Gas sensor, battery leakage monitoring method, system and device

Abstract

The invention relates to a gas sensor and a battery leakage monitoring method, system and device. The gas sensor comprises a gas sensitive material layer, a test electrode, a heating electrode and a heating material layer which are sequentially stacked, the gas sensitive material layer comprises a metal oxide and a noble metal which are coated on the test electrode, and the heating material layer comprises a metal oxide which is coated on the heating electrode. The gas sensor can support accurate monitoring of battery leakage. The method comprises the following steps: when it is detected that the change amplitude of battery monitoring parameters output by the gas sensor in a photo-thermal combined excitation environment exceeds a preset change amplitude threshold for the first time, sending a light switching instruction to a connected light source device, and extracting change characteristic data of a plurality of battery monitoring parameters in a preset time period, and carrying out classification identification processing on the change characteristic data to obtain a battery leakage identification result. By adopting the method, accurate monitoring of battery leakage can be realized.

Description

Technical Field

[0001] The present application relates to the field of sensor and battery technology, and in particular to a gas sensor, and a battery leakage monitoring method, device, system, computer equipment, storage medium and computer program product. Background Art

[0002] Battery leakage refers to the phenomenon that liquid, gas or other substances overflow from the inside of the battery. Factors such as overcharging and discharging, high temperature, mechanical damage or improper use may increase the possibility of battery leakage. In order to detect battery leakage in time and take measures to reduce the occurrence of safety accidents within an effective time, it is very necessary to monitor the battery leakage during battery use.

[0003] At present, commonly used battery leakage monitoring solutions mainly include using sensors such as temperature sensors, optical sensors, and corresponding detection devices such as electrolyte chemical sensors to monitor battery leakage. However, the current battery leakage monitoring solutions are still difficult to effectively monitor whether the battery has leakage, etc., and there is a problem of low monitoring accuracy. Summary of the invention

[0004] Based on this, it is necessary to provide a gas sensor that can improve the accuracy of battery leakage monitoring, as well as a battery leakage monitoring method, device, system, computer equipment, storage medium and computer program product to address the above technical problems.

[0005] In a first aspect, the present application provides a gas sensor. The gas sensor comprises: a gas-sensitive material layer, a test electrode, a heating electrode and a heating material layer stacked in sequence;

[0006] The gas-sensitive material layer includes a metal oxide and a noble metal coated on a test electrode;

[0007] The heating material layer includes a metal oxide coated on the heating electrode.

[0008] In the technical solution of the embodiment of the present application, the gas sensor can selectively detect specific gases or gas components through the gas-sensitive material layer to achieve the detection of different gases. It can also quickly increase the temperature of the gas-sensitive material layer by heating the material layer, thereby quickly responding to changes in the gas. In addition, the gas-sensitive material layer uses precious metals to load metal oxides, which can detect low-concentration gases and accurately detect target gases. It has good anti-interference performance against changes in environmental conditions such as temperature and humidity, and can ensure the accuracy and stability of the sensor. In summary, the above-mentioned gas sensor has the characteristics of high sensitivity, high selectivity and high stability, and can support accurate monitoring of battery leakage.

[0009] In some embodiments, the gas-sensitive material layer includes tungsten trioxide doped with palladium, and the molar ratio of palladium to tungsten trioxide is (0.5-2):100.

[0010] In the technical solution of the embodiment of the present application, by selecting palladium-doped tungsten trioxide in the gas-sensitive material, the sensitivity, selectivity and stability of the gas sensor can be effectively improved, thereby supporting more accurate monitoring of battery leakage.

[0011] In some embodiments, the gas sensitive material layer includes palladium and tungsten trioxide coated in an overlying manner.

[0012] In the technical solution of the embodiment of the present application, by adopting the superimposed coating of tungsten trioxide and palladium, the contact area between the gas-sensitive material and the gas can be effectively expanded, thereby improving the response rate and sensitivity of gas detection, thereby improving the accuracy and reliability of the detection results.

[0013] In some embodiments, the molar ratio of palladium to tungsten trioxide is (0.5-2):100.

[0014] In the technical solution of the embodiment of the present application, by setting the molar ratio of palladium and tungsten trioxide to (0.5-2):100, the sensitivity of the gas sensor can be further improved, so that the gas sensor can detect lower concentrations of gas and achieve better detection effects.

[0015] In some embodiments, the gas-sensitive material layer has a porous structure.

[0016] In the technical solution of the embodiment of the present application, a porous structure is left in the gas-sensitive material layer to expand the contact area between the gas-sensitive material and the gas, so that more gas molecules react with the gas-sensitive material, thereby improving the sensitivity of gas detection and accelerating the reaction rate between the gas molecules and the gas-sensitive material, thereby improving the response speed. In addition, the stability of the gas-sensitive material can also be increased, thereby improving the accuracy and reliability of the detection results.

[0017] In some embodiments, the test electrodes include arc-shaped interdigitated electrodes.

[0018] In the technical solution of the embodiment of the present application, by selecting arc-shaped interdigitated electrodes, the current distribution can be made more uniform, and the resistance of the gas-sensitive material layer can be reduced to a certain extent, which facilitates testing.

[0019] In a second aspect, the present application also provides a battery leakage monitoring method. The method comprises:

[0020] Obtaining battery monitoring parameters output by a gas sensor in a light-heat combined excitation environment, the gas sensor including the gas sensor in each of the above gas sensor embodiments;

[0021] When it is detected that the change amplitude of the battery monitoring parameter exceeds the preset change amplitude threshold for the first time, a light switching instruction is sent to the connected light source device, so that the light source device switches the type of light emitted, and the wavelength of the light after switching is shorter than the wavelength of the light before switching;

[0022] Extracting characteristic data of changes in a plurality of battery monitoring parameters within a preset time period, wherein the plurality of battery monitoring parameters within the preset time period include battery monitoring parameters output by the gas sensor after the wavelength of the light is switched;

[0023] The change feature data is classified and identified to obtain the battery leakage identification result.

