Battery thermal runaway prediction method and system, and storage medium

Abstract

The present application provides a battery thermal runaway prediction method and system, and a storage medium. The method comprises: acquiring voltage data of a battery under test under a preset condition; according to the preset condition and the voltage data, obtaining internal resistance data corresponding to said battery; on the basis of a preset activation energy model, processing the internal resistance data, and obtaining activation energy data corresponding to said battery; and, according to the activation energy data, determining a probability of thermal runaway of said battery, thereby achieving accurate prediction of thermal runaway of the battery.

Description

Battery thermal runaway prediction method, system and storage medium

[0001] The present application claims priority to the Chinese patent application No. 2024108666616 filed on June 28, 2024 with the China Patent Office, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of batteries, in particular to a battery thermal runaway prediction method, system and storage medium. BACKGROUND

[0003] As the main energy output source and storage device of electrical equipment, batteries can be used as power sources and energy carriers for new energy vehicles, for example, to bear the functions of vehicle acceleration, speed maintenance and energy recovery. Thermal runaway of a battery refers to a cumulative enhancement of current and temperature during charging or discharging of the battery, which leads to rapid temperature rise of the battery and may eventually cause damage, fire or explosion of the battery. Therefore, it is necessary to predict the thermal runaway of the battery to remind users to detect and replace the battery in time to ensure the safe operation of the battery. TECHNICAL PROBLEM

[0004] In the related art, the discreteness and rate of change of parameters such as internal resistance are usually used to predict the thermal runaway of a battery, but this prediction method is easily affected by external environment, connection and other conditions, resulting in low accuracy of thermal runaway prediction and easy false positives. TECHNICAL SOLUTION

[0005] In a first aspect, the present application provides a battery thermal runaway prediction method, comprising the following steps:

[0006] Obtaining voltage data of a battery to be tested under a preset condition;

[0007] According to the preset condition and the voltage data, obtaining internal resistance data corresponding to the battery to be tested;

[0008] Processing the internal resistance data based on a preset activation energy model to obtain activation energy data corresponding to the battery to be tested;

[0009] According to the activation energy data, determining the thermal runaway probability of the battery to be tested.

[0010] In a second aspect, the present application provides a battery thermal runaway prediction system, comprising a processing device connected to a battery to be tested.

[0011] The processing device is configured to execute the steps of the battery thermal runaway prediction method provided by the present application.

[0012] In a third aspect, the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the battery thermal runaway prediction method provided by the present application. Advantages

[0013] The battery thermal runaway prediction method provided by the present application has the advantages that: the voltage data of the battery to be measured under a preset condition is obtained; the internal resistance data corresponding to the battery to be measured is obtained according to the preset condition and the voltage data; the activation energy data corresponding to the battery to be measured is obtained by processing the internal resistance data based on a preset activation energy model; and the thermal runaway probability of the battery to be measured is determined according to the activation energy data, thereby realizing accurate prediction of the thermal runaway of the battery to be measured. The present application obtains the voltage data of the battery to be measured under the preset condition, calculates the internal resistance data of the battery to be measured, and calculates the activation energy data of the battery to be measured according to the internal resistance data, and then determines the probability of thermal runaway of the battery to be measured according to the activation energy data, thereby realizing early and accurate prediction of the thermal runaway of the battery to be measured, improving the thermal runaway prediction accuracy, and effectively improving the use safety of the battery to be measured. BRIEF DESCRIPTION OF DRAWINGS

[0014] FIG. 1 is a schematic diagram of an application scenario of the battery thermal runaway prediction method provided by the present application;

[0015] FIG. 2 is a first flowchart of the battery thermal runaway prediction method provided by the present application;

[0016] FIG. 3 is a flowchart of the voltage data obtaining step provided by the present application;

[0017] FIG. 4 is a flowchart of the internal resistance data obtaining step provided by the present application;

[0018] FIG. 5 is a second flowchart of the battery thermal runaway prediction method provided by the present application;

[0019] FIG. 6 is a flowchart of the battery pack thermal runaway probability determining step provided by the present application;

[0020] FIG. 7 is a third flowchart of the battery thermal runaway prediction method provided by the present application;

[0021] FIG. 8 is a fourth flowchart of the battery thermal runaway prediction method provided by the present application;

[0022] FIG. 9 is a fifth flowchart of the battery thermal runaway prediction method provided by the present application;

[0023] FIG. 10 is a structural schematic diagram of the battery thermal runaway prediction system provided by the present application. Embodiments of the present application

[0024] The battery thermal runaway prediction method provided in the application can be applied in an application environment as shown in FIG. 1. The processing device can include a processor 102 and a memory 104, and the memory 104 can be used to store voltage data, internal resistance data, activation energy data, and thermal runaway probability data. The processor 102 can be used to obtain voltage data of a battery to be tested under a preset condition; obtain internal resistance data corresponding to the battery to be tested according to the preset condition and the voltage data; process the internal resistance data based on a preset activation energy model to obtain activation energy data corresponding to the battery to be tested; and determine the thermal runaway probability of the battery to be tested according to the activation energy data. The processing device can also include a display 106, which can display voltage data, internal resistance data, activation energy data, and thermal runaway probability data through a graphical interface. For example, the processing device can be a battery management system (BMS), and the processing device can be arranged in a vehicle. The battery to be tested can be a lithium ion single cell, for example, a plurality of single cells can be connected in series or in parallel, or both.

[0025] In one embodiment, as shown in FIG. 2, a battery thermal runaway prediction method is provided, which is applied to the processor 102 in FIG. 1 as an example, including the following steps:

[0026] Step S210, obtaining voltage data of a battery to be tested under a preset condition.

