the real-time expansion force exceeds the threshold again, use the peak detection algorithm to determine the peak.

[0063] In some embodiments, in response to the usage time of the k value reaching the adjustment period of the k value, the processor determines the adjusted k value according to the contrast boundary determination model based on the historical expansion force value, operating environment data, real-time SOC, battery state data, and magnetic field environment data, and can replace the original k value with the adjusted k value.

[0064] The usage time of the k value is the time elapsed since the constant k was set and continuously used from step S41 to step S43.

[0065] The adjustment period of the k value is the time period for updating the k value. The adjustment period of the k value may be preset based on prior experience.

[0066] In some embodiments, the adjustment period of the k value has a negative correlation with the expansion force fluctuation range.

[0067] The expansion force fluctuation range can reflect whether the change in the expansion force value is intense. The processor can determine the variance of the expansion force values in the k seconds before and after the current expansion force value as the expansion force fluctuation range.

[0068] In some embodiments of the present specification, the adjustment period of the k value is adjusted according to the expansion force fluctuation range, thereby enabling the k value to be effectively and flexibly optimized and adjusted based on actual conditions, thereby enhancing the dynamic response capability and application range of the system for identifying and early warning the thermal runaway expansion force characteristics of batteries.

[0069] The contrast boundary determination model is a predictive model that determines the adjusted k value. In some embodiments, the contrast boundary determination model is a machine learning model. For example, the contrast boundary model may be any one or a combination of a long short-term memory neural network (LSTM) or other customized model structures.

[0070] In some embodiments, the contrast boundary determination model is stored in a memory communicatively coupled to the processor.

[0071] In some embodiments, inputs to the contrast boundary model include historical expansion force values, real-time SOC, operating environment data, battery condition data, and magnetic field environment data, and outputs include adjusted k values.

[0072] The magnetic field environment data is magnetic field data of an operating scenario of the battery. In some embodiments, the magnetic field environment data can be obtained by placing a magnetic sensor in the operating environment.

[0073] For more details on the real-time SOC, operating environment data and battery status data, please refer to FIG. 1 and related description.

[0074] In some embodiments, the contrast boundary determination model may be obtained by training with first training samples and a first label. The first training samples include a historical expansion force value of the sample, a real-time SOC of the sample, operating environment data of the sample, battery status data of the sample, and magnetic field environment data of the sample. The first label is a k value of the sample.

[0075] In some embodiments, the first training sample can be obtained based on historical data, and the processor can obtain historical distension force values, preset a large number of different k values, detect theoretical distension peaks from the historical distension force values ​​using the peak detection algorithm, and select one preset k value at which the detected theoretical distension peak is closest to the actual distension force peak as the k value of the sample corresponding to the first training sample.

[0076] In some embodiments, the contrast boundary determination model is obtained by training in the following manner: A processor inputs a plurality of first training samples having a first label into an initial contrast boundary determination model, constructs a loss function based on the first label and the output result of the initial contrast boundary determination model, and iteratively updates parameters of the initial contrast boundary determination model based on the loss function. When the loss function of the initial contrast boundary determination model satisfies an iteration condition, model training is completed and a trained contrast boundary determination model is obtained. Here, the iteration condition may be that the loss function converges, the number of iterations reaches a threshold, etc.

[0077] S5, preset different early warning levels, and determine the early warning level corresponding to the battery under test according to each expansion force peak and the time corresponding to each expansion force peak.

[0078] In some embodiments, the early warning may have multiple early warning levels, such as a level 1 early warning, a level 2 early warning, a level 3 early warning, a level 4 early warning, and a level 5 early warning. A higher early warning level indicates a greater tendency of the battery under test to thermal runaway. The number of early warning levels may be preset based on prior experience.

