Electrochemical energy storage power station early warning method and system based on multiple physical parameters

Abstract

The invention discloses an electrochemical energy storage power station early warning method and system based on multiple physical parameters, and the method comprises the steps: building a battery internal temperature prediction model according to the relation between the internal impedance of a battery and the temperature; the battery internal impedance is monitored in real time, the battery internal temperature prediction model is utilized to predict the battery internal temperature so as to judge the abnormal condition of the battery, and primary early warning of the battery abnormity is achieved; characteristic gas and characteristic sound in the battery are monitored, the thermal runaway condition of the battery is judged, and secondary early warning of battery abnormity is realized; image information in the battery is collected, the thermal runaway diffusion condition in the battery is judged, and three-level early warning of battery abnormity is achieved. According to the invention, the cooperative cooperation of the multi-stage early warning system is utilized, the early warning signal is sent by identifying the internal resistance, internal temperature, gas, sound and other characteristics, and the thermal runaway state of the battery can be processed in time.

Description

Technical Field

[0001] This invention relates to the field of safety protection technology for energy storage power stations, and in particular to an early warning method and system for electrochemical energy storage power stations based on multiple physical parameters. Background Technology

[0002] Lithium battery fires can have serious consequences, so it is especially important to identify characteristic parameters of lithium-ion battery thermal accidents, provide early warnings of thermal runaway, implement safety linkages, and provide fire protection.

[0003] Traditional battery temperature monitoring is achieved through thermocouples mounted on the surface of lithium batteries. However, this method has low accuracy and cannot respond promptly to rapid temperature changes in lithium batteries. Current research, both domestically and internationally, primarily focuses on establishing thermal models of lithium batteries and combining these models with external operating characteristics and surface temperature to predict their operating temperature. However, temperature increases in lithium-ion batteries due to changes in output power often originate from within the battery itself.

[0004] Currently, safety warnings for energy storage systems primarily rely on threshold values ​​of certain characteristic parameters of the battery management system (BMS) to identify the risk of thermal runaway. This reliance on the BMS for thermal runaway warnings is problematic. While changes in these characteristic parameters indicate internal reactions, the BMS's assessments often have a lag. By the time a warning is issued, the battery's internal temperature has already reached the threshold, increasing the possibility of further thermal propagation and runaway. The internal chain reaction has already occurred, and the thermal runaway is irreversible. Summary of the Invention

[0005] The purpose of this invention is to provide an early warning method and system for electrochemical energy storage power stations based on multiple physical parameters. It utilizes the coordinated operation of a multi-level early warning system to issue early warning signals by identifying features such as internal resistance, internal temperature, gas, and sound, and can promptly handle battery thermal runaway.

[0006] To achieve the above objectives, the present invention provides an early warning method for electrochemical energy storage power stations based on multiple physical parameters, comprising:

[0007] Based on the relationship between battery internal impedance and temperature, a battery internal temperature prediction model is established; the battery internal impedance is monitored in real time, and the battery internal temperature is predicted using the battery internal temperature prediction model to determine battery abnormalities and achieve a first-level early warning for battery abnormalities.

[0008] Monitor characteristic gases and sounds inside the battery to determine the battery's thermal runaway status and achieve a two-level early warning for battery anomalies;

[0009] By acquiring image information of the battery's interior, the system can determine the extent of thermal runaway propagation within the battery, enabling a three-level early warning system for battery anomalies.

[0010] Furthermore, based on the relationship between battery internal impedance and temperature, a battery internal temperature prediction model is established, including:

[0011] Using the multi-frequency fitting method, the single-frequency impedance of the battery is measured by frequency measurement EIS to obtain the electrochemical impedance spectrum and the imaginary impedance measurement value corresponding to each single frequency point.

[0012] During the measurement of the single-frequency impedance of the battery, the internal temperature of the battery corresponding to each single-frequency point is measured.

[0013] The imaginary impedance measurement value and the internal temperature of the battery corresponding to each single frequency point are fitted multiple times to obtain the single frequency point temperature model.

[0014] Based on the single-frequency temperature model, the internal temperature of the battery corresponding to each single frequency point is corrected to obtain a battery internal temperature prediction model.

[0015] Among them, the impedance of the battery at a single frequency point within 10Hz-100Hz is measured by frequency measurement EIS.

[0016] Furthermore, real-time monitoring of the battery's internal impedance and prediction of the battery's internal temperature using an internal temperature prediction model are employed to identify abnormal battery conditions, enabling a first-level early warning system for battery anomalies, including:

[0017] If the predicted value of the battery internal temperature prediction model exceeds a certain range of the set average battery temperature, a first-level warning signal for battery abnormality will be issued, and battery charging will be stopped.

[0018] The single-frequency temperature model is as follows:

[0019]

[0020] In the formula, T x Z″ represents the predicted internal temperature of the battery at an impedance of x Hz, where x represents the impedance at a single frequency point. x c represents the imaginary part of the impedance measured at x Hz. x d x Both represent Z at impedance x Hz. x With T x Parameters after nonlinear fitting;

[0021] After temperature correction of the battery internal temperature for each single frequency point, the following results were obtained:

[0022] T = y1T 10 +y2T 20 +…+y 10 T 100 +a (2),

[0023] In the formula, T represents the predicted value of the battery's internal temperature. 10 T 20 ...T 100 These represent the predicted internal battery temperatures from 10Hz to 100Hz, y1, y2...y 10 T represents respectively 10 T 20 ...T 100 The coefficient, where a represents a constant;

[0024] Substituting the single-frequency temperature model into formula (2), the battery internal temperature prediction model is obtained as follows:

[0025]

[0026] Polynomial fitting is performed on each single-frequency temperature model in the battery internal temperature prediction model to obtain parameters y1, y2...y 10 And the value of a.