[0027] The battery to be tested can be applied in a vehicle. The battery to be tested can be a regular shape structure such as a square or a circle. It should be noted that the battery to be tested can also be an irregular shape structure. The battery to be tested can be a lithium ion cell, for example, a lithium iron phosphate cell, a ternary cell, a lithium manganate cell, or a lithium manganate iron phosphate cell.

[0028] The preset condition refers to the charge and discharge condition of the battery to be tested, for example, the preset condition can be the charge and discharge current, the charge and discharge time, and the preset temperature of the battery to be tested. It should be noted that the preset temperature can be the ambient temperature or the surface temperature of the battery to be tested.

[0029] The voltage data of the battery to be tested under the preset condition is obtained by performing charge and discharge tests on the battery to be tested under the preset condition, and collecting the voltage of the battery to be tested during the test.

[0030] Step S220, obtaining internal resistance data corresponding to the battery to be tested according to the preset condition and the voltage data.

[0031] The internal resistance data can be the internal resistance of the SEI film of the battery to be tested. It should be noted that the SEI film, i.e. the solid electrolyte interface film, is a passivation layer formed on the surface of the electrode material during the first charge and discharge process of the battery to be tested, which is formed by the reaction of the electrode material and the electrolyte at the solid-liquid interface.

[0032] For example, according to the preset condition, the preset temperature and the charge-discharge current are obtained; according to the preset temperature and the voltage data, the starting voltage and the terminal voltage of the battery to be tested at the corresponding preset temperature are determined, and then according to the starting voltage, the terminal voltage and the charge-discharge current, the internal resistance data of the corresponding battery to be tested at the corresponding temperature are obtained.

[0033] In step S230, the internal resistance data is processed based on a preset activation energy model to obtain activation energy data of the battery to be tested.

[0034] The preset activation energy model can be obtained according to historical test data.

[0035] For example, by inputting the internal resistance data of the corresponding battery to be tested at different temperatures into the preset activation energy model for fitting processing, the activation energy data of the corresponding battery to be tested are obtained.

[0036] In step S240, the thermal runaway probability of the battery to be tested is determined according to the activation energy data.

[0037] The higher the activation energy of the battery to be tested is, the stronger the stability of the battery to be tested is.

[0038] According to the numerical value of the activation energy data, the probability of thermal runaway of the battery to be tested is determined, and accurate prediction of thermal runaway of the battery to be tested is realized.

[0039] For example, a mapping relationship table between activation energy and thermal runaway probability can be established in advance, and according to the activation energy data obtained by processing, the thermal runaway probability corresponding to the activation energy data is obtained by querying the mapping relationship table, and accurate prediction of thermal runaway of the battery to be tested is realized.

[0040] In the above embodiment, by obtaining the voltage data of the battery to be tested under the preset condition, the internal resistance data of the battery to be tested is calculated, and according to the internal resistance data, the activation energy data of the battery to be tested is calculated, and then according to the activation energy data, the probability of thermal runaway of the battery to be tested is determined, and the thermal runaway of the battery to be tested is predicted in advance and accurately, the prediction accuracy of thermal runaway is improved, and the use safety of the battery to be tested is effectively improved.

[0041] In one embodiment, as shown in FIG. 3, the step of obtaining the voltage data of the battery to be tested under the preset condition comprises:

[0042] At step S310, the battery to be tested is discharged or charged based on the preset current, the preset test time, and the at least one preset temperature.

[0043] The battery to be tested can be charged or discharged using a constant current or a pulse current. For example, when the battery to be tested is charged or discharged using a constant current, the preset current is the constant current. For another example, when the battery to be tested is charged or discharged using a pulse current, the preset current is the average current of the battery to be tested in the charging and discharging interval. In an example, the preset current can be set to be within ±5% of the average current. It should be noted that the preset current (such as the average value of the constant current or the pulse current) for charging or discharging the battery to be tested is the same at different temperatures.

[0044] The preset test time refers to the charging and discharging time of the battery to be tested. For example, the preset test time can be 5 seconds, that is, the battery to be tested can be discharged based on the preset current for 5 seconds.

[0045] The preset temperature can be the ambient temperature of the battery to be tested. For example, in order to more accurately predict the thermal runaway of the battery to be tested, voltage data of the battery to be tested at different temperatures can be collected, such as voltage data at at least four temperatures. For example, the preset temperature is set to be -10℃, 10℃, 25℃, and 45℃. It should be noted that the range of the preset temperature can be set to be between -30℃ and 55℃, and the gradient of the collected temperature can be set to be greater than 5℃. In an example, the gradient of the collected temperature can be set to be between 10℃ and 20℃.

[0046] For example, the preset test time is set to be 5 seconds, the preset temperature is set to be -10℃, 10℃, 25℃, and 45℃, and the preset current is set to be a constant current. Then, the battery to be tested is discharged or charged based on the constant current for 5 seconds at -10℃, 10℃, 25℃, and 45℃ in sequence, so as to realize the charging and discharging test of the battery to be tested.

[0047] At step S320, the battery to be tested is collected according to the preset collection frequency when the battery to be tested is discharged or charged, so as to obtain voltage data corresponding to each preset temperature.

[0048] The preset collection frequency is obtained according to the system preset. For example, the preset collection frequency can be set to be 100Hz (that is, collected once every 0.01 seconds).

[0049] For example, the preset collection frequency is set to 100 Hz, the battery to be tested is discharged or charged at a preset current for a preset test time based on a corresponding preset temperature, and the voltage of the battery to be tested is collected to obtain voltage data at the corresponding preset temperature. According to each preset temperature and the voltage data at the corresponding preset temperature, the internal resistance data of each corresponding preset temperature is obtained; the internal resistance data is processed based on a preset activation energy model to obtain activation energy data corresponding to the battery to be tested; and the thermal runaway probability of the battery to be tested is determined according to the activation energy data, thereby accurately predicting the thermal runaway of the battery to be tested, realizing early and accurate prediction of the thermal runaway of the battery to be tested, improving the thermal runaway prediction accuracy, and thus effectively improving the use safety of the battery to be tested.

