Thermal runaway detection method and device of lithium ion battery, electronic equipment and medium

Abstract

The invention provides a thermal runaway detection method and device of a lithium ion battery, electronic equipment and a medium. The thermal runaway detection method and device of the lithium ion battery are based on battery monomer voltage of each battery monomer in a target lithium ion battery pack and a charge state of the target lithium ion battery pack collected at multiple sampling moments; determining a voltage matrix of each single battery and a charge state matrix of the target lithium ion battery pack; based on the voltage matrix, determining a voltage difference matrix of each single battery; based on the charge state matrix and the voltage difference matrix, performing fuzzy reasoning on the thermal runaway risk of each battery monomer to obtain a thermal runaway risk scoring matrix of each battery monomer; and determining the thermal runaway risk grade of the target lithium ion battery pack based on the thermal runaway risk scoring matrix. Therefore, the thermal runaway risk of each single battery in the lithium ion battery pack is evaluated through the state-of-charge matrix and the voltage difference matrix, and thermal runaway early warning of the lithium ion battery pack can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of lithium-ion batteries, and in particular to a thermal runaway detection method, device, electronic equipment and medium for lithium-ion batteries. Background Art

[0002] Lithium-ion batteries have the advantages of high energy density, long cycle life, and wide operating conditions, and are widely used as power sources for electric vehicles. However, as the energy density of lithium-ion batteries continues to increase, safety accidents such as smoke and fire in new energy vehicles caused by thermal runaway of lithium-ion batteries are gradually increasing, and it is necessary to monitor the thermal runaway of lithium-ion batteries.

[0003] Existing thermal runaway detection methods for lithium-ion batteries usually monitor the surface temperature of the lithium-ion battery pack and provide thermal runaway warning by determining whether the temperature exceeds the maximum temperature threshold or whether the temperature rise rate is greater than the temperature rise rate threshold. However, since the surface temperature of the lithium-ion battery pack is monitored, when the judgment condition is triggered, the lithium-ion battery is already in a very dangerous state and may leak, catch fire or even explode at any time, so the thermal runaway detection of lithium-ion batteries is not timely enough. Summary of the invention

[0004] In view of this, the embodiments of the present application at least provide a thermal runaway detection method, device, electronic device and medium for lithium-ion batteries, which evaluates the thermal runaway risk of each battery cell in a lithium-ion battery pack through a state of charge matrix and a voltage difference matrix, and can provide thermal runaway warning for the lithium-ion battery pack.

[0005] This application mainly includes the following aspects:

[0006] In a first aspect, an embodiment of the present application provides a method for detecting thermal runaway of a lithium-ion battery, the method comprising:

[0007] Determine a voltage matrix of each battery cell and a state-of-charge matrix of the target lithium-ion battery pack based on the battery cell voltages of each battery cell in the target lithium-ion battery pack collected at multiple sampling moments and the state-of-charge of the target lithium-ion battery pack; the multiple sampling moments are multiple moments within a preset time period after the target lithium-ion battery pack is charged;

[0008] Based on the voltage matrix, determining a voltage difference matrix of each battery cell;

[0009] Based on the state of charge matrix and the voltage differential matrix, fuzzy reasoning is performed on the thermal runaway risk of each of the battery cells to obtain a thermal runaway risk scoring matrix for each of the battery cells;

[0010] Based on the thermal runaway risk scoring matrix, a thermal runaway risk level of the target lithium-ion battery pack is determined.

[0011] In a second aspect, an embodiment of the present application further provides a thermal runaway detection device for a lithium-ion battery, the thermal runaway detection device for a lithium-ion battery comprising:

[0012] A data acquisition module, for determining a voltage matrix of each battery cell and a state-of-charge matrix of the target lithium-ion battery pack based on the battery cell voltages of each battery cell in the target lithium-ion battery pack and the state-of-charge of the target lithium-ion battery pack acquired at multiple sampling moments; the multiple sampling moments are multiple moments within a preset time period after the target lithium-ion battery pack is charged;

[0013] A data extraction module, used to determine a voltage difference matrix of each battery cell based on the voltage matrix;

[0014] A fuzzy reasoning module, used for performing fuzzy reasoning on the thermal runaway risk of each of the battery cells based on the state of charge matrix and the voltage differential matrix to obtain a thermal runaway risk scoring matrix for each of the battery cells;

[0015] A risk determination module is used to determine the thermal runaway risk level of the target lithium-ion battery pack based on the thermal runaway risk scoring matrix.

[0016] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the thermal runaway detection method for lithium-ion batteries as described above.

[0017] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the thermal runaway detection method for a lithium-ion battery as described above are executed.

[0018] The thermal runaway detection method, device, electronic device and medium of lithium-ion batteries provided in the embodiments of the present application determine the voltage matrix of each battery cell and the state of charge matrix of the target lithium-ion battery pack based on the battery cell voltage of each battery cell in the target lithium-ion battery pack collected at multiple sampling moments and the state of charge of the target lithium-ion battery pack; the multiple sampling moments are multiple moments within a preset time period after the target lithium-ion battery pack is charged; based on the voltage matrix, the voltage differential matrix of each battery cell is determined; based on the state of charge matrix and the voltage differential matrix, the thermal runaway risk of each battery cell is fuzzy inferred to obtain the thermal runaway risk scoring matrix of each battery cell; based on the thermal runaway risk scoring matrix, the thermal runaway risk level of the target lithium-ion battery pack is determined. In this way, the thermal runaway risk of each battery cell in the lithium-ion battery pack is evaluated by the state of charge matrix and the voltage differential matrix, so that the thermal runaway early warning of the lithium-ion battery pack can be realized.

