A thermal runaway early warning system and early warning method for a lithium ion battery energy storage cabin

Abstract

The application discloses a thermal runaway early warning system and method of a lithium ion battery energy storage cabin, and relates to the technical field of battery safety online monitoring. The thermal runaway early warning system comprises: a plurality of detection devices arranged in the housings of a plurality of battery pack units; the detection device comprises a pressure detection module for monitoring the internal air pressure of the battery pack unit, a sound detection module for monitoring the sound of the opening process of the pressure relief valve of the battery cell, and a particle detection module for monitoring the concentration of nanoparticles in the battery pack unit; a monitoring host installed in the battery cluster, the monitoring host being connected to all detection modules of the plurality of detection devices; the monitoring host is used for receiving the monitoring signals of all detection modules and for alarming externally when an abnormal signal is received. The thermal runaway early warning system and method can monitor each battery pack unit individually, can pre-position the early warning time, and can improve the accuracy of early warning.

Description

Technical Field

[0001] This application relates to the field of online battery safety monitoring technology, specifically to a thermal runaway early warning system and method for a lithium-ion battery energy storage compartment. Background Technology

[0002] According to incomplete statistics, more than 40 fire and explosion accidents involving energy storage power stations have occurred globally in the past 10 years. More than 90% of these accidents involved lithium-ion batteries, and more than 70% of these fires and explosions occurred during or after charging. Therefore, the urgency of effectively monitoring the safety of lithium-ion battery energy storage power stations is particularly prominent.

[0003] To ensure efficient electrochemical reactions within the battery pack units of energy storage power stations, both the battery cells and the battery pack units employ a sealed design. Currently, the sealing rating of the entire lithium-ion battery can reach IP65-67.

[0004] In related technologies, such as Figure 1 As shown, an energy storage compartment typically consists of multiple battery clusters. Each battery cluster contains a BMS monitoring unit and multiple battery pack units. Each battery pack unit integrates several battery cells. When a single battery cell experiences thermal runaway and breaks through the cell's pressure relief valve, the runaway gradually spreads to the surrounding area. Once it reaches a certain level, the temperature and pressure inside the battery pack unit will gradually increase and break through the pack's pressure relief valve. At this point, the heated battery pack unit will transfer the high temperature to the surrounding battery pack units, causing the thermal runaway of the battery pack unit to gradually spread. If intervention is not taken in time, thermal runaway at the energy storage compartment level will occur, resulting in a serious safety accident.

[0005] Specifically, existing thermal runaway detection devices operate on a per-cell basis, with one device installed within each cell cluster. These devices primarily rely on smoke alarms to monitor thermal runaway risk. However, in actual testing, because the battery pack units are strictly sealed, current monitoring methods can only detect thermal runaway risk after the pack's pressure relief valve opens. The time between the opening of the pressure relief valve due to thermal runaway and the occurrence of a fire or explosion is extremely short, leaving limited time for fire and emergency response. Improper handling can easily lead to the spread of thermal runaway and even explosions, resulting in major safety incidents. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this application is to provide a thermal runaway early warning system and method for lithium-ion battery energy storage compartments, which monitors each battery pack unit individually, enabling earlier warnings and improving the accuracy of warnings.

[0007] To achieve the above objectives, the technical solution adopted is: a thermal runaway early warning system for a lithium-ion battery energy storage compartment, wherein the battery energy storage compartment comprises several battery clusters, each battery cluster comprises several battery pack units, each battery pack unit comprises several battery cells, and the thermal runaway early warning system includes:

[0008] Several detection devices are installed inside the housings of several battery pack units; the detection devices include a pressure detection module for monitoring the internal air pressure of the battery pack unit, a sound detection module for monitoring the sound of the cell pressure relief valve opening process, and a particle detection module for monitoring the concentration of nanoparticles inside the battery pack unit.

[0009] The monitoring host is installed inside the battery cluster and is connected to all the detection modules of several detection devices. The monitoring host is used to receive monitoring signals from all the detection modules and to issue an alarm when an abnormal signal is received.

[0010] Based on the above technical solution, the battery cluster and the battery pack unit within the battery cluster are all numbered. The monitoring host is used to provide external alarms and also to provide corresponding numbering information.

[0011] Based on the above technical solution, the detection device also includes a characteristic gas detection module for monitoring the concentration of characteristic gases.

[0012] Based on the above technical solution, the detection device also includes an active flow disruptor for accelerating the flow of characteristic gas inside the battery pack unit, wherein the active flow disruptor is an electric fan.

[0013] This application also discloses an early warning method for the above-mentioned thermal runaway detection system, comprising the following steps:

[0014] S1: The monitoring host collects signals detected by the particle detection module, pressure detection module, and sound detection module at small period intervals of t. The host collects n signals within each large period T; n = T / t; the monitoring host stores all signals collected within the i-th large period T.