[0027] Furthermore, by monitoring characteristic gases and sounds inside the battery, the thermal runaway situation can be determined, enabling a two-level early warning system for battery anomalies, including:

[0028] Using hydrogen and carbon monoxide sensors, the concentration of combustible gases in the battery storage compartment is monitored in real time. If the concentration of combustible gases reaches a set threshold, a secondary warning for battery anomalies is activated, and measures to suppress battery thermal runaway are implemented; and / or,

[0029] The sound signal inside the battery storage compartment is monitored by an audio acquisition device, and it is identified whether the sound signal is the characteristic sound signal emitted when the battery safety valve is opened; if the sound signal is the characteristic sound signal, a second-level warning for battery abnormality is activated, and measures to suppress battery thermal runaway are implemented.

[0030] This includes initiating a level-two warning for battery anomalies and implementing measures to suppress battery thermal runaway, including:

[0031] The battery was de-energized, and fire extinguishing agents were used to extinguish the fire at the location of the battery hazard to prevent the spread of heat.

[0032] The extinguishing medium is perfluorohexanone or heptafluoropropane.

[0033] Furthermore, by acquiring image information from inside the battery, the extent of thermal runaway propagation within the battery is determined, enabling a three-level early warning system for battery anomalies, including:

[0034] A high-speed camera is used to continuously capture images of the battery module, and the grayscale of the images is processed to identify areas of white smoke, thereby determining the area where the battery is experiencing thermal runaway.

[0035] Once the thermal runaway area is identified, a three-level warning for battery anomalies is activated. Based on the image processing results, the stage of battery thermal runaway is determined, and different fire extinguishing media are applied to the battery in different stages to prevent the spread of battery thermal runaway.

[0036] Different fire extinguishing media are used for the batteries in different stages, including:

[0037] When the image grayscale value exceeds the first predetermined value, it indicates that the battery has experienced thermal runaway and is accompanied by smoke generation. In this case, perfluorohexanone or heptafluoropropane is activated to extinguish the fire in the battery module and suppress the spread of thermal runaway to other battery modules or PACKs.

[0038] When the image grayscale value exceeds the second predetermined value, it indicates that the battery has experienced thermal runaway. In this case, water spraying is used to extinguish the fire and suppress the continued spread of thermal runaway.

[0039] Furthermore, a high-speed camera is used to continuously capture images of the battery module, and the image grayscale is processed to identify areas of white smoke, thereby determining the areas where thermal runaway of the battery has occurred, including:

[0040] The process of white smoke spreading was captured using a high-speed camera, resulting in multiple RGB images.

[0041] The captured RGB image is represented using a two-dimensional matrix. Based on the two-dimensional array or matrix of the RGB image, the RGB image is converted to grayscale to obtain a grayscale image. The grayscale image is then processed to obtain a smoke grayscale image.

[0042] The smoke grayscale image is divided into several parts based on a two-dimensional matrix, thus obtaining a matrix with multiple parts; each part of the matrix is ​​discretized to obtain the corresponding gridded image;

[0043] By processing the grayscale values ​​of the gridded images, the average grayscale value of each part of the matrix is ​​obtained; the white smoke concentration is determined by the grayscale values ​​of each gridded image or by the grayscale values ​​of each part of the matrix, so as to obtain the white smoke region.

[0044] Among them, the Gamma correction algorithm is used to calculate the gray value of each gridded image, and the average gray value of each part of the matrix is ​​obtained from the gray value of each gridded image.

[0045] The average gray value is the sum of the gray values ​​of each gridded image and divided by the set number of grids.

[0046] Furthermore, the battery undergoes primary monitoring: real-time monitoring of the battery's internal impedance and prediction of whether the battery's internal temperature is abnormal using a battery internal temperature prediction model; if an abnormal battery internal temperature is predicted, the battery is deemed to have malfunctioned, and primary warning processing is initiated, while secondary monitoring is activated; otherwise, primary monitoring is maintained.

[0047] Secondary monitoring of the battery: Monitoring characteristic gases and sounds inside the battery to determine the extent of thermal runaway.

[0048] The characteristic gas and characteristic sound inside the battery are monitored separately. If the concentration of the characteristic gas or the characteristic sound inside the battery is abnormal, the second-level early warning process is initiated, the third-level monitoring state is activated, and measures to suppress battery thermal runaway are implemented. Check whether the risk of battery thermal runaway is eliminated. If it is eliminated, the second-level monitoring state is maintained. If it is not eliminated, the third-level monitoring state is activated.

[0049] If no abnormalities are found in the concentration of characteristic gases and characteristic sounds inside the battery, continue to maintain the level 2 monitoring status;

[0050] The battery is monitored in three levels: image information inside the battery is collected, and the thermal runaway propagation inside the battery is judged by the image grayscale value; if the image grayscale value exceeds the abnormal range, the three-level early warning and fire protection measures are activated; if the thermal runaway propagation is resolved, the three-level early warning measures are deactivated and the three-level monitoring status is maintained; if the thermal runaway propagation is not resolved, the three-level early warning measures are maintained.

