Methods, systems, and devices for identifying and early warning characteristics of thermal runaway expansion forces in batteries.

Abstract

This invention provides a method, system, and device for identifying and early warning of thermal runaway expansion force characteristics in batteries. [Solution] The method includes the steps of: setting a threshold and an initial expansion force value based on the characteristics of the change in expansion force during the charging and discharging process of a battery at different current rates; 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 the relationship between the current expansion force value of the battery to be measured and the threshold, and if the current expansion force value is greater than or equal to the threshold, issuing a Level 1 early alarm and proceeding to the next step, otherwise 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 to be measured; setting different early alarm levels and determining the early alarm level corresponding to the battery to be measured; and issuing different early alarms according to the early alarm level.

Description

Technical Field

[0001] This specification relates to the technical field of batteries, and particularly to methods, systems, and devices for identifying the thermal runaway expansion force characteristics of batteries and early warning.

[0002] [Incorporation by Reference] This application claims the priority of Chinese Application No. 202411474210.4 filed on October 22, 2024, and all of its contents are incorporated herein by reference.

Background Art

[0003] In recent years, the number of fire accidents caused by new energy vehicles has been on the rise, becoming one of the biggest obstacles to the current commercialization of electric vehicles. Due to the extremely complex electrochemical system of lithium-ion batteries, both the normal charge-discharge process and the thermal runaway process involve the interaction of various processes such as electrostatics, thermotics, chemistry, mechanics, and gas generation dynamics.

[0004] Regarding the early warning of thermal runaway of lithium-ion batteries, the current common methods mainly rely on voltage, temperature, internal resistance, released gas, smoke, etc. However, these methods cannot provide a timely response and early warning for thermal runaway inside the battery.

[0005] When thermal runaway occurs, the expansion force signal appears earlier than conventional early warning signals such as voltage and temperature, and changes as the thermal runaway progresses. Therefore, it is desired to provide methods, systems, and devices for identifying the thermal runaway expansion force characteristics of batteries and early warning, which can help accurately judge the thermal runaway state in a timely manner and provide timely early warning.

Summary of the Invention

Means for Solving the Problems

[0006] To address the above technical challenges, embodiments of this specification provide methods, systems, and devices for identifying and early warning of thermal runaway expansion force characteristics of batteries, thereby providing an early warning regarding the degree of battery hazard, giving sufficient time for active heat dissipation, active gas release, and personnel evacuation at the battery system level, and 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 this specification includes: step S1 of presetting a threshold and an initial expansion force value based on the characteristics of the change in expansion force during the charging and discharging process of the battery at different current rates; step S2 of charging, discharging or leaving the battery under test at a pre-set current rate to obtain the current expansion force value of the battery under test; step S3 of determining the relationship between the current expansion force value of the battery under test and a threshold, and if the current expansion force value is greater than or equal to the threshold, issuing a level 1 early warning and proceeding to S4, otherwise proceeding to S2; step S4 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 initial expansion force value of the battery under test, wherein the expansion force peak includes a first expansion force peak, a second expansion force peak and a third expansion force peak; and step S5 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. Furthermore, the step of calculating each expansion force peak using the peak detection algorithm includes a step S41 in which the expansion force values ​​for k seconds before and k seconds after the current expansion force value are obtained, where k is a constant other than 0; a step S42 in which the relationship between the current expansion force value and the expansion force values ​​for k seconds before and k seconds after is determined, and if the current expansion force value is greater than or equal to the expansion force value for k seconds before and greater than or equal to the expansion force value for k seconds after, it is determined that the current expansion force value is the expansion force peak; otherwise, it is determined that the current expansion force value is not the expansion force peak; and a step S43 in which the calculation process is terminated if the current expansion force value is less than the initial expansion force value, and the process proceeds to S41 if the current expansion force value is greater than or equal to the initial expansion force value.Furthermore, the step of pre-setting different early alarm levels and determining the early alarm level corresponding to the battery under test based on each inflation force peak and the time corresponding to each inflation force peak includes the steps of pre-setting a first preset value and a second preset value; if a first inflation force peak is detected, a level 2 early alarm is performed; if a second inflation force peak is detected, a level 3 early alarm is performed 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 the first preset value and less than the second preset value; if the difference between the time corresponding to the first inflation force peak and the time corresponding to the second inflation force peak is less than the first preset value, a level 4 early alarm is performed; and if a third inflation force peak is detected, a level 5 early alarm is performed. Furthermore, the step of obtaining the current inflation force value of the battery under test includes the step of obtaining the current inflation force value by installing a pressure sensor on the battery under test. Furthermore, k is positively correlated 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 this specification includes a preset module, a calculation module, and an early warning module. The preset module is configured to perform the step of presetting a threshold and an initial expansion force value based on the characteristics of the change in expansion force during the charging and discharging process of a battery at different current rates. The calculation module is connected to the preset module and is configured to perform the steps of: charging, discharging or leaving a battery under test at a preset current rate to obtain the current expansion force value of the battery under test; determining the relationship between the current expansion force value of the battery and a threshold; if the current expansion force value is greater than or equal to the threshold, issuing a Level 1 early alarm and proceeding to the early alarm module; otherwise, repeating the calculation in the calculation module. The early alarm module is connected to the calculation module and is 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 initial expansion force value of the battery under test, wherein the expansion force peak includes a first expansion force peak, a second expansion force peak, and a third expansion force peak; and preset different early alarm levels and determining the early alarm level corresponding to the battery under test based on each expansion force peak and the time corresponding to each expansion force peak.