[0024] In the technical solution of the embodiment of the present application, by conducting a combined photothermal excitation test on the precious metal-loaded metal oxide gas sensor, the sensitivity of the gas sensor can be greatly improved, and the stability of the gas sensor is greatly improved while ensuring the sensitivity and detection limit. When a drastic change in the resistance output of the gas sensor is detected for the first time, switching the wavelength of light can amplify the change amplitude of the battery monitoring parameters, thereby enabling classification and identification of the change characteristic data of the battery monitoring parameters with more obvious change amplitudes, and obtaining more accurate monitoring results.

[0025] In some embodiments, extracting the change characteristic data of multiple battery monitoring parameters within a preset time period includes:

[0026] The variation characteristics of a plurality of battery monitoring parameters within a first preset time period are extracted, and the starting point of the first preset time period is the moment when the light source device switches the wavelength of the light.

[0027] In the technical solution of the embodiment of the present application, by extracting the change characteristic data of the battery monitoring parameters after the light is switched, and performing classification and identification based on the transformed characteristic data, not only can the time for gas classification and identification be significantly shortened, but also it can be more accurately determined whether the battery is leaking.

[0028] In some embodiments, extracting the change characteristic data of multiple battery monitoring parameters within a preset time period includes:

[0029] The variation characteristics of multiple battery monitoring parameters within a second preset time period are extracted, the starting point of the second preset time period is before the moment when the light source device switches the wavelength of the light, and the end point of the second time period is after the moment when the light source device switches the wavelength of the light.

[0030] In the technical solution of the embodiment of the present application, the change characteristic data of the battery monitoring parameters in the preset time period from before the light switching to after the light switching is extracted, and classification and identification are performed based on the transformed characteristic data. This can not only more comprehensively and accurately reflect the changes in the battery monitoring parameters during the monitoring process and improve the accuracy of the battery leakage monitoring results, but also effectively shorten the time for gas classification and identification compared to the traditional extraction of battery monitoring parameters during the entire monitoring process.

[0031] In some embodiments, the change feature data is classified and identified to obtain a battery leakage identification result, including:

[0032] Taking the change feature data as input, calling the trained battery leakage recognition model to classify and recognize the change feature data, and obtaining the battery leakage recognition result;

[0033] Among them, the battery leakage identification model is trained based on historical battery monitoring parameter change characteristic data and battery leakage annotation data.

[0034] In the technical solution of the embodiment of the present application, by calling the trained battery leakage recognition model to classify and identify the change feature data, it is possible to accurately identify battery leakage and classify different types of leakage situations, thereby improving battery monitoring efficiency.

[0035] In some embodiments, extracting the change characteristic data of multiple battery monitoring parameters within a preset time period includes:

[0036] Generate a battery monitoring parameter change curve according to multiple battery monitoring parameters within a preset time period;

[0037] The slope characteristic data of the battery monitoring parameter change curve is extracted, and the change characteristic data includes the slope characteristic data.

[0038] In the technical solution of the embodiment of the present application, by extracting the slope characteristics of the battery monitoring parameter change curve, the change amplitude of the battery monitoring parameters can be accurately determined. Compared with directly processing the original battery monitoring parameters, the recognition time can be significantly shortened, the data processing process is simplified, and the processing speed and efficiency are improved.

[0039] In some embodiments, the battery monitoring parameter includes a resistance value;

[0040] Generating a battery monitoring parameter change curve according to multiple battery monitoring parameters within a preset time period includes:

[0041] A resistance change curve is generated according to a plurality of resistance values ​​within a preset time period, and the battery monitoring parameter change curve includes a resistance change curve.

[0042] In the technical solution of the embodiment of the present application, by extracting the slope characteristics of the resistance change curve and classifying and identifying the resistance slope characteristics, battery leakage identification and classification can be achieved quickly and accurately, and battery leakage conditions can be monitored in real time.

[0043] In some embodiments, before extracting the change characteristic data of multiple battery monitoring parameters within a preset time period, the method further includes:

[0044] Performing data preprocessing on a plurality of battery monitoring parameters within a preset time period, wherein the data preprocessing includes at least one of data cleaning, data normalization, and feature selection;

[0045] Extracting the change characteristic data of multiple battery monitoring parameters within a preset time period includes:

[0046] Extract the change characteristic data of multiple battery monitoring parameters within a preset time period after data preprocessing.

[0047] In the technical solution of the embodiment of the present application, by performing data preprocessing on the battery monitoring parameters, the standardization and quality of the data can be improved, the adverse effect of some noise data on the results of battery leakage identification can be reduced, and the accuracy of the battery leakage identification results can be improved.

[0048] In a third aspect, the present application also provides a battery leakage monitoring system. The system comprises: a battery, a gas sensor and a controller connected in communication;

[0049] The gas sensor includes the gas sensor in the above-mentioned gas sensor embodiment, and the controller is used to execute the steps in the above-mentioned battery leakage monitoring method embodiment to monitor whether the battery is leaking.

[0050] In a fourth aspect, the present application provides a battery leakage monitoring device. The device comprises:

[0051] A data acquisition module, used to acquire battery monitoring parameters output by a gas sensor in a light-heat combined excitation environment, wherein the gas sensor includes the gas sensor in each of the above gas sensor embodiments;

[0052] The instruction sending module is used to send a light switching instruction to the connected light source device when it is detected that the change amplitude of the battery monitoring parameter exceeds the preset change amplitude threshold for the first time, so that the light source device switches the type of light emitted, and the wavelength of the light after switching is shorter than the wavelength of the light before switching;

[0053] A data extraction module, used to extract characteristic data of changes in a plurality of battery monitoring parameters within a preset time period, wherein the plurality of battery monitoring parameters within the preset time period include battery monitoring parameters output by the gas sensor after the wavelength of the light is switched;

[0054] The leakage identification module is used to classify and identify the change feature data to obtain the battery leakage identification result.