[0079] In some embodiments, different early warning levels correspond to different early warning measures. The higher the early warning level, the more severe the corresponding early warning measures. For example, the higher the early warning level, the brighter and more frequently the early warning lamp will flash, the louder and sharper the alarm will sound, and the more frequently short messages will be sent to relevant parties or warning notifications will pop up. In some embodiments, the early warning measures corresponding to different early warning levels may be determined based on prior experience.

[0080] In some embodiments, different early warning levels may correspond to different handling measures. For example, when an early warning is triggered, the battery power may be controlled to be turned off, the power of cooling devices such as fans and pumps may be controlled, the battery may be forced to dissipate heat, the air convection rate within the battery system may be increased, and generated aerosols, smoke, etc. may be expelled outside the battery system housing to minimize the occurrence of a thermal runaway situation. At the same time as forcing the battery to dissipate heat, a fire extinguishing system may be activated to inject a fire-extinguishing flame retardant into the battery to prevent any fire or combustion that may exist in the battery system, and an early warning notification may be provided via audio and light.

[0081] In some embodiments, the higher the early warning level, the higher the power of the cooling device, such as a fan, a pump, etc. Specific early warning measures corresponding to different early warning levels can be set according to actual situations, and are not described in this application.

[0082] In some embodiments, the first preset value and the second preset value may be preset.

[0083] In some embodiments, the processor may determine the adjusted first preset value and the adjusted second preset value using a preset value determination model based on the battery attribute data, the operating environment data, the battery monitoring data, the battery state data, the gas data, and the vibration data, and replace the original first preset value and the second preset value.

[0084] The preset value determination model is a predictive model that determines the first preset value and the second preset value. In some embodiments, the preset value determination model is a machine learning model. For example, the preset value determination model may be any one or a combination of a recurrent neural network (RNN) or other customized model structures.

[0085] In some embodiments, the preset value determination model is stored in a memory communicatively coupled to the processor.

[0086] In some embodiments, inputs of the preset value determination model include battery attribute data, operating environment data, battery monitoring data, battery condition data, gas data, and vibration data, and outputs include an adjusted first preset value and an adjusted second preset value.

[0087] Battery attribute data is data related to the characteristics of a battery. The battery attribute data may include the type of battery material, the type of battery, the size and shape of the battery, etc. Here, the type of battery material refers to the type and content of materials such as the positive and negative electrodes and electrolyte in the battery. The type of battery includes lithium batteries, lead batteries, etc. The size and shape of the battery may include cylindrical, rectangular, soft pack, hard pack, etc.

[0088] Battery detection data is battery data recorded by a battery management system (BMS). For example, the battery detection data includes the battery's SOC, SOH, SOP, etc. Here, SOC represents the percentage of remaining battery power, SOH represents the remaining usable capacity of the battery, and SOP represents the maximum charge / discharge power that the current battery can supply.

[0089] The gas data includes the rate at which the battery releases gas, the gas composition, and the like.

[0090] Vibration data is data on vibrations occurring in the battery, and can be acquired by a vibration sensor.

[0091] In some embodiments, the preset value determination model may be obtained by training with second training samples and second labels. The second training samples include battery attribute data of the samples, operating environment data of the samples, battery monitoring data of the samples, battery status data of the samples, gas data of the samples, and vibration data of the samples. The second labels are optimal first preset values ​​and second preset values ​​corresponding to the samples.

[0092] In some embodiments, the second training sample can be obtained based on historical data. For example, the processor can obtain a historical time interval between a first historical inflation force peak and a second historical inflation force peak based on the historical data, and determine the first historical preset value and the second historical preset value as the second label based on the historical time interval and the corresponding historical early warning level.

[0093] For example, if a phenomenon corresponding to a level 3 early warning (ejection of secondary aerosols, smoke, or jet flames generated in the battery) occurs, the time interval between the first and second expansion force peaks is 3 to 13 seconds, and if a phenomenon corresponding to a level 4 early warning (secondary jet flames generated in the battery) occurs, the time interval between the first and second expansion force peaks is less than 3 seconds, and the first preset value can be set to 3 seconds and the second preset value can be set to 13 seconds.