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

[0010] The examples described herein include at least the following technical effects:

[0011] In the embodiments described herein, a peak detection algorithm for expansion force peaks is proposed for the characteristics of multiple jet flame ejections present in the thermal runaway process of a battery, and for the characteristics of multiple expansion force peaks associated with those characteristics. Based on the presence or absence of peaks, the time interval between occurrences, and the number of occurrences, the algorithm categorizes the thermal runaway state into levels and provides early warnings. Each level indicates the danger of the thermal runaway state, allowing for accurate response to the progress inside the battery, helping personnel to accurately understand the battery state, reducing decision-making errors due to misjudgment, reducing losses, and improving the safety level of electric vehicle applications.

[0012] This specification will be further described by exemplary embodiments, which will be described in detail with reference to the drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structure. [Brief explanation of the drawing]

[0013] [Figure 1] This is a flowchart illustrating a method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to the embodiments described herein. [Figure 2] This 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% of a 51Ah rectangular hard-shell ternary / graphite battery according to the embodiments of this specification. [Figure 3] This 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% of a 51Ah rectangular hard-shell ternary / graphite battery according to the embodiments of this specification. [Figure 4] This 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% of a 51Ah rectangular hard-shell ternary / graphite battery according to the embodiments of this specification. [Figure 5]This 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% of a 51Ah rectangular hard-shell ternary / graphite battery according to the embodiments of this specification. [Figure 6] This 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% in a 51Ah rectangular hard-shell ternary / graphite battery according to the embodiments of this specification. [Modes for carrying out the invention]

[0014] To more clearly illustrate the technical means of the embodiments described herein, the drawings necessary for describing the embodiments are briefly described below. Clearly, the drawings described below are only a part of the examples or embodiments of this specification, and those skilled in the art can apply this specification to other similar scenarios based on these drawings without requiring any creative effort. Unless otherwise stated or otherwise evident from the context, the same reference numerals in the figures represent the same structure or operation.

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

[0016] As described herein and in the claims, unless the context explicitly indicates otherwise, terms such as “one,” “one,” “one kind,” and / or “the” do not specifically refer to the singular form, but may include the plural form. Generally, the terms “includes” and “contains” merely indicate the inclusion of clearly identified steps and elements, and these steps and elements are not an exclusive list; the method or device may include other steps or elements.

[0017] This specification uses flowcharts to illustrate the operations performed by the systems according to the embodiments described herein. It should be understood that the preceding and succeeding operations are not necessarily performed in exact order. Instead, each step may be performed in reverse order or simultaneously. Furthermore, 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 specific conditions. It involves a rapid increase in internal temperature, acceleration of chemical reactions, and generation and accumulation of gases. These phenomena interact to form a vicious cycle, leading to battery failure and ultimately explosion.