[0015] S2: Determine if there is any abnormality in the signal collected within the i-th large cycle T; if yes, go to S3; if no, let i = i + 1 and go to S1.

[0016] S3: Extract and save the abnormal signals collected in the i-th large cycle;

[0017] S4: Determine whether the abnormal signal stored in the i-th large cycle is significantly different from the abnormal signal in the (i-1)-th large cycle; if not, go to S6; if yes, go to S5.

[0018] S5: Increment the number of anomalies by one, and check if the number of anomalies is greater than 3. If yes, go to S6; if no, let i = i + 1, and go to S1.

[0019] S6: The monitoring host sends alarm signals to the outside world.

[0020] Based on the above technical solution, before step S1, it also includes: setting the time lengths T and t, and numbering the battery pack units and battery clusters;

[0021] Step S6 also includes: the monitoring host sending the codes of the potentially hazardous battery pack unit and the battery cluster where the potentially hazardous battery pack unit is located.

[0022] Based on the above technical solution, in step S1, within each large cycle T, at intervals of small cycles t, the electrical signal parameters of the particle detection module, pressure detection module, and sound detection module are collected n times, and the particle concentration electrical signal array is a1, a2, a3, ..., a n The pressure electrical signal array is b1, b2, b3, ..., b n The audio electrical signal array is c1, c2, c3, ..., c n Construct an electrical signal matrix [a1, a2, a3, ..., a...] n b1, b2, b3, ..., b n c1, c2, c3, ..., c n 】

[0023] Based on the above technical solution, in step S2, determining whether the signal collected within the i-th large period T is abnormal includes:

[0024] The monitoring host will display the particle concentration electrical signal array [a1, a2, a3, ..., a...] n The conversion yields a particle concentration array [A1, A2, A3, ..., A...]. n The pressure electrical signal parameters [b1, b2, b3, ..., b] will be used to input the pressure signals. n The conversion yields a pressure value array [B1, B2, B3, ..., B]. n ]; The sound electrical signal array [c1, c2, c3, ..., c n The conversion yields an array of audio signals [C1, C2, C3, ..., C]. n 】;

[0025] The monitoring host is pre-set with corresponding thresholds. When [A1, A2, A3, ..., A...]... n 】,

【b1, b2, b3,...,b n [C1, C2, C3, ..., C] n When each signal in the latter half of the signal is abnormal compared to the corresponding threshold, the signal acquired in the i-th large cycle is determined to be abnormal.

[0026] In step S3, the abnormal signal collected in the i-th large cycle is extracted and saved, including:

[0027] Convert and save the exception signals using a standard format, specifying [A1, A2, A3, ..., A...]. n Save as a particle concentration anomaly array [BA1, BA2, BA3, ..., BA] n ], and select [B1, B2, B3, ..., B] n Save as a pressure anomaly array [BB1, BB2, BB3, ..., BB] n 】, select [C1, C2, C3, ..., C n Save as an array of sound anomalies: [BC1, BC2, BC3, ..., BC] n 】

[0028] Based on the above technical solution, in step S4, determining whether the abnormal signal stored in the i-th large cycle is significantly different from the abnormal signal in the (i-1)-th large cycle includes:

[0029] The i-th large-cycle abnormal signal matrix is ​​[BA1, BA2, BA3, ..., BA...]. n ;BB1, BB2, BB3, ..., BB n ;BC1, BC2, BC3, ..., BC n The (i-1)th large-cycle abnormal signal is constructed into a matrix as [BA1', BA2', BA3', ..., BA...]. n ';BB1', BB2', BB3', ..., BB n ';BC1', BC2', BC3', ..., BC n '】;

[0030] Based on the correlation coefficient formula, calculate [BA] n / 2 BA 1+n / 2 BA 2+n / 2 , ..., BA n 】 respectively with [BA1', BA2', BA3', ..., BA n / 2 '】、

【BA n / 4 '、BA 1+n / 4 '、BA 2+n / 4 '、…、BA 3n / 4The mean U1 of the absolute values ​​of the correlation coefficients between ' and '.

[0031] Calculate [BB] n / 2 BB 1+n / 2 BB 2+n / 2 , ..., BB n 】 respectively with [BB1', BB2', BB3', ..., BB n / 2 '】、

【BB n / 4 '、BB 1+n / 4 '、BB 2+n / 4 '、…、BB 3n / 4 The mean U2 of the absolute values ​​of the correlation coefficients between the two '].

[0032] Calculate [BC] n / 2 BC 1+n / 2 BC 2+n / 2 , ..., BC n 】 respectively with [BC1', BC2', BC3', ..., BC n / 2 '】、

【BC n / 4 '、BC 1+n / 4 '、BC 2+n / 4 '、…、BC 3n / 4 The mean U3 of the absolute values ​​of the correlation coefficients between ' and '.