[0055] In a fifth aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the above-mentioned battery leakage monitoring method embodiment are implemented.

[0056] In a sixth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned battery leakage monitoring method embodiment are implemented.

[0057] In a seventh aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned battery leakage monitoring method embodiment are implemented.

[0058] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a schematic diagram of the structure of a gas sensor in some embodiments of the present application;

[0060] Figure 2 A comparison diagram of the response of the gas sensor to different palladium doping ratios in some embodiments of the present application;

[0061] Figure 3 A comparison diagram of the resistance of the gas sensitive material layer monitored by the gas sensor for different palladium doping ratios in some embodiments of the present application;

[0062] Figure 4 This is a schematic diagram of the structure of a gas sensor in some embodiments of the present application;

[0063] Figure 5 This is a diagram of the application environment of the battery leakage monitoring method in some embodiments of the present application;

[0064] Figure 6 A schematic diagram of a flow chart of a battery leakage monitoring method in some embodiments of the present application;

[0065] Figure 7 This is a schematic diagram of the change in resistance of the gas-sensitive material layer monitored by the gas sensor after the light source device switches the light in some embodiments of the present application;

[0066] Figure 8 A schematic diagram of a flow chart of a battery leakage monitoring method in some other embodiments of the present application;

[0067] Fig. 9 A schematic diagram of the change in resistance of the gas-sensitive material layer monitored by the gas sensor in the case of battery leakage in some embodiments of the present application;

[0068] Fig.10 A schematic diagram of the change in resistance of the gas sensitive material layer monitored by the gas sensor when the gas sensor is aged in some other embodiments of the present application;

[0069] Fig.11 Detailed flowchart of the battery leakage monitoring method in some embodiments of the present application;

[0070] Fig.12 This is an architectural diagram of a battery leakage monitoring system in some embodiments of the present application;

[0071] Fig.13 This is a structural block diagram of a battery leakage monitoring device in some embodiments of the present application;

[0072] Fig.14 A structural block diagram of a battery leakage monitoring device in some other embodiments of the present application;

[0073] Fig.15 This is a diagram of the internal structure of a computer device in some embodiments of the present application. DETAILED DESCRIPTION

[0074] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0076] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0077] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0078] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0079] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0080] At present, from the perspective of market development, the application of power batteries is becoming more and more extensive. Lithium batteries have been widely used in power station energy storage, home appliances, automobiles and other consumer fields due to their high energy density and long cycle life. Especially with the increasing energy problems, the application of lithium batteries in electric vehicles and electrochemical energy storage has developed rapidly and gradually formed a scale. However, with the occurrence of battery safety accidents, battery safety issues have attracted more and more attention.

[0081] Battery leakage refers to the phenomenon that electrolyte, gas or other substances overflow from the inside of the battery. Storing batteries in a high temperature, humid or damp environment will accelerate the corrosion and damage of the battery. Overcharging and discharging, high temperature, mechanical damage to the battery shell, or improper use such as reversed positive and negative poles may increase the possibility of battery leakage. In order to detect battery leakage in time and take measures within an effective time to reduce the occurrence of safety accidents, it is very necessary to monitor the battery for leakage during battery use.

[0082] At present, common battery leakage monitoring solutions mainly include installing temperature sensors around the battery to monitor temperature changes to determine whether there is battery leakage, or using gas sensors to monitor volatile organic compounds generated by battery leakage to determine whether the battery is leaking, or using liquid level sensors to monitor the battery liquid level to detect liquid overflow in time, or using electrolyte chemical sensors to monitor whether the battery is leaking. However, the above battery leakage monitoring solutions are still difficult to effectively monitor whether there is battery leakage, etc., and there is a problem of low monitoring accuracy.

[0083] Based on the above considerations, in order to improve the accuracy of battery leakage monitoring, the present application designs a gas sensor, in which a gas-sensitive material layer and a heating material layer are stacked, and the gas-sensitive material of the gas-sensitive material layer includes a noble metal-loaded metal oxide. By arranging a heating material layer and a gas-sensitive material layer on the gas sensor, it is possible to quickly respond to gas changes. The gas-sensitive material of the gas-sensitive material layer includes noble metals and metal oxides, and can detect gases with very low concentrations, and can accurately detect target gases and exclude the influence of other interfering gases, thereby supporting accurate monitoring of battery leakage.

[0084] In addition, based on the above-mentioned gas sensor, a battery leakage monitoring method is also proposed, which specifically includes:

[0085] First, different from the thermal excitation method of traditional gas sensors, light excitation is introduced on the basis of thermal excitation, and the gas sensor with a heating material layer and a gas-sensitive material layer is subjected to combined photothermal excitation, that is, on the one hand, the voltage applied to the heating electrode can be controlled by the circuit to adjust the heating temperature, and the heating material layer generates heat under the action of the voltage, which serves as a heat source to provide temperature for the gas sensor. On the other hand, the gas-sensitive material layer is continuously irradiated with an external light source device to provide stable light for the gas sensor. By introducing light excitation, the surface of the sensitive material can be effectively activated, the carrier concentration can be increased, the initial resistance can be reduced, and the detection of the gas to be tested can be realized at room temperature. In addition, the photogenerated adsorbed oxygen ions formed after oxygen captures the photogenerated electrons are more reactive, which is beneficial to improving the gas-sensitive response. The strong oxidizing property of the photogenerated holes is also beneficial to promote the adsorption of the gas to be tested, thereby improving the recovery process of the gas sensor.