[0019] During a thermal runaway process, the temperature and pressure inside the battery rise rapidly, causing thermal expansion of the battery materials and electrolyte, as well as the generation and accumulation of gas. These phenomena together generate significant expansion forces, and if these forces exceed the capacity of the battery's explosion-proof valve or housing, it can cause the valve or housing to rupture and a sudden release of gas. This sudden release of gas can 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, increasing the expansion force of the battery as the positive and negative electrodes expand. If the expansion force of the battery is less than the opening pressure of the battery's relief valve, the expansion force continues to increase and eventually reaches a threshold. Typically, whether or not the expansion force reaches this threshold can be used to detect whether the battery is thermally runaway earlier than based on changes in the battery's voltage and temperature. If the internal pressure of the battery is greater than the opening pressure of the battery's relief valve, the explosion-proof valve opens, and a first expansion force peak appears in the expansion force. Subsequently, as multiple jet flames occur in the battery, multiple peaks appear in the expansion force. Therefore, using the threshold and the first expansion force peak to perform early warning of thermal runaway has high predictive performance, and using multiple peaks to perform early warning of the danger level has high accuracy.

[0021] Before the battery undergoes thermal runaway, the expansion force exhibits abnormal characteristics. Therefore, by monitoring the peak of the expansion force signal, the risk of the battery's thermal runaway can be determined.

[0022] Figure 1 is an exemplary flowchart of a method for identifying and early warning of the thermal runaway expansion force characteristics of a battery according to some embodiments of this specification.

[0023] Figure 1 is a flowchart of a method for identifying and early warning of the thermal runaway expansion force characteristics of a battery according to an embodiment of this specification. In some embodiments, as shown in Figure 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 charge and discharge process of the battery at different current rates.

[0025] The expansion force value is the acting 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 in the normal state of the battery. In some embodiments, the initial expansion force value can be preset and obtained based on prior experience.

[0027] The change characteristics refer to the change characteristics of the expansion force during the charge and discharge process of the battery at different current rates. In some embodiments, the change characteristics include the maximum value, minimum value, average value, etc. of the expansion force value. The change characteristics of the battery's expansion force can be obtained through the process of charging and discharging the battery at different current rates.

[0028] The threshold value is the critical value of the preset expansion force value during the charge and discharge process of the battery. Exemplarily, the threshold value may be 4000N. When the current expansion force value reaches the threshold value, thermal runaway may occur inside the battery, and the risk of jet flame ejection increases.

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

[0030] In some embodiments, the processor may pre-set thresholds and initial expansion force values ​​based on change characteristics. For example, the average expansion force value during the battery's charging and discharging process may be determined as the initial expansion force value, and the maximum value of the expansion force value during the battery's charging and discharging process may be determined as the threshold.

[0031] In some embodiments, the processor can construct a battery feature vector based on change features, operating environment data, and battery state data, and then determine a threshold using a vector database based on the battery feature vector.

[0032] Operating environment data may include ambient temperature, ambient humidity, ambient pressure, etc.

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

[0034] S2, the battery under test is charged and discharged or left standing at a preset current rate, and the current expansion force value of the battery under test is obtained.

[0035] In some embodiments, the preset current rate for charging and discharging the battery under test can be determined according to the actual application scenario and needs. Exemplarily, the preset current rate may be the charge and discharge current recommended in the specification of the battery.

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

[0037] In some embodiments, the current expansion force value may be obtained by installing a pressure sensor on the battery under test.

[0038] Furthermore, if the condition of "charging, discharging, or leaving the battery stationary at a predetermined current rate" is met, it is sufficient to directly monitor the actual expansion force of the battery without specifically limiting the state of charge (SOC) or other conditions of the battery.

[0039] In some embodiments of this specification, the current expansion force of a 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 can determine the expansion collection frequency based on the expansion growth rate.

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

[0042] In some embodiments, the expansion collection frequency is positively correlated with the expansion growth rate. For example, the higher the expansion growth rate, the higher the expansion collection frequency.

[0043] In some embodiments of this specification, by determining the expansion collection frequency, the expansion force value inside the battery can be monitored in a timely and efficient manner, thereby preventing thermal runaway.

[0044] In S3, the system determines the 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, it issues a Level 1 early alarm and proceeds to S4; otherwise, it proceeds to S2.

