A battery explosion-proof prediction method and device

Abstract

The present invention relates to the field of battery explosion-proof prediction, and specifically discloses a battery explosion-proof prediction method and device, including: classifying all sub-batteries in a battery based on positive and negative pole temperature data, obtaining all normal sub-batteries and all abnormal sub-batteries of the battery; obtaining the temperature coefficient of the battery at each sampling moment in a preset time period based on the real-time parameters of the battery at each sampling moment in a preset time period, obtaining the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative pole temperature data of each abnormal sub-battery; obtaining the predicted explosion rate of each abnormal sub-battery based on the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, and obtaining explosion-proof intervention measures based on the predicted explosion rates of all abnormal sub-batteries. The present invention realizes the calculation of the predicted explosion rate of each abnormal sub-battery in the battery, and more targetedly selects the explosion-proof intervention measures of the battery according to the predicted explosion rates of all abnormal sub-batteries.

Description

Technical Field

[0001] The present invention relates to the field of battery explosion-proof prediction, and in particular to a battery explosion-proof prediction method and device. Background Art

[0002] At present, as the core component of new energy vehicles, the safety of lithium-ion power batteries has attracted widespread attention, especially core safety issues such as battery explosion and thermal runaway, which need to be solved urgently. The new energy vehicle industry urgently needs effective thermal runaway explosion prevention solutions to respond to consumer doubts.

[0003] However, the existing battery explosion-proof prediction method and device only determine the predicted fire time of the battery pack from the occurrence of thermal runaway of the starting battery cell to the occurrence of thermal runaway and thermal radiation of the target battery cell, and do not calculate the predicted explosion rate of each abnormal sub-battery in the battery, and do not select the explosion-proof intervention measures of the battery in a more targeted manner according to the predicted explosion rate of all abnormal sub-batteries. For example, the patent with the publication number "CN113673102A" and the patent name "A method, device and equipment for calculating the predicted fire time of a battery pack" includes the following steps: obtaining the first time from the occurrence of thermal runaway of the starting battery cell to the occurrence of thermal runaway of the target battery cell of the same module. There is an overlapping part between the flame radius of the starting battery cell and the flame radius of the target battery cell. In the process from the occurrence of thermal runaway of the starting battery cell to the occurrence of thermal runaway and thermal radiation of the target battery cell, the overlapping part is thermally radiated a preset number of times. Determine the minimum number of thermal runaways corresponding to the shortest thermal interlock path between the battery cell to which the overlapping part belongs and the terminating battery cell. The product of the number of thermal interlocks and the minimum thermal runaway interval time is the second time. The third time is the time from when the target cell experiences thermal runaway to when the cell belonging to the overlapping part experiences thermal runaway. The fourth time is the time from when the termination cell experiences thermal runaway to when the explosion-proof valve leaks fire. The sum of the first time, the second time, the third time, and the fourth time is the predicted fire time of the battery pack. However, this patent only determines the predicted fire time of the battery pack from when the cell experiences thermal runaway to when the target cell experiences thermal runaway and generates thermal radiation. It does not predict the predicted explosion rate of each abnormal sub-battery in the battery, nor does it select the explosion-proof intervention measures for the battery in a more targeted manner based on the predicted explosion rates of all abnormal sub-batteries.

[0004] Therefore, the present invention proposes a battery explosion-proof prediction method and device. Summary of the invention

[0005] The present invention provides a battery explosion-proof prediction method and device, which are used to obtain the temperature coefficient of the battery at each sampling moment according to the real-time parameters of the battery at each sampling moment, and to construct a heating matrix of each abnormal sub-battery according to the basic information of the battery and the positive and negative temperature data of each abnormal sub-battery, so as to facilitate the subsequent calculation of the predicted explosion rate of the abnormal sub-battery. According to the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, the predicted explosion rate of each abnormal sub-battery is obtained more accurately, and the explosion-proof intervention measures of the battery are selected more specifically according to the predicted explosion rates of all abnormal sub-batteries.

[0006] The present invention provides a battery explosion-proof prediction method, comprising:

[0007] S1: Acquire the positive and negative electrode temperature data of the battery within a preset time period, and classify all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery;

[0008] S2: Obtain basic information of the battery and real-time parameters of the battery at each sampling moment within a preset time period, obtain the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period, and obtain the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery;

[0009] S3: Based on the heat generation matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, the predicted explosion rate of each abnormal sub-battery is obtained, and the explosion prediction result of the battery is obtained based on the predicted explosion rates of all abnormal sub-batteries, and the explosion-proof intervention measures are obtained based on the explosion prediction results of the battery.

[0010] Preferably, the battery explosion-proof prediction method, S1: obtaining the positive and negative electrode temperature data of the battery within a preset time period, and classifying all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery, including:

[0011] Based on the temperature sensors disposed at the positive and negative ends of each sub-battery in the battery, the temperature monitoring values ​​at the positive and negative ends of each sub-battery in the battery are obtained as the positive and negative temperature data of the battery in a preset time period;

[0012] All sub-batteries in the battery are classified based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery.