[0019] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A flow chart of a thermal runaway detection method for a lithium-ion battery provided in an embodiment of the present application is shown;

[0022] Figure 2 A schematic diagram showing a first membership function curve in an embodiment of the present application is shown;

[0023] Figure 3 A schematic diagram showing a second membership function curve in an embodiment of the present application is shown;

[0024] Figure 4 One of the functional module diagrams of a thermal runaway detection device for a lithium-ion battery provided in an embodiment of the present application is shown;

[0025] Figure 5 A second functional module diagram of a thermal runaway detection device for a lithium-ion battery provided in an embodiment of the present application is shown;

[0026] Figure 6 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0028] To facilitate the understanding of the present application, the technical solution provided by the present application is described in detail below in conjunction with specific embodiments.

[0029] The embodiments of the present application provide a method, device, electronic device and medium for detecting thermal runaway of lithium-ion batteries. The thermal runaway risk of each battery cell in a lithium-ion battery pack is evaluated by a state of charge matrix and a voltage differential matrix, so as to provide a thermal runaway warning for the lithium-ion battery pack.

[0030] See also Figure 1 , Figure 1 This is a flow chart of a method for detecting thermal runaway of a lithium-ion battery provided in an embodiment of the present application. Figure 1 As shown, the thermal runaway detection method of a lithium-ion battery provided in an embodiment of the present application comprises the following steps:

[0031] S101, based on the battery cell voltages of the battery cells in the target lithium-ion battery pack and the charge state of the target lithium-ion battery pack collected at multiple sampling moments, determine the voltage matrix of the battery cells and the charge state matrix of the target lithium-ion battery pack; the multiple sampling moments are multiple moments within a preset time period after the charging of the target lithium-ion battery pack is completed.

[0032] Here, firstly, the battery operation data after the target lithium-ion battery pack is charged is collected. Among them, the battery operation data includes the operation time data of the lithium-ion battery pack, the battery cell voltage data of each battery cell constituting the lithium-ion battery pack, and the state of charge data of the lithium-ion battery pack. Specifically, the battery cell voltage of each battery cell in the target lithium-ion battery pack collected at multiple sampling moments and the state of charge of the target lithium-ion battery pack are stored in a matrix to obtain the voltage matrix of each battery cell and the state of charge matrix of the target lithium-ion battery pack. Among them, the multiple sampling moments are multiple moments within a preset time period after the target lithium-ion battery pack is charged. Specifically, taking the target lithium-ion battery pack after the i-th charging is completed as an example, the battery operation data of k time points are collected at multiple moments within the preset time period, and the time matrix t of the target lithium-ion battery pack is collected. i =[t 1 ,t 2 ,…,t k ]; Voltage matrix of each battery cell And the state of charge matrix of the target lithium-ion battery pack By combining the above matrices, we can get the battery operation data matrix D after the i-th charging is completed. i =[t i ,SOC i ,U i ]; where m is the number of battery cells, m>1, The state of charge of the target lithium-ion battery pack at the kth time point after the i-th charging is completed. In the embodiment of the present application, the battery cell voltage of each battery cell can be collected by the voltage sensor at both ends of the positive and negative electrodes of the battery cell, and the state of charge value can be obtained from the battery management system (Battery Management System, BMS) of the lithium-ion battery pack, so that there is no need to add new sensors to the lithium-ion battery pack, which can reduce hardware costs.

[0033] S102: Determine a voltage difference matrix of each battery cell based on the voltage matrix.

[0034] Here, the voltage difference is an important indicator for judging the performance of battery cells in a lithium-ion battery pack. The voltage difference can reflect the change in voltage of the battery cell during the charge and discharge process, and is used to help monitor whether the performance of the battery cell is normal. Therefore, the voltage difference information of each battery cell in the target lithium-ion battery pack is extracted through the voltage matrix to obtain the voltage difference matrix of each battery cell.

[0035] S103, performing fuzzy reasoning on the thermal runaway risk of each of the battery cells based on the state of charge matrix and the voltage differential matrix to obtain a thermal runaway risk scoring matrix for each of the battery cells.

[0036] Here, since the early signs before thermal runaway of lithium-ion battery packs are often complex and nonlinear, it is difficult to describe them using traditional linear models or precise mathematical models. Fuzzy reasoning can integrate experts' understanding and experience of battery thermal runaway behavior, and convert these difficult-to-quantify knowledge into rules that can be automatically executed. Therefore, through fuzzy reasoning, the early signs of thermal runaway of lithium-ion battery packs can be more accurately identified and quantified into thermal runaway risk scores, so that early warnings can be issued in advance, which can effectively reduce safety accidents caused by thermal runaway of lithium-ion battery packs. Since the state of charge of the lithium-ion battery pack and the voltage difference of each battery cell in the lithium-ion battery pack have a close impact on the thermal runaway of the lithium-ion battery pack, in the embodiment of the present application, based on the state of charge matrix and the voltage difference matrix, fuzzy reasoning is performed on the thermal runaway risk of each battery cell to obtain a thermal runaway risk score matrix for each battery cell.

[0037] S104: Determine the thermal runaway risk level of the target lithium-ion battery pack based on the thermal runaway risk scoring matrix.

[0038] Here, according to each thermal runaway risk score in the thermal runaway risk scoring matrix, the corresponding thermal runaway risk level is determined for the target lithium-ion battery pack, thereby achieving thermal runaway warning for the target lithium-ion battery pack.

[0039] Further, the determining of the voltage difference matrix of each battery cell based on the voltage matrix includes:

[0040] Step a1, determining the difference between the battery cell voltage of any battery cell in the voltage matrix at the first sampling moment and the battery cell voltage at the second sampling moment as the voltage value of the voltage difference of any battery cell at the first sampling moment; the first sampling moment is any sampling moment among the multiple sampling moments except the first sampling moment; the second sampling moment is the sampling moment before the first sampling moment.

[0041] Here, the difference between the battery cell voltage of any battery cell in the voltage matrix at the first sampling moment and the battery cell voltage at the second sampling moment is determined as the voltage value of the voltage difference of any battery cell at the first sampling moment. The first sampling moment is any sampling moment in the voltage matrix except the first sampling moment, and the second sampling moment is the sampling moment before the first sampling moment. Specifically, it can be expressed as in It is the voltage value of the voltage difference of the mth battery cell at the kth moment.