[0050] In one embodiment, as shown in FIG. 4, the step of obtaining the internal resistance data corresponding to the battery to be tested according to the preset conditions and the voltage data includes:

[0051] In step S410, the SEI film initial voltage at each corresponding preset temperature and the SEI film terminal voltage at each corresponding preset temperature are obtained according to the voltage data at different preset temperatures.

[0052] The voltage data can include at least one collection voltage at a corresponding collection point. The SEI film initial voltage refers to the initial voltage of the SEI film corresponding to the battery to be tested; and the SEI film terminal voltage refers to the terminal voltage of the SEI film corresponding to the battery to be tested.

[0053] For example, the voltage of the battery to be tested is collected at a preset collection frequency based on a corresponding preset temperature to obtain the collection voltage at each corresponding collection point at the corresponding preset temperature, and the SEI film initial voltage and the SEI film terminal voltage at the corresponding preset temperature are obtained by processing the plurality of collection voltages at the corresponding preset temperature.

[0054] In one example, step S410 includes: obtaining a first voltage at a current collection time and a second voltage at a next collection time under the same preset temperature; when the proportion of the first voltage to the second voltage is less than a first threshold, confirming that the first voltage is the SEI film initial voltage at the corresponding preset temperature; obtaining a third voltage corresponding to a preset test time under the same preset temperature; and when the preset test time reaches a second threshold, confirming that the third voltage is the SEI film terminal voltage at the corresponding preset temperature.

[0055] For example, the collection interval is set to 0.01 seconds, the preset test time is t seconds, the first voltage at the current collection time is , the second voltage at the next collection time is , and the first threshold is Any value between 1.2 and 2 can be set. The battery under test is discharged for t seconds at the corresponding preset temperature and preset current, and voltage acquisition is performed on the battery under test based on the set acquisition interval, to obtain the voltage corresponding to the voltage at the current acquisition time and the voltage at the next acquisition time under the same preset temperature, and then the first voltage and the second voltage are obtained. When , it is determined that the first voltage is the SEI film initial voltage at the corresponding preset temperature.

[0056] The third voltage refers to the acquisition voltage corresponding to the preset test time. For example, if the preset test time is 0.5 seconds, the third voltage is the voltage acquired when the battery under test is discharged for 0.5 seconds. The second threshold value can be set to any value between 0.1 and 1. The battery under test is discharged for the preset test time at the corresponding preset temperature and preset current, and voltage acquisition is performed on the battery under test based on the set acquisition interval, to obtain the third voltage corresponding to the preset test time under the same preset temperature. When the preset test time falls within 0.1 to 1, it is determined that the third voltage is the SEI film termination voltage at the corresponding preset temperature.

[0057] In step S420, the internal resistance data of each battery under test corresponding to the preset temperature is obtained based on the preset current, the plurality of SEI film initial voltages, and the plurality of SEI film termination voltages.

[0058] The internal resistance data refers to the internal resistance of the SEI film of the battery under test.

[0059] Based on the preset current, the SEI film initial voltage, and the SEI film termination voltage at the corresponding preset temperature, the preset current, the SEI film initial voltage at the corresponding preset temperature, and the SEI film termination voltage at the corresponding preset temperature are processed, and then the internal resistance data of the battery under test at the corresponding preset temperature is obtained.

[0060] For example, the preset temperature can be set to -10°C, 10°C, 25°C, and 45°C. By calculating the SEI film initial voltage and the SEI film termination voltage at different temperatures, the internal resistance of the battery under test at different temperatures can be obtained. For example, the internal resistance at -10°C is R(-10°C), the internal resistance at 10°C is R(10°C), the internal resistance at 25°C is R(25°C), and the internal resistance at 45°C is R(45°C).

[0061] In one example, step S420 includes: performing difference processing on the SEI film initial voltage and the SEI film termination voltage to obtain a voltage difference; and performing ratio processing on the voltage difference and the preset current to obtain the internal resistance of the corresponding battery under test at the corresponding preset temperature.

[0062] wherein the SEI film initial voltage is SEI film termination voltage is internal resistance is .

[0063] Based on the internal resistance formula , the preset current, the SEI film initial voltage and the SEI film termination voltage are processed, and then the internal resistance R SEI of the corresponding preset temperature of the to-be-tested battery is obtained.

[0064] In the above embodiment, based on the obtained SEI film initial voltage and SEI film termination voltage under the preset condition, the internal resistance data of the to-be-tested battery is calculated, the calculation accuracy of the internal resistance is improved, and then the activation energy data of the to-be-tested battery is calculated according to the internal resistance data, the probability of thermal runaway of the to-be-tested battery is determined according to the activation energy data, the early and accurate prediction of the thermal runaway of the to-be-tested battery is realized, the thermal runaway prediction accuracy is improved, and thus the use safety of the to-be-tested battery can be effectively improved.

[0065] In one embodiment, the step of processing the internal resistance data based on the preset activation energy model to obtain the activation energy data corresponding to the to-be-tested battery comprises:

[0066] The plurality of preset temperatures and the plurality of internal resistances corresponding to the preset temperatures are input into the preset activation energy model for fitting processing to obtain the activation energy data corresponding to the to-be-tested battery.

[0067] The preset activation energy model can be obtained by historical test data.

[0068] By inputting the corresponding preset temperature and the internal resistance corresponding to the preset temperature into the preset activation energy model for processing, and through linear fitting calculation, the activation energy data corresponding to the to-be-tested battery is obtained.

[0069] In one example, the preset activation energy model is: .