[0013] Preferably, the battery explosion-proof prediction method classifies all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery, including:

[0014] Taking every unit time from the start time within the preset time period as the sampling time, determining the average of the temperature monitoring values ​​of the positive and negative electrodes of each sub-battery in the battery at each sampling time as the abnormal judgment temperature of each sub-battery at the corresponding sampling time;

[0015] Determine the mean and standard deviation of the abnormal judgment temperature of a single sub-battery at all sampling moments, and use the sum of the corresponding mean and twice the standard deviation as the temperature comparison value of the corresponding sub-battery;

[0016] The abnormal judgment temperature and temperature comparison value of each sub-battery at the last sampling moment are used as the abnormal judgment temperature and temperature comparison value of each sub-battery at the current moment, and it is determined whether the abnormal judgment temperature of each sub-battery in the battery at the current moment is greater than the temperature comparison value of the corresponding sub-battery. If so, the corresponding sub-battery is determined to be an abnormal sub-battery, otherwise, the corresponding sub-battery is determined to be a normal sub-battery.

[0017] Preferably, the battery explosion-proof prediction method, S2: obtaining basic information of the battery and real-time parameters of the battery at each sampling moment within a preset time period, obtaining the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period, and obtaining the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery, including:

[0018] Obtain the battery shell thickness, the length of each sub-battery, the battery pole length and the battery rated capacity as the basic battery information;

[0019] Obtaining the real-time remaining power of the battery and the real-time temperature of the battery shell at each sampling moment within the preset time period as the real-time parameters of the battery at each sampling moment within the preset time period;

[0020] Obtaining a temperature coefficient of the battery at each sampling moment within a preset time period based on the real-time parameters of the battery at each sampling moment within a preset time period and basic information of the battery;

[0021] A heating matrix of each abnormal sub-battery is obtained based on the basic information of the battery and the positive and negative temperature data of each abnormal sub-battery at all sampling times, wherein the temperature data of the abnormal sub-battery at all sampling times includes the temperature monitoring values ​​of the positive and negative ends of the abnormal sub-battery at all sampling times.

[0022] Preferably, the battery explosion-proof prediction method obtains the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period and the basic information of the battery, including:

[0023] Determine the rated capacity of the battery based on the basic information of the battery, and use the quotient of the real-time remaining capacity in the real-time parameters at each sampling moment and the rated capacity of the battery as the capacity ratio of the battery at each sampling moment;

[0024] The quotient of the real-time temperature of the battery shell in the battery real-time parameters at each sampling moment and the preset battery initial temperature, and the product of the battery power ratio at the corresponding sampling moment, are taken as the temperature coefficient of the battery at the corresponding sampling moment to obtain the temperature coefficient of the battery at each sampling moment.

[0025] Preferably, the battery explosion-proof prediction method obtains the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery at all sampling moments, including:

[0026] The length of each sub-battery is determined based on the basic information of the battery, and the quotient of the difference between the temperature monitoring values ​​at both ends of the positive and negative electrodes in the positive and negative electrode temperature data of each abnormal sub-battery at each sampling moment and the length of the corresponding abnormal sub-battery is used as the first heat transfer value of each abnormal sub-battery at each sampling moment;

[0027] Based on the basic information of the battery, the battery pole length is determined, and the remaining abnormal sub-batteries closest to each abnormal sub-battery are used as the thermal conductive sub-batteries of the corresponding abnormal sub-battery, and the difference between the positive extreme temperature monitoring value of each abnormal sub-battery and the positive extreme temperature monitoring value of the corresponding thermal conductive sub-battery and the quotient of the minimum battery pole connection distance between the corresponding abnormal sub-battery and the corresponding thermal conductive sub-battery are used as the second heat transfer value of each abnormal sub-battery at each sampling time;

[0028] The shell thickness of the battery is determined based on the basic information of the battery, and the larger value of the temperature monitoring values ​​at the positive and negative ends of each abnormal sub-battery and the shell thickness of the battery are used as the third heat transfer value of each abnormal sub-battery at each sampling moment;

[0029] Based on the first heat transfer value, the second heat transfer value and the third heat transfer value of each abnormal sub-cell at all sampling moments, a heat generation matrix of each abnormal sub-cell is obtained.

[0030] Preferably, the battery explosion-proof prediction method obtains the heating matrix of each abnormal sub-battery based on the first heat transfer value, the second heat transfer value and the third heat transfer value of each abnormal sub-battery at all sampling moments, including:

[0031] All sampling moments are ordinal-defined starting from 1 in time sequence to obtain ordinal definition results;

[0032] Based on the first heat transfer value, the second heat transfer value, the third heat transfer value and the ordinal definition results of each abnormal sub-cell at all sampling moments, the heating matrix of each abnormal sub-cell is obtained, which is:

[0033]

[0034] Among them, 6 is the heating matrix of each abnormal sub-battery, q1 is the first heat transfer value of the abnormal sub-battery at the sampling time with sequence number 1, q2 is the first heat transfer value of the abnormal sub-battery at the sampling time with sequence number 2, and q n is the first heat transfer value of the abnormal sub-cell at the sampling time with ordinal number n, n is the total number of sampling times, e1 is the second heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 1, e2 is the second heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 2, e n is the second heat transfer value of the abnormal sub-cell at the sampling time with ordinal number n, z1 is the third heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 1, z2 is the third heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 2, and z n is the third heat transfer value of the abnormal sub-cell at the sampling moment with ordinal number n.