[0033] If E(U1+U2+U3)≥0.5, then there is no significant difference between the i-th large-cycle abnormal signal and the (i-1)-th large-cycle abnormal signal; if E(U1+U2+U3)<0.5, then there is a significant difference between the i-th large-cycle abnormal signal and the (i-1)-th large-cycle abnormal signal.

[0034] Based on the above technical solution, the detection device also includes a characteristic gas detection module for monitoring the concentration of characteristic gas. In step S1, the monitoring host also collects the characteristic gas concentration signal, and in steps S2 to S5, the characteristic gas concentration signal is added and an abnormal signal is determined.

[0035] The beneficial effects of the technical solution provided in this application include:

[0036] 1. The thermal runaway early warning system for the battery storage compartment of this application uses several detection devices to monitor several battery pack units, realizing the monitoring of battery pack units during the cell thermal runaway stage. Compared with the detection devices of the prior art that monitor the battery clusters during the battery pack unit thermal runaway stage, the thermal runaway early warning system of this application significantly advances the warning time, providing a much earlier warning. Simultaneously, the detection devices integrate multiple monitoring methods, with each device including sound monitoring, pressure monitoring, and nanoparticle concentration monitoring. Compared with the smoke alarm method of the prior art, the detection devices monitor from multiple angles, preventing false alarms and providing more accurate warnings.

[0037] 2. The early warning method of the thermal runaway detection system of this application does not directly and generally issue an alarm after an abnormal signal is detected. Instead, it uses rigorous classification and calculation analysis to determine whether the abnormality of the current large cycle is the same as the abnormality of the previous large cycle. If the two abnormalities are the same, it is determined to be a thermal runaway abnormality. If the two abnormalities are different, the next cycle is verified. If three different abnormalities occur, the same alarm is triggered. The calculation is rigorous, which avoids false alarms from the root and improves the accuracy of the early warning. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of a battery energy storage compartment and its detection device in the prior art;

[0040] Figure 2 A schematic diagram of the detection device provided in Embodiment 1 of this application;

[0041] Figure 3 A schematic diagram of a detection device including a characteristic gas detection module, provided in Embodiment 2 of this application;

[0042] Figure 4 A schematic diagram of a detection device including a characteristic gas detection module and an active flow disruptor provided in Embodiment 3 of this application;

[0043] Figure 5 This is a schematic diagram of the internal structure of the battery pack unit provided in an embodiment of this application;

[0044] Figure 6 This is a schematic diagram of the battery cluster, detection device, and monitoring host according to an embodiment of this application;

[0045] Figure 7 This is a schematic diagram of the battery storage compartment and thermal runaway early warning system according to an embodiment of this application;

[0046] Figure 8 A flowchart of the early warning method provided in the embodiments of this application;

[0047] Reference numerals: 100, Battery storage compartment; 10, Battery cluster; 1, Battery pack unit; 2, Detection device; 3, BMS system; 4, Monitoring host; 5, CAN communication line; 11, Battery cell; 12, Pack casing; 21, Sound detection module; 22, Pressure detection module; 23, Particle detection module; 24, Characteristic gas detection module; 25, Communication interface; 26, Active aerodynamic element. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0049] like Figure 2 , Figure 6 and Figure 7 As shown, this application discloses an embodiment of a thermal runaway early warning system for a lithium-ion battery energy storage compartment, which is used to solve the problem of untimely early warning when a traditional battery energy storage compartment experiences a risk of thermal runaway.

[0050] Specifically, the battery storage compartment 100 includes several battery clusters 10, each battery cluster 10 includes several battery pack units 1, and each battery pack unit 1 includes several battery cells 11. Each battery pack unit 1 has a pack pressure relief valve, and each battery cell 11 has a cell pressure relief valve. The process of thermal runaway risk in the battery storage compartment 100 is as follows: first, a certain battery cell 11 experiences thermal runaway, which then forces open the cell pressure relief valve; as the cell pressure relief valve is forced open, the pressure inside the battery pack unit continues to increase until the pack pressure relief valve of the battery pack unit is forced open. At the same time, during the process of thermal runaway of the battery cell, the temperature of the individual battery cell 11 continues to rise and gradually spreads to the surrounding area, causing the surrounding battery cells 11 to experience the same thermal runaway. Finally, the entire battery pack unit 1 is affected, and the thermal runaway spreads to the entire battery storage compartment 100.

[0051] The thermal runaway early warning system includes a monitoring host 4 and several detection devices 2. The detection devices 2 are installed inside the housings of several battery pack units 1 to monitor the thermal runaway stage of the battery cells.

[0052] The detection device 2 includes a pressure detection module 22, a sound detection module 21 and a particle detection module 23. The pressure detection module 22 is used to monitor the internal air pressure of the battery pack unit 1. When the battery cell 11 in the battery pack unit 1 is at risk of thermal runaway, the air pressure in the battery pack unit 1 gradually increases.