[0070] wherein, is the activation energy data; is the internal resistance of the corresponding preset temperature; A is a first constant; B is a second constant; T is the preset temperature. For example, the preset activation energy model can be converted into a linear fitting model , wherein k is the slope. It should be noted that A is the reaction rate constant, and the value of B can be set to 8.31 J / mol·K.

[0071] By inputting the corresponding preset temperature and the internal resistance corresponding to the preset temperature into the preset activation energy model for processing, and through linear fitting calculation, the slope k of the linear fitting model can be obtained, and then the activation energy data corresponding to the to-be-tested battery can be calculated.

[0072] In the above embodiment, by acquiring the voltage data of the battery to be tested under the preset condition, calculating the internal resistance data of the battery to be tested, and fitting the internal resistance of each preset temperature and each corresponding preset temperature based on the preset activation energy model, the activation energy data corresponding to the battery to be tested is obtained, and then the probability of thermal runaway of the battery to be tested is determined according to the activation energy data, so as to realize the early and accurate prediction of the thermal runaway of the battery to be tested, improve the thermal runaway prediction accuracy, and effectively improve the use safety of the battery to be tested.

[0073] In one embodiment, the step of discharging or charging the battery to be tested based on the preset current, the preset test time and the at least one preset temperature comprises:

[0074] When the resting time of the battery to be tested reaches the preset time length or the battery to be tested is detected to be in a starting state, the battery to be tested is discharged or charged based on the preset current, the preset test time and the at least one preset temperature.

[0075] The preset time length can be obtained according to the system preset, for example, the preset time length can be set to 30 minutes. The battery to be tested in the starting state means that the battery to be tested is in the state of just starting.

[0076] For example, before collecting the voltage of the battery to be tested, the battery to be tested is rested for 30 minutes, and when the resting time of the battery to be tested reaches 30 minutes, the battery to be tested is discharged or charged based on the preset current, the preset test time and the at least one preset temperature, and the voltage of the battery to be tested is collected based on the preset collection frequency, so as to obtain the voltage data of the battery to be tested under the corresponding preset temperature, thereby improving the accuracy of voltage collection, so as to accurately calculate the internal resistance and activation energy of the battery to be tested in the subsequent internal resistance calculation step and activation energy step, and then determine the probability of thermal runaway of the battery to be tested according to the activation energy data, realize the early and accurate prediction of the thermal runaway of the battery to be tested, improve the thermal runaway prediction accuracy, and effectively improve the use safety of the battery to be tested.

[0077] For example, in order to improve the thermal runaway prediction efficiency of the battery to be tested, when the battery to be tested is detected to be in a starting state, the battery to be tested is discharged or charged based on the preset current, the preset test time and the at least one preset temperature, and the voltage of the battery to be tested is collected based on the preset collection frequency, so as to obtain the voltage data of the battery to be tested under the corresponding preset temperature. For example, for a new energy vehicle, the voltage of the battery to be tested can be collected when the vehicle is just started, so as to obtain the voltage data of the battery to be tested under the corresponding preset temperature.

[0078] In one embodiment, the step of discharging or charging the battery to be tested based on the preset current, the preset test time and the at least one preset temperature further comprises:

[0079] Discharge or charge the battery under test based on the preset SOC, the preset current, the preset test time, and at least one preset temperature.

[0080] The preset SOC is obtained by system preset.

[0081] To improve the accuracy of voltage collection, the battery under test is discharged or charged based on the preset SOC, the preset current, the preset test time, and at least one preset temperature by setting the SOC, and voltage collection is performed on the battery under test based on a preset collection frequency to obtain voltage data of the battery under test at the corresponding preset temperature, thereby improving the accuracy of voltage collection, so that the internal resistance and activation energy of the battery under test can be accurately calculated in subsequent internal resistance calculation and activation energy steps, and the probability of thermal runaway of the battery under test can be determined according to the activation energy data, thereby realizing early and accurate prediction of thermal runaway of the battery under test, improving the accuracy of thermal runaway prediction, and effectively improving the use safety of the battery under test.

[0082] It should be noted that the voltage of the battery under test is collected based on different preset temperatures, and the SOC (state of charge) of the battery under test before standing is in the same range, for example, the SOC difference is within ±5%.

[0083] In one embodiment, as shown in FIG. 5, a battery thermal runaway prediction method is provided, which is applied to the processor 102 in FIG. 1 as an example, and includes the following steps:

[0084] Step S510, obtaining voltage data of the battery under test under preset conditions.

[0085] The specific description of step S510 is described above, and will not be repeated here.

[0086] Step S520, obtaining internal resistance data corresponding to the battery under test according to the preset conditions and the voltage data.

[0087] The specific description of step S520 is described above, and will not be repeated here.

[0088] Step S530, processing the internal resistance data based on a preset activation energy model to obtain activation energy data corresponding to the battery under test.

[0089] The specific description of step S530 is described above, and will not be repeated here.

[0090] Step S540, determining the thermal runaway probability of the battery under test according to the activation energy data.

[0091] The specific description of step S540 is described in the above embodiment, and will not be repeated here.

[0092] In step S550, the activation energy data of each battery to be measured in the same battery pack is obtained.

[0093] The battery pack includes at least one battery to be measured; the multiple batteries to be measured in the same battery pack can be connected in series and / or parallel.

[0094] For example, by collecting the voltage data of each battery to be measured in the same battery pack, the voltage data of each corresponding battery to be measured is obtained, the internal resistance data of the corresponding battery to be measured is calculated, and the activation energy data of each battery to be measured in the same battery pack is obtained according to the internal resistance data of the corresponding battery to be measured.

[0095] In step S560, the minimum activation energy data is obtained according to the multiple activation energy data.

[0096] The minimum activation energy data refers to the activation energy with the smallest value in the multiple activation energy data of the corresponding battery pack.

[0097] The activation energy data of the multiple batteries to be measured in the same battery pack is screened to obtain the minimum activation energy data.