[0086] Then, the battery monitoring parameters such as current, resistance and other battery monitoring parameters output by the gas sensor under the light-heat combined excitation environment are obtained. When the change amplitude of the battery monitoring parameters is detected to exceed the preset change amplitude threshold for the first time, a light switching instruction is sent to the connected light source device. The light source device can be pre-set with the wavelength switching sequence and switching interval time. After receiving the light switching instruction, the wavelength of the currently emitted light is changed according to the preset wavelength switching sequence and switching interval time. For example, the light source device can be switched from the initial red light to blue light, and then from blue light to ultraviolet light. The switching time interval can be 3 minutes, so that light with different energies can be used to provide illumination for the gas sensor and amplify the change amplitude of the current monitoring parameter.

[0087] Next, the variation characteristic data of a plurality of battery monitoring parameters within a preset time period are extracted, wherein the plurality of battery monitoring parameters within the preset time period include the battery monitoring parameters output by the gas sensor after the wavelength of the light is switched.

[0088] Finally, the changing characteristic data of the battery monitoring parameters are classified and identified to determine whether the battery is leaking, or whether the gas environment has changed due to uncontrollable factors such as sensor aging and thermal runaway, causing a drastic change in the battery monitoring parameters. Through the above judgment and identification, the battery leakage identification result is obtained.

[0089] The above-mentioned battery leakage monitoring method can greatly improve the sensitivity of the gas sensor by conducting a combined photothermal excitation test on the precious metal-loaded metal oxide gas sensor, and greatly improve the stability of the gas sensor while ensuring the sensitivity and detection limit. When the change amplitude of the battery monitoring parameter exceeds the preset change amplitude threshold for the first time, a light switching instruction is issued to switch the light wavelength and amplify the change amplitude of the battery monitoring parameter, thereby making it possible to classify and identify the change characteristics of the battery monitoring parameter after the change amplitude is amplified, and the monitoring result obtained is more accurate.

[0090] In some embodiments, Figure 1 As shown, a gas sensor is provided, comprising: a gas-sensitive material layer 110, a test electrode 120, a heating electrode 130 and a heating material layer 140 arranged in a stacked manner; wherein,

[0091] The gas-sensing material layer 110 includes a metal oxide and a noble metal coated on the test electrode 120 , and the heating material layer 140 includes a metal oxide coated on the heating electrode 130 .

[0092] In this embodiment, the gas sensor may be a stacked arrangement of a heating material layer 140 , a heating electrode 130 , a test electrode 120 , and a gas-sensitive material layer 110 .

[0093] Wherein, the heating material layer 140 is coated on the heating electrode 130, and the heating material selected for the heating material layer 140 includes metal oxides, and the heating material may include but is not limited to ruthenium oxide. The gas-sensitive material layer 110 is coated on the test electrode 120, and the gas-sensitive material selected for the gas-sensitive material layer 110 is a metal oxide semiconductor and a precious metal that are sensitive to the battery electrolyte. Wherein, the metal oxide includes but is not limited to tungsten trioxide or tungsten oxide, etc., and the precious metal includes but is not limited to platinum, silver or palladium, etc. In other embodiments, the gas-sensitive material layer 110 can also use other materials that are sensitive to the electrolyte, such as metal sulfides, organic polymers, etc. In this embodiment, the test electrode 120 and the heating electrode 130 include but are not limited to gold electrodes. In other embodiments, the test electrode 120 and the heating electrode 130 can be precious metal electrodes such as platinum, silver or palladium.

[0094] It is understood that in other embodiments, in addition to the above-mentioned gas-sensitive material layer 110, test electrode 120, heating electrode 130 and heating material layer 140, the gas sensor may also include an insulating substrate and four external pins for measurement. Specifically, the external pins, heating material layer 140, heating electrode 130, insulating substrate, test electrode 120 and gas-sensitive material layer 110 may be stacked in sequence in the vertical direction. If the gas sensor is a pancake-shaped sensor, the external pins, heating material layer 140, heating electrode 130, insulating substrate, test electrode 120 and gas-sensitive material layer 110 may be stacked in sequence in the axial direction of the pancake-shaped sensor. Among them, the substrate may be a ceramic substrate, specifically, an alumina ceramic sheet. In order to make the substrate smaller in size and lighter in weight, and to better withstand various environmental influences such as forces and vibrations during actual use, after experiments, the diameter of the alumina ceramic sheet in this embodiment may be 3 mm and the thickness may be 0.5 mm. The four external pins are divided into two pairs, located at both ends of the ceramic substrate, and are connected to the test electrode 120 and the heating electrode 130 respectively. The test electrode 120 and the heating electrode 130 are arranged on both sides of the ceramic substrate, and are both gold electrodes. The heating material layer 140 is ruthenium oxide, connected to the heating electrode 130, and located in the middle of the ceramic substrate, which can provide a uniform and stable thermal field for the sensor. The gas-sensitive material layer 110 uses metal oxide semiconductors and precious metals that are sensitive to electrolytes. It is understandable that in other embodiments, the insulating substrate includes but is not limited to glass and silicon wafers. The heating electrode 130 and the test electrode 120 may include but are not limited to precious metal electrodes, and the gas-sensitive material layer 110 may include but are not limited to metal sulfides or organic polymers.

[0095] In the technical solution of the embodiment of the present application, the gas sensor can selectively detect specific gases or gas components through the gas-sensitive material layer to realize the detection of different gases. By heating the material layer, the temperature of the gas-sensitive material layer can be quickly increased, so that the gas sensor can quickly respond to changes in the gas. In addition, the gas-sensitive material layer uses precious metals to load metal oxides, which can detect very low concentrations of gas, and can accurately detect target gases and eliminate the influence of other interfering gases. It has good anti-interference performance against changes in environmental conditions such as temperature and humidity, and can ensure the accuracy and stability of the sensor. In summary, the above-mentioned gas sensor has the characteristics of high sensitivity, high selectivity and high stability, and can support accurate monitoring of battery leakage.

