Multi-stage pre-warning method and system for thermal runaway of energy storage lithium ion battery

Abstract

The application discloses a multi-stage early warning method and system for thermal runaway of energy storage lithium ion batteries, and belongs to the technical field of lithium battery safety. The method comprises the following steps: constructing a three-dimensional closed-loop optical fiber monitoring network, wherein the distributed temperature sensing optical fiber is laid along the path of the side surface, the top surface and then the side surface of the battery, and actively avoids the area not less than 20mm from the center of the safety valve and not less than 15mm from the tab welding point; obtaining the centimeter-level temperature field data through an optical fiber demodulator; the processor calculates the temperature rise rate R and the temperature gradient G based on the temperature field in real time, and executes the composite condition early warning. The trigger condition of the early warning is: when the temperature T of any measuring point reaches the preset threshold value, and the temperature rise rate R or the temperature gradient G reaches the preset threshold value, and the proportion of all abnormal areas exceeding the threshold value in the total laying length of the optical fiber is not less than 30%, the corresponding level of early warning is triggered. The application can effectively filter local benign fluctuations, greatly reduce the false positive rate, and realize earlier and more accurate early prediction of thermal runaway.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of lithium battery safety, and particularly relates to a multi-stage early warning method with high precision and low false alarm rate for thermal runaway of large-capacity energy storage lithium ion batteries, and an intelligent early warning system for implementing the method. BACKGROUND

[0002] With the increasing urgency of global energy transformation and climate change, electrochemical energy storage systems centered on lithium ion batteries have been widely used in power grid peak shaving, renewable energy grid connection, smart grids, electric transportation, and data centers, as key support. Lithium ion batteries, with their high energy density, long cycle life, and increasing cost-effectiveness, have become the mainstream choice for large-scale energy storage.

[0003] However, the intrinsic safety of lithium ion batteries has always been a serious challenge to their large-scale application. Under the influence of extreme working conditions, mechanical abuse, electrical abuse, or internal defects, a single battery (cell) may experience thermal runaway. Thermal runaway is a chain reaction process, often accompanied by a rapid rise in internal battery temperature, gas production, expansion, and even rupture of high-temperature and high-pressure gas and flames, which quickly spread to adjacent cells, forming a catastrophic chain combustion and explosion, posing a significant threat to human life and property. Therefore, how to achieve early warning, accurate positioning, and rapid intervention of lithium ion battery thermal runaway has become the core focus of current battery technology and energy storage system safety research.

[0004] Currently, for battery thermal runaway warning, it mainly relies on the monitoring of key battery parameters, including temperature, voltage, current, gas composition, and expansion force, etc. Among them, temperature is one of the most direct and important thermal runaway characterization parameters. Distributed optical fiber sensing technology, due to its inherent electrical insulation, anti-electromagnetic interference capability, high temperature resistance, and unique advantage of continuous measurement along the line, is considered an ideal choice for battery temperature monitoring, and has been preliminarily applied in some battery management systems (BMS).

[0005] However, the existing distributed optical fiber battery thermal runaway warning scheme still has significant limitations:

[0006] First, the robustness of optical fiber deployment is insufficient. In many existing schemes, optical fibers are usually laid on the surface of the battery in a simple straight line or planar winding manner. This layout does not fully consider the extreme physical environment when thermal runaway occurs. In particular, when the internal pressure of the battery is too high, causing the safety valve to open, the high-temperature and high-pressure (up to 800°C or more, with a flow rate exceeding 200m / s) gas and flames ejected will cause devastating impact on the optical fiber, easily causing the optical fiber to melt, resulting in a loss of monitoring capability at the most critical warning moment, causing the failure of the entire warning system.

[0007] Second, the accuracy and anti-interference ability of the early warning criterion are poor. Most existing early warning systems still rely on single parameter fixed threshold judgment (such as temperature exceeding a certain set value to alarm). This "point-based" judgment method is easily affected by local temperature fluctuations, environmental temperature changes, battery charging and discharging heating or accidental contact failure under normal working conditions in actual operation, thereby generating a large number of false alarms, and the false alarm rate can be as high as 35%. This frequent false alarm of the "Wolf is coming" type not only reduces the system reliability, but also increases unnecessary operation and maintenance costs and emergency response burden, and cannot effectively distinguish between real potential thermal runaway risks and harmless local hot spots.

[0008] Third, the dynamic perception of fault development trend is insufficient. Existing systems often fail to fully utilize the large amount of spatial temperature data provided by distributed optical fibers, only focus on the instantaneous value of a certain point, lack of in-depth analysis and utilization of the dynamic evolution characteristics of temperature field in space and time (such as hot spot formation, diffusion rate and direction), and thus it is difficult to warn at the sprouting stage of the fault, missing the best intervention opportunity.

[0009] Therefore, it is urgent to develop a new type of battery thermal runaway early warning method and system which can overcome the above technical problems, so as to improve the reliability, accuracy and timeliness of early warning, and fundamentally ensure the safe operation of energy storage system. SUMMARY

[0010] The present application aims to solve the problems in the prior art that the distributed optical fiber sensor is easily damaged by thermal runaway jet flow due to improper deployment, resulting in monitoring failure, and the false alarm rate is high due to the dependence of early warning criterion on single point threshold, thereby providing a more reliable and accurate multi-level early warning method and system for thermal runaway of energy storage lithium ion battery.