[0051] Based on the same inventive concept, this invention also provides an early warning system for electrochemical energy storage power stations based on multiple physical parameters, comprising:

[0052] The temperature warning module is used to establish a battery internal temperature prediction model based on the relationship between battery internal impedance and temperature; it uses a monitoring system to monitor the battery internal impedance in real time and uses the battery internal temperature prediction model to predict the battery internal temperature to determine abnormal battery conditions and achieve a first-level warning for battery abnormalities.

[0053] The gas and sound early warning module is used to monitor the characteristic gases and sounds inside the battery using the monitoring system, to determine the battery thermal runaway situation, and to achieve a two-level early warning of battery abnormalities.

[0054] The image warning module is used to collect image information inside the battery, determine the thermal runaway propagation situation inside the battery, and realize a three-level warning for battery abnormalities.

[0055] Furthermore, the gas sound warning module includes a hydrogen sensor, a carbon monoxide sensor, and an audio acquisition device, and the image warning module includes a high-speed camera and an image signal processor;

[0056] The gaseous warning module is used to monitor the hydrogen and carbon monoxide concentrations in the battery storage compartment in real time via the hydrogen sensor and the carbon monoxide sensor. If the hydrogen and carbon monoxide concentrations reach a set threshold, a secondary warning for battery anomalies is activated, and measures to suppress battery thermal runaway are implemented; and / or,

[0057] The gas sound warning module is also used to monitor the sound signal in the battery energy storage compartment in real time through the audio collector, and identify whether the sound signal is the characteristic sound signal emitted when the battery safety valve is opened; if the sound signal is the characteristic sound signal, then a secondary warning for battery abnormality is activated, and measures to suppress battery thermal runaway are implemented.

[0058] The image early warning module is used to continuously capture images of the battery module using a high-speed camera, and to process the image grayscale using an image signal processor to identify areas of white smoke, thereby determining the area where the battery is experiencing thermal runaway. Once the thermal runaway area is identified, a three-level warning for battery abnormality is activated, and the stage of battery thermal runaway is determined based on the image processing results. Different fire extinguishing media are applied to the battery at different stages to prevent the spread of battery thermal runaway.

[0059] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, including: a memory and a processor; the processor is used to read and execute a computer program stored in the memory to implement the aforementioned early warning method for an electrochemical energy storage power station based on multiple physical parameters.

[0060] Based on the same inventive concept, embodiments of the present invention also provide a computer storage medium storing computer-executable instructions, which, when executed, implement the aforementioned early warning method for an electrochemical energy storage power station based on multiple physical parameters.

[0061] The technical effects and advantages of this invention are as follows: This invention fully utilizes the coordinated operation of a multi-level early warning system, issuing early warning signals by identifying features such as internal resistance, internal temperature, gas, and sound. The multi-level early warning system for energy storage batteries can achieve active monitoring and identification; traditional thermal runaway alarms, when the external temperature reaches a threshold, indicate that the battery has already entered a thermal runaway state, and heat propagation is often unavoidable; the three-level early warning system can detect thermal runaway fires 10 minutes earlier, effectively preventing safety accidents; this invention can determine the internal temperature of the cell by monitoring battery impedance online, judging internal temperature changes based on impedance fluctuations and issuing timely warnings without damaging the cell itself. Simultaneously, a gas and sound early warning module is set up to suppress heat propagation, accurately locate abnormal battery modules, and finally, through image recognition, determine the battery thermal runaway process and promptly link fire-fighting facilities to suppress the spread of thermal runaway.

[0062] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating an early warning method for an electrochemical energy storage power station based on multiple physical parameters, according to an embodiment of the present invention.

[0065] Figure 2 This is a flowchart of the first-level monitoring in an embodiment of the present invention;

[0066] Figure 3 This is a flowchart of the secondary monitoring process in an embodiment of the present invention;

[0067] Figure 4 This is a flowchart of the three-level monitoring system in an embodiment of the present invention;

[0068] Figure 5 This is a schematic diagram of the structure of an electrochemical energy storage power station early warning system based on multiple physical parameters, according to an embodiment of the present invention.

[0069] Figure 6 This is a structural schematic diagram showing the locations of some data acquisition devices in an embodiment of the present invention.

[0070] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] To address the shortcomings of existing technologies, this invention discloses an early warning method for electrochemical energy storage power stations based on multiple physical parameters, such as... Figure 1 As shown, it includes the following steps:

[0073] Step S1: Without affecting the normal operation of the battery and without requiring modification to the battery structure, establish a battery internal temperature prediction model based on the relationship between the battery's internal impedance and temperature; monitor the battery's internal impedance in real time, and use the battery internal temperature prediction model to predict the battery's internal temperature to determine abnormal battery conditions, achieving a first-level early warning for battery abnormalities (also called non-destructive early warning); specifically including:

[0074] Since temperature is the first abnormal change to occur when a lithium battery malfunctions, a multi-frequency fitting method is used. Electrochemical impedance spectroscopy (EIS) is employed to measure the impedance at each 10Hz frequency point within the 10Hz-100Hz range, obtaining the electrochemical impedance spectrum and the imaginary impedance measurement value corresponding to each single frequency point. The impedance warning function uses the 10Hz single-frequency point to monitor temperature, but other frequencies can also be selected to determine the internal temperature of the cell.