[0053] The sound detection module 21 is used to monitor the sound emitted during the opening process of the cell pressure relief valve. When the cell 11 in the battery pack unit 1 is at risk of thermal runaway, the sound emitted during the opening process of the cell pressure relief valve will be captured.

[0054] The particle detection module 23 is used to monitor the concentration of nanoparticles inside the battery pack unit 1. When the cell 11 inside the battery pack unit 1 is at risk of thermal runaway, the overheating of the cell 11 and its surrounding structure will cause the concentration of nanoparticles to gradually increase. Since nanoparticles are difficult to detect by traditional smoke detection methods, a specific particle detection module 23 is required for detection. As the concentration of nanoparticles inside the battery pack unit 1 gradually increases, the particle detection module 23 determines the risk of thermal runaway based on the increased concentration of nanoparticles.

[0055] The monitoring host 4 is installed inside the battery cluster 10, with one monitoring host 4 corresponding to each battery cluster 10. The monitoring host 4 is connected to all the detection modules of the several detection devices 2 via CAN communication lines 5. The monitoring host 4 is used to receive monitoring signals from all detection modules and to issue an alarm when an abnormal signal is received. Specifically, the monitoring host 4 is installed in the BMS system 3 of the battery cluster.

[0056] The thermal runaway early warning system of the battery storage compartment 100 of this application includes several detection devices 2 that monitor several battery pack units 1, enabling monitoring of the battery pack unit 1 during the cell thermal runaway stage. Compared with the detection devices of existing technologies that monitor the battery cluster during the thermal runaway stage of the battery pack unit 1, the thermal runaway early warning system of this application significantly advances the warning time, providing a much earlier warning. Simultaneously, the detection devices 2 integrate multiple monitoring methods, with each device including sound monitoring, pressure monitoring, and nanoparticle concentration monitoring. Compared with the smoke alarm method in existing technologies, the detection devices 2 monitor from multiple angles, preventing false alarms and providing more accurate warnings.

[0057] Specifically, the pressure detection module 22 uses a barometric pressure sensor, the sound detection module 21 uses a sound sensor, and the particle detection module 23 uses a laser emitter and a light receiver sensor. The laser emitter is used to emit laser light, and the light receiver sensor senses the laser light intensity on the side of the laser. The laser emitter and the light receiver sensor are used together. As the concentration of nanoparticles changes, the laser light intensity sensed by the light receiver sensor changes accordingly. The concentration of nanoparticles is monitored by the change in light intensity.

[0058] Furthermore, the battery cluster 10 and the battery pack unit 1 within the battery cluster 10 are all numbered. While the monitoring host 4 is issuing an alarm, it also sends out the numbering information, which not only alerts the staff to the risk of thermal runaway, but also tells the staff which battery pack unit 1 in which battery cluster 10 has a problem, thus buying more time for fire rescue or maintenance.

[0059] like Figure 3 As shown in Embodiment 2, the thermal runaway early warning system for the lithium-ion battery energy storage compartment, based on Embodiment 1, further includes a characteristic gas detection module 24 for monitoring the concentration of characteristic gases in the detection device 2. Specifically, the characteristic gas refers to at least one of VOC, CO, CO2, H2, HF, and CH4, preferably VOC. Preferably, the characteristic gas detection module 24 employs a semiconductor characteristic gas sensor. The semiconductor characteristic gas sensor monitors the concentration of the characteristic gas to determine whether thermal runaway has occurred.

[0060] In other embodiments, the characteristic gas detection module 24 is an NDIR optical characteristic gas detection sensor used to detect at least one of VOC, CO, CO2, H2, HF and CH4, and is respectively disposed outside and inside the detection device along with the pressure detection module 22, the sound detection module 21 and the particle detection module 23. It can be detachably connected and can be flexibly arranged in the small space inside the battery pack unit.

[0061] like Figure 4 As shown in Embodiment 3, the thermal runaway early warning system for the lithium-ion battery energy storage compartment, based on Embodiment 1, further includes an active flow disruptor 26 in the detection device 2 to accelerate the flow of characteristic gas inside the battery pack unit. The active flow disruptor 26 is an electric fan. The electric fan can accelerate the gas flow inside the battery pack unit, making the characteristic gas distribution more uniform and enabling faster and more accurate measurement of the concentration of characteristic gas and nanoparticles.

[0062] Preferably, the pressure detection module 22, sound detection module 21, particle detection module 23, and characteristic gas detection module 24 share a single communication interface 25, which is located on the side wall of the pack casing 12 of the battery pack unit 1. The pack casing 12 of the battery pack unit 1 is made of an insulating and corrosion-resistant material.