[0042] Step a2, calculating the voltage value of the voltage difference of each of the battery cells at each of the first sampling moments, and obtaining a voltage difference matrix of each of the battery cells.

[0043] Here, the voltage value of the voltage difference of each battery cell at each first sampling moment is calculated. Get the voltage difference matrix ΔU of each battery cell i , which can be expressed as

[0044] Furthermore, based on the state of charge matrix and the voltage differential matrix, fuzzy reasoning is performed on the thermal runaway risk of each battery cell to obtain a thermal runaway risk scoring matrix for each battery cell, including:

[0045] Step b1, inputting each state of charge value in the state of charge matrix into a preset first membership function, and calculating the membership of each state of charge value under each first fuzzy set; each first fuzzy set is obtained based on the state of charge division.

[0046] Here, each state of charge value in the state of charge matrix is ​​input into a preset first membership function, and the membership of each state of charge value under each first fuzzy set is calculated; wherein, the first membership function is a preset function for mapping the exact value of the state of charge to the interval [0,1]. This function defines how to determine the degree to which the state of charge value belongs to each first fuzzy set according to its size. Among them, each first fuzzy set is obtained based on the state of charge division. In the embodiment of the present application, the state of charge is divided into three fuzzy sets: low, medium and high. After each state of charge value is processed by the first membership function, a corresponding array is generated. This array reflects the degree of membership of the state of charge value in each first fuzzy set (low, medium and high). In the embodiment of the present application, the first membership function is a parabolic membership function. Please refer to Figure 2 , Figure 2 Schematic diagram of the first membership function curve in the embodiment of the present application. Figure 2 As shown, A 1 , B 1 The two points divide the first membership function into three parts. The calculation formula of the first membership function is as follows:

[0047] Here, A 1 , B 1 The value of is set according to experience. In the embodiment of the present application, A 1 is 0.4, B 1 =0.9. The state of charge matrix SOC i Each state of charge value in Input into the first membership function to get the state of charge value The membership degree μ under each first fuzzy set SOC =[μ SOC低 ,μ SOC中 ,μ SOC高 ]; if the state of charge value is not within the corresponding value range, μ SOC低 , μ SOC中 , μ SOC高 The default value is 0. For example, when the state of charge value is 0.8, 0.8 is not in μ SOC低 and μ SOC高 In the range of values, μ SOC低 and μ SOC高 The default value is 0, that is, μ SOC =[0,0.64,0].

[0048] Step b2, inputting each voltage value in the voltage differential matrix into a preset second membership function, and calculating the membership of each voltage value under each second fuzzy set; each second fuzzy set is obtained based on the voltage differential division.

[0049] Here, each voltage value in the voltage difference matrix is ​​input into a preset second membership function, and the membership of each voltage value under each second fuzzy set is calculated; wherein, the second membership function is a preset function for mapping the exact value of the voltage difference to the interval [0,1]. This function defines how to determine the degree to which the voltage difference belongs to each second fuzzy set according to the size of the voltage value of the voltage difference. Among them, each second fuzzy set is obtained based on the voltage difference division. In the embodiment of the present application, the voltage difference is divided into three fuzzy sets: small, medium and large. After each voltage difference voltage value is processed by the second membership function, a corresponding array will be generated. This array reflects the degree of membership of the voltage value of the voltage difference in each second fuzzy set (small, medium and large). In the embodiment of the present application, the second membership function is a trapezoidal membership function. Please refer to Figure 3 , Figure 3 Schematic diagram of the second membership function curve in the embodiment of the present application. Figure 3 As shown, A 2 , B 1 The two points divide the second membership function into three parts. The calculation formula of the second membership function is as follows:

[0050] Here, A 2 , B 2 The value of is set according to experience. In the embodiment of the present application, A 2 is -0.5, B 2= -0.05. The voltage difference matrix ΔU i The voltage values ​​in Input into the second membership function to get the voltage values The membership degree μ under each second fuzzy set ΔV =[μ ΔV大 ,μ ΔV中 ,μ ΔV小 ]; if the voltage value is not within the corresponding value range, μ ΔV大 , μ ΔV中 , μ ΔV小 The default value is 0. For example, when the voltage value is -0.3, -0.3 is not in μ ΔV大 and μ ΔV小 In the range of values, μ ΔV大 and μ ΔV小 The default value is 0, that is, μ ΔV =[0,0.62,0].

[0051] Step b3, according to the membership of each state of charge value under each first fuzzy set, the membership of each voltage value under each second fuzzy set and a preset fuzzy rule base, calculate the membership of the fuzzy conclusion of each battery cell under each fuzzy rule in the fuzzy rule base at each sampling moment.

[0052] Here, according to the membership degree μ of each state of charge value under each first fuzzy set SOC , the membership degree μ of each voltage value under each second fuzzy set ΔV And the preset fuzzy rule base calculates the membership degree of the fuzzy conclusion of each battery cell under each fuzzy rule in the fuzzy rule base at each sampling moment. Among them, the preset fuzzy rule base is as follows:

[0053] Rule 1: SOC high and If it is large, then the risk is very high;

[0054] Rule 2: SOC High and Medium, then the risk is high;

[0055] Rule 3: SOC High and Small, then the risk is medium;

[0056] Rule 4: SOC Neutralization If it is large, then the risk is high;

[0057] Rule 5: SOC Neutralization Medium, then the risk is medium;

[0058] Rule 6: SOC Neutralization Small, so the risk is low;

[0059] Rule 7: SOC is low and Small, then the risk is very low;

[0060] Rule 8: Low SOC and Medium, then the risk is medium;

[0061] Rule 9: Low SOC and Big, so the risk is high.