[0096] In some embodiments, the gas sensitive material layer 110 includes tungsten trioxide doped with palladium.

[0097] In this embodiment, the gas-sensitive material of the gas-sensitive material layer 110 includes tungsten trioxide and palladium. Palladium is an excellent gas-sensitive material that can adsorb and desorb hydrogen at room temperature, and therefore has high gas sensitivity. Doping palladium into other materials can improve the gas sensitivity of the material, especially for some gases that are easily oxidized. Tungsten trioxide itself is also a good gas-sensitive material with high sensitivity, selectivity and repeatability. Doping tungsten trioxide can improve its electrical properties and response speed, and can also increase its life cycle and anti-interference ability. Based on the above considerations, in this embodiment, the gas-sensitive material layer 110 can be a tungsten trioxide doped with a certain proportion of palladium to improve the sensitivity of the gas sensor.

[0098] In the technical solution of the embodiment of the present application, by selecting palladium-doped tungsten trioxide in the gas-sensitive material, the sensitivity, selectivity and stability of the gas sensor can be effectively improved, thereby supporting more accurate monitoring of battery leakage.

[0099] In some embodiments, in order to enhance the gas sensing response capability of the gas-sensitive material, the ratio of the molar number of palladium to tungsten trioxide (hereinafter referred to as the doping ratio of palladium) is studied in depth, and relevant experiments are conducted. The experimental data show that when the doping ratio of palladium is in the range of 0.5% to 2.0%, the gas response capability of the gas-sensitive material layer is better. Therefore, in this embodiment, the preferred ratio range of palladium to tungsten trioxide can be: the molar ratio of palladium to tungsten trioxide is (0.5 to 2):100.

[0100] In the technical solution of the embodiment of the present application, by setting the molar ratio of palladium and tungsten trioxide to (0.5-2):100, the sensitivity of the gas sensor can be further improved, so that the gas sensor can detect lower concentrations of gas and achieve better detection effects.

[0101] In other embodiments, the doping ratio of palladium (also called the loading ratio of palladium) can be 0.5%, 1%, 1.5% and 2%. In practical applications, experiments were conducted with the doping ratio of palladium being 0.5%, 1%, 1.5% or 2%, respectively. The experimental results can be found in Figure 2 and Figure 3 It has been found through experiments that when the doping ratio of palladium is 1.5%, the response capability of the gas-sensitive material layer is optimal and the resistance value output by the gas sensor is larger. Therefore, in a preferred embodiment, the molar ratio of palladium to tungsten trioxide can be 1.5:100.

[0102] In some embodiments, the gas sensitive material layer 110 includes stacked coatings of tungsten trioxide and palladium.

[0103] In this embodiment, different from the doping method of the previous embodiment, a method of superimposed coating of tungsten trioxide and a preset proportion of palladium is adopted, and superimposed coating can also be understood as superimposed printing. Specifically, tungsten trioxide may be first coated on the test electrode 120, and then a preset proportion of palladium may be superimposed on the surface of the tungsten trioxide. Alternatively, a preset proportion of palladium may be first coated on the test electrode 120, and then tungsten trioxide may be superimposed on the surface of the palladium. Similarly, in this embodiment, the molar ratio of palladium to tungsten trioxide may be (0.5-2):100. In other embodiments, the molar ratio of palladium to tungsten trioxide may include but is not limited to 1.5:100. In other embodiments, the gas-sensitive material layer 110 may also be formed by multiple superimposed coatings of other different sensitive materials.

[0104] In the technical solution of the embodiment of the present application, by adopting the method of superimposed coating of tungsten trioxide and palladium, the contact area between the gas-sensitive material and the gas can be effectively expanded, thereby improving the response rate and sensitivity of gas detection, thereby improving the accuracy and reliability of the detection results.

[0105] In some embodiments, the gas-sensitive material layer 110 has a porous structure.

[0106] In this embodiment, the gas-sensitive material layer 110 may have a large number of porous structures to further expand the contact area and reaction degree between the gas-sensitive material and the gas. Specifically, following the previous embodiment, if the method of superimposing tungsten trioxide and palladium is adopted, a certain proportion of pore-forming agent can be added during the coating of tungsten trioxide and palladium, and the pore-forming agent is removed during the sintering process, thereby leaving a large number of porous structures in the gas-sensitive material layer 110.

[0107] In the technical solution of the embodiment of the present application, a porous structure is left in the gas-sensitive material layer to expand the contact area between the gas-sensitive material and the gas, so that more gas molecules react with the gas-sensitive material, thereby improving the sensitivity of gas detection and accelerating the reaction rate between the gas molecules and the gas-sensitive material, thereby improving the response speed. In addition, the stability of the gas-sensitive material can also be increased, thereby improving the accuracy and reliability of the detection results.

[0108] In some embodiments, the test electrode 120 includes arc-shaped interdigitated electrodes.

[0109] In this embodiment, different from the traditional planar electrode, the test electrode can be an arc-shaped interdigital electrode. The arc-shaped electrode can better concentrate the current and make the current distribution more uniform, which can reduce the resistance of the gas-sensitive material layer to a certain extent and facilitate the test.

[0110] In the technical solution of the embodiment of the present application, by selecting arc-shaped interdigitated electrodes, the current distribution can be made more uniform, and the resistance of the gas-sensitive material layer can be reduced to a certain extent, which facilitates testing.