[0063] This application also discloses an embodiment of an early warning method for the above-mentioned thermal runaway detection system, comprising the following steps:

[0064] S1: The monitoring host 4 collects signals detected by the particle detection module 23, pressure detection module 22, and sound detection module 21 at small period intervals of t. Within each large period T, n signals are collected; n = T / t; where n is a positive multiple of four. The monitoring host 4 stores all signals collected within the i-th large period T (including particle concentration signal, pressure signal, and sound signal).

[0065] S2: Calculate and determine whether the signal collected within the i-th large cycle T is abnormal; if yes, go to S3; if no, let i = i + 1, and go to S1 to determine the next large cycle. When determining abnormalities, a one-to-one comparison principle is adopted.

[0066] S3: Extract and save the abnormal signals collected within the i-th large cycle T;

[0067] S4: Determine whether the abnormal signal stored in the i-th large cycle T is significantly different from the abnormal signal in the (i-1)-th large cycle; if not, it indicates that there are two consecutive identical differences, and an alarm can be triggered directly, proceeding to S6; if yes, it indicates that there are two different differences, and proceeding to S5.

[0068] S5: Increment the number of anomalies by one, and determine whether the number of anomalies is greater than or equal to 3. If yes, it means that the system has accumulated three different anomalies, and go to S6. If no, let i = i + 1, and go to S1 to continue the observation of the next large cycle.

[0069] S6: Monitoring host 4 sends alarm signals to external systems.

[0070] The early warning method of the thermal runaway detection system of this application stores and compares various signals related to thermal runaway. During the comparison, all abnormal signals are summarized through rigorous analysis. If the summarized signal is abnormal and the same abnormality occurs twice consecutively, an alarm is triggered. If the summarized signal is abnormal and more than three different abnormalities occur consecutively, an alarm is also triggered. The early warning method of this application is rigorous, reliable, and accurate, and avoids false alarms from the perspective of process logic.

[0071] Furthermore, prior to step S1, the process includes: defining the time length of period T and numbering the battery pack units 1.

[0072] Step S6 also includes: the monitoring host 4 sending the code of the hidden danger battery pack unit 1 and the code of the battery cluster where the hidden danger battery pack unit 1 is located.

[0073] The early warning method of this application not only alerts staff to the risk of thermal runaway, but also tells staff which battery pack unit 1 of which battery cluster 10 has a problem, which can buy more time for fire rescue or repair.

[0074] Furthermore, in step S1, within each large period T, at intervals of small periods t, the electrical signal parameters of the particle detection module 23, pressure detection module 22, and sound detection module 21 are collected n times, and the particle concentration electrical signal parameters are a1, a2, a3, ..., a n The pressure electrical signal parameters are b1, b2, b3, ..., b n The audio electrical signal parameters are c1, c2, c3, ..., c n Construct the electrical signal parameter matrix [a1, a2, a3, ..., a...] n b1, b2, b3, ..., b n c1, c2, c3, ..., c n 】

[0075] In step S2, it is determined whether the signal acquired within the i-th large period T is abnormal, including:

[0076] The monitoring host (4) transmits the particle concentration electrical signal array [a1, a2, a3, ..., a...] n The conversion yields a particle concentration array [A1, A2, A3, ..., A...]. n The pressure electrical signal parameters [b1, b2, b3, ..., b] will be used to input the pressure signals. n The conversion yields a pressure value array [B1, B2, B3, ..., B]. n ]; The sound electrical signal array [c1, c2, c3, ..., c n The conversion yields an array of audio signals [C1, C2, C3, ..., C]. n 】;

[0077] The monitoring host (4) is pre-set with corresponding thresholds. When [A1, A2, A3, ..., A...] n 】,

【b1, b2, b3,...,b n [C1, C2, C3, ..., C] n When each signal in the latter half of the signal is abnormal compared to the corresponding threshold, the signal acquired in the i-th large cycle is determined to be abnormal.

[0078] In step S3, the abnormal signal collected in the i-th large cycle is extracted and saved, including:

[0079] Convert and save the exception signals using a standard format, specifying [A1, A2, A3, ..., A...]. n Save as a particle concentration anomaly array [BA1, BA2, BA3, ..., BA] n ], and select [B1, B2, B3, ..., B] n Save as a pressure anomaly array [BB1, BB2, BB3, ..., BB] n 】, select [C1, C2, C3, ..., Cn Save as an array of sound anomalies: [BC1, BC2, BC3, ..., BC] n 】

[0080] Specifically as follows:

[0081] Determine if there are any anomalies in the particle concentration signal during the i-th large period, including:

[0082] The monitoring host 4 will collect the particle concentration electrical signal array [a1, a2, a3, ..., a] from the particle detection module 23. n Convert the values ​​to the corresponding particle concentrations under these parameters to obtain the particle concentration array [A1, A2, A3, ..., A...]. n 】;

[0083] The particle concentration threshold Y1 is preset in the monitoring host 4;

[0084] When [A1, A2, A3, ..., A n Each signal in the latter half of [A] is greater than the particle concentration threshold Y1, that is, when [A] n / 2 A 1+n / 2 A 2+n / 2 A n When the value of each particle concentration parameter in [A1, A2, A3, ..., A] is greater than the particle concentration threshold Y1, determine [A1, A2, A3, ..., A]. n The entire set of particle concentration signals is abnormal, and appears in the standard format [BA1, BA2, BA3, ..., BA]. n Storing abnormal particle concentration signals. Specifically, the standard format stores parameters with the same length and relative size, performs normalization processing, does not affect the original pattern of the data, and facilitates subsequent analysis.