[0011] To achieve the above-mentioned purpose, the present application provides a multi-level early warning method for thermal runaway of energy storage lithium ion battery, comprising the following steps:

[0012] S1: constructing a three-dimensional monitoring network: adopting a distributed temperature sensing optical fiber, laying along at least one side, top and another side of a single energy storage lithium ion battery to form a closed loop monitoring path;

[0013] Wherein, the monitoring path maintains a radial distance of not less than 20mm from the center of the safety valve of the battery, and maintains a distance of not less than 15mm from the welding point of the battery tab of the battery;

[0014] S2: obtaining and analyzing temperature field data: obtaining real-time temperature data of multiple measuring points on the distributed temperature sensing optical fiber through a fiber demodulator to form a centimeter-level resolution battery surface temperature field;

[0015] S3: calculating dynamic evolution parameters: based on the temperature field data, calculating the temperature rise rate R of each measuring point and the temperature gradient G along the length direction of the optical fiber in real time;

[0016] S4: executing compound condition early warning: when both of the following a) and b) are met, triggering the early warning of the corresponding level:

[0017] a) the real-time temperature T of at least one measuring point reaches the preset temperature threshold Tx, and the real-time temperature rise rate R reaches the preset rate threshold Rx, or the real-time temperature gradient G reaches the preset gradient threshold Gx;

[0018] b) the abnormal area whose temperature, temperature rise rate or temperature gradient exceeds the respective preset threshold accounts for no less than 30% of the total laying length of the distributed temperature sensing optical fiber (2).

[0019] Preferably, in step S3, the temperature rise rate R is obtained by time derivation dT / dt of the temperature of a single measuring point, and the temperature gradient G is obtained by spatial derivation dT / dL of the temperature field data along the laying path of the optical fiber.

[0020] Preferably, it further comprises synchronously collecting the expansion force signal of the battery, the gas concentration signal after the safety valve is opened, and the voltage signal of the battery as auxiliary early warning judgment basis.

[0021] Preferably, the early warning is multi-level early warning, including at least one-level, two-level and three-level early warning;

[0022] Wherein, the preset temperature threshold Tx, rate threshold Rx and gradient threshold Gx are divided according to the early warning level x, x is 1, 2, 3, and satisfies T1<T2<T3, R1≤R2<R3, G1<G2<G3, so as to distinguish different fault severity.

[0023] Preferably, between step S2 and step S3, it further comprises: adopting sliding average filtering to the real-time temperature data obtained by the optical fiber demodulator for noise reduction processing, and the window width is 20 sampling points.

[0024] Correspondingly, the application also provides a multi-level early warning system for thermal runaway of energy storage lithium ion battery, which executes the above method, comprising:

[0025] A distributed temperature sensing optical fiber: configured to be laid along at least one side, top and another side of a single energy storage lithium ion battery, forming a closed loop monitoring path; the monitoring path maintains a radial distance of no less than 20mm from the center of the safety valve of the battery, and maintains a distance of no less than 15mm from the welding point of the battery tab of the battery;

[0026] an optical fiber demodulator optically connected to the distributed temperature sensing fiber, configured to obtain centimeter-level real-time temperature data of a plurality of measuring points on the fiber based on the Brillouin scattering principle;

[0027] a processor electrically connected to the optical fiber demodulator, the processor being configured to:

[0028] receive the real-time temperature data to construct a battery surface temperature field;

[0029] based on the temperature field data, calculate the temperature rise rate R of each measuring point and the temperature gradient G along the length direction of the fiber in real time;

[0030] when the real-time temperature T and the real-time temperature rise rate R or the real-time temperature gradient G of at least one measuring point reach the respective preset threshold, and the total length of all abnormal regions exceeding the corresponding threshold accounts for not less than 30% of the total laying length of the fiber, output a pre-warning signal.

[0031] Preferably, further comprising:

[0032] a pressure sensor arranged between a metal clamp for clamping the battery and the surface of the battery, for monitoring the swelling force of the battery;

[0033] a gas sensor connected to the top of the safety valve through a gas extraction pipe, for monitoring the gas concentration;

[0034] wherein the pressure sensor and the gas sensor are electrically connected to the processor.

[0035] Preferably, the distributed temperature sensing fiber comprises a fiber core, an aramid woven layer, a Teflon outer sheath, and a built-in metal reinforcing wire, and the overall temperature resistance is not less than 300℃.

[0036] Preferably, further comprising a plurality of polyimide high-temperature insulation adhesive tapes for fixing the distributed temperature sensing fiber, the tapes fixing the fiber at an interval of 5cm along the fiber laying path, and each fixing point can withstand a pulling force of not less than 50N.

[0037] Preferably, the total laying length L of the distributed temperature sensing fiber on the surface of a single battery and the number N of surface temperature measuring points satisfy the relationship:

[0038] L≈5cm×N, and N≥12.

[0039] Compared with the prior art, the present application fundamentally solves the fatal defect that the optical fiber sensor is easily fused by high-temperature high-speed jet or arc in the initial stage of thermal runaway by the innovative "side surface→top surface→side surface" three-dimensional closed-loop fiber layout and actively avoiding a specific safety distance from the safety valve and the battery tab, ensuring the survival ability of the monitoring system and the continuous reliability of data acquisition at the most critical moment. On the basis of high-reliable data, the present application further discards the traditional single-point threshold model and innovatively introduces a composite early warning criterion combining "physical quantity threshold" and "space abnormal area proportion", which can effectively distinguish the dangerous heat source with spreading trend from the benign and isolated local hot spot in the spatial dimension, thereby accurately identifying the true signs of early thermal runaway and greatly reducing the high false alarm rate caused by normal working condition fluctuations. It is the organic combination of high-reliable data acquisition and high-accuracy dynamic analysis capability that enables the present application to capture the subtle features of heat abnormal aggregation and diffusion in the extremely early stage of the thermal runaway fault chain (such as abnormal expansion force, valve opening), even before the dramatic changes of macroscopic parameters such as voltage and temperature, thereby enabling the present application to issue an early warning 200 seconds or more in advance than the traditional method, which provides valuable response time for taking emergency intervention measures.