[0045] Early warnings may include, but are not limited to, one or more of the following: light early warnings, audible early warnings, and informational early warnings (e.g., sending a short message).

[0046] In some embodiments, the early alarm execution device may be multiple distributed alarm devices or a single integrated alarm device. For example, the alarm device may be a buzzer, warning light, or other device distributed in multiple locations, or it may be an integrated monitoring and early alarm system located in a monitoring room.

[0047] Early warnings may have multiple levels, such as Level 2 early warnings and Level 3 early warnings. For further details on this, please refer to the following.

[0048] If the current expansion force value exceeds the threshold, the battery may be at risk of localized arc discharge, as the housing may visibly bulge, deforming the battery module connection sheet. In this case, there is a high probability of thermal runaway, and a Level 1 early warning will be issued to alert nearby personnel to evacuate as quickly as possible.

[0049] S4. Based on the current and initial expansion force values ​​of the battery under test, the peak detection algorithm is used to calculate each expansion force peak and the time corresponding to each expansion force peak.

[0050] A peak detection algorithm is an algorithm that detects the extreme values ​​of the current expansion force 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 or heapsort.

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

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

[0054] In S42, the relationship between the current expansion force value and the expansion force values ​​at the previous k seconds and the following k seconds is determined. If the current expansion force value is greater than or equal to the expansion force value at the previous k seconds and greater than or equal to the expansion force value at the following k seconds, it is determined that the current expansion force value is the expansion force peak. Otherwise, it is determined that the current expansion force value is not the expansion force peak. In other words, in the following cases, it is determined that the current expansion force value is not the expansion force peak.

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

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

[0057] In S43, if the current expansion force value is less than the initial expansion force value, the calculation process ends. If the current expansion force value is greater than or equal to the initial expansion force value, the process proceeds to S41.

[0058] The peak detection algorithm described above sets a time window, i.e., a time range of k seconds before and after the real-time expansion value, and determines whether the current expansion force value is at its maximum within that time window range, thereby determining whether the current expansion force value is the expansion force peak.

[0059] In some embodiments of this specification, by calculating each expansion force peak using a peak detection algorithm, expansion force peaks can be detected efficiently and accurately, thereby enabling accurate determination of the degree 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 battery charge. The larger the SOC, the smaller k becomes; in other words, 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, the detection of the peak is stopped. When the real-time expansion force exceeds the threshold again, the peak detection algorithm is used 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 in steps S41 to S43 and continuously used.

[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 this specification, by adjusting the adjustment period of the k value according to the expansion force fluctuation range, the k value can be effectively and flexibly optimized and adjusted based on actual conditions, thereby enhancing the dynamic response capability and applicability of the system for identifying and early warning of thermal runaway expansion force characteristics of batteries.

[0069] A 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 a long short-term memory neural network (LSTM) or any other customized model structure or a combination thereof.

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

[0071] In some embodiments, the input to the contrast boundary model includes hysteretic expansion force values, real-time SOC, operating environment data, battery status data, and magnetic field environment data, and the output includes the adjusted k value.

[0072] Magnetic field environment data refers to magnetic field data in the battery's operating scenario. In some embodiments, magnetic field environment data can be obtained by placing magnetic sensors in the operating environment.

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

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

[0075] In some embodiments, the first training sample can be acquired based on historical data. The processor can acquire historical expansion force values, pre-set a large number of different k values, detect the theoretical expansion peak from the historical expansion force values ​​using the peak detection algorithm, and select the one pre-set k value that is closest to the actual expansion force peak as the k value for the sample corresponding to the first training sample.

[0076] In some embodiments, a contrast boundary determination model is obtained by training in the following manner. The processor inputs a plurality of first training samples having a first label into the initial contrast boundary determination model, constructs a loss function using the first label and the output of the initial contrast boundary determination model, and can iteratively update the 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 the 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, or that the number of iterations reaches a threshold.

[0077] S5, different early alarm levels are pre-set, and the early alarm level corresponding to the battery under test is determined based on each inflation force peak and the time corresponding to each inflation force peak.