[0085] Determine if there are any abnormalities in the pressure signal within the i-th large cycle, including:

[0086] The monitoring host 4 will collect the pressure electrical signal array [b1, b2, b3, ..., b] from the pressure detection module. n Convert the values ​​to the corresponding pressure values ​​under the given parameters to obtain the internal pressure value array [B1, B2, B3, ..., B] of the battery pack unit (1). n 】;

[0087] The particle concentration threshold Y2 is preset in the monitoring host 4;

[0088] When [B1, B2, B3, ..., B n When each signal in the latter half of the sequence is greater than the pressure threshold Y2, that is, [B] n / 2 B 1+n / 2 B 2+n / 2 B nIf each pressure value in the sequence is greater than the pressure threshold Y2, the pressure signal is determined to be abnormal, and displayed in the standard format [BB1, BB2, BB3, ..., BB]. n Storage pressure abnormality signal.

[0089] Determine if there are any abnormalities in the sound signal within the i-th large period, including:

[0090] The monitoring host 4 will collect the audio electrical signal array [c1, c2, c3, ..., c] from the audio detection module. n First, perform preliminary noise filtering, then convert it into the corresponding sound parameters under these parameters, resulting in a sound parameter array [C1, C2, C3, ..., C]. n 】;

[0091] When [C1, C2, C3, ..., C n When each signal in the latter half of the sequence is greater than the pressure threshold Y2, that is, when the sound parameter array [C]... n / 2 C 1+n / 2 C 2+n / 2 ..., C n When any sound signal is abnormal, it is displayed in the standard format [BC1, BC2, BC3, ..., BC]. n Stores abnormal sound signals.

[0092] Specifically, the characteristics of sound include time-domain features and frequency-domain features. The time-domain features include energy parameters and time threshold peak values, while the frequency-domain features include frequency peak values ​​and frequency standard deviations. The monitoring host 4 sets thresholds for the energy parameters, time threshold peak values, frequency peak values, and frequency standard deviations of the sound. When any two characteristics of any sound exceed their respective thresholds, the sound is judged to be abnormal.

[0093] The early warning method of this application performs normalization processing on the sound parameter array in a standard format after an abnormal signal is detected, which facilitates subsequent calculation and analysis.

[0094] Furthermore, in step S4, determining whether the abnormal signal stored in the i-th large cycle is significantly different from the abnormal signal in the (i-1)-th large cycle includes:

[0095] The i-th large-cycle abnormal signal matrix is ​​[BA1, BA2, BA3, ..., BA...]. n ;BB1, BB2, BB3, ..., BB n ;BC1, BC2, BC3, ..., BC n 】

[0096] The (i-1)th large-cycle abnormal signal is constructed into a matrix as [BA1', BA2', BA3', ..., BA1']. n ';BB1', BB2', BB3', ..., BB n';BC1', BC2', BC3', ..., BC n '】

[0097] In this application, U1, U2, and U3 are calculated as follows:

[0098] Calculate [BA] n / 2 BA 1+n / 2 BA 2+n / 2 , ..., BA n 】 respectively with [BA1', BA2', BA3', ..., BA n / 2 '】、

【BA n / 4 '、BA 1+n / 4 '、BA 2+n / 4 '、…、BA 3n / 4 The mean U1 of the absolute values ​​of the correlation coefficients between ' and '.

[0099]

[0100] Calculate [BB] n / 2 BB 1+n / 2 BB 2+n / 2 , ..., BB n 】 respectively with [BB1', BB2', BB3', ..., BB n / 2 '】、

【BB n / 4 '、BB 1+n / 4 '、BB 2+n / 4 '、…、BB 3n / 4 The mean U2 of the absolute values ​​of the correlation coefficients between the two '].

[0101]

[0102] Calculate [BC] n / 2 BC 1+n / 2 BC 2+n / 2 , ..., BC n 】 respectively with [BC1', BC2', BC3', ..., BC n / 2 '】、

【BC n / 4 '、BC 1+n / 4 '、BC 2+n / 4 '、…、BC 3n / 4 The mean U3 of the absolute values ​​of the correlation coefficients between ' and '.