[0111] In order to make a clearer description of the gas sensor provided in this application, Figure 4 and one A specific embodiment is described, wherein the Figure 4 A top view, a front view and a bottom view of the gas sensor are provided. The embodiment includes the following contents:

[0112] In this embodiment, taking the gas sensor as a circular gas sensor as an example, in the axial direction of the circular gas sensor, four external pins (not shown in the figure), a heating material layer 140, a heating electrode 130, a ceramic substrate 122, a test electrode 120 and a gas-sensitive material layer 110 are stacked in sequence. Among them, the ceramic substrate 122 can be an alumina ceramic sheet, and its diameter can be 3 mm and its thickness can be 0.5 mm. The four external pins are divided into two pairs, located at both ends of the ceramic substrate 122, and are respectively connected to the test electrode 120 and the heating electrode 130. The test electrode 120 and the heating electrode 130 are located on the front and back sides of the ceramic substrate, and are both gold electrodes. The heating material layer 140 is ruthenium oxide, connected to the heating electrode 130, and is located in the middle of the ceramic substrate. The gas-sensitive material layer 110 uses tungsten trioxide doped with 1.5% palladium, and the gas-sensitive material layer 110 has a large number of pore structures.

[0113] Based on the design of the above gas sensor, the present application also provides a battery leakage monitoring method, which can be applied to Figure 5In the application environment shown. The gas sensor 102 communicates with the control terminal 104 through the network. Specifically, the gas sensor 102 and the battery to be tested 103 can be placed in the same space, and a light-heat combined excitation method is adopted, that is, on the one hand, the voltage applied to the heating electrode is controlled by the circuit to adjust the heating temperature, and the heating material layer generates heat under the action of the voltage, which serves as a heat source to provide temperature for the gas sensor. On the other hand, the light source device 101 is used to continuously irradiate the gas-sensitive material layer to provide stable light for the gas sensor 102. The gas sensor 102 continuously outputs the battery monitoring parameters and transmits the battery monitoring parameters to the control end 104. The control end 104 receives the battery monitoring parameters, and when it is detected that the change amplitude of the battery monitoring parameters exceeds the preset change amplitude threshold for the first time, sends a light switching instruction to the connected light source device to switch the type of light emitted by the light source device, wherein the wavelength of the light after switching is shorter than the wavelength of the light before switching, and accordingly, the energy of the light after switching is higher than the light before switching. Then, multiple battery monitoring parameters within a preset time period are extracted to construct a battery monitoring parameter set. Finally, the battery monitoring parameter set is classified and identified to obtain a battery leakage identification result.

[0114] The control terminal 104 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The control terminal 104 may also be a server or a cloud server. The server or cloud server may be implemented as an independent server or a server cluster consisting of multiple servers.

[0115] In some embodiments, Figure 6 As shown, a battery leakage monitoring method is provided, which is applied to Figure 5 The control terminal 104 in the example is used for explanation. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0116] S200, obtaining battery monitoring parameters output by the gas sensor under a light-heat combined excitation environment.

[0117] In this embodiment, the gas sensor may be the gas sensor described in the above gas sensor embodiments. Battery monitoring parameters refer to parameters output by the gas sensor during the monitoring process when the gas sensor is used for battery leakage monitoring. Specifically, the battery monitoring parameters include current, resistance and other parameters.

[0118] In actual applications, the gas sensor and the battery to be tested are placed in the same space. Before testing, the external test circuit is connected to the four pins of the gas sensor. The circuit can control the voltage applied to the heating electrode to adjust the heating temperature. It also provides a fixed voltage to the test electrode and detects the current and resistance of the gas-sensitive material layer in real time. During the test, you only need to turn on the test circuit and continuously illuminate the gas-sensitive material layer with an external light source. Among them, the external test circuit adopts a constant current design to ensure that the current of the heating electrode and the light source path is constant, providing a stable operating temperature and light intensity.

[0119] When a battery leaks, specific gases will be released, mainly including carbonate gases. The released gases interact with the gas-sensitive material, causing the impedance of the gas-sensitive material layer to change dramatically in a short period of time. The impedance and load state of the gas sensor will also change accordingly. The gas sensor will convert the monitored changes into battery monitoring parameters in real time. The battery monitoring parameters can then be transmitted to the control end in the form of electrical signals.

[0120] S400, when it is detected that the change amplitude of the battery monitoring parameter exceeds the preset change amplitude threshold for the first time, a light switching instruction is sent to the connected light source device to switch the type of light emitted by the light source device, and the wavelength of the light after switching is shorter than the wavelength of the light before switching.

[0121] The light source device may include but is not limited to an LED (Light Emitting Diode) light source, as long as it is a light source device that can provide light of multiple different wavelengths.

[0122] In practical applications, the light source device may be pre-set with light switching logic, including pre-setting the switching order and switching duration of light types. Among them, the switching order of light types may be to switch the light currently emitted by the light source device to light with shorter wavelength and higher energy in sequence, so as to amplify the degree of change in the response value. The switching duration refers to the length of time it takes for the light source to switch from one wavelength to another, and can also be understood as the duration of exposure to light after the wavelength is switched.

[0123] After receiving the electrical signal, the control end converts the electrical signal into a recognizable battery monitoring parameter and monitors the changes in the battery monitoring parameter. When a drastic change in the battery monitoring parameter is detected for the first time, that is, the change amplitude exceeds the preset change amplitude, it is determined that the resistance or impedance of the gas-sensitive material layer has changed drastically. At this time, a light switching command is sent to the light source device.

[0124] After receiving the light switching instruction, the light source device can switch the type of light according to the preset light switching order and switching duration, and switch the light currently emitted by the light source device to light with shorter wavelength and higher energy according to the preset switching duration, so as to intensify the changes in battery monitoring parameters.