[0040] In summary, the present application solves the three industry pain points of sensor survivability, early warning accuracy and response timeliness through systematic scheme design, and provides more comprehensive and reliable technical support for the intrinsic safety of energy storage systems. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 is a schematic diagram of the instrument equipment in the embodiment of the present application;

[0042] Figure 2 is a layout schematic diagram of the battery thermal runaway test system in the embodiment of the present application;

[0043] Figure 3 is a schematic diagram of the battery surface fiber layout in the embodiment of the present application;

[0044] Figure 4 is a time sequence diagram of multi-dimensional signals and temperature cross-sectional diagrams at different times in the process of battery thermal runaway;

[0045] Figure 5 is a spatiotemporal evolution graph of multiple physical quantities measured by the distributed optical fiber sensor in the process of thermal runaway experiment. Specifically, it includes:

[0046] Figure 5 (a) in the above is a 2D pseudo-color graph of the evolution of the battery surface temperature field along the fiber position and time;

[0047] Figure 5 (b) in the above is the 2D pseudo-color graph of the evolution of the battery surface temperature field along the fiber position and time in the process of thermal runaway caused by abnormal expansion force (t Abn), safety valve opening (t v ), and internal short circuit (t ISC ) three key moments;

[0048] Figure 5 (c) in FIG. 12 is a 2D pseudo-color plot of the temperature rise rate field along the fiber position and time evolution;

[0049] Figure 5 (d) in FIG. 13 is a one-dimensional distribution curve of the temperature rise rate at the three key moments of abnormal expansion force (t Abn ), safety valve opening (t v ), and internal short circuit (t ISC );

[0050] Figure 5 (e) in FIG. 14 is a 2D pseudo-color plot of the temperature gradient field along the fiber position and time evolution;

[0051] Figure 5 (f) in FIG. 15 is a one-dimensional distribution curve of the temperature gradient at the three key moments of abnormal expansion force (t Abn ), safety valve opening (t v ), and internal short circuit (t ISC ).

[0052] Figure 6 FIG. 16 is a spatiotemporal evolution map of multiple physical quantities measured by the battery under normal 1C rate cycling conditions (a total of 3 cycles), for comparison with the thermal runaway condition. Specifically, it includes:

[0053] Figure 6 (a) in FIG. 17 is a 2D spatiotemporal distribution map of the battery surface temperature field;

[0054] Figure 6 (b) in FIG. 18 is a 2D spatiotemporal distribution map of the temperature rise rate field;

[0055] Figure 6 (c) in FIG. 19 is a 2D spatiotemporal distribution map of the temperature gradient field.

[0056] Figure 7 FIG. 20 is a three-dimensional scatter plot of the correlation between the three key indicators of the battery surface maximum temperature, maximum temperature rise rate, and maximum temperature gradient under two different conditions.

[0057] Figure 7 (a) in FIG. 21 shows the data point distribution of the three normal cycles;

[0058] Figure 7 (b) in FIG. 22 shows the data point distribution of the thermal abuse (500W heating) until the thermal runaway process, for intuitive comparison of the evolution path differences of the early warning parameters under normal and abnormal conditions;

[0059] In the figure: 1-fiber demodulator, 2-temperature sensing fiber, 3-processor, 4-paperless recorder, 5-thermocouple, 6-24V power supply, 7-pressure sensor, 8-gas sensor, 9-pumping tube, 10-video recorder, 11-battery, 12-safety valve, 13-battery tab, 14-heating plate, 15-insulating cotton, 16-metal clamp, 17-stud bolt, 18-high temperature resistance test bench. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application. Embodiment 1

[0061] This embodiment describes in detail a multi-stage early warning method for thermal runaway of energy storage lithium ion batteries. The method aims to realize early, high-precision and low false alarm rate monitoring and early warning of the risk of thermal runaway of lithium ion batteries. The implementation of the method not only depends on advanced sensing technology, but also relies on its innovative monitoring network layout and composite intelligent early warning criterion. The various links of the method will be described in depth and detail below with reference to the accompanying drawings.

[0062] In the initial stage of the early warning process, a high-reliability three-dimensional monitoring network is constructed, which is step S1. This step is the physical basis for all subsequent data analysis and early warning decisions. The core goal of its design is to ensure comprehensive and accurate temperature measurement while ensuring the survival ability of the sensor itself in extreme thermal runaway events. In specific implementation, a distributed temperature sensing fiber 2 with centimeter-level spatial resolution is selected, for example, an optical fiber with an outer diameter of 2.4 mm and special reinforcement treatment can be selected. Its structure can include a fiber core, aramid woven layer, Teflon outer sheath and built-in metal reinforcing wire, ensuring that its overall temperature resistance is not less than 300°C, so as to maintain the integrity of structure and function when the battery surface temperature rises sharply. This distributed temperature sensing fiber 2 will be laid along the surface of a single energy storage lithium ion battery 11 in a specific path, which can be a large-capacity square battery, such as the 280 Ah lithium iron phosphate energy storage battery used for experimental verification in this specification. The laying path is not a simple two-dimensional plane winding, but follows an optimized three-dimensional closed-loop topology of "side surface→top surface→another side surface". Specifically, the distributed temperature sensing fiber 2 can start from one main side of the battery 11, extend upward to cover the top surface, and then extend downward to the other main side of the battery 11, finally forming a closed-loop monitoring path that can monitor the key heat-producing areas of the battery in a three-dimensional and dead-angle-free manner. This closed-loop design also makes it possible to check the integrity of the signal.