[0103]

[0104] If E(U1+U2+U3)≥0.5, then there is no significant difference between the i-th large-cycle abnormal signal and the (i-1)-th large-cycle abnormal signal; if E(U1+U2+U3)<0.5, then there is a significant difference between the i-th large-cycle abnormal signal and the (i-1)-th large-cycle abnormal signal.

[0105] The early warning method of this application does not directly issue an alarm in a general way after an abnormal signal appears. Instead, it uses rigorous classification and calculation analysis to determine whether the abnormality of the current large cycle is the same as the abnormality of the previous large cycle. If the two abnormalities are the same, it is determined to be a thermal runaway abnormality. If the two abnormalities are different, the next cycle is verified. If three different abnormalities occur, the same alarm is triggered. The calculation is rigorous and avoids false alarms from the root.

[0106] In one embodiment, the detection device 2 further includes a characteristic gas detection module for monitoring the concentration of characteristic gas. In step S1, the monitoring host 4 also collects the characteristic gas concentration signal, and in steps S2 to S5, the characteristic gas concentration signal is added and an abnormal signal is determined.

[0107] Specifically, let's take a characteristic gas such as VOC as an example:

[0108] In step S1, characteristic gas electrical signal arrays d1, d2, d3, ..., d are acquired. n .

[0109] In step S2, the monitoring host 4 transmits the characteristic gas electrical signal array [d1, d2, d3, ..., d... n The conversion yields a characteristic gas concentration array [D1, D2, D3, ..., D...]. n The monitoring host 4 is pre-set with corresponding thresholds. When [D1, D2, D3, ..., D...], the thresholds are set accordingly. n If each signal in the latter half of the sequence is abnormal when compared to the corresponding threshold, the entire set of signals acquired in the i-th large cycle is determined to be abnormal.

[0110] In step S3, the abnormal signals are converted and saved using a standard format, and [D1, D2, D3, ..., D...] are stored in the format specified by the standard format. n Save as a particle concentration anomaly array [BD1, BD2, BD3, ..., BD] n 】;

[0111] Following the calculation steps above, U4 is calculated.

[0112] If E(U1+U2+U3+U4)≥0.5, then there is no significant difference between the i-th large-cycle abnormal signal and the (i-1)-th large-cycle abnormal signal; if E(U1+U2+U3+U4)<0.5, then there is a significant difference between the i-th large-cycle abnormal signal and the (i-1)-th large-cycle abnormal signal, further improving the accuracy of the alarm.

[0113] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0114] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0115] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for early warning of thermal runaway in a lithium-ion battery storage compartment, wherein the battery storage compartment (100) comprises a plurality of battery clusters (10), each battery cluster (10) comprises a plurality of battery pack units (1), and each battery pack unit (1) comprises a plurality of battery cells (11), characterized in that, The thermal runaway early warning system includes a monitoring host (4) and several detection devices (2), each of which is installed inside the housing of a number of battery pack units (1). Each detection device (2) includes a pressure detection module (22) for monitoring the internal air pressure of the battery pack unit (1), a sound detection module (21) for monitoring the sound of the cell pressure relief valve opening process, and a particle detection module (23) for monitoring the concentration of nanoparticles inside the battery pack unit (1). The monitoring host (4) is installed inside the battery cluster (10) and is connected to all the detection modules of the several detection devices (2). The monitoring host (4) receives monitoring signals from all detection modules and issues an alarm when an abnormal signal is received. The thermal runaway early warning method includes the following steps: S1: The monitoring host (4) collects the signals detected by the particle detection module (23), pressure detection module (22) and sound detection module (21) at small period intervals of t. The signal is collected n times in each large period T; n=T / t; The monitoring host (4) stores all the signals collected in the i-th large period T. S2: Determine if there is any abnormality in the signal collected within the i-th large cycle T; if so, go to S3; If not, let i = i + 1, and go to S1; S3: Extract and save the abnormal signals collected in the i-th large cycle; S4: Determine whether the abnormal signal stored in the i-th large cycle is significantly different from the abnormal signal in the (i-1)-th large cycle; if not, go to S6; if yes, go to S5. S5: Increment the number of anomalies by one, and check if the number of anomalies is greater than 3. If yes, go to S6; otherwise, set i = i + 1 and go to S1. S6: The monitoring host (4) sends alarm signals to the outside world.

2. The thermal runaway early warning method for a lithium-ion battery energy storage chamber as described in claim 1, characterized in that, Before step S1, the process also includes: setting the time lengths T and t, and numbering the battery pack unit (1) and battery cluster (10). In step S6, the monitoring host (4) also sends the codes of the hidden danger battery pack unit (1) and the battery cluster (10) where the hidden danger battery pack unit (1) is located.