[0062] The details are shown in Table 1 below:

[0063] Table 1

[0064]

[0065] Among them, VS, S, M, H, and VH represent the fuzzy conclusions of very low, low, medium, high, and very high thermal runaway risks, respectively. SOC =[μ SOC低 , μ SOc中 , μ SOC高 ],μ ΔV =[μ ΔV大 , μ ΔV中 , μ ΔV小 ], as shown in Table 1, when μ SOC低 and μ ΔV小 When pairing, the minimum non-zero value of the two memberships is taken as the membership of the fuzzy conclusion VS corresponding to rule 7; when μ SOC低 and μ ΔV中 When pairing, the minimum non-zero value of the two memberships is taken as the membership of the fuzzy conclusion M corresponding to rule 8; and so on, the membership of the fuzzy conclusion of each battery cell under each fuzzy rule at each sampling time is calculated, and the membership of the fuzzy conclusion under the 9 fuzzy rules can be obtained. 1 -r 9 ; expressed as:

[0066] Result rule =[VS SMMMHHH VH] = [r 1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 ].

[0067] Step b4, calculating the target membership degree of each fuzzy conclusion of each battery cell at each sampling moment according to all the membership degrees of each fuzzy conclusion of each battery cell at each sampling moment.

[0068] Here, according to all the membership degrees r of each fuzzy conclusion of each battery cell at each sampling time 1 -r 9 , calculate the target membership of each fuzzy conclusion of each battery cell at each sampling time. Specifically, for all membership r of each fuzzy conclusion VS, S, M, L, VL 1 -r 9 Screening is performed to obtain the target membership degree k of each fuzzy conclusion 1 -k 5 , represented by result risk =[k 1 k 2 k 3 k 4 k 5 ]. The specific screening process is:

[0069] In result rule There is only one VS in the vector, so k 1 =r 1 ;

[0070] In result rule There is only one S in the vector, so k 2 =r 2 ;

[0071] In result rule There are three values ​​r in the vector M 3 , r 4 , r 5 , at this time k 3 For r 3 , r 4 , r 5 The minimum non-zero value in ;

[0072] In result rule There are three values ​​r in the vector H 6 , r 7 , r 8 , at this time k4 is r 6 , r 7 , r 8 The minimum non-zero value in ;

[0073] In result rule There is only one VH in the vector, so k 5 =r 9 .

[0074] Step b5, inputting the target membership of each fuzzy conclusion of each battery cell at each sampling moment into a preset third membership function, and calculating the thermal runaway risk score of each fuzzy conclusion of each battery cell at each sampling moment.

[0075] Here, the target membership of each fuzzy conclusion of each battery cell at each sampling time is input into the preset third membership function, and the thermal runaway risk score of each fuzzy conclusion of each battery cell at each sampling time is calculated. Specifically, the third membership function is a mapping relationship between the preset thermal runaway risk score and the membership of each fuzzy conclusion. The calculation formula of the third membership function is as follows:

[0076]

[0077] Where z is the thermal runaway risk score; the target membership degree k of each fuzzy conclusion of each battery cell at each sampling time is 1 -k 5 Substitute μ into VS ,μ S ,μ M ,μ H ,μ VH , the thermal runaway risk score of each fuzzy conclusion of each battery cell at each sampling time can be calculated in reverse. Specifically, for example, k 1 The corresponding membership degree is μ VS ,because Reverse z 1 =15-15×μ VS , we can get the thermal runaway risk score μ of the fuzzy conclusion VS risk (k 1 )=z 1 ;k 2 The corresponding membership degree is μ S ,because Reverse z 2 =15μ S ,z 3 =40-25μ S , we can get the thermal runaway risk score μ of the fuzzy conclusion S risk (k 2 )=z 2 +z 2 ; Similarly, the thermal runaway risk score μ of each fuzzy conclusion of each battery cell at each sampling time is calculated risk (k 1 )-μ risk (k 5 ).

[0078] Step b6, according to the target membership of each fuzzy conclusion of each battery cell at each sampling moment, weighted average is performed on the thermal runaway risk score of each fuzzy conclusion of each battery cell at each sampling moment, and the thermal runaway risk score of each battery cell at each sampling moment is calculated.

[0079] Here, according to the target membership degree k of each fuzzy conclusion of each battery cell at each sampling time 1 -k 5 , the thermal runaway risk score μ for each fuzzy conclusion of each battery cell at each sampling time riSk (k 1 )-μ risk (k 5 ) is weighted averaged to calculate the thermal runaway risk score of each battery cell at each sampling time Specifically, the centroid method is used for weighted averaging, and the specific formula is shown as follows:

[0080] Among them, k l is the target membership of the lth fuzzy conclusion; μ risk (k l ) is represented as the thermal runaway risk score of the lth fuzzy conclusion.

[0081] Step b7, determining a thermal runaway risk score matrix for each battery cell according to the thermal runaway risk score of each battery cell at each sampling time.

[0082] Here, the thermal runaway risk score of each battery cell at each sampling time is calculated Get the thermal runaway risk score matrix risk for each battery cell i ; can be expressed as

[0083] Further, determining the thermal runaway risk level of the target lithium-ion battery pack based on the thermal runaway risk scoring matrix includes:

[0084] Step c1, determining the arithmetic mean of the thermal runaway risk scores of each battery cell at each sampling moment in the thermal runaway risk score matrix as the comprehensive thermal runaway risk score of each battery cell.

[0085] Here, the arithmetic mean of the thermal runaway risk scores of each battery cell at each sampling moment in the thermal runaway risk score matrix is ​​determined as the comprehensive thermal runaway risk score of each battery cell. Specifically, after the i-th charging, the comprehensive thermal runaway risk score of the m-th battery cell can be expressed as The thermal runaway risk scores of all battery cells can be expressed as the thermal runaway risk score vector Specifically expressed as

[0086] Step c2: if the comprehensive thermal runaway risk score of any of the battery cells is greater than or equal to a preset first thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be the first thermal runaway risk level.

[0087] Here, if there is a comprehensive thermal runaway risk score for the mth battery cell If the value is greater than or equal to the preset first thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be the first thermal runaway risk level. At this time, the mth battery cell has an extremely high thermal runaway risk, and a prompt message needs to be output to remind the user to stay away from the target lithium-ion battery pack. The preset first thermal runaway risk score threshold is set based on experience. In the embodiment of the present application, the first thermal runaway risk score threshold is set to 80.