[0094] The step of training to obtain a preset value determination model based on a second training sample having a second label is similar to the step of training to obtain a contrast boundary determination model, and reference can be made to the related content above.

[0095] In some embodiments, the processor may determine the expansion collection frequency based on a first preset value and a second preset value.

[0096] In some embodiments, the expansion collection frequency is negatively correlated with the absolute value of the difference between the first preset value and the second preset value. For example, the smaller the absolute value of the difference between the first preset value and the second preset value, the higher the expansion collection frequency. By way of example only, the expansion collection frequency can be obtained based on the following equation (1):

[0097]

number

[0098] where Z represents the expansion collection frequency, k1 is a coefficient, Y1 represents a first preset value, and Y2 represents a second preset value. The value of the coefficient k4 may be determined manually or based on prior experience.

[0099] In some embodiments of the present specification, the expansion collection frequency is determined based on the first preset value and the second preset value, thereby allowing the first preset value and the second preset value to be flexibly adjusted.

[0100] When the peak detection algorithm detects the first expansion peak, a Level 2 early warning is issued. The first expansion peak indicates that the battery has experienced thermal runaway, which may cause the battery's explosion-proof valve to open, emitting large amounts of aerosols, smoke, or a violent jet flame. The emitted materials are likely to cause short circuits outside the battery, overheating of adjacent batteries, and failure of the high-voltage insulation protection within the battery system.

[0101] In some embodiments, when a second inflation force peak is detected by the peak detection algorithm, i.e., when a second inflation force peak is detected, a level 3 early warning is issued if the difference between the time corresponding to the first inflation force peak and the time corresponding to the second inflation force peak is greater than or equal to a first preset value and less than a second preset value.

[0102] The specific values ​​of the first preset value and the second preset value are set according to actual needs. For example, the first preset value is 2.5 seconds, and the second preset value is 15 seconds.

[0103] This poses a greater risk as secondary aerosols, smoke or jet flames are more likely to be emitted into the battery, causing heat diffusion within the battery system.

[0104] In some embodiments, no early warning is provided if the difference between the time corresponding to the first inflation force peak and the time corresponding to the second inflation force peak is greater than or equal to a second preset value.

[0105] In some embodiments, a level 4 early warning is issued when the difference between the time corresponding to the first and second peaks of expansion force is less than a first preset value. At this time, the aerosols and particulate matter emitted from the battery must be discharged through the battery pack's relief valve. The shorter the interval between the occurrence of the aerosols and particulate matter, the more likely a secondary jet flame will occur within a short period of time, which increases the challenges and risks associated with battery pack pressure relief, temperature drop, and arc discharge.

[0106] In some embodiments, when the peak detection algorithm detects a third expansion force peak, i.e., when the third expansion force peak is detected, a Level 5 early warning is issued. At this time, the battery thermal runaway reaction is a sustained, long-term process, and the multiple peaks correspond to multiple jet flames, causing multiple extrusions and thermal shocks in adjacent batteries, making it easier to cause thermal diffusion in the adjacent batteries and making it more difficult to eliminate the risk of battery thermal runaway through external fire extinguishing measures.

[0107] As for the fourth peak, fifth peak, etc., the expansion force indicated is insufficient and is similar to the situation of the third expansion force peak, and the early warning effect is also similar, so this application is limited to monitoring only the third expansion force peak.

[0108] In some embodiments of the present specification, the determination conditions for different early warning levels and corresponding early warning measures are preset to contribute to targeted early warning and processing for different thermal diffusion situations, so as to provide early warning for battery thermal diffusion and avoid battery burning as much as possible.

[0109] In some embodiments, the processor can determine an inflation increase rate based on real-time SOC and battery status data, and determine an execution interval period based on the inflation increase rate, where the execution interval period is the interval time at which the processor executes steps S2 to S4.

[0110] For more information about real-time SOC and battery status data, please refer to the related content above.