[0063] In the process of constructing this three-dimensional monitoring network, the key innovation of this embodiment lies in the precise layout requirement of actively avoiding two known high-risk areas. Please refer to Figure 2 and Figure 3 schematic, Figure 2 shows the arrangement of the entire test system, and Figure 3The specific location of the optical fiber on the surface of the battery is more clearly depicted. The monitoring path of the distributed temperature sensing fiber 2 must be strictly regulated to maintain a radial distance of no less than 20 mm from the geometric center of the safety valve 12 on the top of the battery 11. This distance is determined based on in-depth analysis of the thermal runaway physical process. When the pressure inside the battery reaches the design threshold due to gas production from side reactions, the safety valve 12 will rupture or open, instantaneously ejecting a mixture of high-temperature and high-pressure gas, electrolyte vapor, and flame with a temperature up to 800°C and a flow rate exceeding 200 m / s, with a strong destructive force in the core impact area. More than 90% of the failures of optical fiber sensors in the prior art are due to the laying path being too close to this area. By forcing a safety distance of more than 20 mm, the distributed temperature sensing fiber 2 can be ensured to be in the marginal zone of the jet stream, greatly improving its survival probability in a thermal runaway event. Similarly, the monitoring path must also maintain a distance of no less than 15 mm from the welding point of the battery tab 13 of the battery 11. The battery tab 13 is a key component for connecting the internal and external circuits of the battery, and in the case of external or internal short circuit deterioration, this area may become a region of high concentration of Joule heat, even producing a high-temperature arc, causing thermal damage to materials in close proximity. Maintaining a safety distance of more than 15 mm can effectively avoid damage caused by this local extreme high temperature.

[0064] At the same time, in order to ensure that the distributed temperature sensing fiber 2 maintains close and stable thermal contact with the surface of the battery during long-term operation of the battery due to environmental vibration or thermal expansion and contraction of itself, thereby ensuring the accuracy and response speed of temperature measurement, the present embodiment uses polyimide high-temperature resistant insulation tape for segmented fixation. Optionally, the fixed interval can be set to 5 cm. This tape not only resists high temperatures, but also has excellent mechanical properties, such as a thickness of less than or equal to 0.1 mm, and each fixed point can withstand a pulling force of no less than 50 N, ensuring that it can still provide firm and reliable fixation at an environmental temperature of up to 200°C. In addition, in order to ensure the fineness of the monitoring, the total laying length L of the distributed temperature sensing fiber 2 on the surface of a single battery 11 and the number N of surface temperature measurement points should satisfy a certain relationship, such as L ≈ 5 cm × N, and N ≥ 12, to ensure effective capture of the early thermal runaway core area of less than 5 cm 2 .

[0065] After the construction of the high-reliability three-dimensional monitoring network is completed, step S2 of acquiring and analyzing temperature field data is entered. This step is completed by the optical fiber demodulator 1 optically connected to the distributed temperature sensing optical fiber 2. The optical fiber demodulator 1 is the core of the entire sensing system, and its working principle is preferably based on the sensing technology of Brillouin scattering effect. The laser inside the optical fiber demodulator 1 periodically emits a probe light pulse into the distributed temperature sensing optical fiber 2. When the light pulse propagates forward in the optical fiber, it interacts with the optical fiber medium to produce various backscattered lights. Among them, the center frequency of the Brillouin backscattered light has a small frequency shift compared to the incident light, i.e. the Brillouin frequency shift, and the size of the frequency shift is linearly proportional to the temperature and strain at the position of the optical fiber. The optical fiber demodulator 1 receives and analyzes the backscattered Brillouin light carrying position information (determined by the time of flight of the light pulse) and temperature information through a high-sensitivity photodetector and a high-speed signal processing unit, thereby being able to analyze the accurate temperature values of multiple measuring points along the optical fiber in real time and in a distributed manner. The system used in this method has a spatial resolution of ±0.05 m and a temperature accuracy of ±0.05°C, thereby forming an extremely fine dynamic "thermal map" of the battery surface, which provides a high-density data basis for subsequent dynamic evolution analysis.

[0066] After obtaining the high-resolution temperature field data, step S3 of calculating dynamic evolution parameters is performed. This step is usually completed by the processor 3 electrically connected to the optical fiber demodulator 1. After receiving the continuous temperature field data stream transmitted by the optical fiber demodulator 1, the algorithm program built-in the processor 3 does not only focus on the static absolute value of the temperature, but further deeply mines the dynamic change characteristics of the temperature field in the time and space dimensions, because these dynamic characteristics can better reveal the trend of heat accumulation and diffusion, and are the key to distinguishing between normal temperature rise and abnormal heat runaway precursor. Specifically, the processor 3 calculates two core dynamic parameters, temperature rise rate R and temperature gradient G, for each measuring point in real time.

[0067] The temperature rise rate R is obtained by performing a time first-order derivative on the temperature data of a single measuring point at consecutive time points, and its mathematical expression can be simplified as R=dT / dt. This parameter intuitively reflects the speed of temperature change at a fixed position, and a continuously and rapidly increasing positive value R usually indicates that dangerous heat accumulation is occurring at this point.

[0068] The temperature gradient G is obtained by performing a spatial first-order derivative on the temperature field data along the laying path of the distributed temperature sensing optical fiber 2 at the same time, and its mathematical expression can be simplified as G=dT / dL. This parameter represents the degree of temperature difference between physically adjacent measuring points, and an abnormally large G value indicates the formation of a local hot spot, with heat spreading from this point to the surrounding area.

[0069] Before the calculation of the two parameters, as a preferred technical solution, the processor 3 can first perform a step of noise reduction processing on the original real-time temperature data obtained from the optical fiber demodulator 1, for example, a sliding average filtering algorithm can be used, and a sliding window with a width of 20 sampling points is set, and the data in the window is arithmetically averaged, which can effectively smooth out random noise introduced by electromagnetic interference or the system itself, thereby significantly improving the stability and accuracy of the subsequent temperature rise rate R and temperature gradient G calculation results.

[0070] Finally, the most core decision-making link in the method flow is step S4, i.e. executing compound condition early warning. This is the fundamental difference between the present application and the prior art in early warning logic, aiming to fundamentally solve the problem of 35% false alarm rate of traditional single-point threshold early warning method. The processor 3 internally presets a plurality of early warning criteria corresponding to different fault severity levels. Optionally, the early warning levels can be divided into at least one level, two levels and three levels. Each level of early warning corresponds to a set of accurate threshold values, including temperature threshold Tx, rate threshold Rx and gradient threshold Gx. These threshold values are not set arbitrarily, but are obtained based on a large amount of battery thermal abuse experimental data and analysis and statistics of thermal failure mechanism.