[0035] Preferably, the battery explosion-proof prediction method, S3: based on the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, obtain the predicted explosion rate of each abnormal sub-battery, and obtain the explosion prediction result of the battery based on the predicted explosion rates of all abnormal sub-batteries, and obtain the explosion-proof intervention measures based on the explosion prediction result of the battery, including:

[0036] S301: Based on the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, the predicted explosion rate of each abnormal sub-battery is obtained, which is:

[0037]

[0038] Where C is the predicted explosion rate of the abnormal sub-battery, σ n is the temperature coefficient of the battery at the sampling time with ordinal number n, σ max is the maximum value of the battery's temperature coefficient at all sampling moments, σ min is the minimum value of the battery's temperature coefficient at all sampling moments, P δ is the rank of the heating matrix of the abnormal sub-battery, ln is the natural logarithm, and the value of the natural constant e is 2.718;

[0039] S302: Obtain explosion prediction results of the battery based on the predicted explosion rates of all abnormal sub-batteries, and obtain explosion-proof intervention measures based on the explosion prediction results of the battery.

[0040] Preferably, the battery explosion-proof prediction method, S302: obtaining a battery explosion prediction result based on the predicted explosion rates of all abnormal sub-batteries, and obtaining explosion-proof intervention measures based on the battery explosion prediction result, including:

[0041] S3021: taking the maximum value of the predicted explosion rates of all abnormal sub-batteries as the predicted explosion rate of the battery, and judging whether the predicted explosion rate of the battery is greater than a preset explosion rate threshold; if so, taking the predicted explosion rate of the battery as too large as the battery explosion prediction result; otherwise, taking the predicted explosion rate of the battery as not too large as the battery explosion prediction result;

[0042] S3022: When the explosion prediction result of the battery is that the predicted explosion rate of the battery is not too large, the preset cooling device on the battery will be turned on as an explosion-proof intervention measure; when the explosion prediction result of the battery is that the predicted explosion rate of the battery is too large, the output power of the battery will be limited to a preset power value as an explosion-proof intervention measure.

[0043] The present invention provides a battery explosion-proof prediction device, which is used to execute any one of the battery explosion-proof prediction methods in Embodiments 1 to 9, comprising:

[0044] A classification module is used to obtain the positive and negative electrode temperature data of the battery within a preset time period, and classify all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery;

[0045] A construction module is used to obtain basic information of the battery and real-time parameters of the battery at each sampling moment within a preset time period, obtain the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period, and obtain the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery;

[0046] The prediction module is used to obtain the predicted explosion rate of each abnormal sub-battery based on the heat matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, and to obtain the explosion prediction result of the battery based on the predicted explosion rates of all abnormal sub-batteries, and to obtain explosion-proof intervention measures based on the explosion prediction result of the battery.

[0047] The beneficial effects of the present invention compared to the prior art are as follows: the temperature coefficient of the battery at each sampling moment is obtained according to the real-time parameters of the battery at each sampling moment, the heating matrix of each abnormal sub-battery is constructed according to the basic information of the battery and the positive and negative temperature data of each abnormal sub-battery, so as to facilitate the subsequent calculation of the predicted explosion rate of the abnormal sub-battery, and the predicted explosion rate of each abnormal sub-battery is obtained more accurately according to the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, and the explosion-proof intervention measures of the battery are selected more specifically according to the predicted explosion rates of all abnormal sub-batteries.

[0048] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structure specifically pointed out in the written application document.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 This is a flow chart of a battery explosion-proof prediction method in an embodiment of the present invention;

[0052] Figure 2 Schematic diagram of a battery explosion-proof prediction device in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] Embodiment 1:

[0055] The present invention provides a battery explosion-proof prediction method, referring to Figure 1 ,include:

[0056] S1: Acquire the positive and negative electrode temperature data of the battery within a preset time period, and classify all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery;

[0057] S2: Obtain basic information of the battery and real-time parameters of the battery at each sampling moment within a preset time period, obtain the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period, and obtain the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery;

[0058] S3: Based on the heat generation matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, the predicted explosion rate of each abnormal sub-battery is obtained, and the explosion prediction result of the battery is obtained based on the predicted explosion rates of all abnormal sub-batteries, and the explosion-proof intervention measures are obtained based on the explosion prediction results of the battery.

[0059] In this embodiment, the preset time period is a preset time period for collecting the positive and negative electrode temperature data, for example, 10 minutes.

[0060] In this embodiment, the positive and negative electrode temperature data are the temperature values ​​of the positive and negative electrodes of each sub-battery (single cell, multiple single cells connected in series or in parallel to form a battery pack) in the battery within a preset time period.

[0061] In this embodiment, normal sub-cells are some sub-cells without explosion risk among all sub-cells of the battery.

[0062] In this embodiment, the abnormal sub-cells are some of the sub-cells that have explosion risks among all the sub-cells of the battery.

[0063] In this embodiment, the basic information of the battery is the thickness of the battery shell, the length of each sub-battery, the length of the battery pole and the rated capacity of the battery.

[0064] In this embodiment, the sampling moment is the moment every unit time from the starting moment within the preset time period.

[0065] In this embodiment, the real-time parameters are the real-time remaining power of the battery and the real-time temperature of the battery casing at each sampling moment within a preset time period.