[0111] The expansion increase rate is the rate of increase in the expansion force value over a certain period of time.

[0112] In some embodiments, the processor can balance the energy consumption and response time of the calculation by determining the execution interval period according to the actual application scenario and needs.

[0113] In some embodiments, the processor may determine the expansion increase rate based on the real-time SOC, battery temperature in the battery state data. By way of example only, the expansion increase rate may be obtained based on the following equation (2):

[0114]

number

[0115] Here, y represents the rate of increase of the expansion force value in the future time period, k2, k3, k4, and k5 are coefficients, a represents the real-time SOC, b represents the battery temperature, c represents the current battery charge / discharge rate, and d represents the expansion force value.

[0116] In some embodiments, the values ​​of the coefficients k2, k3, k4, and k5 may be determined based on manual input or prior experience.

[0117] In some embodiments, the processor can obtain a large amount of historical real-time SOC, historical battery temperature, historical current battery charge / discharge rate, and historical expansion force values, calculate the expansion increase rate y based on the historical expansion force values, and obtain the values ​​of the coefficients k1, k2, k3, and k4 through a fitting algorithm, which can include, but is not limited to, linear regression, nonlinear fitting, etc.

[0118] For convenience of explanation, the expansion increase rate can be obtained based on the following formula (3).

[0119]

number

[0120] Here, y represents the inflation increase rate during a certain historical time period, p2 represents the inflation force value at the end of the historical time period, and p1 represents the inflation force value at the start of the historical time period.

[0121] In some embodiments, the processor may periodically update the expansion increase rate based on the expansion collection frequency, which may be the frequency at which steps S2 to S4 are repeatedly executed.

[0122] As described above, the present application uses the expansion force peak as a judgment criterion to quantify the thermal runaway state of the battery, and by dividing the early warning level according to the peak, it is possible to classify the risk of battery thermal runaway into levels, and as the risk increases from level 2 to level 5, it is possible to take corresponding fire-fighting measures in a timely manner to reduce the risk.

[0123] The following provides a more detailed description of specific implementations. Please note that the following relevant descriptions are for illustrative purposes only and are not intended to limit the scope of this specification.

[0124] 1) Thermal runaway tests were conducted on lithium-ion batteries. The lithium-ion batteries were 51Ah rectangular hard-shell ternary / graphite batteries, and thermal runaway was triggered by external heating to more realistically simulate the thermal runaway scenario of batteries in actual use.

[0125] 2) Test the characteristic parameters in the thermal runaway process of the battery, including battery temperature, voltage, expansion force and thermal runaway combustion image of the battery.

[0126] Here, the battery temperature includes the temperature of the positive and negative electrode tabs and the housing temperature of the battery and is obtained by a temperature sensor; the voltage is the voltage at the positive and negative electrode terminals of the battery and is obtained by a voltage sensor; the battery expansion force is the expansion force of the side of the rectangular hard shell and is obtained by a pressure sensor; for the pressure sensor, in the battery system, one pressure sensor can be placed in a single battery module to obtain the expansion force of the entire battery module, or one can be placed on the side of a single battery cell to obtain the expansion force value of each cell; and images of the thermal runaway combustion of the battery are collected by a camera.

[0127] Peaks were detected from the battery swelling force data at different SOCs, and the detected swelling force peaks are shown in Figures 2, 3, 4, 5, and 6, respectively.

[0128] When SOC=110%, there are four swelling force peaks, the first swelling force peak being the highest at 7222.6 N, and the interval between the first and second swelling force peaks is 2.26 s.

[0129] When SOC=100%, there are four swelling force peaks, the first swelling force peak being the highest at 8222.2 N, and the interval between the first and second swelling force peaks is 1.9 s.

[0130] When SOC=50%, there are four swelling force peaks, the first swelling force peak being the highest at 7036.4 N, and the interval between the first and second swelling force peaks is 1.8 s.