[0071] For example, in the present embodiment, based on the analysis of the experimental data shown in Figure 4 、 Figure 5 、 Figure 6 and Figure 7 , a set of optimized three-level early warning threshold values can be determined:

[0072] The temperature three-level threshold values are T1=100℃, T2=160℃ and T3=240℃, respectively;

[0073] The temperature rise rate three-level threshold values are R1=0.2℃ / s, R2=0.2℃ / s and R3=1.0℃ / s, respectively;

[0074] The temperature gradient three-level threshold values are G1=4℃ / cm, G2=6℃ / cm and G3=8℃ / cm, respectively.

[0075] Among them, the size relationship of the threshold values satisfies T1<T2<T3, R1≤R2<R3, G1<G2<G3, in order to distinguish different stages such as the germination, development and near-loss-of-control of the fault.

[0076] The triggering of early warning is not simply a single-point overrun, but needs to meet the following a) and b) two logical and conditions:

[0077] Condition a) is the trigger condition of "point", which guarantees the sensitivity of the early warning. That is, in all the measuring points of the whole distributed temperature sensing fiber 2, as long as the real-time temperature T of at least one measuring point reaches the preset temperature threshold Tx of the corresponding level, and the real-time temperature rise rate R reaches the preset rate threshold Rx of the corresponding level, or the real-time temperature gradient G reaches the preset gradient threshold Gx of the corresponding level, the condition is satisfied.

[0078] However, only satisfying condition a) is not enough to immediately trigger the early warning, condition b) must also be met, which is the key to suppressing false positives in this method. Condition b) requires a calculation of the spatial area ratio.

[0079] First, the fiber area covered by all measuring points whose real-time values exceed their respective preset thresholds of the same level is defined as the abnormal area. Then, the total length of these abnormal areas accounts for the proportion of the total length of the distributed temperature sensing fiber 2 laid on the surface of the battery. The calculation method is as follows:

[0080] Abnormal area length = Σ (abnormal area fiber length x 0.05 m);

[0081] Total area = total length L of single battery surface laying x 0.05 m;

[0082] Ratio = abnormal area length / total area.

[0083] Only when the calculated "spatial abnormal area ratio" is not less than 30%, condition b) is satisfied. When and only when conditions a) and b) are met simultaneously, the processor 3 will finally confirm and trigger the early warning of the corresponding level.

[0084] In addition, in order to objectively and comprehensively verify and compare the early warning effect of the method of the present application, traditional monitoring methods will also be used and relevant data will be recorded during the experiment. As shown in Figure 1 and Figure 2 A traditional thermocouple 5 can be arranged at the center of the front and back surfaces of the battery 11 respectively to measure the temperature of a single point. The signal of the thermocouple 5 will be connected to a paperless recorder 4, which is responsible for real-time acquisition and display of the temperature data of the thermocouple 5, and can store it for subsequent analysis. At the same time, in order to directly observe the whole process of thermal runaway, a video recorder 10 will be set up to record the whole process of the test device. The data collected by the thermocouple 5, the paperless recorder 4 and the video recorder 10 will be used as a reference benchmark to evaluate the improvement of the method of the present application in timeliness and accuracy of early warning.

[0085] In order to more vividly illustrate the effectiveness of the method, please refer to Figures 4 to 7The experimental data shown in the figure. The experiment was carried out on a 280 Ah lithium iron phosphate battery, which was heated by a 500 W hot plate 14 to simulate the process of thermal abuse. Figure 4 The upper half of the figure shows the battery voltage, back center temperature T back and heating plate temperature T heat over time, and the lower half shows the swelling force, carbon monoxide and carbon dioxide concentration over time. Several key failure feature moments are marked with vertical dashed lines in the figure. In the figure, the times of abnormal swelling force, valve opening, internal short circuit and thermal runaway of the battery are 1022s, 2420s, 3266s and 3343s respectively.

[0086] Now we apply the method of the present application to analyze the same process. Please refer to Figure 5 , which shows the multi-physical field spatiotemporal evolution pattern monitored by the method of the present application during thermal runaway in detail. Specifically: Figure 5 (a) in Figure 5 (c) in Figure 5 (b) and Figure 5 (d) give the one-dimensional cross-sectional distribution curves of temperature and temperature rise rate along the fiber position at the three key moments of abnormal swelling force, safety valve opening and internal short circuit. Most importantly, Figure 5 (e) and Figure 5 (f) clearly reveal the evolution process of another core parameter, temperature gradient. From the spatiotemporal graph in Figure 5 (e), it can be seen that as thermal runaway approaches, a local high temperature area appears on the surface of the battery, resulting in a sharp temperature gradient. From the one-dimensional cross-sectional graph in Figure 5 (f), it can be quantitatively seen that at the moment of internal short circuit (t ISC ), the maximum temperature gradient soars to 14.03℃ / cm, which strongly proves the formation of a localized dangerous hot spot, which is the key feature that the early warning method of the present application can accurately capture.

[0087] At the moment of abnormal swelling force, i.e. 1022 seconds, the highest temperature, maximum temperature rise rate and maximum temperature gradient monitored by the fiber have reached 102.8℃, 0.23℃ / s and 5.56℃ / cm respectively.

[0088] At the moment of pressure relief, i.e. 2420 seconds, these three values have reached 187.2℃, -0.58℃ / s and 9.66℃ / cm respectively.

[0089] At the moment of internal short circuit, i.e. 3266 seconds, these three values have soared to 275.1℃, 7.31℃ / s and 14.03℃ / cm respectively.