[0078] In some embodiments, the early warning system may have multiple early warning levels. For example, Level 1 early warning, Level 2 early warning, Level 3 early warning, Level 4 early warning, and Level 5 early warning. A higher early warning level indicates a greater tendency for the battery under test to experience thermal runaway. The number of early warning levels may be predetermined 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 intense the corresponding early warning measures. For example, a higher early warning level results in a brighter and more frequent flashing of the early warning lamp, a louder and sharper alarm volume, and a more frequent sending of short messages to relevant parties or pop-up warning notifications. 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 countermeasures. For example, when an early warning is triggered, the battery's power can be controlled to turn off, the power of cooling devices such as fans and pumps can be controlled to force the battery to dissipate heat, increase the air convection velocity inside the battery system, and expel any generated aerosols, smoke, etc., outside the battery system casing, thereby avoiding thermal runaway as much as possible. Simultaneously with forcibly dissipating heat from the battery, a fire suppression system can be activated to spray a fire retardant onto the battery, providing fire retardant to any fires or combustions that may be present in the battery system, and early warning notifications can be provided via sound and light.

[0081] In some embodiments, the higher the early warning level, the higher the power of cooling devices such as fans and pumps. Specific early warning measures corresponding to different early warning levels should be set according to the actual situation and are not described in this application.

[0082] In some embodiments, a first preset value and a second preset value can be set in advance.

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

[0084] The preset value determination model is a predictive model that determines a first preset value and a 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 a recurrent neural network (RNN) or any other customized model structure or a combination thereof.

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

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

[0087] Battery attribute data refers to data about the characteristics of a battery. 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 within the battery. The type of battery includes lithium batteries, lead-acid batteries, etc. The size and shape of the battery may include cylindrical, rectangular, soft-pack, hard-pack, etc.

[0088] Battery detection data refers to battery data recorded by a battery management system (BMS). For example, battery detection data includes the battery's State of Charge (SOC), State of Health (SOH), and State of Operating Power (SOP). Here, SOC represents the battery's remaining charge percentage, SOH represents the battery's remaining usable capacity, and SOP represents the maximum charge / discharge power that the battery can currently supply.

[0089] Gas data includes the rate at which the battery releases gas, the gas composition, and other factors.

[0090] Vibration data refers to data indicating vibrations occurring in the battery, and can be acquired using a vibration sensor.

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

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

[0093] For example, when a phenomenon corresponding to a Level 3 early warning occurs (emission of secondary aerosols, smoke, or jet flames generated in the battery), the time interval between the first and second expansion force peaks is 3s to 13s. When a phenomenon corresponding to a Level 4 early warning occurs (secondary jet flames generated in the battery), the time interval between the first and second expansion force peaks is less than 3s. In this case, the first preset value can be set to 3s and the second preset value to 13s.

[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 the relevant content above can be referenced.

[0095] In some embodiments, the processor may determine the bloat 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. As a simple example, the expansion collection frequency can be obtained based on the following equation (1).

[0097]

number

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

[0099] In some embodiments of this specification, the first and second preset values ​​can be flexibly adjusted by determining the expansion collection frequency based on the first and second preset values.

[0100] If the peak detection algorithm detects a first expansion force peak, i.e., the first expansion force peak to appear, a Level 2 early warning will be issued. The appearance of a first expansion force peak indicates that thermal runaway has occurred in the battery, at which point the explosion-proof valve in the battery may open, potentially accompanied by the emission of a large amount of aerosol, smoke, or a violent jet of flames. The emitted material is likely to cause a short circuit outside the battery, overheating of adjacent batteries, and failure of high-voltage insulation protection inside the battery system.

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

[0102] The specific values ​​for the first and second preset values ​​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] In this case, the risk is greater because secondary aerosols, smoke, or jet flames are more likely to be emitted from the battery, and heat diffusion is more likely to occur within the battery system.

[0104] In some embodiments, if the difference between the time corresponding to the first expansion force peak and the time corresponding to the second expansion force peak is greater than or equal to a second preset value, an early alarm is not issued.

[0105] In some embodiments, a Level 4 early warning is issued if the difference between the time corresponding to the first expansion force peak and the time corresponding to the second expansion force peak is smaller than a first preset value. At this time, the aerosols and particulate matter ejected from the battery need to be discharged from the relief valve of the battery pack. The shorter the interval between occurrences, the more likely it is that a secondary jet flame will occur in a short time, which presents greater challenges and higher risks in avoiding pressure release, cooling, and arc discharge in the battery pack.