[0098] In step S570, the thermal runaway probability of the battery pack is determined according to the minimum activation energy data.

[0099] The smaller the value of the minimum activation energy data in the same battery pack, the worse the stability of the battery pack.

[0100] According to the value of the minimum activation energy data, the probability of thermal runaway of the corresponding battery pack is determined, and the accurate prediction of thermal runaway of the battery pack is realized.

[0101] For example, a mapping relationship table between the minimum activation energy and the thermal runaway probability can be established in advance, and the thermal runaway probability corresponding to the minimum activation energy data is obtained by querying the mapping relationship table according to the obtained minimum activation energy data, so as to realize the accurate prediction of thermal runaway of the battery pack.

[0102] In the above embodiment, by obtaining the voltage data of each battery to be measured in the same battery pack under the preset condition, the internal resistance data of each battery to be measured is calculated, and the activation energy data of each battery to be measured in the same battery pack is calculated according to the internal resistance data of each battery to be measured, and then the minimum activation energy data of the corresponding battery pack is determined according to the multiple activation energy data, and the probability of thermal runaway of the corresponding battery pack is determined according to the minimum activation energy data, so as to realize the early and accurate prediction of thermal runaway of the battery pack, improve the prediction accuracy of thermal runaway, and effectively improve the use safety of the battery pack.

[0103] In one embodiment, as shown in FIG. 6, the step of determining the thermal runaway probability of the battery pack according to the minimum activation energy data comprises:

[0104] In step S610, the average activation energy data is obtained according to the plurality of activation energy data.

[0105] The average activation energy data refers to the average value of each activation energy data of the corresponding battery pack.

[0106] For example, the battery pack includes n batteries to be tested. By calculating the activation energy of each battery to be tested in the same battery pack, the plurality of activation energy data are obtained as follows: By averaging the activation energy data from to , the average activation energy data is obtained.

[0107] In step S620, the average activation energy data is compared with each activation energy data to obtain a first ratio value corresponding to each activation energy data.

[0108] The first ratio value is obtained by dividing the average activation energy data by the activation energy data of the corresponding battery pack.

[0109] By comparing the average activation energy data with the plurality of activation energy data of each battery to be tested in the same battery pack, the first ratio value of each corresponding battery to be tested is obtained.

[0110] In step S630, the thermal runaway probability of the battery pack is determined according to the minimum activation energy data and the number of activation energy data whose corresponding first ratio value is greater than a third threshold value.

[0111] The third threshold value can be obtained by system presetting, for example, the third threshold value can be set to any value between 1.05 and 1.5. When the first ratio value of the corresponding battery to be tested is greater than the third threshold value, the probability of thermal runaway of the battery to be tested is relatively high. For example, the third threshold value is set to Y2, and the average activation energy data is compared with the first ratio value of the activation energy data of a battery to be tested i, i.e. , the probability of thermal runaway of the battery to be tested i is relatively high.

[0112] According to the value of the minimum activation energy data and the number of activation energy data whose corresponding first ratio value is greater than the third threshold value, the probability of thermal runaway of the corresponding battery pack is determined, and accurate prediction of the thermal runaway of the battery pack is realized.

[0113] ​In the above embodiment, by acquiring the voltage data of each to-be-tested battery in the same battery pack under the preset condition, calculating the internal resistance data of each to-be-tested battery, and calculating the activation energy data of each to-be-tested battery in the same battery pack according to the internal resistance data of each to-be-tested battery, and then determining the minimum activation energy data and the average activation energy data of the corresponding battery pack according to each activation energy data, and determining the probability of thermal runaway of the corresponding battery pack according to the number of first ratios greater than the third threshold value between the minimum activation energy data and the average activation energy data and the activation energy data of the corresponding to-be-tested battery, the method realizes the early and accurate prediction of the thermal runaway of the battery pack, improves the prediction accuracy of the thermal runaway of the battery pack, and thus effectively improves the use safety of the battery pack.

[0114] In one example, the step of performing ratio processing on the average activation energy data and the plurality of activation energy data to obtain a plurality of first ratios includes:

[0115] When any one of the first ratios is greater than the third threshold value, outputting a thermal runaway warning of the to-be-tested battery corresponding to the first ratio greater than the third threshold value.

[0116] The thermal runaway warning can be a sound warning, an information warning, or a light warning. For example, when the thermal runaway warning of the to-be-tested battery is triggered, a sound warning, an information warning, or a light warning can be output to remind the user to maintain or replace the corresponding to-be-tested battery in time.

[0117] By comparing the ratio of the average activation energy data to the activation energy data of each to-be-tested battery in the same battery pack, a plurality of first ratios in the same battery pack are obtained. By comparing the plurality of first ratios in the same battery pack with the third threshold value, and according to the comparison result, when any one of the first ratios is greater than the third threshold value, it is determined that the probability of thermal runaway of the corresponding to-be-tested battery is relatively large, and then a thermal runaway warning of the to-be-tested battery corresponding to the first ratio greater than the third threshold value is output to timely remind the user.

[0118] In one embodiment, as shown in FIG. 7, a method for predicting battery thermal runaway is provided. The method is applied to the processor 102 in FIG. 1 as an example for illustration, and includes the following steps:

[0119] In step S710, voltage data of a to-be-tested battery under a preset condition is acquired.

[0120] The specific description of step S710 is described above, and will not be repeated here.

[0121] In step S720, internal resistance data corresponding to the to-be-tested battery is obtained according to the preset condition and the voltage data.

[0122] The specific description of step S720 is described above, and will not be repeated here.

[0123] Step S730, processing the internal resistance data based on the preset activation energy model to obtain the activation energy data corresponding to the battery to be measured.

[0124] The specific description of step S730 is described above in the embodiments, which is not repeated here.