[0088] Step c3: if the comprehensive thermal runaway risk score of any of the battery cells is greater than or equal to a preset second thermal runaway risk score threshold, and the comprehensive thermal runaway risk scores of each of the battery cells are less than the first thermal runaway risk score threshold, then the thermal runaway risk level of the target lithium-ion battery pack is determined to be the second thermal runaway risk level; the second thermal runaway risk score threshold is less than the first thermal runaway risk score threshold.

[0089] Here, if there is a comprehensive thermal runaway risk score for the mth battery cell If the thermal runaway risk score of the target lithium-ion battery pack is greater than or equal to the preset second thermal runaway risk score threshold, and the comprehensive thermal runaway risk score of each battery cell is less than the first thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be the second thermal runaway risk level. At this time, the mth battery cell has a high thermal runaway risk, and the target lithium-ion battery pack, especially the mth battery cell, needs to be maintained. Among them, the second thermal runaway risk score threshold is less than the first thermal runaway risk score threshold, and the second thermal runaway risk score threshold is set according to experience. In the embodiment of the present application, the second score threshold is set to 60.

[0090] Step c4: if the comprehensive thermal runaway risk score of each of the battery cells is less than the second thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be a third thermal runaway risk level.

[0091] Here, if the comprehensive thermal runaway risk scores of each battery cell are less than the second thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be the third thermal runaway risk level. At this time, the possibility of thermal runaway risk in the target lithium-ion battery pack is low, and no prompt or maintenance is required.

[0092] In a possible implementation manner, before performing fuzzy reasoning on the thermal runaway risk of each of the battery cells based on the state of charge matrix and the voltage differential matrix, the method further includes:

[0093] Step d1, determining a temperature matrix of the target lithium-ion battery pack based on the battery temperatures collected by each temperature sensor in the target lithium-ion battery pack at the plurality of sampling moments.

[0094] Here, before fuzzy reasoning, a preliminary judgment is first made on whether the target lithium-ion battery pack has thermal runaway. In the embodiment of the present application, the battery operation data also includes temperature data collected by each temperature sensor in the target lithium-ion battery pack. Specifically, the battery temperatures collected by each temperature sensor in the target lithium-ion battery pack at multiple sampling times are stored in a matrix to obtain a temperature matrix of the target lithium-ion battery pack, which can be expressed as Wherein, x is the number of temperature sensors. In addition, the temperature data is collected by the temperature sensor provided in the lithium-ion battery pack, and there is no need to add a new sensor to the lithium-ion battery pack, which can reduce the hardware cost.

[0095] Step d2: determining a voltage range difference matrix of each battery cell based on the voltage matrix.

[0096] Here, the voltage range difference can reflect the consistency of each battery cell in the lithium-ion battery pack. If the voltage range difference is too large, it may mean that the lithium-ion battery pack is experiencing thermal runaway. Therefore, the voltage range difference information of each battery cell in the target lithium-ion battery pack is extracted through the voltage matrix to obtain the voltage range difference matrix of each battery cell.

[0097] Step d3, based on the voltage extreme difference matrix and the temperature matrix, determine whether thermal runaway occurs in the target lithium-ion battery pack.

[0098] Here, the voltage extreme difference and temperature are used as the basis for judgment, and whether the target lithium-ion battery pack has thermal runaway is judged according to the voltage extreme difference matrix and the temperature matrix.

[0099] Step d4: If yes, output thermal runaway alarm prompt information.

[0100] Here, if it is determined that the target lithium-ion battery pack has thermal runaway, a thermal runaway alarm prompt message is output to issue a thermal runaway alarm. Moreover, since the target lithium-ion battery pack has already experienced thermal runaway and triggered an alarm, there is no need to perform a subsequent fuzzy reasoning process.

[0101] Step d5: If not, jump to performing fuzzy reasoning on the thermal runaway risk of each battery cell based on the state of charge matrix and the voltage difference matrix.

[0102] Here, if it is determined that the target lithium-ion battery pack does not have thermal runaway, there is no need to issue a thermal runaway alarm, and the process directly jumps to step S103.

[0103] Further, the determining of the voltage range difference matrix of each battery cell based on the voltage matrix includes:

[0104] Step e1, determining the difference between the maximum value and the minimum value of each battery cell voltage at any sampling moment in the voltage matrix as the voltage value of the voltage extreme difference at any sampling moment.

[0105] Here, the difference between the maximum and minimum values ​​of the voltages of each battery cell at any sampling moment in the voltage matrix is ​​determined as the voltage value of the voltage extreme difference at any sampling moment. The specific formula is as follows:

[0106] in, is the voltage range difference at the kth moment after the i-th charging is completed. For example, at the second moment, the voltages of the battery cells in the voltage matrix are 3.55V, 3.6V, 3.7V and 3.65V respectively. At this time, the maximum voltage is 3.7V and the minimum voltage is 3.55V, so the voltage range difference is 3.7-3.55=0.15V.

[0107] Step e2, calculating the voltage value of the voltage extreme difference at each sampling moment to obtain the voltage extreme difference matrix of each battery cell.

[0108] Here, the voltage value of the voltage difference at each sampling time is calculated Get the voltage difference matrix ΔV of each battery cell after the i-th charge i , which can be expressed as

[0109] Further, judging whether thermal runaway occurs in the target lithium-ion battery pack based on the temperature matrix and the voltage extreme difference matrix includes:

[0110] Step f1: If any voltage value in the voltage extreme difference matrix is ​​greater than or equal to a preset first voltage threshold, or any temperature value in the temperature matrix is ​​greater than or equal to a preset first temperature threshold, it is determined that thermal runaway occurs in the target lithium-ion battery pack.