[0075] During the measurement of the battery's single-frequency impedance, the internal temperature of the battery corresponding to each single-frequency point is measured in the laboratory, including:

[0076] Select a battery sample for testing, measure its surface temperature using a thermocouple, and allow it to stand for 30 minutes after the surface temperature reaches a set value. This can be considered as the internal temperature of the battery being the same as the surface temperature. Then, perform multiple fitting operations on the imaginary impedance measurement value and the internal temperature of the battery corresponding to each single-frequency point to obtain the single-frequency temperature model:

[0077]

[0078] In the formula, T x Z″ represents the predicted internal temperature of the battery at an impedance of x Hz, where x represents the impedance at a single frequency point. x c represents the imaginary part of the impedance measured at x Hz. x d x Both represent Z when the impedance is x Hz. x With T x The parameters after nonlinear fitting.

[0079] Among them, for different batteries, the measured c for each impedance value x d x The parameters are different.

[0080] Based on the single-frequency temperature model, after temperature correction of the battery internal temperature corresponding to each single frequency point, we obtain: T = y1T 10 +y2T 20 +…+y 10 T 100 +a (2),

[0081] In the formula, T represents the predicted value of the battery's internal temperature. 10 T 20 ...T100 These represent the predicted battery temperatures from 10Hz to 100Hz, y1, y2...y 10 T represents respectively 10 T 20 ...T 100 The coefficient of , where a represents a constant.

[0082] Substituting the single-frequency temperature model into formula (2), the battery internal temperature prediction model is obtained as follows:

[0083]

[0084] Then, polynomial fitting is performed on each single-frequency temperature model in the battery internal temperature prediction model to obtain parameters y1, y2...y 10 And the value of a.

[0085] The single-frequency impedance values ​​of the 10 impedance measurement points are recorded by the battery management system (BMS), and the temperature prediction value of the battery internal temperature prediction model can be obtained through BMS backend calculation.

[0086] If the temperature prediction value of the battery's internal temperature prediction model exceeds the set average battery temperature by more than 10%, a first-level warning signal for battery abnormality will be issued, and battery charging will be stopped. The battery in question is not limited to individual cells, battery modules, or battery packs.

[0087] Step S2: Monitor the characteristic gases and sounds inside the battery to determine the battery's thermal runaway status and achieve a secondary early warning system for battery anomalies (also called a minor damage early warning system); specifically including:

[0088] Step S201: Monitor the battery for combustible gases:

[0089] Because lithium dendrites react with polymer binders to produce hydrogen gas in the early stages of overcharging of lithium-ion batteries, this gas can be detected earlier than other thermal runaway gases. Therefore, hydrogen and carbon monoxide sensors are used to monitor the concentration of combustible gases (hydrogen and carbon monoxide) in the battery storage compartment in real time. If the concentration of combustible gases reaches a set threshold, a secondary warning for battery anomalies is activated, and measures to suppress battery thermal runaway are implemented. The hydrogen sensor used has a measurement range of 1×10⁻⁶ to 1000×10⁻⁶ and a response time of less than 3 seconds.

[0090] Step S202: Monitor the characteristic sounds of the battery:

[0091] Because hydrogen will rapidly decompose and produce gas inside the lithium-ion battery after a period of time, causing the internal gas pressure of the lithium battery to rise, the pressure relief device (such as the battery safety valve) will emit a sound at a specific frequency when it opens. Therefore, multiple audio collectors are set up in the battery energy storage compartment to collect the sound signals inside the battery energy storage compartment in order to identify whether the sound signal is the characteristic sound signal emitted when the battery safety valve opens. If the sound signal is the characteristic sound signal, the second-level warning of battery abnormality is activated and measures to suppress battery thermal runaway are implemented.

[0092] In some specific embodiments, a secondary warning for battery anomalies is activated, and measures to suppress battery thermal runaway are implemented, including:

[0093] The battery should be de-energized, and fire extinguishing agents (perfluorohexanone or heptafluoropropane) should be used to extinguish the fire at the location of the battery hazard to prevent the spread of heat.

[0094] In some specific embodiments, since the cabin noise signal affects the audio acquisition unit's capture of characteristic sounds during actual battery operation, a spectral subtraction method can be used to remove the influence of background sound signals before the audio acquisition unit is actually running, thereby improving the accuracy of characteristic sound capture. First, the audio acquisition unit collects the spectrum of the noisy signal, then the spectrum of the characteristic sound is collected separately, and the ambient noise spectrum is subtracted. The signal after spectral subtraction is relatively pure.

[0095] Among them, the use of spectral subtraction to remove the influence of background sound signals includes:

[0096] The useful signal can be obtained by subtracting the spectrum of the noisy signal from the spectrum of the noisy signal. Noise reduction uses a hard thresholding method: wavelet coefficients with absolute values ​​less than a threshold are reduced to zero, while wavelet coefficients with absolute values ​​greater than the threshold are retained without any processing. The threshold calculation formula is either the Minmax thresholding method or the Sqtwolog thresholding method, as shown in the following formula:

[0097]

[0098] In the formula, ω is the wavelet coefficient and λ is the threshold.

[0099] Each collected sound signal undergoes noise reduction processing. Under normal operating conditions, the noise-reduced sound signal is the normal sound signal of the battery compartment, and this signal shows almost no fluctuation at different times. Each module or PACK is equipped with a sound acquisition device. When a characteristic sound signal is detected, the spectrum will fluctuate significantly. If the signal continues to fluctuate for one minute, it indicates that a characteristic sound has been generated. At this point, a three-level warning system is activated, the module or PACK that generated the characteristic signal is disconnected, and it is reconnected after the safety hazard is eliminated.

[0100] It should be noted that when performing secondary monitoring of the battery, the present invention can first monitor the combustible gas and then monitor the characteristic sound, or it can monitor the combustible gas or the characteristic sound separately.