3. The thermal runaway early warning method for a lithium-ion battery energy storage chamber as described in claim 1, characterized in that, In step S1, within each large period T, at intervals of small periods t, the electrical signal parameters of the particle detection module (23), pressure detection module (22), and sound detection module (21) are collected n times. The particle concentration electrical signal array is a1, a2, a3, ..., a n The pressure electrical signal array is b1, b2, b3, ..., b n The audio electrical signal array is c1, c2, c3, ..., c n Construct an electrical signal matrix [a1, a2, a3, ..., a...] n b1, b2, b3, ..., b n c1, c2, c3, ..., c n 】 4. The thermal runaway early warning method for a lithium-ion battery energy storage chamber as described in claim 3, characterized in that, In step S2, it is determined whether the signal acquired within the i-th large period T is abnormal, including: The monitoring host (4) transmits the particle concentration electrical signal array [a1, a2, a3, ..., a...] to the control unit. n The conversion yields a particle concentration array [A1, A2, A3, ..., A...]. n The pressure electrical signal parameters [b1, b2, b3, ..., b] will be transferred. n The conversion yields a pressure value array [B1, B2, B3, ..., B]. n ]; The sound electrical signal array [c1, c2, c3, ..., c n The conversion yields an array of audio signals [C1, C2, C3, ..., C]. n 】; The monitoring host (4) pre-sets the corresponding thresholds. When [A1, A2, A3, ..., A...] n 】,【b1, b2, b3,...,b n [C1, C2, C3, ..., C] n If each signal in the latter half of the sequence is abnormal when compared with the corresponding threshold, the signal acquired in the i-th large cycle is determined to be abnormal. In step S3, the abnormal signal collected in the i-th large cycle is extracted and saved, including: Convert and save the exception signals using a standard format, specifying [A1, A2, A3, ..., A...]. n Save as a particle concentration anomaly array [BA1, BA2, BA3, ..., BA] n ], and select [B1, B2, B3, ..., B] n Save as a pressure anomaly array [BB1, BB2, BB3, ..., BB] n 】, select [C1, C2, C3, ..., C n Save as an array of sound anomalies: [BC1, BC2, BC3, ..., BC] n 】 5. A thermal runaway early warning method for a lithium-ion battery energy storage chamber as described in claim 4, characterized in that, In step S4, it is determined whether the abnormal signal stored in the i-th large cycle is significantly different from the abnormal signal in the (i-1)-th large cycle, including: The i-th large-cycle abnormal signal matrix is ​​[BA1, BA2, BA3, ..., BA...]. n ; BB1, BB2, BB3,…,BB n ; BC1, BC2, BC3,…,BC n The (i-1)th large-cycle abnormal signal is constructed into a matrix as [BA1', BA2', BA3', ..., BA1']. n ';BB1', BB2', BB3', ..., BB n ';BC1', BC2', BC3', ..., BC n '】; Based on the correlation coefficient formula, calculate [BA] n / 2 BA 1+n / 2 BA 2+n / 2 , ..., BA n 】 respectively with [BA1', BA2', BA3', ..., BA n / 2 '】、【BA n / 4 '、BA 1+n / 4 '、BA 2+n / 4 '、…、BA 3n / 4 The mean U1 of the absolute values ​​of the correlation coefficients between ' and '. Calculate [BB] n / 2 BB 1+n / 2 BB 2+n / 2 , ..., BB n 】 respectively with [BB1', BB2', BB3', ..., BB n / 2 '】、【BB n / 4 '、BB 1+n / 4 '、BB 2+n / 4 '、…、BB 3n / 4 The mean U2 of the absolute values ​​of the correlation coefficients between ' and '. Calculate [BC] n / 2 BC 1+n / 2 BC 2+n / 2 , ..., BC n 】 respectively with [BC1', BC2', BC3', ..., BC n / 2 '】、【BC n / 4 '、BC 1+n / 4 '、BC 2+n / 4 '、…、BC 3n / 4 The mean U3 of the absolute values ​​of the correlation coefficients between ' and '. If E(U1+U2+U3)≥0.5, then there is no significant difference between the i-th large-cycle abnormal signal and the (i-1)-th large-cycle abnormal signal. If E(U1+U2+U3)<0.5, then the i-th large-cycle abnormal signal is significantly different from the (i-1)-th large-cycle abnormal signal.

6. The thermal runaway early warning method for a lithium-ion battery energy storage chamber as described in claim 1, characterized in that: The detection device (2) also includes a characteristic gas detection module (24) for monitoring the concentration of characteristic gas. In step S1, the monitoring host (4) also collects the characteristic gas concentration signal, and in steps S2 to S5, the characteristic gas concentration signal is added and abnormal signal is determined.

7. A thermal runaway early warning method for a lithium-ion battery energy storage chamber as described in claim 1, characterized in that: The detection device (2) also includes an active flow deflector (26) for accelerating the flow of characteristic gas inside the battery pack unit (1), the active flow deflector (26) being an electric fan.

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