[0131] When SOC=25%, there are three swelling force peaks, the first swelling force peak being the highest at 6036.8 N, and the interval between the first and second swelling force peaks is 11.39 s.

[0132] When SOC=0%, there is only one expansive force peak, which is 4968.6N.

[0133] At the same time, before the first peak of the expansion force appears, after the expansion force reaches 4000 N, the expansion force curve changes sharply and then quickly reaches the first peak, which proves the feasibility of the threshold value set in Example 1 of the present application being 4000 N. Therefore, by monitoring the expansion force peak in real time and monitoring whether it exceeds the threshold value, the risk of battery combustion and explosion can be effectively reduced.

[0134] In some embodiments, the battery temperature includes the positive and negative electrode tab temperatures and the housing temperature of the battery and is acquired by a temperature sensor; the voltage is the positive and negative electrode terminal voltage of the battery and is acquired by a voltage sensor; the battery expansion force is the expansion force of the side of the rectangular hard shell and is acquired by a pressure sensor; for the pressure sensor, in the battery system, one pressure sensor can be placed on a single battery module to acquire the expansion force of the entire battery module, or the expansion force value of each cell can be acquired by placing it on the side of a single battery cell; and the thermal runaway combustion image of the battery is collected by a camera.

[0135] A system for identifying and early warning of thermal runaway expansion force characteristics of a battery according to one or more embodiments of the present disclosure includes a pre-setting module, a calculation module, and an early warning module.

[0136] In some embodiments, the presetting module is configured to perform the step of presetting the threshold value and the initial expansion force value based on a change characteristic of the expansion force during charging and discharging of the battery at different current rates.

[0137] In some embodiments, the calculation module is connected to the setting module in advance and configured to perform the steps of charging / discharging or leaving the pre-measured battery at a preset current rate, obtaining the current expansion force value of the measured battery, determining whether the current expansion force value of the battery is greater than or equal to a threshold value, issuing a level 1 early warning and proceeding to the early warning module if the current expansion force value is greater than or equal to the threshold value, and repeating the calculation in the calculation module if not.

[0138] In some embodiments, the early warning module is connected to the calculation module and configured to perform the steps of: calculating each expansion force peak and the time corresponding to each expansion force peak using a peak detection algorithm based on the current expansion force value and the initial expansion force value of the measured battery, where the expansion force peaks include a first expansion force peak, a second expansion force peak, and a third expansion force peak; and pre-setting different early warning levels and determining the early warning level corresponding to the measured battery based on each expansion force peak and the time corresponding to each expansion force peak.

[0139] For more specific details of the above, please refer to the relevant descriptions of Figures 1, 2, 3, 4, 5 and 6, and the description will be omitted here.

[0140] An electronic device according to an embodiment of the present specification includes a processor and a memory, and the processor executes the steps of the method for identifying and early warning of thermal runaway expansion force characteristics of a battery described in any one of the above embodiments by calling a program or instruction stored in the memory.

[0141] The processor may be a central processing unit (CPU) or other type of processing unit having data processing and / or instruction execution capabilities, and may control other assemblies in the electronic device to perform desired functions.

[0142] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. The computer-readable storage medium may store one or more computer program instructions, and a processor may execute the program instructions to realize the method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to any embodiment of the present application described above and / or other desired functions. The computer-readable storage medium may store various contents, such as initial external parameters, threshold values, etc.

[0143] In addition to the methods and devices described above, embodiments of the present application may also be a computer program product including computer program instructions that, when executed by a processor, cause the processor to perform steps of a method for identifying and early warning of a thermal runaway expansion force signature of a battery according to any embodiment of the present application.

[0144] The program code for performing operations in the embodiments of the present application according to the computer program product may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and further including conventional process programming languages ​​such as "C" or similar programming languages. The program code may run entirely on the user's computing device, partially on the user's device, as a separate software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0145] An embodiment of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps of a method for identifying and early warning of a thermal runaway expansion force signature of a battery according to any embodiment of the present application.