[0111] Here, if any voltage value in the voltage extreme difference matrix is ​​greater than or equal to the preset first voltage threshold, or any temperature value in the temperature matrix is ​​greater than or equal to the preset first temperature threshold, it means that there is a significant difference in voltage between the battery cells in the target lithium-ion battery pack, or the target lithium-ion battery pack is overheated, and it can be determined that the target lithium-ion battery pack has thermal runaway after this charge. Among them, the first voltage threshold and the first temperature threshold are set according to experience. In the embodiment of the present application, the first voltage threshold is set to 0.2V, and the first temperature threshold is set to 60°C.

[0112] Step f2: If the voltage values ​​in the voltage extreme difference matrix are all less than the first voltage threshold, and the temperature values ​​in the temperature matrix are all less than the first temperature threshold, it is determined that thermal runaway does not occur in the target lithium-ion battery pack.

[0113] Here, if the voltage values ​​in the voltage extreme difference matrix are all less than the first voltage threshold, and the temperature values ​​in the temperature matrix are all less than the first temperature threshold, it means that the consistency of each battery cell of the target lithium-ion battery pack is good, and the target lithium-ion battery pack has not overheated. It can be determined that the target lithium-ion battery pack has not experienced thermal runaway after this charge.

[0114] The thermal runaway detection method for lithium-ion batteries provided in the embodiment of the present application determines the voltage matrix of each battery cell and the state of charge matrix of the target lithium-ion battery pack based on the battery cell voltage of each battery cell in the target lithium-ion battery pack collected at multiple sampling moments and the state of charge of the target lithium-ion battery pack; the multiple sampling moments are multiple moments within a preset time period after the target lithium-ion battery pack is charged; based on the voltage matrix, the voltage differential matrix of each battery cell is determined; based on the state of charge matrix and the voltage differential matrix, the thermal runaway risk of each battery cell is fuzzy inferred to obtain the thermal runaway risk scoring matrix of each battery cell; based on the thermal runaway risk scoring matrix, the thermal runaway risk level of the target lithium-ion battery pack is determined. In this way, the thermal runaway risk of each battery cell in the lithium-ion battery pack is evaluated by the state of charge matrix and the voltage differential matrix, so that the thermal runaway early warning of the lithium-ion battery pack can be achieved.

[0115] Based on the same application concept, the embodiments of the present application also provide a thermal runaway detection device for a lithium ion battery corresponding to the thermal runaway detection method for a lithium ion battery provided in the above embodiments. Since the principle of solving the problem by the device in the embodiments of the present application is similar to the thermal runaway detection method for a lithium ion battery in the above embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0116] See also Figure 4 , Figure 4 This is one of the functional module diagrams of a thermal runaway detection device for a lithium-ion battery provided in an embodiment of the present application. Figure 4 As shown, the thermal runaway detection device 400 for a lithium-ion battery includes:

[0117] The data acquisition module 410 is used to determine the voltage matrix of each battery cell and the charge state matrix of the target lithium-ion battery pack based on the battery cell voltage of each battery cell in the target lithium-ion battery pack and the charge state of the target lithium-ion battery pack collected at multiple sampling moments; the multiple sampling moments are multiple moments within a preset time period after the charging of the target lithium-ion battery pack is completed.

[0118] The data extraction module 420 is used to determine the voltage difference matrix of each battery cell based on the voltage matrix.

[0119] The fuzzy reasoning module 430 is used to perform fuzzy reasoning on the thermal runaway risk of each of the battery cells based on the state of charge matrix and the voltage differential matrix to obtain a thermal runaway risk scoring matrix for each of the battery cells.

[0120] The risk determination module 430 is used to determine the thermal runaway risk level of the target lithium-ion battery pack based on the thermal runaway risk scoring matrix.

[0121] Further, when the data extraction module 420 is used to determine the voltage difference matrix of each battery cell based on the voltage matrix, the data extraction module 420 is specifically used to:

[0122] Determine the difference between the battery cell voltage of any battery cell in the voltage matrix at the first sampling moment and the battery cell voltage at the second sampling moment as the voltage value of the voltage difference of any battery cell at the first sampling moment; the first sampling moment is any sampling moment among the multiple sampling moments except the first sampling moment; the second sampling moment is a sampling moment before the first sampling moment;

[0123] The voltage value of the voltage difference of each of the battery cells at each of the first sampling moments is calculated to obtain a voltage difference matrix of each of the battery cells.

[0124] Further, when the fuzzy reasoning module 430 is used to perform fuzzy reasoning on the thermal runaway risk of each battery cell based on the state of charge matrix and the voltage differential matrix to obtain the thermal runaway risk scoring matrix of each battery cell, the fuzzy reasoning module 430 is specifically used to:

[0125] Inputting each state of charge value in the state of charge matrix into a preset first membership function, and calculating the membership of each state of charge value under each first fuzzy set; each first fuzzy set is obtained based on the state of charge division;

[0126] Input each voltage value in the voltage differential matrix into a preset second membership function, and calculate the membership of each voltage value under each second fuzzy set; each second fuzzy set is obtained based on the voltage differential division;

[0127] Calculate the membership of the fuzzy conclusion of each battery cell under each fuzzy rule in the fuzzy rule base at each sampling time according to the membership of each state of charge value under each first fuzzy set, the membership of each voltage value under each second fuzzy set and a preset fuzzy rule base;

[0128] Calculating a target membership degree of each fuzzy conclusion of each battery cell at each sampling moment according to all membership degrees of each fuzzy conclusion of each battery cell at each sampling moment;

[0129] Inputting the target membership of each fuzzy conclusion of each battery cell at each sampling moment into a preset third membership function, and calculating the thermal runaway risk score of each fuzzy conclusion of each battery cell at each sampling moment;

[0130] According to the target membership of each fuzzy conclusion of each battery cell at each sampling moment, a weighted average of the thermal runaway risk scores of each fuzzy conclusion of each battery cell at each sampling moment is performed to calculate the thermal runaway risk score of each battery cell at each sampling moment;

[0131] According to the thermal runaway risk scores of the battery cells at each sampling moment, a thermal runaway risk score matrix of the battery cells is determined.