[0125] In the specific embodiment, taking the light source device as an LED array as an example, the LED array may include a blue light LED array, an ultraviolet light LED array and a red light LED array, which can provide three different wavelengths of light: red light, blue light and ultraviolet light. The operator pre-sets the light switching order of the LED array to red light, blue light and ultraviolet light, and the switching time is 3 minutes. At the beginning of the test, the LED array emits red light from the red light LED array to irradiate the gas-sensitive material layer. When receiving the light switching instruction, according to the switching order and switching time, the light source is switched to the blue light LED array in turn, and the blue light is used to irradiate the gas-sensitive material layer, and then the blue light LED array is switched to the ultraviolet light LED array, and the ultraviolet light is used to irradiate the gas-sensitive material layer. The time interval of each light source switching, that is, the switching time, can be 3 minutes, wherein the energy of ultraviolet light is greater than the energy of blue light, and the energy of blue light is greater than the energy of red light. Therefore, in the process of switching the wavelength of light, the changes in the gas-sensitive material layer will become more and more obvious as the energy of the light increases, and the changes in the battery monitoring parameters will also change accordingly.

[0126] Specifically, the gas-sensitive material of noble metal-loaded metal oxide (such as tungsten trioxide doped with palladium) is an n-type semiconductor material whose carriers are free electrons. When the thermodynamic temperature is zero degrees and there is no external energy excitation, the valence electrons are bound by covalent bonds, there are no free-moving electrons in the crystal, and the semiconductor cannot conduct electricity. When the temperature of the semiconductor rises or is affected by external factors such as light, the valence electrons in some covalent bonds gain enough energy to break free from the constraints of the covalent bonds, transition to the conduction band, and become free electrons, while leaving the same number of holes in the covalent bonds.

[0127] In practical applications, light sources are used as energy inputs, and electrons in the valence band of semiconductor materials absorb energy and transition to the conduction band to become free electrons. As the carrier concentration increases, the conductivity of semiconductor materials will gradually increase, and the resistance of semiconductor materials will decrease accordingly. The generation of free electrons will form corresponding holes. As free electrons and holes gradually diffuse, space charge regions will be formed, which will hinder the further increase of carrier concentration. Accordingly, the resistance of semiconductor materials will gradually stabilize. When switching to light with higher energy, the balance of the space charge region will be broken, and the carrier concentration will further increase. Accordingly, the resistance of semiconductor materials will decrease again. Similarly, as time goes by, the resistance of semiconductor materials will stabilize again. Therefore, when the light emitted by the LED array switches from red light to blue light, the resistance of the gas-sensitive material layer monitored by the gas sensor will drop slightly and then stabilize. When the light switches from blue light to ultraviolet light, the resistance of the gas-sensitive material layer will drop slightly again and then stabilize over time. In this way, by gradually switching the light to higher energy light, the change in resistance of the gas-sensitive material layer can be further aggravated. Accordingly, the more obvious the change in resistance is, the more conducive it is to judging whether the battery is leaking, reducing the possibility of misjudgment of battery leakage, and improving the accuracy of battery leakage judgment. Specifically, during the entire battery leakage monitoring process, when the light emitted by the LED array switches from red light to blue light and then to ultraviolet light, the changes in resistance monitored can be seen in Figure 7 .

[0128] S600, extracting change characteristic data of multiple battery monitoring parameters within a preset time period, where the multiple battery monitoring parameters within the preset time period include battery monitoring parameters output by the gas sensor after the wavelength of the light is switched.

[0129] The change characteristic data of the battery monitoring parameters refers to the data that characterizes the change of the battery monitoring parameters, which can be the change amplitude data, or the slope characteristics of the curve corresponding to the battery monitoring parameters, etc. Continuing from the previous embodiment, when the light is switched to light with a shorter wavelength, the battery monitoring parameters will change significantly. Therefore, in this embodiment, the battery monitoring parameters extracted within the preset time period may include the battery monitoring parameters output by the gas sensor after the wavelength of the light is switched, so as to make a more accurate judgment on whether the battery is leaking.

[0130] S800, classifying and identifying the change feature data to obtain a battery leakage identification result.

[0131] After obtaining the change characteristic data, the change characteristic data can be classified and identified to determine whether the battery is actually leaking, or whether the gas environment has changed due to uncontrollable factors such as sensor aging, severe vibration, or battery thermal runaway, which in turn causes a drastic change in the battery monitoring parameters. By classifying and identifying the above situations, the battery leakage identification result is obtained. Specifically, the change characteristic data can be classified and identified using a pre-trained battery leakage classification model, or the change characteristics of the battery monitoring parameters after the battery monitoring parameters have changed drastically due to various environmental factors that have been sorted out in advance, and then the correspondence between the environmental factors and the change characteristics of the battery monitoring parameters is preset, and the change characteristic data is classified and identified based on the correspondence to obtain the battery leakage identification result.

[0132] In the technical solution of the embodiment of the present application, by conducting a combined photothermal excitation test on the precious metal-loaded metal oxide gas sensor, the sensitivity of the gas sensor can be greatly improved, and the stability of the gas sensor is greatly improved while ensuring the sensitivity and detection limit. When a drastic change in the battery monitoring parameter output by the gas sensor is detected for the first time, the wavelength of light is switched to amplify the change amplitude of the battery monitoring parameter, and then the change characteristic data of the battery monitoring parameter is extracted, thereby making the monitoring results obtained by classification and identification based on the change characteristics after the change amplitude is amplified more accurate.

[0133] In some embodiments, S600 includes: extracting change characteristics of multiple battery monitoring parameters within a first preset time period, where the starting point of the first preset time period is the moment when the light source device switches the wavelength of the light.