[0146] The computer-readable storage medium may be any combination of one or more computer-readable media. The computer-readable medium may be a readable signal medium or a readable storage medium. The computer-readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include an electrical connection having one or more conductors, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0147] Although the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure above is merely provided by way of example and is not intended to limit the present specification. Although not expressly described herein, those skilled in the art may make various changes, improvements, and modifications to the present specification. These changes, improvements, and modifications are intended to be suggested by the present specification and are therefore within the spirit and scope of the exemplary embodiments of the present specification.

[0148] Additionally, certain terms are used herein to describe embodiments of the present specification. For example, "one embodiment," "one embodiment," and / or "some embodiments" refer to particular features, structures, or characteristics associated with at least one embodiment of the present specification. Therefore, it is emphasized and understood that two or more references to "one embodiment" or "one embodiment" or "one alternative embodiment" in various parts of the present specification do not necessarily all refer to the same embodiment. Furthermore, particular features, structures, or characteristics of one or more embodiments of the present specification may be combined as appropriate.

[0149] Additionally, unless expressly stated in the claims, the enumerated order of processing elements or sequences described herein, the use of alphanumeric characters, or the use of other designations does not limit the order of the procedures and methods herein. While the above disclosure has set forth through various examples what are presently believed to be various useful embodiments of the invention, it should be understood that such details are merely illustrative, and that the appended claims are not limited to the disclosed embodiments, but rather are intended to cover all modifications and equivalent combinations within the spirit and scope of the embodiments herein. For example, the system assembly described above may be implemented by a hardware device, or may be implemented as a software-only solution, e.g., by installing the described system on an existing server or mobile device.

[0150] Similarly, in the foregoing description of embodiments herein, it should be understood that various features may be grouped together in a single embodiment, drawing, or description for the purpose of simplifying the description and facilitating an understanding of one or more embodiments of the invention. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed subject matter requires more features than are recited in each claim. In fact, an embodiment may include fewer than all features of a single embodiment disclosed above.

[0151] In some examples, numbers describing the number of components and attributes are used, and it should be understood that the numbers describing such examples are in some instances modified by the modifiers "about," "approximately," or "generally." Unless otherwise specified, "about," "approximately," or "generally" indicates that the number can vary by ±20%. Thus, in some examples, all numerical parameters used in the specification and claims are approximations that may vary depending on the specific requirements of a particular example. In some examples, ordinary placekeeping techniques should be applied to numerical parameters, taking into account the number of significant digits provided. While in some examples herein the numerical ranges and parameters determining ranges are approximations, in specific examples, such numerical values ​​are set as precisely as possible.

[0152] All patents, patent applications, published patent applications, and other materials, such as papers, books, specifications, publications, and documents, referenced herein are incorporated herein by reference in their entirety, except for prosecution history documents that are inconsistent with or inconsistent with the content of this specification and documents that may have a limiting effect on the broadest scope of the claims herein (now or later related to this specification). Furthermore, to the extent that explanations, definitions, and / or term usage in the accompanying materials herein are inconsistent with or inconsistent with the content set forth herein, the explanations, definitions, and / or term usage in this specification shall control.

[0153] Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments herein. Other variations may be within the scope of the present disclosure. Thus, by way of example, and not of limitation, alternative configurations of the embodiments herein may be considered consistent with the teachings herein. Thus, the embodiments herein are not limited to the embodiments expressly introduced and described herein.

Claims

1. Step S1: preset a threshold value and an initial expansion force value based on the change characteristics of the expansion force during the charging and discharging process of the battery at different current rates; Step S2: charging / discharging the battery under test at a preset current rate or leaving the battery undisturbed, and acquiring the current expansion force value of the battery under test; Step S3: Determine whether the current expansion force value of the battery under test is greater than or equal to the threshold value. If the current expansion force value is greater than or equal to the threshold value, a level 1 early warning is issued and the process proceeds to S4; otherwise, the process proceeds to S2; Step S4: calculating each expansion force peak and the corresponding time of each expansion force peak using a peak detection algorithm based on the current expansion force value and the initial expansion force value of the battery to be measured, wherein the expansion force peaks include a first expansion force peak, a second expansion force peak and a third expansion force peak; and (S5) presetting different early warning levels and determining the early warning level corresponding to the battery under test based on each expansion force peak and the time corresponding to each expansion force peak.