[0125] Step S740, determining the thermal runaway probability of the battery to be measured according to the activation energy data.

[0126] The specific description of step S740 is described above in the embodiments, which is not repeated here.

[0127] Step S750, obtaining the activation energy data of the battery to be measured in at least one monitoring period.

[0128] The monitoring period refers to the time point of monitoring the aging process of the battery to be measured. For example, the activation energy of the battery to be measured is monitored after a period of use, and then the activation energy data of the battery to be measured in at least one monitoring period is obtained.

[0129] Step S760, performing ratio processing on the activation energy data of the last monitoring period and the activation energy data of the current monitoring period to obtain a second ratio.

[0130] The second ratio can be obtained by dividing the activation energy data of the last monitoring period by the activation energy data of the current monitoring period.

[0131] By performing ratio processing on the activation energy data of the last monitoring period and the activation energy data of the current monitoring period, the second ratio of the battery to be measured is obtained.

[0132] For example, considering that the activation energy of the battery to be measured decreases with the aging of the battery during use due to continuous aging, thermal runaway may occur. By comparing the activation energy of the battery to be measured in different monitoring periods, the activation energy data of the battery to be measured when it is just off the line is , the activation energy data after a period of use is , and , etc. The activation energy data of the last monitoring period is set to , the activation energy data of the current monitoring period is , the second ratio is F, and .

[0133] Step S770, when the second ratio is greater than a fourth threshold, outputting a thermal runaway warning of the battery to be measured corresponding to the current monitoring period.

[0134] The fourth threshold value can be set to any value between 1.1 and 1.8. The thermal runaway warning can be a sound warning, an information warning, or a light warning. For example, when the thermal runaway warning of the battery under test is triggered, a sound warning, an information warning, or a light warning can be output to remind the user to maintain or replace the corresponding battery under test in time.

[0135] The fourth threshold value is set as Y3, the activation energy data of the previous monitoring period is , and the activation energy data of the current monitoring period is When , it is determined that the corresponding battery under test has a high probability of thermal runaway in the current monitoring period.

[0136] By comparing the ratio of the activation energy data of the previous monitoring period to the activation energy data of the current monitoring period of the corresponding battery under test, the second ratio of the corresponding battery under test at different monitoring times is obtained. By comparing the plurality of second ratios of the corresponding battery under test with the fourth threshold value, and according to the comparison result, when the second ratio is greater than the fourth threshold value, it is determined that the corresponding battery under test has a high probability of thermal runaway in the current monitoring period, and the thermal runaway warning of the battery under test corresponding to the second ratio greater than the third threshold value is output, thereby reminding the user in time.

[0137] In the above embodiments, by obtaining the voltage data of the corresponding battery under test under the preset condition, the internal resistance data of the battery under test is calculated, and the activation energy data of the corresponding battery under test at different monitoring periods is calculated according to the internal resistance data of the battery under test. Then, according to the activation energy data of each monitoring period, the ratio of the activation energy data of the previous monitoring period to the activation energy data of the current monitoring period is determined, and when the second ratio is greater than the fourth threshold value, it is determined that the corresponding battery under test has a high probability of thermal runaway in the current monitoring period, and the thermal runaway warning is output, thereby realizing the early and accurate prediction of the thermal runaway of the battery under test, reminding the user in time, improving the prediction accuracy of the thermal runaway of the battery under test, and effectively improving the use safety of the battery under test.

[0138] It should be understood that although the plurality of steps in the flowcharts of FIGS. 2 to 7 are displayed in sequence according to the arrows, these steps are not necessarily executed in sequence according to the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least a part of the steps in FIGS. 2 to 7 can include a plurality of sub-steps or a plurality of stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least a part of other steps or sub-steps or stages of other steps.

[0139] In one embodiment, as shown in Figure 8, taking a power battery used in a new energy vehicle as an example, the voltage data of each battery under test in the battery pack can be collected during the vehicle's start-up and driving process, and the accuracy of the collected voltage is 0.01 seconds. 1. When the vehicle is started at different temperatures, the SOC of the battery under test at -15℃, 3℃, 18℃, and 35℃ is collected respectively, and the voltage is discharged for 10 seconds at an average current I of approximately 88A. The activation energy data of each battery under test in the same battery pack is obtained through the following steps: 2. Set the first threshold Y1 to 1.3, and compare the curves... The magnitude of the initial voltage of the SEI film at different preset temperatures was used to obtain the initial voltage of the SEI film. That is, at a temperature of -15℃ It is 3.195V; at a temperature of 3℃ It is 3.226V; at 18℃ It is 3.246V; at a temperature of 35℃ It is 3.267V.

[0140] 3. Regarding the SEI film termination voltage With a time t set to 0.8 s, the SEI film termination voltage at different temperatures was obtained. That is, at a temperature of -15℃ It is 3.107V; at a temperature of 3℃ The voltage is 3.169V; at 18°C. It is 3.205V; at a temperature of 35℃ It is 3.244V.

[0141] 4. Through the formula The internal resistance was calculated at different preset temperatures; where, Let be the internal resistance of the SEI film of the battery under test. That is, the resistance at -15℃ is calculated. It is 1mΩ; at 3℃ It is 0.478 mΩ; at 18℃ It is 0.466 mΩ; at 35℃ It is 0.261mΩ.

[0142] 5. Using a preset activation energy model relating preset temperature and internal resistance: By performing linear fitting, the activation energy of the battery under test can be obtained as 17.66 kJ / mol.