[0101] Step S3: Acquire image information of the battery's interior to determine the extent of thermal runaway propagation and achieve a three-level early warning system (also called a limit warning) for battery anomalies; including:

[0102] As the temperature continues to rise, high heat is generated inside the lithium battery, and the electrolyte continues to vaporize. Therefore, a high-speed camera is used to continuously capture images of the battery module and process the grayscale of the images to identify areas of white smoke, thereby determining the area where the battery is experiencing thermal runaway. Once the thermal runaway area is identified, a three-level warning for battery anomalies is activated, and the stage of battery thermal runaway is determined based on the image processing results. Different fire extinguishing media are applied to the battery at different stages to prevent the spread of battery thermal runaway.

[0103] In some specific embodiments, a high-speed camera is used to continuously capture images of the battery module, and an image signal processor processes the image grayscale to identify areas of white smoke, thereby determining the area where thermal runaway of the battery has occurred; including:

[0104] The captured RGB image is represented using a two-dimensional array or two-dimensional matrix. Based on the two-dimensional array or two-dimensional matrix of the RGB image, the RGB image is converted to grayscale to obtain a grayscale image. The grayscale image is then processed to obtain a smoke grayscale image.

[0105] The smoke grayscale image is divided into several parts based on a two-dimensional matrix, thus obtaining a matrix with multiple parts; each part of the matrix is ​​discretized to obtain the corresponding gridded image;

[0106] By processing the grayscale values ​​of the gridded images, the average grayscale value of each part of the matrix is ​​obtained; the white smoke concentration is determined by the grayscale values ​​of each gridded image or by the grayscale values ​​of each part of the matrix, so as to obtain the white smoke region.

[0107] Among them, the Gamma correction algorithm is used to calculate the gray value of each gridded image, and the average gray value of each part of the matrix is ​​obtained from the gray value of each gridded image.

[0108] The average gray value is the sum of the gray values ​​of each gridded image and divided by the set number of grids.

[0109] The Gamma correction algorithm is used to calculate the grayscale value of each gridded image using the following formula:

[0110]

[0111] In the formula, Grey represents the grayscale value of the grid image, and R, G, and B represent the color components corresponding to red, green, and blue, respectively.

[0112] In some specific embodiments, a high-speed camera is used to continuously capture images of the battery module, and an image signal processor processes the image grayscale to identify areas of white smoke, thereby determining the area where the battery has experienced thermal runaway; the specific process is as follows:

[0113] First, the captured image is represented. Image representation methods are fundamental to describing image processing algorithms and processing images using computers. A two-dimensional image is typically represented in a computer as a two-dimensional array f = (x, y), or as an M × N two-dimensional matrix (where M is the number of rows and N is the number of columns). The two-dimensional matrix of the captured image is:

[0114]

[0115] The process of smoke diffusion was captured using a high-speed camera, resulting in several RGB images. RGB images, also known as true color, are a method of representing color images. They use three identical two-dimensional arrays to represent a pixel. The three arrays represent the three components: R, G, and B. R represents red, G represents green, and B represents blue. Any color can be synthesized using these three basic colors. Each color component in each pixel occupies 8 bits, with each bit represented by any value in the range [0, 255]. Therefore, a pixel is represented by 24 bits, and the maximum allowed value is 2^24 (i.e., 1677216, usually written as 16M).

[0116] The background was black and the smoke was white. To simplify the processing of the RGB image, it was usually converted to a grayscale image. A grayscale image, also known as a monochrome image, is typically represented by a two-dimensional array, with 8 bits representing one pixel. 0 represents black, 255 represents white, and 1–254 represent different shades of gray. Grayscale images usually display many levels of color depth between black and white, much wider than the range of color depths the human eye can perceive. Furthermore, because the photos were obtained experimentally from the same perspective and under the same conditions, batch processing can offset interference from external conditions. Different shades of gray represent different concentrations of smoke; a higher grayscale value indicates a higher smoke concentration, and vice versa.

[0117] An RGB image is converted to grayscale using a two-dimensional array or matrix. Using an initial image without smoke as the background, a smoke image is selected, the background is subtracted, and then processed to obtain the desired "smoke image," i.e., the smoke grayscale image.

[0118] The process yields a 1024×1280 matrix of the image (with element values ​​ranging from 0 to 255), where 0 represents black and 255 represents white. The values ​​from 0 to 255 represent different grayscale levels, indirectly reflecting the concentration of smoke.

[0119] The image is divided into a 4×4 matrix, which is 16 parts. Each part of the matrix is ​​then discretized to obtain the corresponding gridded images. By calculating the grayscale values ​​of all gridded images in each part of the matrix, and then processing these grayscale values, the average grayscale value of each part of the matrix can be obtained.

[0120] A higher grayscale value indicates a higher smoke concentration, and vice versa. When the smoke concentration reaches the upper limit, the fire suppression system should be activated immediately.

[0121] In some specific embodiments, different fire extinguishing media are applied to the battery in stages, including:

[0122] When the image grayscale value exceeds the first predetermined value (the grayscale value of more than 50% of the grids in the gridded image exceeds the original grayscale value by more than 20%, or the average value of each part of the image matrix exceeds 100), it indicates that the battery has experienced thermal runaway and is accompanied by smoke generation. In this case, perfluorohexanone or heptafluoropropane is activated to extinguish the fire in the battery module and suppress the thermal runaway from spreading to other battery modules or PACKs.