[0106] In some embodiments, a Level 5 early warning is issued when a third expansion force peak is detected by the peak detection algorithm, i.e., when the third expansion force peak that appears is detected. At this time, the thermal runaway reaction of the battery is a sustained, long-duration process, and the multiple jet flames corresponding to multiple peaks cause multiple pushes and thermal shocks to adjacent batteries, making it easier to cause thermal diffusive effects in adjacent batteries and making it more difficult to eliminate the risk of thermal runaway of the battery by external fire suppression measures.

[0107] Furthermore, regarding the fourth and fifth peaks, the expansion force they exhibit is insufficient and similar to that of the third expansion force peak, and the early warning effect is also similar; therefore, this application is limited to monitoring only the third expansion force peak.

[0108] In some embodiments of this specification, by pre-setting different early warning level determination conditions and corresponding early warning countermeasures, it is possible to provide targeted early warnings and processing for different thermal diffusion conditions, thereby providing early warnings for thermal diffusion in batteries and avoiding battery combustion as much as possible.

[0109] In some embodiments, the processor can determine the expansion rate based on real-time SOC and battery status data, and determine the execution interval period based on the expansion rate, the execution interval period being the time interval during which the processor executes steps S2 to S4.

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

[0111] The expansion growth 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 computational energy consumption and response time by determining the execution interval period according to the actual application scenario and needs.

[0113] In some embodiments, the processor can determine the expansion rate based on the real-time SOC and battery temperature in the battery state data. As just one example, the expansion rate can be obtained based on the following equation (2).

[0114]

number

[0115] Here, y represents the rate of increase in 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 coefficients k2, k3, k4, and k5 may be determined based on manual input or prior experience.

[0117] In some embodiments, the processor acquires a large amount of historical real-time SOC, historical battery temperature, historical current battery charge / discharge rate, and historical expansion force value, calculates the expansion increase rate y based on the historical expansion force value, and obtains the values ​​of coefficients k1, k2, k3, and k4 using a fitting algorithm. The fitting algorithm includes, but is not limited to, methods such as linear regression and nonlinear fitting.

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

[0119]

number

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

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

[0122] As described above, this invention quantifies the thermal runaway state of a battery using the expansion force peak as a determination criterion, and by classifying the early warning level according to the peak, the risk of thermal runaway of a battery is categorized into levels. As the risk increases from level 2 to level 5, corresponding fire extinguishing measures are used in a timely manner to reduce the risk.

[0123] The specific implementation details will be explained below, but please note that the following related explanations are for illustrative purposes only and are not intended to limit the scope of this specification.

[0124] 1) A thermal runaway test was conducted on a lithium-ion battery. The lithium-ion battery was a 51Ah rectangular hard-shell ternary / graphite battery, and thermal runaway was triggered by external heating to more realistically simulate a thermal runaway scenario for a battery in actual use.

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

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

[0127] Peaks were detected in the battery expansion force data for different states of charge (SOCs). The detected expansion force peaks are shown in Figures 2, 3, 4, 5, and 6, respectively.

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

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

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

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

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

[0133] At the same time, before the first expansion force peak appears, the expansion force curve changes sharply after the expansion force reaches 4000N, and then quickly reaches the first expansion force peak. This demonstrates the feasibility of the threshold set at 4000N in Embodiment 1 of the present invention. Therefore, by monitoring the expansion force peak in real time and checking whether it exceeds the threshold, the risk of combustion explosion of the battery can be effectively reduced.

[0134] In some embodiments, the battery temperature includes the temperatures of the battery's positive and negative electrode tabs and the housing temperature and is obtained by a temperature sensor; the voltage is the voltage of the battery's positive and negative electrode terminals and is obtained by a voltage sensor; the battery expansion force is the expansion force on the sides of the rectangular hard shell and is obtained by a pressure sensor; for the pressure sensor, in the battery system, the expansion force of the entire battery module can be obtained by placing one pressure sensor in a single battery module, or the expansion force value of each cell can be obtained by placing it on the side of a single battery cell; and images of the battery's thermal runaway combustion are 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 this specification includes a preset module, a calculation module, and an early warning module.