[0143] Based on steps 1 to 4 above, the activation energy data of all the batteries to be tested in the corresponding battery pack are obtained, as shown in the table below:

[0144]

[0145] 6、According to the activation energy of the plurality of to-be-tested batteries in the same battery pack, the to-be-tested battery with high risk of thermal runaway is predicted and investigated. By comparing the activation energy of the single battery with the average activation energy of the battery, the third threshold Y2 is set to 1.1, the activation energy of the 56th to-be-tested battery is found to be low and exceeds the third threshold, it is determined that the 56th to-be-tested battery has a high probability of thermal runaway, the prediction of whether a to-be-tested battery in the battery pack has thermal runaway is realized, and the accuracy and robustness of the prediction of the to-be-tested battery thermal runaway are improved.

[0146] In one embodiment, as shown in FIG. 9, the to-be-tested battery is a power battery used in a new energy vehicle, which is taken as an example for illustration, and the risk of thermal runaway of the to-be-tested battery after aging in use can be tracked and detected. For the to-be-tested battery, the voltage data of pulse discharge at different preset temperatures in the initial state can be collected, and the precision of the voltage collection is 0.01 seconds. 1, when the vehicle is running, the voltage data of the to-be-tested battery at-7℃, 10℃, 24℃ and 38℃ when the SOC is about 75% and the to-be-tested battery is pulse discharged at an average current of 106A for 7s is collected. The activation energy of the to-be-tested battery is obtained by the following steps: 2, the first threshold Y1 is set to 1.36, and the size of the curve is compared to obtain the initial voltage of the SEI film at different preset temperatures

[0147] 3, for the SEI film termination voltage , the time t is set to 0.8s, and the SEI film termination voltage at different temperatures is obtained. That is, the SEI film termination voltage at-7℃ is 3.516V; the SEI film termination voltage at 10℃ is 3.631V; the SEI film termination voltage at 24℃ is 3.710V; and the SEI film termination voltage at 38℃ is 3.753V.

[0148] 4, the internal resistance at different preset temperatures is calculated by the formula ; wherein, is the internal resistance of the SEI film of the to-be-tested battery. That is, the internal resistance at-7℃ is calculated to be ​​​​​​1.405 mΩ; at a temperature of 10°C 0.877 mΩ; at a temperature of 24°C 0.509 mΩ; at a temperature of 38°C 0.349 mΩ.

[0149] 5. Through a preset activation energy model between the preset temperature and the internal resistance: , linear fitting is performed, and then the activation energy of the battery to be tested in the initial state can be obtained as 22.35 kJ / mol.

[0150] Based on steps 1 to 4 above, through long-term monitoring of the battery to be tested, the battery activation energies in the first to eighth years can be obtained as shown in the following table:

[0151]

[0152] 6. By comparing the activation energy attenuation of the battery to be tested during use, the time and probability of thermal runaway of the battery to be tested are predicted. By comparing the activation energy decline trend, a fourth threshold Y3 is set as 1.2, and when , the battery to be tested has a risk of thermal runaway. It can be found that the activation energy of the battery to be tested in the eighth year decreases by more than the threshold, and the probability of thermal runaway is relatively large, thereby realizing prediction of the risk of thermal runaway of the battery to be tested after performance attenuation during long-term use.

[0153] In one embodiment, the present application provides a battery thermal runaway prediction device, comprising:

[0154] A voltage acquisition unit is configured to acquire voltage data of a battery to be tested under a preset condition.

[0155] An internal resistance calculation unit is configured to obtain internal resistance data corresponding to the battery to be tested according to the preset condition and the voltage data.

[0156] An activation energy calculation unit is configured to process the internal resistance data based on a preset activation energy model to obtain activation energy data corresponding to the battery to be tested.

[0157] A thermal runaway prediction unit is configured to determine a thermal runaway probability of the battery to be tested according to the activation energy data.

[0158] The limitations of the battery thermal runaway prediction device can refer to the limitations of the battery thermal runaway prediction method described above, which will not be repeated here. Each module in the above battery thermal runaway prediction device can be implemented by software, hardware, and a combination thereof, in whole or in part. Each of the above modules can be embedded in the processor of the battery thermal runaway prediction system in hardware form or independent of the processor, or stored in the memory of the battery thermal runaway prediction system in software form, so that the processor invokes the corresponding operation of each of the above modules.

[0159] In one embodiment, as shown in FIG. 10, a battery thermal runaway prediction system is also provided, which includes a processing device 110 connected to a battery under test 120; the processing device 110 is used to perform the steps of the battery thermal runaway prediction method according to any one of the above.

[0160] The processing device 110 can include a BMS (Battery Management System). The processing device 110 is connected to the battery under test 120, which can be a lithium-ion single cell.

[0161] The processing device 110 obtains voltage data of the battery under test 120 under a preset condition; obtains internal resistance data corresponding to the battery under test 120 according to the preset condition and the voltage data; processes the internal resistance data based on a preset activation energy model to obtain activation energy data corresponding to the battery under test 120; and determines the thermal runaway probability of the battery under test 120 according to the activation energy data, thereby achieving accurate prediction of the thermal runaway of the battery under test 120.

[0162] In the above embodiment, the processing device 110 obtains the voltage data of the battery under test 120 under the preset condition, calculates the internal resistance data of the battery under test 120, and calculates the activation energy data of the battery under test 120 according to the internal resistance data, and then determines the probability of thermal runaway of the battery under test 120 according to the activation energy data, thereby achieving early and accurate prediction of the thermal runaway of the battery under test 120, improving the thermal runaway prediction accuracy, and thereby effectively improving the use safety of the battery under test 120.

[0163] In one embodiment, a computer readable storage medium is also provided, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the battery thermal runaway prediction method according to any one of the above are implemented.

[0164] For example, when the computer program is executed by the processor, the steps of the battery thermal runaway prediction method are as follows:

[0165] The voltage data of the to-be-tested battery under preset conditions is acquired; the internal resistance data corresponding to the to-be-tested battery is obtained according to the preset conditions and the voltage data; the activation energy data corresponding to the to-be-tested battery is obtained by processing the internal resistance data based on a preset activation energy model; and the thermal runaway probability of the to-be-tested battery is determined according to the activation energy data, so as to realize accurate prediction of the thermal runaway of the to-be-tested battery.