[0123] When the image grayscale value exceeds the second predetermined value (the grayscale value of more than 80% of the grids in the gridded image exceeds 50% of the original grayscale value, or the average value of each part of the image matrix exceeds 200), it indicates that the battery has experienced thermal runaway. In this case, water spraying is used to extinguish the fire and suppress the continued spread of thermal runaway.

[0124] The original grayscale value represents the grayscale value of a normally operating energy storage station, and this value usually does not change unless an accident occurs.

[0125] In some specific embodiments, the battery is monitored and alerted at three levels, including:

[0126] like Figure 2 As shown, the battery undergoes primary monitoring: real-time monitoring of the battery's internal impedance and prediction of whether the battery's internal temperature is abnormal using a battery internal temperature prediction model; if the predicted battery internal temperature is abnormal (greater than 40°C), the battery is judged to have malfunctioned, and primary warning processing is initiated and secondary monitoring is activated; otherwise, primary monitoring is maintained.

[0127] like Figure 3 As shown, secondary monitoring is performed on the battery: monitoring characteristic gases and sounds inside the battery to determine the battery's thermal runaway status.

[0128] The characteristic gas and characteristic sound inside the battery are monitored separately. If the concentration of the characteristic gas or the characteristic sound inside the battery is abnormal, the second-level early warning process is initiated, the third-level monitoring state is activated, and measures to suppress battery thermal runaway are implemented. Check whether the risk of battery thermal runaway is eliminated. If it is eliminated, the second-level monitoring state is maintained. If it is not eliminated, the third-level monitoring state is activated.

[0129] If no abnormalities are found in the concentration of characteristic gases and characteristic sounds inside the battery, continue to maintain the level 2 monitoring status;

[0130] like Figure 4 As shown, the battery is monitored in three levels: image information inside the battery is collected, and the thermal runaway propagation inside the battery is judged by the image grayscale value; if the image grayscale value exceeds the abnormal range, the three-level early warning and fire protection processes are activated; if the thermal runaway propagation is resolved, the three-level early warning process is turned off, and the three-level monitoring status is maintained; if the thermal runaway propagation is not resolved, the three-level early warning process is maintained.

[0131] It is important to note that the three warning levels are progressive. A level two warning is triggered only after a level one warning has been triggered, and a level three warning is triggered only after a level two warning has been triggered. These three warning levels can also be set up in parallel, with the triggering of each warning method unaffected by other warnings. They can be deployed individually in the energy storage compartment, or any two can be deployed together in the energy storage compartment.

[0132] Based on the same inventive concept, embodiments of the present invention also provide an early warning system for electrochemical energy storage power stations based on multiple physical parameters, such as... Figure 5 As shown, it includes:

[0133] The temperature warning module is used to establish a battery internal temperature prediction model based on the relationship between battery internal impedance and temperature; it uses a monitoring system to monitor the battery internal impedance in real time and uses the battery internal temperature prediction model to predict the battery internal temperature to determine abnormal battery conditions and achieve a first-level warning for battery abnormalities.

[0134] The gas and sound early warning module is used to monitor the characteristic gases and sounds inside the battery using the monitoring system, to determine the battery thermal runaway situation, and to achieve a two-level early warning of battery abnormalities.

[0135] The image warning module is used to collect image information inside the battery, determine the thermal runaway propagation situation inside the battery, and realize a three-level warning for battery abnormalities.

[0136] In some specific embodiments, the gas sound warning module includes a hydrogen sensor, a carbon monoxide sensor, and an audio acquisition unit.

[0137] The gas-sound early warning module is used to monitor the hydrogen and carbon monoxide concentrations in the battery storage compartment in real time via a hydrogen sensor and a carbon monoxide sensor. If the hydrogen and carbon monoxide concentrations reach a set threshold, a secondary early warning for battery anomalies is activated, and measures to suppress battery thermal runaway are implemented; and / or,

[0138] The air-sound warning module is also used to monitor the sound signals inside the battery storage compartment in real time through the audio acquisition device, and to identify whether the sound signal is the characteristic sound signal emitted when the battery safety valve is opened; if the sound signal is the characteristic sound signal, a secondary warning for battery abnormality is activated, and measures to suppress battery thermal runaway are implemented.

[0139] In some specific embodiments, the image warning module includes a high-speed camera and an image signal processor;

[0140] The image early warning module uses a high-speed camera to continuously capture images of the battery module and processes the image grayscale through an image signal processor to identify areas of white smoke, thereby determining the area where the battery is experiencing thermal runaway. Once the thermal runaway area is identified, a three-level warning for battery abnormality is activated, and the stage of battery thermal runaway is determined based on the image processing results. Different fire extinguishing media are applied to the battery at different stages to prevent the spread of battery thermal runaway.

[0141] In some specific embodiments, such as Figure 6 As shown, in one of the energy storage compartments of the battery, each battery module is equipped with an impedance testing device (frequency measurement EIS) to measure the impedance information of the battery module. A hydrogen sensor, a carbon monoxide sensor and a sound signal detector are installed above each battery cluster, and a high-speed camera is installed on the left and right sides of the battery compartment for filming.

[0142] Regarding the system in the above embodiments, the specific manner in which each unit module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0143] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, the structure of which is as follows: Figure 7 As shown, it includes: a memory and a processor, wherein the processor is used to read and execute the computer program stored in the memory to implement the aforementioned early warning method for an electrochemical energy storage power station based on multiple physical parameters.

[0144] Based on the same inventive concept, embodiments of the present invention also provide a computer storage medium storing computer-executable instructions, which, when executed, implement the aforementioned early warning method for an electrochemical energy storage power station based on multiple physical parameters.