[0132] Further, when the risk determination module 440 is used to determine the thermal runaway risk level of the target lithium-ion battery pack based on the thermal runaway risk scoring matrix, the risk determination module 440 is specifically used to:

[0133] Determine the arithmetic mean of the thermal runaway risk scores of each battery cell at each sampling moment in the thermal runaway risk score matrix as a comprehensive thermal runaway risk score of each battery cell;

[0134] If the comprehensive thermal runaway risk score of any of the battery cells is greater than or equal to a preset first thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be the first thermal runaway risk level;

[0135] If the comprehensive thermal runaway risk score of any of the battery cells is greater than or equal to a preset second thermal runaway risk score threshold, and the comprehensive thermal runaway risk scores of each of the battery cells are less than the first thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be the second thermal runaway risk level; the second thermal runaway risk score threshold is less than the first thermal runaway risk score threshold;

[0136] If the comprehensive thermal runaway risk scores of the battery cells are all less than the second thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be a third thermal runaway risk level.

[0137] For further information, see Figure 5 , Figure 5 This is a second functional module diagram of a thermal runaway detection device for a lithium-ion battery provided in an embodiment of the present application. Figure 5 As shown, the thermal runaway detection device 400 for a lithium-ion battery further includes:

[0138] The data acquisition module 410 is further configured to determine a temperature matrix of the target lithium-ion battery pack based on the battery temperatures acquired by each temperature sensor in the target lithium-ion battery pack at the plurality of sampling moments.

[0139] The data extraction module 420 is further configured to determine a voltage range matrix of each battery cell based on the voltage matrix.

[0140] The preliminary judgment module 450 is used to judge whether the target lithium-ion battery pack has thermal runaway based on the voltage extreme difference matrix and the temperature matrix; if so, output a thermal runaway alarm prompt message; if not, jump to fuzzy reasoning on the thermal runaway risk of each battery cell based on the state of charge matrix and the voltage differential matrix.

[0141] Furthermore, when the data extraction module 420 is used to determine the voltage range difference matrix of each battery cell based on the voltage matrix, the data extraction module 420 is specifically used to:

[0142] Determine the difference between the maximum value and the minimum value of each of the battery cell voltages at any sampling moment in the voltage matrix as the voltage value of the voltage extreme difference at any sampling moment;

[0143] The voltage values ​​of the voltage extreme differences at each of the sampling moments are calculated to obtain a voltage extreme difference matrix of each of the battery cells.

[0144] Further, when the preliminary judgment module 450 is used to judge whether the target lithium-ion battery pack has thermal runaway based on the temperature matrix and the voltage extreme difference matrix, the preliminary judgment module 450 is specifically used to:

[0145] If any voltage value in the voltage extreme difference matrix is ​​greater than or equal to a preset first voltage threshold, or any temperature value in the temperature matrix is ​​greater than or equal to a preset first temperature threshold, it is determined that thermal runaway occurs in the target lithium-ion battery pack;

[0146] If the voltage values ​​in the voltage extreme difference matrix are all less than the first voltage threshold, and the temperature values ​​in the temperature matrix are all less than the first temperature threshold, it is determined that thermal runaway does not occur in the target lithium-ion battery pack.

[0147] The thermal runaway detection device for lithium-ion batteries provided in the embodiment of the present application includes: a data acquisition module, which is used to determine the voltage matrix of each battery cell and the state of charge matrix of the target lithium-ion battery pack based on the battery cell voltage of each battery cell in the target lithium-ion battery pack and the state of charge of the target lithium-ion battery pack collected at multiple sampling moments; the multiple sampling moments are multiple moments within a preset time period after the target lithium-ion battery pack is charged; a data extraction module, which is used to determine the voltage differential matrix of each battery cell based on the voltage matrix; a fuzzy reasoning module, which is used to perform fuzzy reasoning on the thermal runaway risk of each battery cell based on the state of charge matrix and the voltage differential matrix to obtain the thermal runaway risk scoring matrix of each battery cell; a risk determination module, which is used to determine the thermal runaway risk level of the target lithium-ion battery pack using the thermal runaway risk scoring matrix. In this way, by evaluating the thermal runaway risk of each battery cell in the lithium-ion battery pack through the state of charge matrix and the voltage differential matrix, a thermal runaway warning for the lithium-ion battery pack can be achieved.

[0148] Based on the same application idea, please refer to Figure 6 , Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 600 includes a processor 610 , a memory 620 and a bus 630 .

[0149] The memory 620 stores machine-readable instructions executable by the processor 610. When the electronic device 600 is running, the processor 610 communicates with the memory 620 via the bus 630. When the processor 610 is running, the machine-readable instructions execute the steps of the thermal runaway detection method for lithium-ion batteries provided in the above embodiment. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0150] Based on the same application concept, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the thermal runaway detection method for lithium-ion batteries provided in the above embodiment are executed. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0156] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0157] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for detecting thermal runaway of a lithium-ion battery, characterized in that: The method comprises: Determine a voltage matrix of each battery cell and a state-of-charge matrix of the target lithium-ion battery pack based on the battery cell voltages of each battery cell in the target lithium-ion battery pack collected at multiple sampling moments and the state-of-charge of the target lithium-ion battery pack; the multiple sampling moments are multiple moments within a preset time period after the target lithium-ion battery pack is charged; Based on the voltage matrix, determining a voltage difference matrix of each battery cell; Based on the state of charge matrix and the voltage differential matrix, fuzzy reasoning is performed on the thermal runaway risk of each of the battery cells to obtain a thermal runaway risk scoring matrix for each of the battery cells; Based on the thermal runaway risk scoring matrix, a thermal runaway risk level of the target lithium-ion battery pack is determined.

2. The thermal runaway detection method for a lithium-ion battery according to claim 1, characterized in that: The step of determining the voltage difference matrix of each battery cell based on the voltage matrix includes: Determine the difference between the battery cell voltage of any battery cell in the voltage matrix at the first sampling moment and the battery cell voltage at the second sampling moment as the voltage value of the voltage difference of any battery cell at the first sampling moment; the first sampling moment is any sampling moment among the multiple sampling moments except the first sampling moment; the second sampling moment is a sampling moment before the first sampling moment; The voltage value of the voltage difference of each of the battery cells at each of the first sampling moments is calculated to obtain a voltage difference matrix of each of the battery cells.