[0136] In some embodiments, the preset module is configured to perform the step of presetting threshold and initial expansion force values ​​based on the characteristics of the change in expansion force during the charging and discharging process of the battery at different current rates.

[0137] In some embodiments, the calculation module is connected to a preset module and is configured to perform the following steps: charge / discharge or leave a battery under test at a preset current rate to obtain the current expansion force value of the battery under test; determine the relationship between the current expansion force value of the battery and a threshold value; if the current expansion force value is greater than or equal to the threshold value, issue a Level 1 early alarm and proceed to the early alarm module; otherwise, repeat the calculation in the calculation module.

[0138] In some embodiments, the early warning module is connected to a calculation module and is configured to perform the following steps: calculate 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 initial expansion force value of the battery under test, wherein the expansion force peak includes a first expansion force peak, a second expansion force peak, and a third expansion force peak; and preset different early warning levels and determine 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.

[0139] For more specific details, please refer to the related explanations in Figures 1, 2, 3, 4, 5, and 6; the explanation is omitted here.

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

[0141] The processor may be a central processing unit (CPU) or another type of processing unit having data processing capability and / or instruction execution capability, which can control other assemblies in an electronic device to perform a desired function.

[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), hard disk, flash memory, etc. The computer-readable storage media can store one or more computer program instructions, and the processor can execute the program instructions to implement a method for identifying and early warning of thermal runaway expansion force characteristics of a battery and / or other desired functions according to any embodiment of the present application described above. The computer-readable storage media may store various contents such as initial external parameters and thresholds.

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

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

[0145] Furthermore, the embodiments of the present application may also be computer-readable storage media in which computer program instructions are stored, and when the computer program instructions are executed by a processor, the processor is instructed to perform the steps of the method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to any embodiment of the present application.

[0146] The computer-readable storage medium described above may be any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any combination of more than these. More specific examples of readable storage media (a non-exhaustive list) include electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0147] Having explained the basic concepts above, it will be clear to those skilled in the art that the above detailed disclosures are merely examples and do not limit this specification. Although not explicitly stated herein, those skilled in the art can make various changes, improvements, and modifications to this specification. These changes, improvements, and modifications are intended to be suggested herein and are therefore within the spirit and scope of the exemplary embodiments herein.

[0148] Furthermore, certain terms are used herein to describe the embodiments. For example, “one embodiment,” “one embodiment,” and / or “several embodiments” mean certain features, structures, or properties related to at least one embodiment of this specification. Therefore, it should be emphasized and understood that two or more references to “one embodiment,” “one embodiment,” or “one alternative embodiment” in various parts of this specification do not necessarily all refer to the same embodiment. Also, certain features, structures, or properties in one or more embodiments of this specification may be combined appropriately.

[0149] Furthermore, unless explicitly stated in the claims, the enumerated order, use of alphanumeric characters, or use of other names of the processing elements or sequences described herein does not limit the order of the procedures and methods herein. While various examples illustrate what are currently considered useful embodiments of the invention in the above disclosure, such details are for illustrative purposes only, and it should be understood 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 assemblies described above may be implemented by hardware devices, but may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

[0150] Similarly, in the foregoing description of the embodiments in this specification, it should be understood that, for the purpose of simplifying this specification and aiding in the understanding of embodiments of one or more inventions, various features may be grouped together in a single embodiment, drawing, or description thereof. However, such disclosure methods should not be interpreted as reflecting an intention that the claimed subject matter requires more features than those enumerated in each claim. In fact, the features of an embodiment may be fewer than all the features of a single embodiment disclosed above.

[0151] In some embodiments, numbers are used to describe the number of components and attributes, and it should be understood that these numbers describing such embodiments are modified in some cases by the modifiers “about,” “approximately,” or “generally.” Unless otherwise specified, “about,” “approximately,” or “generally” indicates that the above numbers are allowed to vary by ±20%. Therefore, in some embodiments, the numerical parameters used in the specification and claims are all approximations that may vary depending on the characteristics required for the individual embodiment. In some embodiments, the numerical parameters should be treated with the specified number of significant figures and the usual place-keep method. In some embodiments of this specification, the numerical ranges and parameters used to determine the range are approximations, but in specific embodiments, such numbers are set as precisely as possible.