[0090] Combining these data with the three-level early warning thresholds and the 30% rule we set, we can conclude that:

[0091] The first-level early warning (T≥100℃ and R≥0.2℃ / s or T≥100℃ and G≥4℃ / cm, and the proportion of abnormal area ≥30%) is triggered at about 787 seconds, which is about 235 seconds earlier than the abnormal swelling force of 1022 seconds.

[0092] The second-level early warning (T≥160℃ and R≥0.2℃ / s or T≥100℃ and G≥6℃ / cm, and the proportion of abnormal area ≥30%) is triggered at about 1876 seconds, which is about 544 seconds earlier than the valve opening of 2420 seconds.

[0093] The third-level early warning (T≥240℃ and R≥1.0℃ / s or T≥100℃ and G≥8℃ / cm, and the proportion of abnormal area ≥30%) is triggered at about 3001 seconds, which is about 265 seconds earlier than the internal short circuit of 3266 seconds.

[0094] This fully proves the great advantage of the invention in early warning timeliness.

[0095] In order to show the excellent anti-interference ability of the invention, we compare its performance under normal working conditions with the above thermal runaway process. Please refer to Figure 6 , which shows the monitoring results of the same battery under normal 1C rate cycling conditions (a total of 3 cycles). Among them, Figure 6 (a) shows the spatiotemporal distribution of the temperature field, Figure 6 (b) shows the spatiotemporal distribution of the temperature rise rate field, and Figure 6 (c) shows the spatiotemporal distribution of the temperature gradient field. It can be seen that although in normal cycling, the highest temperature of the battery can reach 97.7℃, the maximum temperature rise rate reaches 0.99℃ / s, and the maximum temperature gradient reaches 3.3°C / cm, some parameter values may exceed the threshold of the first-level early warning at some time, meeting condition a). However, since these temperature rises are overall and uniform, they do not form localized abnormal heat zones with spreading tendency, so the proportion of spatial abnormal area is always much lower than 30%, not meeting condition b). This can be more intuitively verified from the three-dimensional scatter plot comparison between Figure 7 (a) and Figure 7 (b): Figure 7 (a) The data points under normal cycling are densely distributed in a low-risk area, while Figure 7 (b) The data points under the thermal runaway process show a clear evolution path migrating to the high-risk area. Therefore, the method of the invention will not produce any false positives during the entire normal cycling process, demonstrating its excellent anti-interference ability.

[0096] The multi-stage early warning method for thermal runaway of energy storage lithium ion batteries described in detail in this embodiment ensures the stability of the sensing data through its forward-looking avoidance design in physical deployment; improves the insight into the nature of the fault through the deep mining of multi-dimensional dynamic evolution parameters of the temperature field; and finally realizes the high unification of early warning sensitivity and reliability through its innovative "point and surface combination" composite early warning criterion. It constitutes a complete and effective battery safety early protection technology scheme, which has significant technical effect and can greatly improve the safety of energy storage systems in practical applications. Embodiment 2

[0097] The embodiment provides a multi-stage early warning system for thermal runaway of energy storage lithium ion batteries. The system is a specific physical implementation and engineering carrier of the method described in embodiment one. As an integrated hardware and software platform, the design goal of the system is to solidify each step and algorithm of the aforementioned method into a stable and reliable entity product to ensure that it can run efficiently and autonomously in various actual energy storage application scenarios such as large container-type energy storage power stations, electric vehicle battery packs, or backup power systems in data centers. Please refer to Figure 1 With Figure 2 The core components of the system mainly include a distributed temperature sensing optical fiber 2, an optical fiber demodulator 1, and a processor 3 as the central processing unit, and can also integrate various auxiliary sensors. These components work together through precise physical installation positioning and clear signal connection relationship to realize real-time, multi-dimensional monitoring and hierarchical early warning of the thermal runaway risk of energy storage lithium ion batteries 11.

[0098] Firstly, the sensing basis and core sensing element of the system is a specially designed and deployed distributed temperature sensing fiber 2. In terms of physical installation, the fiber is configured to closely adhere to the surface of a single energy storage lithium ion battery 11. Its installation position and laying path strictly follow the three-dimensional monitoring network construction principles elaborated in embodiment one, that is, along at least one side of the battery 11, through its top surface, and to the other side, forming a closed loop monitoring path. In the specific installation construction, high-precision positioning tools must be used to ensure that the fiber laying path maintains a radial distance of not less than 20 mm from the geometric center point of the safety valve 12 of the battery 11, and also maintains a distance of not less than 15 mm from the welding point of the battery tab 13 of the battery 11. This installation layout that avoids specific dangerous areas is an inherent requirement of the system design and is the key to ensuring that it can still work normally in extreme cases. In order to ensure the performance and life of the sensor, the distributed temperature sensing fiber 2 itself is optionally made of high-temperature-resistant and high-strength materials, with a overall temperature resistance of not less than 300°C, and its physical structure can include in turn a central fiber core, a Kevlar woven layer for reinforcement, a Teflon outer sheath for chemical and physical protection, and a built-in metal reinforcing wire for improved tensile strength. In addition, the system also includes a plurality of polyimide high-temperature-resistant insulation tapes for firmly fixing the distributed temperature sensing fiber 2 on the surface of the battery. These tapes are configured to tightly fix the fiber on the surface of the battery along the laying path of the fiber, for example, at an interval of 5 cm, thereby ensuring efficient and rapid heat conduction and long-term structural stability, with each fixing point being able to withstand a tensile force of not less than 50 N.

[0099] Next, the signal demodulation and data conversion unit of the system is an optical fiber demodulator 1. In terms of physical connection, one optical port of the optical fiber demodulator 1 is optically connected to one end of the distributed temperature sensing fiber 2 through standard fiber jumpers and connectors, thereby forming a complete and closed optical path. The optical fiber demodulator 1 is configured to work based on the Brillouin scattering principle, and its core function is to emit probe light pulses into the fiber and receive and demodulate backscattered light signals returned from each point along the fiber with high sensitivity. It integrates precise light sources, optical circulators, photodetectors, and high-speed signal processing modules inside, and can accurately convert the slight drift of optical wave frequency into temperature readings of all measurement points along the line and output them in the form of digital signals. These data are connected to the central processing unit processor 3 of the system through its data communication interface, such as an Ethernet port or an RS485 interface, for reliable electrical connection and real-time data transmission.