[0066] In this embodiment, the temperature coefficient is a coefficient obtained based on the battery power ratio at each sampling moment and the battery power ratio at the corresponding sampling moment, and characterizes the relationship between the battery power release at each sampling moment and the battery temperature.

[0067] In this embodiment, the heating matrix of the abnormal sub-battery is a matrix constructed by using the first heat transfer value, the second heat transfer value and the third heat transfer value of the abnormal sub-battery at all sampling moments as matrix elements to characterize the heating condition of the abnormal sub-battery.

[0068] In this embodiment, the predicted explosion rate is the probability of the corresponding abnormal sub-battery exploding in the future, obtained based on the heat generation matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment.

[0069] In this embodiment, the battery explosion prediction result is a result of determining whether the possibility of the battery exploding in the future is too high based on the predicted explosion rates of all abnormal sub-batteries, and the battery explosion prediction result includes the battery predicted explosion rate is not too high and the battery predicted explosion rate is too high.

[0070] In this embodiment, the explosion-proof intervention measure is an explosion-proof measure based on the explosion prediction result of the battery, and the explosion-proof intervention measure includes turning on a preset cooling device on the battery and limiting the output power of the battery to a preset power value.

[0071] The beneficial effects of the above technology are as follows: the temperature coefficient of the battery at each sampling moment is obtained based on the real-time parameters of the battery at each sampling moment, and the heating matrix of each abnormal sub-battery is constructed based on the basic information of the battery and the positive and negative temperature data of each abnormal sub-battery, which is convenient for the subsequent calculation of the predicted explosion rate of the abnormal sub-battery, and the predicted explosion rate of each abnormal sub-battery is obtained more accurately based on the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, and the explosion-proof intervention measures of the battery are selected more specifically based on the predicted explosion rates of all abnormal sub-batteries.

[0072] Embodiment 2:

[0073] Based on Example 1, a battery explosion-proof prediction method, S1: obtaining the positive and negative electrode temperature data of the battery within a preset time period, and classifying all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery, including:

[0074] Based on the temperature sensors disposed at the positive and negative ends of each sub-battery in the battery, the temperature monitoring values ​​at the positive and negative ends of each sub-battery in the battery are obtained as the positive and negative temperature data of the battery in a preset time period;

[0075] All sub-batteries in the battery are classified based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery.

[0076] In this embodiment, the temperature monitoring value is the temperature value at both ends of the positive electrode and the negative electrode of each sub-battery in the battery.

[0077] The beneficial effect of the above technology is: all sub-batteries in the battery can be classified more accurately according to the positive and negative electrode temperature data of all sub-batteries in the battery, and all normal sub-batteries and all abnormal sub-batteries of the battery can be obtained, which can effectively reduce the amount of calculation for subsequent prediction of explosion rate.

[0078] Embodiment 3:

[0079] On the basis of Example 2, the battery explosion-proof prediction method classifies all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery, including:

[0080] Taking every unit time from the start time within the preset time period as the sampling time, determining the average of the temperature monitoring values ​​of the positive and negative electrodes of each sub-battery in the battery at each sampling time as the abnormal judgment temperature of each sub-battery at the corresponding sampling time;

[0081] Determine the mean and standard deviation of the abnormal judgment temperature of a single sub-battery at all sampling moments, and use the sum of the corresponding mean and twice the standard deviation as the temperature comparison value of the corresponding sub-battery;

[0082] The abnormal judgment temperature and temperature comparison value of each sub-battery at the last sampling moment are used as the abnormal judgment temperature and temperature comparison value of each sub-battery at the current moment, and it is determined whether the abnormal judgment temperature of each sub-battery in the battery at the current moment is greater than the temperature comparison value of the corresponding sub-battery. If so, the corresponding sub-battery is determined to be an abnormal sub-battery, otherwise, the corresponding sub-battery is determined to be a normal sub-battery.

[0083] In this embodiment, the unit time is one time unit, and in this embodiment, the unit time is 1 minute.

[0084] In this embodiment, the abnormal determination temperature is a determination temperature value for determining whether each sub-battery in the battery is an abnormal sub-battery.

[0085] In this embodiment, the temperature comparison value is a comparison temperature value for determining whether each sub-battery in the battery is an abnormal sub-battery.

[0086] The beneficial effects of the above technology are: based on the temperature monitoring values ​​at both ends of the positive and negative electrodes of each sub-battery in the battery at each sampling moment, the abnormal judgment temperature and temperature comparison value of each sub-battery at the current moment are more accurately obtained; based on the abnormal judgment temperatures and temperature comparison values ​​of all sub-batteries at the current moment, the normal sub-batteries and abnormal sub-batteries in the battery are more accurately determined. This embodiment provides a method for more accurately determining the normal sub-batteries and abnormal sub-batteries in the battery.