[0152] All patents, patent applications, published patent gazettes, and other materials such as articles, books, specifications, publications, and documents referenced herein are incorporated herein by reference in their entirety, with the exception of any prosecution history documents that are inconsistent with or contradict the content of this specification, and any documents that may have a limited effect on the broadest scope of the claims herein (currently or later relating to this specification). In the event of any inconsistency between the use of explanations, definitions, and / or terms in the accompanying materials of this specification and the content herein, the use of explanations, definitions, and / or terms herein shall prevail.

[0153] Finally, it should be understood that the examples described herein are merely illustrative of the principles of the examples herein. Other modifications may also be within the scope of this specification. Therefore, alternative configurations of the examples herein may be considered consistent with the teachings herein, for example, without limitation. Thus, the examples herein are not limited to those explicitly introduced and described herein.

Claims

1. Step S1 involves pre-setting a threshold and an initial expansion force value based on the characteristics of the change in expansion force during the charging and discharging process of a battery at different current rates. Step S2 involves charging or discharging the battery under test at a preset current rate or leaving it stationary, and obtaining the current expansion force value of the battery under test. Step S3 involves determining the 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, a Level 1 early warning is issued, and the process proceeds to S4; otherwise, the process proceeds to S2. Step S4, which calculates 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 initial expansion force value of the battery under test, wherein the expansion force peak includes a first expansion force peak, a second expansion force peak and a third expansion force peak, A method for identifying and early warning of thermal runaway expansion force characteristics of a battery, comprising step S5 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.

2. The step of calculating each expansion force peak using the aforementioned peak detection algorithm is: Step S41 is to obtain the current expansion force value for k seconds before and k seconds after, wherein k is a constant that is not 0. Step S42 involves determining the relationship between the current expansion force value and the expansion force values ​​at the previous k seconds and the following k seconds. If the current expansion force value is greater than or equal to the expansion force value at the previous k seconds and greater than or equal to the expansion force value at the following k seconds, it is determined that the current expansion force value is the expansion force peak. Otherwise, it is determined that the current expansion force value is not the expansion force peak. A method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to claim 1, characterized by including step S43, which involves terminating the calculation process if the current expansion force value is less than or equal to the initial expansion force value, and proceeding to S41 if the current expansion force value is greater than or equal to the initial expansion force value.

3. The step of pre-setting different early warning levels and determining the appropriate early warning level for the battery under test based on each inflation force peak and the time corresponding to each inflation force peak is: A step of pre-setting a first preset value and a second preset value, If a first expansion force peak is detected, the step is to issue a Level 2 early warning, If a second expansion force peak is detected, a Level 3 early warning is issued if the difference between the time corresponding to the first expansion force peak and the time corresponding to the second expansion force peak is greater than or equal to the first preset value and less than the second preset value; and a Level 4 early warning is issued if 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. A method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to claim 1, characterized by comprising the step of issuing a level 5 early warning when a third expansion force peak is detected.

4. The step of obtaining the current expansion force value of the battery under test is: A method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to claim 1, characterized by including the step of obtaining the current expansion force value by installing a pressure sensor on the battery to be measured.

5. The method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to claim 2, characterized in that k has a positive correlation 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 performing a method for identifying and early warning of thermal runaway expansion force characteristics of a battery according to any one of claims 1 to 5, A preset module configured to perform the step of presetting threshold and initial expansion force values ​​based on the characteristics of changes in expansion force during the charging and discharging process of a battery at different current rates, A calculation module configured to perform the following steps: connect to a pre-configuration module, charge / discharge or leave a battery under test at a pre-configured current rate, and obtain the current expansion force value of the battery under test; determine the relationship between the current expansion force value of the battery and a threshold value, and if the current expansion force value is greater than or equal to the threshold value, issue a Level 1 early alarm and proceed to the early alarm module; otherwise, repeat the calculation in the calculation module; A system comprising an early warning module connected to a calculation module, 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 initial expansion force value of the battery under test, wherein the expansion force peak includes 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 under test based on each expansion force peak and the time corresponding to each expansion force peak.

7. Including the processor and memory, The electronic device is characterized in that the processor performs the steps of 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 by calling a program or instruction stored in the memory.

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