[0100] The core control and decision unit of the system is a processor 3. As shown in the figure, the processor 3 is connected to the optical fiber demodulator 1 through a data communication interface, such as an Ethernet port or an RS485 interface, for reliable electrical connection and real-time data transmission. The processor 3 is configured to receive the temperature data of the battery 11 from the optical fiber demodulator 1 and perform real-time analysis and processing on the data. The processor 3 is also connected to the alarm unit 4 through a data communication interface, such as an Ethernet port or an RS485 interface, for reliable electrical connection and real-time data transmission. The processor 3 is configured to send alarm signals to the alarm unit 4 based on the analysis and processing results of the temperature data of the battery 11. Figure 1In the shown laboratory environment, the processor 3 can be undertaken by a data logging computer with computing and data recording capabilities. However, in more extensive industrial or commercial applications, it is more likely to be a dedicated, high-reliability industrial control computer (IPC), a programmable logic controller (PLC), or a high-performance embedded system motherboard developed according to specific needs. In the connection relationship of the system, the processor 3 is electrically connected to the fiber demodulator 1 to continuously receive the output of the real-time temperature data stream with centimeter-level resolution.

[0101] The processor 3 is the core embodiment of the intelligence of the whole system, and the software program inside or running is precisely configured to execute all core algorithms of the method described in embodiment one.

[0102] Specifically, the processor 3 is first configured to receive continuous real-time temperature data and build a battery surface dynamic temperature field model that can be refreshed in real time in its memory.

[0103] Subsequently, it is configured to calculate the two key dynamic evolution parameters, the temperature rise rate R and the temperature gradient G along the length direction of the optical fiber, in real time and point by point based on the temperature field data.

[0104] Finally, and most importantly, the processor 3 is configured to execute the "point-surface combination" composite condition warning logic of the present application. It will continuously compare the real-time temperature T, real-time temperature rise rate R and real-time temperature gradient G of each measuring point with the multi-level threshold values (Tx, Rx, Gx) preset in the program at high speed, and simultaneously calculate the proportion of the total length of all abnormal areas that exceed the corresponding threshold values to the total laying length of the optical fiber in real time. Once it is detected that the parameter value of a measuring point reaches the preset threshold value (satisfies condition a), and the calculated proportion of the abnormal area reaches or exceeds the preset proportion, for example, 30% (satisfies condition b), the processor 3 will immediately generate a corresponding level of warning signal on its output port. This warning signal can take many forms, such as the level inversion of one or more digital I / O ports, which can directly drive a relay to shut down the charging circuit or start the fire sprinkler; it can also be an audible and light alarm; or a network data packet following a specific protocol (such as Modbus TCP or CAN), which sends detailed warning information (including warning level, trigger time, abnormal area location, etc.) to the central monitoring system or battery management system (BMS) of the entire energy storage power station through industrial Ethernet or CAN bus for higher-level decision-making and response.

[0105] In order to more fully show the composition of the system and its application in the experimental environment, Figure 2The whole test device is placed on a solid high-temperature test table 18. The battery 11 to be tested and the heating plate 14 for simulating thermal abuse are clamped by a metal clamp 16, and a certain pre-tightening force is applied and maintained by the stud bolt 17. In order to reduce the heat loss to the outside, so as to more accurately simulate the adiabatic environment of the battery in the module or battery pack, the battery 11 and the heating plate 14 are wrapped by a layer of heat insulation cotton 15. This complete experimental device ensures the validity and repeatability of the experimental data, and provides a verification platform close to the actual working condition for the system of the present application.

[0106] In order to further enhance the early warning capability and fault diagnosis dimension of the system, the system of the embodiment can also selectively integrate other types of sensors to form a multi-dimensional information fusion platform. For example, as shown in Figure 2 The system can include a pressure sensor 7, which is carefully installed between the metal clamp 16 for clamping the battery 11 and the surface of the battery 11, so that the macroscopic expansion force change caused by the internal irreversible side reaction gas production of the battery can be monitored in real time in a non-invasive manner. The system can also include a gas sensor 8, which is installed by an air extraction pipe 9, with its sampling port accurately positioned above the space of the safety valve 12, so that the appearance of the escaped characteristic gas (such as carbon monoxide CO, hydrogen H2, etc.) and its concentration change can be monitored in the first time after the safety valve 12 is opened. In terms of connection relationship of the system, these additional pressure sensor 7 and gas sensor 8 are electrically connected to the processor 3 through their respective signal cables, and the software of the processor 3 is configured to be able to collect and analyze their signals, and use them as a powerful supplement and cross-verification of the optical fiber temperature data, for multi-information fusion judgment, so as to further improve the accuracy of early warning decision and the diagnosis precision of fault state. The whole system, including the optical fiber demodulator 1, the processor 3 and all additional sensors, can be uniformly powered by a stable and reliable DC power supply 6, for example, a 24V industrial-grade switching power supply.

[0107] The thermal runaway multi-stage early warning system of the energy storage lithium ion battery described in detail in this embodiment, through the accurate configuration of each component, the unique physical installation position and the clear definition of the signal connection relationship, realizes all the innovative methods proposed in embodiment one completely and reliably. From the front-end fiber sensor network layout with high survivability, to the middle-end high-precision photoelectric demodulation unit, to the rear-end processor with high intelligent composite algorithm, the whole system forms a seamless closed loop from physical perception to intelligent decision. It can be used as an independent and complete technical solution to stably and reliably perform the thermal runaway early warning task in various complex practical applications, effectively cope with the severe safety challenges of energy storage systems, and fully prove its advanced nature, practical value and huge market application potential as a whole technical solution.