[0166] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program provided by the present application can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of the division operation method. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

Claims

1. A method for battery thermal runaway prediction, comprising the steps of: obtaining voltage data of a battery to be tested under a preset condition; obtaining internal resistance data corresponding to the battery to be tested according to the preset condition and the voltage data; processing the internal resistance data based on a preset activation energy model to obtain activation energy data corresponding to the battery to be tested; determining a thermal runaway probability of the battery to be tested according to the activation energy data.

2. The method of battery thermal runaway prediction of claim 1, wherein, The step of obtaining voltage data of a battery to be tested under a preset condition comprises: discharging or charging the battery to be tested based on a preset current, a preset test time and at least one preset temperature; collecting the battery to be tested according to a preset collection frequency to obtain voltage data corresponding to each preset temperature while the battery to be tested is discharging or charging.

3. The method of battery thermal runaway prediction of claim 2, wherein, The step of obtaining internal resistance data corresponding to the battery to be tested according to the preset condition and the voltage data comprises: obtaining SEI film initial voltage corresponding to each preset temperature and SEI film termination voltage corresponding to each preset temperature according to voltage data under different preset temperatures; obtaining internal resistance data corresponding to each battery to be tested and each preset temperature according to the preset current, each SEI film initial voltage and each SEI film termination voltage.

4. The method of battery thermal runaway prediction of claim 3, wherein, The step of obtaining SEI film initial voltage corresponding to each preset temperature and SEI film termination voltage corresponding to each preset temperature according to voltage data under different preset temperatures comprises: obtaining a first voltage at a current collection time and a second voltage at a next collection time under the same preset temperature; confirming the first voltage as SEI film initial voltage of the corresponding preset temperature when the proportion of the first voltage to the second voltage is less than a first threshold value; obtaining a third voltage corresponding to the preset test time under the same preset temperature; confirming the third voltage as SEI film termination voltage of the corresponding preset temperature when the preset test time reaches a second threshold value.

5. The method of battery thermal runaway prediction of claim 3, wherein, The step of obtaining internal resistance data corresponding to each battery to be tested and each preset temperature according to the preset current, each SEI film initial voltage and each SEI film termination voltage comprises: differentially processing the SEI film initial voltage and the SEI film termination voltage to obtain a voltage difference; ratio processing the voltage difference and the preset current to obtain internal resistance corresponding to the battery to be tested and the preset temperature.

6. The method of battery thermal runaway prediction of claim 5, wherein, The step of processing the internal resistance data based on a preset activation energy model to obtain activation energy data corresponding to the battery to be tested comprises: inputting a plurality of preset temperatures and a plurality of internal resistances corresponding to the preset temperatures into a preset activation energy model for fitting processing to obtain activation energy data corresponding to the battery to be tested.

7. The method of battery thermal runaway prediction of claim 6, wherein, The preset activation energy model is: , wherein, for said activation energy data; an internal resistance for a corresponding preset temperature; is a first constant; is a second constant; The preset temperature.

8. The method of battery thermal runaway prediction of claim 2, wherein, The step of discharging or charging the battery to be tested based on a preset current, a preset test time and at least one preset temperature comprises: discharging or charging the battery to be tested based on the preset current, the preset test time and at least one preset temperature when the standing time of the battery to be tested reaches a preset length or the battery to be tested is detected to be in a starting state.

9. The method of battery thermal runaway prediction of claim 2, wherein, The discharging or charging the battery under test based on the preset current, the preset test time and the at least one preset temperature further comprises: discharging or charging the battery under test based on the preset SOC, the preset current, the preset test time and the at least one preset temperature.

10. The method of battery thermal runaway prediction of any one of claims 1 to 9, wherein, The battery pack comprises at least one battery under test; The step of determining the thermal runaway probability of the battery under test according to the activation energy data further comprises: obtaining activation energy data of each battery under test in the same battery pack; obtaining minimum activation energy data from the plurality of activation energy data; determining the thermal runaway probability of the battery pack according to the minimum activation energy data.

11. The method of battery thermal runaway prediction of claim 10, wherein, The step of determining the thermal runaway probability of the battery pack according to the minimum activation energy data comprises: obtaining average activation energy data from the plurality of activation energy data; performing ratio processing on the average activation energy data and each activation energy data to obtain a first ratio corresponding to each activation energy data; determining the thermal runaway probability of the battery pack according to the minimum activation energy data and the number of activation energy data whose corresponding first ratio is greater than a third threshold value.

12. The method of battery thermal runaway prediction of claim 11, wherein, The step of performing ratio processing on the average activation energy data and the plurality of activation energy data to obtain a plurality of first ratios further comprises: when any one of the first ratios is greater than a third threshold value, outputting a thermal runaway warning of the battery under test corresponding to the first ratio greater than the third threshold value.

13. The method of battery thermal runaway prediction of any one of claims 1 to 9, wherein, The step of determining the thermal runaway probability of the battery under test according to the activation energy data further comprises: obtaining activation energy data of the battery under test in a plurality of monitoring periods; performing ratio processing on the activation energy data of the previous monitoring period and the activation energy data of the current monitoring period to obtain a second ratio; when the second ratio is greater than a fourth threshold value, outputting a thermal runaway warning of the battery under test corresponding to the current monitoring period.

14. A system for predicting thermal runaway of a battery, comprising a processing device configured to connect to a battery under test; The processing device is configured to perform the steps of the method for predicting thermal runaway of a battery according to any one of claims 1 to 13.

15. A computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the steps of the method for predicting thermal runaway of a battery according to any one of claims 1 to 13.

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