[0087] Embodiment 4:

[0088] On the basis of Example 1, the battery explosion-proof prediction method, S2: obtaining basic information of the battery and real-time parameters of the battery at each sampling moment within a preset time period, obtaining the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period, and obtaining the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery, including:

[0089] Obtain the battery shell thickness, the length of each sub-battery, the battery pole length and the battery rated capacity as the basic battery information;

[0090] Obtaining the real-time remaining power of the battery and the real-time temperature of the battery shell at each sampling moment within the preset time period as the real-time parameters of the battery at each sampling moment within the preset time period;

[0091] Obtaining a temperature coefficient of the battery at each sampling moment within a preset time period based on the real-time parameters of the battery at each sampling moment within a preset time period and basic information of the battery;

[0092] A heating matrix of each abnormal sub-battery is obtained based on the basic information of the battery and the positive and negative temperature data of each abnormal sub-battery at all sampling times, wherein the temperature data of the abnormal sub-battery at all sampling times includes the temperature monitoring values ​​of the positive and negative ends of the abnormal sub-battery at all sampling times.

[0093] In this embodiment, the thickness of the battery shell is the thickness of the shell covering all sub-batteries.

[0094] In this embodiment, the length of the sub-battery is the length between the positive electrode and the negative electrode of the sub-battery.

[0095] In this embodiment, the battery pole length is the length of a component (ie, a battery pole) connecting the same pole of adjacent sub-batteries in a battery.

[0096] In this embodiment, the rated capacity of the battery is the nominal capacity, which refers to the amount of electricity discharged when the battery is discharged to the cut-off voltage under certain discharge conditions.

[0097] In this embodiment, the real-time remaining power is the remaining power of the battery at each sampling moment.

[0098] In this embodiment, the real-time temperature of the battery casing is a real-time temperature value of the battery casing obtained based on a temperature sensor pre-set on the battery casing.

[0099] The beneficial effects of the above technology are: obtaining the temperature coefficient of the battery at each sampling moment based on the real-time parameters of the battery at each sampling moment, and constructing the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative temperature data of each abnormal sub-battery, so as to facilitate the calculation of the predicted explosion rate of the subsequent abnormal sub-battery.

[0100] Embodiment 5:

[0101] On the basis of Example 4, a battery explosion-proof prediction method obtains a temperature coefficient of the battery at each sampling moment within a preset time period based on the real-time parameters of the battery at each sampling moment within a preset time period and basic information of the battery, including:

[0102] Determine the rated capacity of the battery based on the basic information of the battery, and use the quotient of the real-time remaining capacity in the real-time parameters at each sampling moment and the rated capacity of the battery as the capacity ratio of the battery at each sampling moment;

[0103] The quotient of the real-time temperature of the battery shell in the battery real-time parameters at each sampling moment and the preset battery initial temperature is multiplied by the battery power ratio at the corresponding sampling moment as the temperature coefficient of the battery at the corresponding sampling moment to obtain the temperature coefficient of the battery at each sampling moment.

[0104] The beneficial effect of the above technology is: the temperature coefficient of the battery at each sampling moment is obtained more accurately according to the real-time parameters of the battery at each sampling moment, which is convenient for the subsequent calculation of the predicted explosion rate. This embodiment provides a method for calculating the temperature coefficient of the battery at each sampling moment based on the real-time parameters of the battery at each sampling moment.

[0105] Embodiment 6:

[0106] On the basis of Example 4, the battery explosion-proof prediction method obtains the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery at all sampling moments, including:

[0107] The length of each sub-battery is determined based on the basic information of the battery, and the quotient of the difference between the temperature monitoring values ​​at both ends of the positive and negative electrodes in the positive and negative electrode temperature data of each abnormal sub-battery at each sampling moment and the length of the corresponding abnormal sub-battery is used as the first heat transfer value of each abnormal sub-battery at each sampling moment;

[0108] Based on the basic information of the battery, the battery pole length is determined, and the remaining abnormal sub-batteries closest to each abnormal sub-battery are used as the thermal conductive sub-batteries of the corresponding abnormal sub-battery, and the difference between the positive extreme temperature monitoring value of each abnormal sub-battery and the positive extreme temperature monitoring value of the corresponding thermal conductive sub-battery and the quotient of the minimum battery pole connection distance between the corresponding abnormal sub-battery and the corresponding thermal conductive sub-battery are used as the second heat transfer value of each abnormal sub-battery at each sampling time;

[0109] The shell thickness of the battery is determined based on the basic information of the battery, and the larger value of the temperature monitoring values ​​at the positive and negative ends of each abnormal sub-battery and the shell thickness of the battery are used as the third heat transfer value of each abnormal sub-battery at each sampling moment;

[0110] Based on the first heat transfer value, the second heat transfer value and the third heat transfer value of each abnormal sub-cell at all sampling moments, a heat generation matrix of each abnormal sub-cell is obtained.

[0111] In this embodiment, the first heat transfer value represents the conduction speed value of the heat generated by the abnormal sub-battery on the sub-battery housing at each sampling moment.

[0112] In this embodiment, the minimum battery pole connection distance is the shortest distance between each abnormal sub-battery and the corresponding thermally conductive sub-battery when they are connected through the battery pole (the positive poles of all two adjacent sub-batteries in the battery are connected by the battery pole, so there are multiple connection methods when each abnormal sub-battery and the corresponding thermally conductive sub-battery are connected through the battery pole).

[0113] In this embodiment, the second heat transfer value represents the conduction speed value of the heat generated by the abnormal sub-battery on the battery pole at each sampling moment.

[0114] In this embodiment, the third heat transfer value represents the conduction speed value of the heat generated by the abnormal sub-battery on the battery housing at each sampling moment.