[0145] This invention can determine the internal temperature of the battery cell by monitoring the battery impedance online, judge the internal temperature change based on impedance fluctuations and issue timely warnings without damaging the battery cell itself. At the same time, it is equipped with a gas sound warning module to suppress heat spread, accurately locate the abnormal battery module, and finally determine the battery thermal runaway process through image recognition and promptly link fire-fighting facilities to suppress the spread of thermal runaway.

[0146] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for early warning of electrochemical energy storage power stations based on multiple physical parameters, characterized in that, include: Based on the relationship between battery internal impedance and temperature, a battery internal temperature prediction model is established; the battery internal impedance is monitored in real time, and the battery internal temperature is predicted using the battery internal temperature prediction model to determine battery abnormalities and achieve a first-level early warning for battery abnormalities. Monitor characteristic gases and sounds inside the battery to determine the battery's thermal runaway status and achieve a two-level early warning for battery anomalies; By acquiring image information of the battery's interior, the system can determine the extent of thermal runaway propagation within the battery, enabling a three-level early warning system for battery anomalies.

2. The early warning method for electrochemical energy storage power stations based on multiple physical parameters according to claim 1, characterized in that, Based on the relationship between battery internal impedance and temperature, a battery internal temperature prediction model is established, including: Using the multi-frequency fitting method, the single-frequency impedance of the battery is measured by frequency measurement EIS to obtain the electrochemical impedance spectrum and the imaginary impedance measurement value corresponding to each single frequency point. During the measurement of the single-frequency impedance of the battery, the internal temperature of the battery corresponding to each single-frequency point is measured. The imaginary impedance measurement value and the internal temperature of the battery corresponding to each single frequency point are fitted multiple times to obtain the single frequency point temperature model. Based on the single-frequency temperature model, the internal temperature of the battery corresponding to each single frequency point is corrected to obtain a battery internal temperature prediction model. Among them, the impedance of the battery at a single frequency point within 10Hz-100Hz is measured by frequency measurement EIS.

3. The early warning method for electrochemical energy storage power stations based on multiple physical parameters according to claim 2, characterized in that, Real-time monitoring of battery internal impedance and prediction of battery internal temperature using an internal temperature prediction model to identify abnormal battery conditions, enabling first-level early warning of battery anomalies, including: If the predicted value of the battery internal temperature prediction model exceeds a certain range of the set average battery temperature, a first-level warning signal for battery abnormality will be issued, and battery charging will be stopped. The single-frequency temperature model is as follows: In the formula, T x Z″ represents the predicted internal temperature of the battery at an impedance of x Hz, where x represents the impedance value at a single frequency point. x c represents the imaginary impedance measurement at x Hz. x d x Both represent Z″ at impedance x Hz. x With T x Parameters after nonlinear fitting; After temperature correction of the battery internal temperature for each single frequency point, the following results were obtained: T=y1T 10 +y2T 20 +……+y 10 T 100 +a (2), In the formula, T represents the predicted value of the battery's internal temperature. 10 T 20 ...T 100 These represent the predicted internal battery temperatures from 10Hz to 100Hz, y1, y2...y 10 T represents respectively 10 T 20 ...T 100 The coefficient, where a represents a constant; Substituting the single-frequency temperature model into formula (2), the battery internal temperature prediction model is obtained as follows: Polynomial fitting is performed on each single-frequency temperature model in the battery internal temperature prediction model to obtain parameters y1, y2...y 10 And the value of a.

4. A method for early warning of electrochemical energy storage power stations based on multiple physical parameters according to claim 1, 2, or 3, characterized in that, Monitor characteristic gases and sounds inside the battery to determine the battery's thermal runaway status and achieve a two-level early warning system for battery anomalies, including: Using hydrogen and carbon monoxide sensors, the concentration of combustible gases in the battery storage compartment is monitored in real time. If the concentration of combustible gases reaches a set threshold, a secondary warning for battery anomalies is activated, and measures to suppress battery thermal runaway are implemented; and / or, The sound signal inside the battery storage compartment is monitored by an audio acquisition device, and it is identified whether the sound signal is the characteristic sound signal emitted when the battery safety valve is opened; if the sound signal is the characteristic sound signal, a second-level warning for battery abnormality is activated, and measures to suppress battery thermal runaway are implemented. This includes initiating a level-two warning for battery anomalies and implementing measures to suppress battery thermal runaway, including: The battery was de-energized, and fire extinguishing agents were used to extinguish the fire at the location of the battery hazard to prevent the spread of heat. The extinguishing medium is perfluorohexanone or heptafluoropropane.

5. The early warning method for electrochemical energy storage power stations based on multiple physical parameters according to claim 1, characterized in that, By acquiring internal image information of the battery, determining the spread of thermal runaway within the battery, and implementing a three-level early warning system for battery anomalies, including: A high-speed camera is used to continuously capture images of the battery module, and the grayscale of the images is processed to identify areas of white smoke, thereby determining the area where the battery is experiencing thermal runaway. Once the thermal runaway area is identified, a three-level warning for battery anomalies is activated. Based on the image processing results, the stage of battery thermal runaway is determined, and different fire extinguishing media are applied to the battery in different stages to prevent the spread of battery thermal runaway. Different fire extinguishing media are used for the batteries in different stages, including: When the image grayscale value exceeds the first predetermined value, it indicates that the battery has experienced thermal runaway and is accompanied by smoke generation. In this case, perfluorohexanone or heptafluoropropane is activated to extinguish the fire in the battery module and suppress the spread of thermal runaway to other battery modules or PACKs. When the image grayscale value exceeds the second predetermined value, it indicates that the battery has experienced thermal runaway. In this case, water spraying is used to extinguish the fire and suppress the continued spread of thermal runaway.