3. The thermal runaway detection method for a lithium-ion battery according to claim 1, characterized in that: The method of performing fuzzy reasoning on the thermal runaway risk of each battery cell based on the state of charge matrix and the voltage differential matrix to obtain a thermal runaway risk scoring matrix for each battery cell includes: Inputting each state of charge value in the state of charge matrix into a preset first membership function, and calculating the membership of each state of charge value under each first fuzzy set, wherein each first fuzzy set is obtained based on the state of charge division; Input each voltage value in the voltage differential matrix into a preset second membership function, and calculate the membership of each voltage value under each second fuzzy set, wherein each second fuzzy set is obtained based on the voltage differential division; Calculate the membership of the fuzzy conclusion of each battery cell under each fuzzy rule in the fuzzy rule base at each sampling time according to the membership of each state of charge value under each first fuzzy set, the membership of each voltage value under each second fuzzy set and a preset fuzzy rule base; Calculating a target membership degree of each fuzzy conclusion of each battery cell at each sampling moment according to all membership degrees of each fuzzy conclusion of each battery cell at each sampling moment; Inputting the target membership of each fuzzy conclusion of each battery cell at each sampling moment into a preset third membership function, and calculating the thermal runaway risk score of each fuzzy conclusion of each battery cell at each sampling moment; According to the target membership of each fuzzy conclusion of each battery cell at each sampling moment, a weighted average of the thermal runaway risk scores of each fuzzy conclusion of each battery cell at each sampling moment is performed to calculate the thermal runaway risk score of each battery cell at each sampling moment; A thermal runaway risk score matrix for each battery cell is determined according to the thermal runaway risk score for each battery cell at each sampling time.

4. The thermal runaway detection method for a lithium-ion battery according to claim 1, characterized in that: The step of determining the thermal runaway risk level of the target lithium-ion battery pack based on the thermal runaway risk scoring matrix includes: Determine the arithmetic mean of the thermal runaway risk scores of each battery cell at each sampling moment in the thermal runaway risk score matrix as a comprehensive thermal runaway risk score of each battery cell; If the comprehensive thermal runaway risk score of any of the battery cells is greater than or equal to a preset first thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be the first thermal runaway risk level; If the comprehensive thermal runaway risk score of any of the battery cells is greater than or equal to a preset second thermal runaway risk score threshold, and the comprehensive thermal runaway risk scores of each of the battery cells are less than the first thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be the second thermal runaway risk level; the second thermal runaway risk score threshold is less than the first thermal runaway risk score threshold; If the comprehensive thermal runaway risk scores of the battery cells are all less than the second thermal runaway risk score threshold, the thermal runaway risk level of the target lithium-ion battery pack is determined to be a third thermal runaway risk level.

5. The thermal runaway detection method for a lithium-ion battery according to claim 1, characterized in that: Before performing fuzzy reasoning on the thermal runaway risk of each of the battery cells based on the state of charge matrix and the voltage differential matrix, the method further includes: Determining a temperature matrix of the target lithium-ion battery pack based on battery temperatures collected by each temperature sensor in the target lithium-ion battery pack at the plurality of sampling times; Based on the voltage matrix, determining a voltage range matrix of each battery cell; Based on the voltage extreme difference matrix and the temperature matrix, determining whether thermal runaway occurs in the target lithium-ion battery pack; If so, output thermal runaway alarm prompt information; If not, jump to performing fuzzy reasoning on the thermal runaway risk of each battery cell based on the state of charge matrix and the voltage difference matrix.

6. The thermal runaway detection method for a lithium-ion battery according to claim 5, characterized in that: The step of determining the voltage range difference matrix of each battery cell based on the voltage matrix includes: Determine the difference between the maximum value and the minimum value of each of the battery cell voltages at any sampling moment in the voltage matrix as the voltage value of the voltage extreme difference at any sampling moment; The voltage values ​​of the voltage extreme differences at each of the sampling moments are calculated to obtain a voltage extreme difference matrix of each of the battery cells.

7. The thermal runaway detection method for a lithium-ion battery according to claim 5, characterized in that: The determining whether thermal runaway occurs in the target lithium-ion battery pack based on the temperature matrix and the voltage extreme difference matrix includes: If any voltage value in the voltage extreme difference matrix is ​​greater than or equal to a preset first voltage threshold, or any temperature value in the temperature matrix is ​​greater than or equal to a preset first temperature threshold, it is determined that thermal runaway occurs in the target lithium-ion battery pack; If the voltage values ​​in the voltage extreme difference matrix are all less than the first voltage threshold, and the temperature values ​​in the temperature matrix are all less than the first temperature threshold, it is determined that thermal runaway does not occur in the target lithium-ion battery pack.

8. A thermal runaway detection device for a lithium-ion battery, characterized in that: The thermal runaway detection device of the lithium-ion battery comprises: A data acquisition module, for determining a voltage matrix of each battery cell and a state-of-charge matrix of the target lithium-ion battery pack based on the battery cell voltages of each battery cell in the target lithium-ion battery pack and the state-of-charge of the target lithium-ion battery pack acquired at multiple sampling moments; the multiple sampling moments are multiple moments within a preset time period after the target lithium-ion battery pack is charged; A data extraction module, used to determine a voltage difference matrix of each battery cell based on the voltage matrix; A fuzzy reasoning module, used for performing fuzzy reasoning on the thermal runaway risk of each of the battery cells based on the state of charge matrix and the voltage differential matrix to obtain a thermal runaway risk scoring matrix for each of the battery cells; A risk determination module is used to determine the thermal runaway risk level of the target lithium-ion battery pack based on the thermal runaway risk scoring matrix.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the thermal runaway detection method for a lithium-ion battery as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the thermal runaway detection method for a lithium-ion battery according to any one of claims 1 to 7 are executed.

Images