[0115] The beneficial effect of the above technology is: based on the basic information of the battery and the positive and negative temperature data of each abnormal sub-battery at all sampling moments, the first heat transfer value, the second heat transfer value and the third heat transfer value of each abnormal sub-battery are obtained more accurately. This embodiment provides a method for calculating the first heat transfer value, the second heat transfer value and the third heat transfer value of the abnormal sub-battery.

[0116] Embodiment 7:

[0117] On the basis of Example 6, the battery explosion-proof prediction method obtains the heating matrix of each abnormal sub-battery based on the first heat transfer value, the second heat transfer value and the third heat transfer value of each abnormal sub-battery at all sampling moments, including:

[0118] All sampling moments are ordinal-defined starting from 1 in time sequence to obtain ordinal definition results;

[0119] Based on the first heat transfer value, the second heat transfer value, the third heat transfer value and the ordinal definition results of each abnormal sub-cell at all sampling moments, the heating matrix of each abnormal sub-cell is obtained, which is:

[0120]

[0121] Among them, δ is the heating matrix of each abnormal sub-battery, q1 is the first heat transfer value of the abnormal sub-battery at the sampling time with ordinal number 1, q2 is the first heat transfer value of the abnormal sub-battery at the sampling time with ordinal number 2, and q n is the first heat transfer value of the abnormal sub-cell at the sampling time with ordinal number n, n is the total number of sampling times, e1 is the second heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 1, e2 is the second heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 2, e n is the second heat transfer value of the abnormal sub-cell at the sampling time with ordinal number n, z1 is the third heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 1, z2 is the third heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 2, and z n is the third heat transfer value of the abnormal sub-cell at the sampling moment with ordinal number n.

[0122] The beneficial effect of the above technology is: constructing a heating matrix of the abnormal sub-battery according to the first heat transfer value, the second heat transfer value and the third heat transfer value of the abnormal sub-battery, which facilitates the calculation of the predicted explosion rate of the subsequent abnormal sub-battery.

[0123] Embodiment 8:

[0124] Based on Example 1, a battery explosion-proof prediction method, S3: based on the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, a predicted explosion rate of each abnormal sub-battery is obtained, and based on the predicted explosion rates of all abnormal sub-batteries, an explosion prediction result of the battery is obtained, and based on the explosion prediction result of the battery, an explosion-proof intervention measure is obtained, including:

[0125] S301: Based on the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, the predicted explosion rate of each abnormal sub-battery is obtained, which is:

[0126]

[0127] Where C is the predicted explosion rate of the abnormal sub-battery, σ n is the temperature coefficient of the battery at the sampling time with ordinal number n, σ max is the maximum value of the battery's temperature coefficient at all sampling moments, σ min is the minimum value of the battery's temperature coefficient at all sampling moments, P δ is the rank of the heating matrix of the abnormal sub-battery, ln is the natural logarithm, and the value of the natural constant e is 2.718;

[0128] S302: Obtain explosion prediction results of the battery based on the predicted explosion rates of all abnormal sub-batteries, and obtain explosion-proof intervention measures based on the explosion prediction results of the battery.

[0129] The beneficial effects of the above technology are: based on the heat generation matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, the predicted explosion rate of each abnormal sub-battery is obtained more accurately, and the explosion-proof intervention measures of the battery are selected more accurately based on the predicted explosion rates of all abnormal sub-batteries.

[0130] Embodiment 9:

[0131] Based on Example 8, the battery explosion-proof prediction method, S302: obtaining a battery explosion prediction result based on the predicted explosion rates of all abnormal sub-batteries, and obtaining explosion-proof intervention measures based on the battery explosion prediction result, including:

[0132] S3021: taking the maximum value of the predicted explosion rates of all abnormal sub-batteries as the predicted explosion rate of the battery, and judging whether the predicted explosion rate of the battery is greater than a preset explosion rate threshold; if so, taking the predicted explosion rate of the battery as too large as the battery explosion prediction result; otherwise, taking the predicted explosion rate of the battery as not too large as the battery explosion prediction result;

[0133] S3022: When the explosion prediction result of the battery is that the predicted explosion rate of the battery is not too large, the preset cooling device on the battery will be turned on as an explosion-proof intervention measure; when the explosion prediction result of the battery is that the predicted explosion rate of the battery is too large, the output power of the battery will be limited to a preset power value as an explosion-proof intervention measure.

[0134] In this embodiment, the preset explosion rate threshold is a predicted explosion rate threshold that is preset to obtain a battery explosion prediction result.

[0135] In this embodiment, the preset cooling device is, for example, a fan pre-set on the battery housing.

[0136] In this embodiment, the preset power value is a battery output power that is preset to ensure battery safety.

[0137] The beneficial effects of the above technology are: obtaining the battery explosion prediction result more accurately according to the predicted explosion rates of all abnormal sub-batteries, and selecting more suitable explosion-proof intervention measures in a more targeted manner according to the battery explosion prediction result.