6. The early warning method for electrochemical energy storage power stations based on multiple physical parameters according to claim 5, characterized in that, A high-speed camera is used to continuously capture images of the battery module, and the grayscale of the images is processed to identify areas of white smoke, thereby determining the areas where thermal runaway of the battery has occurred, including: The process of white smoke spreading was captured using a high-speed camera, resulting in multiple RGB images. The captured RGB image is represented using a two-dimensional matrix. Based on the two-dimensional array or matrix of the RGB image, the RGB image is converted to grayscale to obtain a grayscale image. The grayscale image is then processed to obtain a smoke grayscale image. The smoke grayscale image is divided into several parts based on a two-dimensional matrix, thus obtaining a matrix with multiple parts; each part of the matrix is ​​discretized to obtain the corresponding gridded image; By processing the grayscale values ​​of the gridded images, the average grayscale value of each part of the matrix is ​​obtained; the white smoke concentration is determined by the grayscale values ​​of each gridded image or by the grayscale values ​​of each part of the matrix, so as to obtain the white smoke region. Among them, the Gamma correction algorithm is used to calculate the gray value of each gridded image, and the average gray value of each part of the matrix is ​​obtained from the gray value of each gridded image. The average gray value is the sum of the gray values ​​of each gridded image and divided by the set number of grids.

7. The early warning method for electrochemical energy storage power stations based on multiple physical parameters according to claim 1, characterized in that, The battery undergoes primary monitoring: real-time monitoring of the battery's internal impedance and prediction of whether the battery's internal temperature is abnormal using a battery internal temperature prediction model; if an abnormal battery internal temperature is predicted, the battery is judged to have malfunctioned, and primary warning processing is initiated and secondary monitoring is activated; otherwise, primary monitoring is maintained. Secondary monitoring of the battery: Monitoring characteristic gases and sounds inside the battery to determine the extent of thermal runaway. The characteristic gas and characteristic sound inside the battery are monitored separately. If the concentration of the characteristic gas or the characteristic sound inside the battery is abnormal, the second-level early warning process is initiated, the third-level monitoring state is activated, and measures to suppress battery thermal runaway are implemented. Check whether the risk of battery thermal runaway is eliminated. If it is eliminated, the second-level monitoring state is maintained. If it is not eliminated, the third-level monitoring state is activated. If no abnormalities are found in the concentration of characteristic gases and characteristic sounds inside the battery, continue to maintain the level 2 monitoring status; The battery is monitored in three levels: image information inside the battery is collected, and the thermal runaway propagation inside the battery is judged by the image grayscale value; if the image grayscale value exceeds the abnormal range, the three-level early warning and fire protection measures are activated. If the thermal runaway situation is resolved, the Level 3 warning procedure will be deactivated, and the Level 3 monitoring status will continue; if the thermal runaway situation is not resolved, the Level 3 warning procedure will remain in effect.

8. An early warning system for an electrochemical energy storage power station based on multiple physical parameters, characterized in that, include: The temperature warning module is used to establish a battery internal temperature prediction model based on the relationship between battery internal impedance and temperature. The monitoring system monitors the internal impedance of the battery in real time, and the battery internal temperature prediction model is used to predict the internal temperature of the battery to determine abnormal battery conditions, so as to achieve a first-level early warning of battery abnormalities. The gas and sound early warning module is used to monitor the characteristic gases and sounds inside the battery using the monitoring system, to determine the battery thermal runaway situation, and to achieve a two-level early warning of battery abnormalities. The image warning module is used to collect image information inside the battery, determine the thermal runaway propagation situation inside the battery, and realize a three-level warning for battery abnormalities.

9. The early warning system for an electrochemical energy storage power station based on multiple physical parameters according to claim 8, characterized in that, The gas sound warning module includes a hydrogen sensor, a carbon monoxide sensor and an audio collector, and the image warning module includes a high-speed camera and an image signal processor. The gaseous warning module is used to monitor the hydrogen and carbon monoxide concentrations in the battery storage compartment in real time via the hydrogen sensor and the carbon monoxide sensor. If the hydrogen and carbon monoxide concentrations reach a set threshold, a secondary warning for battery anomalies is activated, and measures to suppress battery thermal runaway are implemented; and / or, The gas sound warning module is also used to monitor the sound signal in the battery energy storage compartment in real time through the audio collector, and identify whether the sound signal is the characteristic sound signal emitted when the battery safety valve is opened; if the sound signal is the characteristic sound signal, then a secondary warning for battery abnormality is activated, and measures to suppress battery thermal runaway are implemented. The image early warning module is used to continuously capture images of the battery module using a high-speed camera, and to process the grayscale of the images using an image signal processor to identify areas of white smoke, thereby determining the area where the battery has experienced thermal runaway. Once the thermal runaway area is identified, a three-level warning for battery anomalies is activated. Based on the image processing results, the stage of battery thermal runaway is determined, and different fire extinguishing media are applied to the battery in different stages to prevent the spread of battery thermal runaway.

10. An electronic device, characterized in that, include: Memory, processor; The processor is used to read and execute the computer program stored in the memory to implement the early warning method for an electrochemical energy storage power station based on multiple physical parameters as described in any one of claims 1-7.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, implement the early warning method for an electrochemical energy storage power station based on multiple physical parameters as described in any one of claims 1-7.

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