2. Utilizing a peak detection algorithm to calculate each expansive force peak comprises: Step S41: acquiring expansion force values ​​k seconds before and k seconds after the current expansion force value, where k is a constant other than 0; Step S42: determining whether the current inflation force value is greater than or equal to the inflation force value in the previous k seconds and the inflation force value in the subsequent k seconds; if the current inflation force value is greater than or equal to the inflation force value in the previous k seconds and is greater than or equal to the inflation force value in the subsequent k seconds, determining that the current inflation force value is the inflation force peak; if not, determining that the current inflation force value is not the inflation force peak; The method for identifying and early warning of thermal runaway expansion force characteristics of a battery as described in claim 1, further comprising step S43, wherein if the current expansion force value is smaller than the initial expansion force value, the calculation process is terminated, and if the current expansion force value is equal to or greater than the initial expansion force value, the method proceeds to step S41.

3. The step of presetting different early warning levels and determining the early warning level corresponding to the battery under test based on each expansion force peak and the time corresponding to each expansion force peak includes: Presetting a first preset value and a second preset value; issuing a level 2 early warning if a first inflation force peak is detected; When a second inflation force peak is detected, if the difference between the time corresponding to the first inflation force peak and the time corresponding to the second inflation force peak is equal to or greater than a first preset value and is smaller than a second preset value, a level 3 early warning is issued, and if the difference between the time corresponding to the first inflation force peak and the time corresponding to the second inflation force peak is smaller than the first preset value, a level 4 early warning is issued. and issuing a level 5 early warning if a third expansion force peak is detected.

4. The step of obtaining the current expansion force value of the battery under test includes: The method for identifying and early warning of thermal runaway expansion force characteristics of a battery as claimed in claim 1, characterized in that it includes the step of obtaining a current expansion force value by installing a pressure sensor on the battery under test.

5. The method for identifying and early warning of thermal runaway expansion force characteristics of a battery as claimed in claim 2, wherein k is negatively correlated with the real-time SOC of the battery under test.

6. A system for identifying and early warning of thermal runaway expansion force characteristics of a battery, for carrying out the method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to any one of claims 1 to 5, comprising: A preset module configured to perform a step of presetting a threshold value and an initial expansion force value according to the change characteristics of the expansion force during the charging and discharging process of the battery at different current rates; a calculation module connected to the preset module and configured to charge / discharge or leave the battery under test at a preset current rate, obtain the current expansion force value of the battery under test, determine whether the current expansion force value is greater than or equal to the threshold, issue a level 1 early warning if the current expansion force value is greater than or equal to the threshold, and proceed to a step by the early warning module; and if the current expansion force value is less than the threshold, obtain the current expansion force value of the battery under test again and determine again whether the current expansion force value is greater than or equal to the threshold, before proceeding to a step by the early warning module; The system comprises an early warning module connected to the calculation module and configured to perform the steps of: calculating each expansion force peak and the time corresponding to each expansion force peak using a peak detection algorithm based on the current expansion force value and the initial expansion force value of the battery being measured, wherein the expansion force peaks include a first expansion force peak, a second expansion force peak, and a third expansion force peak; and presetting different early warning levels and determining the early warning level corresponding to the battery being measured based on each expansion force peak and the time corresponding to each expansion force peak.

7. a processor and a memory, The electronic device, characterized in that the processor executes the steps of the method for identifying and early warning of thermal runaway expansion force characteristics of batteries according to any one of claims 1 to 5 by calling a program or instruction stored in the memory.

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