[0138] Embodiment 10:

[0139] The present invention provides a battery explosion-proof prediction device for executing any one of the battery explosion-proof prediction methods in embodiments 1 to 9, referring to Figure 2 ,include:

[0140] A classification module is used to obtain the positive and negative electrode temperature data of the battery within a preset time period, and classify all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery;

[0141] A construction module is used to obtain basic information of the battery and real-time parameters of the battery at each sampling moment within a preset time period, obtain the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period, and obtain the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery;

[0142] The prediction module is used to obtain the predicted explosion rate of each abnormal sub-battery based on the heat matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, and to obtain the explosion prediction result of the battery based on the predicted explosion rates of all abnormal sub-batteries, and to obtain explosion-proof intervention measures based on the explosion prediction result of the battery.

[0143] The beneficial effects of the above technology are: obtaining the temperature coefficient of the battery at each sampling moment according to the real-time parameters of the battery at each sampling moment, constructing the heating matrix of each abnormal sub-battery according to the basic information of the battery and the positive and negative temperature data of each abnormal sub-battery, so as to facilitate the subsequent calculation of the predicted explosion rate of the abnormal sub-battery, and more accurately obtaining the predicted explosion rate of each abnormal sub-battery according to the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, and selecting the explosion-proof intervention measures of the battery in a more targeted manner according to the predicted explosion rates of all abnormal sub-batteries.

[0144] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A battery explosion-proof prediction method, characterized in that: include: S1: Acquire the positive and negative electrode temperature data of the battery within a preset time period, and classify all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery; S2: Obtain basic information of the battery and real-time parameters of the battery at each sampling moment within a preset time period, obtain the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period, and obtain the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery; S3: Based on the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, the predicted explosion rate of each abnormal sub-battery is obtained, and the explosion prediction result of the battery is obtained based on the predicted explosion rates of all abnormal sub-batteries, and the explosion-proof intervention measures are obtained based on the explosion prediction results of the battery; Among them, S2: obtaining basic information of the battery and real-time parameters of the battery at each sampling moment within a preset time period, obtaining the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period, and obtaining the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery, including: Obtain the battery shell thickness, the length of each sub-battery, the battery pole length and the battery rated capacity as the basic battery information; Obtaining the real-time remaining power of the battery and the real-time temperature of the battery shell at each sampling moment within the preset time period as the real-time parameters of the battery at each sampling moment within the preset time period; Obtaining a temperature coefficient of the battery at each sampling moment within a preset time period based on the real-time parameters of the battery at each sampling moment within a preset time period and basic information of the battery; A heating matrix of each abnormal sub-battery is obtained based on the basic information of the battery and the temperature data of the positive and negative electrodes of each abnormal sub-battery at all sampling times, wherein the temperature data of the abnormal sub-battery at all sampling times includes the temperature monitoring values ​​of the positive and negative electrodes of the abnormal sub-battery at all sampling times; Wherein, obtaining the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period and the basic information of the battery includes: Determine the rated capacity of the battery based on the basic information of the battery, and use the quotient of the real-time remaining capacity in the real-time parameters at each sampling moment and the rated capacity of the battery as the battery capacity ratio at each sampling moment; The quotient of the real-time temperature of the battery shell in the real-time parameters of the battery at each sampling moment and the preset initial temperature of the battery is multiplied by the battery power ratio at the corresponding sampling moment as the temperature coefficient of the battery at the corresponding sampling moment, to obtain the temperature coefficient of the battery at each sampling moment; Among them, the heating matrix of each abnormal sub-battery is obtained based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery at all sampling moments, including: The length of each sub-battery is determined based on the basic information of the battery, and the quotient of the difference between the temperature monitoring values ​​at both ends of the positive and negative electrodes in the positive and negative electrode temperature data of each abnormal sub-battery at each sampling moment and the length of the corresponding abnormal sub-battery is used as the first heat transfer value of each abnormal sub-battery at each sampling moment; Based on the basic information of the battery, the battery pole length is determined, and the remaining abnormal sub-batteries closest to each abnormal sub-battery are used as the thermal conductive sub-batteries of the corresponding abnormal sub-battery, and the difference between the positive extreme temperature monitoring value of each abnormal sub-battery and the positive extreme temperature monitoring value of the corresponding thermal conductive sub-battery and the quotient of the minimum battery pole connection distance between the corresponding abnormal sub-battery and the corresponding thermal conductive sub-battery are used as the second heat transfer value of each abnormal sub-battery at each sampling time; The shell thickness of the battery is determined based on the basic information of the battery, and the larger value of the temperature monitoring values ​​at the positive and negative ends of each abnormal sub-battery and the shell thickness of the battery are used as the third heat transfer value of each abnormal sub-battery at each sampling moment; Based on the first heat transfer value, the second heat transfer value and the third heat transfer value of each abnormal sub-cell at all sampling moments, a heat generation matrix of each abnormal sub-cell is obtained; Wherein, based on the first heat transfer value, the second heat transfer value and the third heat transfer value of each abnormal sub-cell at all sampling moments, the heating matrix of each abnormal sub-cell is obtained, including: All sampling moments are ordinal-defined starting from 1 in time sequence to obtain ordinal definition results; Based on the first heat transfer value, the second heat transfer value, the third heat transfer value and the ordinal definition results of each abnormal sub-cell at all sampling moments, the heating matrix of each abnormal sub-cell is obtained, which is: Among them, δ is the heating matrix of each abnormal sub-battery, q1 is the first heat transfer value of the abnormal sub-battery at the sampling time with ordinal number 1, q2 is the first heat transfer value of the abnormal sub-battery at the sampling time with ordinal number 2, and q n is the first heat transfer value of the abnormal sub-cell at the sampling time with ordinal number n, n is the total number of sampling times, e1 is the second heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 1, e2 is the second heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 2, e n is the second heat transfer value of the abnormal sub-cell at the sampling time with ordinal number n, z1 is the third heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 1, z2 is the third heat transfer value of the abnormal sub-cell at the sampling time with ordinal number 2, and z n is the third heat transfer value of the abnormal sub-cell at the sampling time with ordinal number n; Wherein, S3: based on the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, the predicted explosion rate of each abnormal sub-battery is obtained, and the explosion prediction result of the battery is obtained based on the predicted explosion rates of all abnormal sub-batteries, and explosion-proof intervention measures are obtained based on the explosion prediction results of the battery, including: S301: Based on the heating matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, the predicted explosion rate of each abnormal sub-battery is obtained, which is: Where C is the predicted explosion rate of the abnormal sub-battery, σ n is the temperature coefficient of the battery at the sampling time with ordinal number n, σ max is the maximum value of the battery's temperature coefficient at all sampling moments, σ min is the minimum value of the battery's temperature coefficient at all sampling moments, P δ is the rank of the heating matrix of the abnormal sub-battery, ln is the natural logarithm, and the value of the natural constant e is 2.718; S302: Obtain explosion prediction results of the battery based on the predicted explosion rates of all abnormal sub-batteries, and obtain explosion-proof intervention measures based on the explosion prediction results of the battery.