Claims

1. A multi-level early warning method for thermal runaway in energy storage lithium-ion batteries, characterized in that, It includes the following steps: S1: Construct a three-dimensional monitoring network: A distributed temperature sensing optical fiber (2) is used and laid along at least one side surface, the top surface of a single energy storage lithium-ion battery (11), and then to another side surface to form a closed-loop monitoring path; wherein, the monitoring path maintains a radial distance of not less than 20 mm from the center of the safety valve (12) of the battery (11), and a distance of not less than 15 mm from the welding point of the battery tab (13) of the battery (11); S2: Obtain and analyze temperature field data: A fiber optic demodulator (laquo;1raquo;) is used to obtain the real-time temperature data of multiple measurement points on the distributed temperature sensing optical fiber (2) to form a battery surface temperature field with centimeter-level resolution; S3: Calculate dynamic evolution parameters: Based on the temperature field data, the temperature rise rate R of each measurement point and the temperature gradient G along the optical fiber length direction are calculated in real time; S4: Execute composite condition warning: When the following two conditions a) and b) are satisfied simultaneously, a warning of the corresponding level is triggered: a) The real-time temperature T of at least one measurement point reaches the preset temperature threshold Tx, and at the same time the real-time temperature rise rate R reaches the preset rate threshold Rx, or the real-time temperature gradient G reaches the preset gradient threshold Gx; b) The proportion of the abnormal area where the temperature, temperature rise rate or temperature gradient exceeds their respective preset thresholds in the total laying length of the distributed temperature sensing optical fiber (2) is not less than 30%.

2. The multi-level early warning method for thermal runaway of energy storage lithium-ion batteries according to claim 1, characterized in that: In step S3, the temperature rise rate R is obtained by taking the time derivative dT / dt of the temperature of a single measurement point, and the temperature gradient G is obtained by taking the spatial derivative dT / dL of the temperature field data along the optical fiber laying path.

3. The multi-level early warning method for thermal runaway of energy storage lithium-ion batteries according to claim 1, characterized in that, It further includes: Simultaneously collecting the expansion force signal of the battery (11), the gas production concentration signal after the safety valve (12) is opened, and the voltage signal of the battery (11) as the basis for auxiliary warning judgment.

4. The multi-level early warning method for thermal runaway of energy storage lithium-ion batteries according to claim 1, characterized in that: The warning is a multi-level warning, including at least level one, level two and level three warnings; wherein, the preset temperature threshold Tx, rate threshold Rx and gradient threshold Gx are divided according to the warning level x, x is 1, 2, 3, and satisfy T1 < T2 < T3, R1 ≤ R2 < R3, G1 < G2 < G3 to distinguish different degrees of fault severity.

5. The multi-level early warning method for thermal runaway of energy storage lithium-ion batteries according to claim 1, characterized in that, Between step S2 and step S3, it further includes: Denoising the real-time temperature data obtained by the fiber optic demodulator (1) by using moving average filtering, and the window width is 20 sampling points.

6. A multi-level early warning system for thermal runaway of energy storage lithium-ion batteries, comprising the method described in any one of claims 1-5, characterized in that, It includes: A distributed temperature sensing optical fiber (2): Configured to be laid along at least one side surface, the top surface of a single energy storage lithium-ion battery (11), and then to another side surface to form a closed-loop monitoring path; the monitoring path maintains a radial distance of not less than 20 mm from the center of the safety valve (12) of the battery (11), and a distance of not less than 15 mm from the welding point of the battery tab (13) of the battery (11); A fiber optic demodulator (1): Optically connected to the distributed temperature sensing optical fiber (2), configured to obtain centimeter-level real-time temperature data of multiple measurement points on the optical fiber based on the Brillouin scattering principle; A processor (3): electrically connected to the fiber optic demodulator (1), the processor being configured to: Receive the real-time temperature data to construct the battery surface temperature field; Based on the temperature field data, the temperature rise rate R and the temperature gradient G along the fiber length direction of each measuring point are calculated in real time. When the real-time temperature T and the real-time temperature rise rate R or the real-time temperature gradient G of at least one measuring point are detected to reach their respective preset thresholds, and the total length of all abnormal areas exceeding the corresponding thresholds accounts for no less than 30% of the total length of the optical fiber, an early warning signal is output.

7. The multi-level early warning system for thermal runaway of energy storage lithium-ion batteries according to claim 6, characterized in that, Also includes: A pressure sensor (7): disposed between the metal clamp (16) for holding the battery (11) and the surface of the battery (11), for monitoring the expansion force of the battery; A gas sensor (8): connected directly above the safety valve (12) via a suction pipe (9) for monitoring the concentration of generated gas; The pressure sensor (7) and the gas sensor (8) are both electrically connected to the processor (3).

8. The multi-level early warning system for thermal runaway of energy storage lithium-ion batteries according to claim 6, characterized in that: The distributed temperature-sensing optical fiber (2) includes a fiber core, an aramid braided layer, a Teflon outer sheath, and an internal metal reinforcing wire, and its overall temperature resistance is not lower than 300℃.

9. The multi-level early warning system for thermal runaway of energy storage lithium-ion batteries according to claim 6, characterized in that, It also includes multiple polyimide high-temperature resistant insulating tapes for fixing the distributed temperature-sensing optical fiber (2), the tapes fixing the optical fiber at 5cm intervals along the optical fiber laying path, and each fixing point can withstand a tensile force of not less than 50N.

10. The multi-level early warning system for thermal runaway of energy storage lithium-ion batteries according to claim 6, characterized in that, The total laying length L of the distributed temperature-sensing optical fiber (2) on the surface of a single battery (11) satisfies the following relationship with the number of surface temperature measuring points N: L≈5cm×N, and N≥12.

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