2. A battery explosion-proof prediction method according to claim 1, characterized in that: S1: Acquire the positive and negative electrode temperature data of the battery within a preset time period, and classify all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery, including: Based on the temperature sensors disposed at the positive and negative ends of each sub-battery in the battery, the temperature monitoring values ​​at the positive and negative ends of each sub-battery in the battery are obtained as the positive and negative temperature data of the battery in a preset time period; All sub-batteries in the battery are classified based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery.

3. A battery explosion-proof prediction method according to claim 2, characterized in that: Based on the positive and negative electrode temperature data, all sub-batteries in the battery are classified to obtain all normal sub-batteries and all abnormal sub-batteries of the battery, including: Taking every unit time from the start time within the preset time period as the sampling time, determining the average of the temperature monitoring values ​​of the positive and negative terminals of each sub-battery in the battery at each sampling time as the abnormal judgment temperature of each sub-battery at the corresponding sampling time; Determine the mean and standard deviation of the abnormal judgment temperature of a single sub-battery at all sampling moments, and take the sum of the corresponding mean and twice the standard deviation as the temperature comparison value of the corresponding sub-battery; The abnormal judgment temperature and temperature comparison value of each sub-battery at the last sampling moment are used as the abnormal judgment temperature and temperature comparison value of each sub-battery at the current moment, and it is determined whether the abnormal judgment temperature of each sub-battery in the battery at the current moment is greater than the temperature comparison value of the corresponding sub-battery. If so, the corresponding sub-battery is determined to be an abnormal sub-battery, otherwise, the corresponding sub-battery is determined to be a normal sub-battery.

4. A battery explosion-proof prediction method according to claim 1, characterized in that: S302: Obtaining explosion prediction results of the battery based on the predicted explosion rates of all abnormal sub-batteries, and obtaining explosion-proof intervention measures based on the explosion prediction results of the battery, including: S3021: taking the maximum value of the predicted explosion rates of all abnormal sub-batteries as the predicted explosion rate of the battery, and judging whether the predicted explosion rate of the battery is greater than a preset explosion rate threshold; if so, taking the predicted explosion rate of the battery as too large as the battery explosion prediction result; otherwise, taking the predicted explosion rate of the battery as not too large as the battery explosion prediction result; S3022: When the explosion prediction result of the battery is that the predicted explosion rate of the battery is not too large, the preset cooling device on the battery will be turned on as an explosion-proof intervention measure; when the explosion prediction result of the battery is that the predicted explosion rate of the battery is too large, the output power of the battery will be limited to a preset power value as an explosion-proof intervention measure.

5. A battery explosion-proof prediction device, characterized in that: A method for predicting battery explosion prevention according to any one of claims 1 to 4, comprising: A classification module is used to obtain the positive and negative electrode temperature data of the battery within a preset time period, and classify all sub-batteries in the battery based on the positive and negative electrode temperature data to obtain all normal sub-batteries and all abnormal sub-batteries of the battery; A construction module is used to obtain basic information of the battery and real-time parameters of the battery at each sampling moment within a preset time period, obtain the temperature coefficient of the battery at each sampling moment within the preset time period based on the real-time parameters of the battery at each sampling moment within the preset time period, and obtain the heating matrix of each abnormal sub-battery based on the basic information of the battery and the positive and negative electrode temperature data of each abnormal sub-battery; The prediction module is used to obtain the predicted explosion rate of each abnormal sub-battery based on the heat matrix of each abnormal sub-battery and the temperature coefficient of the battery at each sampling moment, and to obtain the explosion prediction result of the battery based on the predicted explosion rates of all abnormal sub-batteries, and to obtain explosion-proof intervention measures based on the explosion prediction result of the battery.

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