[0134] Continuing with the above embodiment, when a drastic change in the battery monitoring parameters is detected, a light switching instruction is sent to the light source device. After the light source device switches the currently emitted light to light with higher energy, the change amplitude of the battery monitoring parameters will increase accordingly. At this time, the battery monitoring parameters of the preset time period after the light switching can be extracted, that is, the starting point of the data extraction time period is the moment when the light source device switches the wavelength of the light. Specifically, the battery monitoring parameters within 100 seconds after the light switching can be extracted, and then the change characteristic data of the extracted battery monitoring parameters within 100 seconds is obtained, and classification and identification are performed based on the change characteristic data to obtain a more accurate battery leakage identification result. It can be understood that the preset time period can include but is not limited to 100 seconds, depending on the actual situation.

[0135] In the technical solution of the embodiment of the present application, by extracting the change characteristic data of the battery monitoring parameters after the light is switched, and performing classification and identification based on the transformed characteristic data, not only can the time for gas classification and identification be significantly shortened, but also it can be more accurately determined whether the battery is leaking.

[0136] In some embodiments, extracting change characteristic data of multiple battery monitoring parameters within a preset time period includes: extracting change characteristics of multiple battery monitoring parameters within a second preset time period, the starting point of the second preset time period is before the moment when the light source device switches the wavelength of the light, and the end point of the second time period is after the moment when the light source device switches the wavelength of the light.

[0137] In this embodiment, in order to more comprehensively monitor the changes in battery monitoring, the battery monitoring parameters within the preset time period from before the light switching to after the light switching can be extracted, that is, the starting point of the data extraction time period is earlier than the moment when the wavelength of the light source device switches the light, and the end point is later than the moment when the wavelength of the light source device switches the light. For example, the battery monitoring parameter takes resistance as an example, and the light switching time is 30 seconds as an example. If the control end detects a drastic change in resistance at the 10th second, at this time, a light switching instruction is sent to the light source device, and the light source device responds to the instruction, and switches the currently emitted light to a shorter wavelength and higher energy light at the 10th second, and at the same time feeds back the instruction reception confirmation signal to the control end, after which the resistance will drop and then stabilize. At the 40th second, the light is switched to a higher energy light again, and the resistance will drop again and then stabilize. In the data extraction stage, the battery monitoring parameters from the 7th second to the 100th second can be extracted.

[0138] In the technical solution of the embodiment of the present application, the change characteristic data of the battery monitoring parameters in the preset time period from before the light switching to after the light switching is extracted, and classification and identification are performed based on the transformed characteristic data. This can not only more comprehensively and accurately reflect the changes in the battery monitoring parameters during the monitoring process and improve the accuracy of the battery leakage monitoring results, but also effectively shorten the time for gas classification and identification compared to the traditional extraction of battery monitoring parameters during the entire monitoring process.

[0139] In some embodiments, S800 includes: taking the change characteristic data as input, calling a trained battery leakage identification model to classify and identify the change characteristic data, and obtaining a battery leakage identification result, wherein the battery leakage identification model is trained based on historical battery monitoring parameter change characteristic data and battery leakage annotation data.

[0140] The battery leakage label data is the label data that marks whether the battery has leaked, wherein the label data that characterizes that the battery has not leaked needs to mark the specific factors that cause the change in the gas environment, such as sensor aging, severe vibration, battery thermal runaway, etc. In this embodiment, the battery monitoring parameter set is classified and identified, which can be based on the historical battery monitoring parameter change feature data and the corresponding battery leakage label data, and the battery leakage identification model is pre-trained using deep learning or machine learning technology. Specifically, the historical battery monitoring parameter change feature collected in the case of battery leakage can be marked as 1, and the historical battery monitoring parameter change feature collected in the case of no battery leakage can be marked as 0, and then the labeled data is input into a pre-constructed neural network such as a fully connected neural network for training, and the loss function is continuously optimized. The fully connected neural network distinguishes the two types of data marked as 1 and 0 through self-iteration. When the fully connected neural network reaches a preset accuracy or convergence, the model training is completed, and a battery leakage identification model is obtained. It can be understood that the neural network includes but is not limited to a fully connected neural network.

[0141] In the actual battery monitoring process, after extracting the change feature data of multiple battery monitoring parameters within a preset time period, the change feature data can be used as input to call the trained battery leakage identification model to classify and identify the change feature data to obtain the corresponding battery leakage identification result. Specifically, if the input change feature data is classified into the category marked as 1, it is determined that a battery leakage has occurred; if it is classified into the category marked as 0, it is determined that no battery leakage has occurred, and the corresponding influencing factors are output.

[0142] In the technical solution of the embodiment of the present application, by calling the trained battery leakage recognition model to classify and identify the change feature data, it is possible to accurately identify battery leakage and classify different types of leakage situations, thereby improving battery monitoring efficiency.

[0143] like Figure 8 As shown, in some embodiments, S600 includes: S620, generating a battery monitoring parameter change curve according to a plurality of battery monitoring parameters within a preset time period, and extracting slope characteristic data of the battery monitoring parameter change curve.

[0144] S800 includes: S822, taking the slope characteristic data at different time points as input, calling the trained battery leakage recognition model to classify and recognize the change characteristic data, and obtaining the battery leakage recognition result, wherein the battery leakage recognition model is trained based on the historical battery monitoring parameter change characteristic data and the battery leakage annotation data.

[0145] In this embodiment, the slope feature is used as an example of the change characteristic data. Specifically, it can be based on multiple battery monitoring parameters within 100 seconds, calling the corresponding visualization tool to generate a battery monitoring parameter change curve in which the battery monitoring parameters change over time. Then, the slope characteristics at different time points in the battery monitoring parameter change curve are extracted, and the slope characteristics at different time points are used as the objects of classification and identification processing to obtain the battery leakage identification result. It can be understood that the battery monitoring parameter change curve includes but is not limited to the resistance change curve, the current change curve, and the impedance change curve.

[0146] In the technical solution of the embodiment of the present application, by extracting the slope characteristics of the battery monitoring parameter change curve, the change amplitude of the battery monitoring parameters can be accurately determined. Compared with directly processing the original battery monitoring parameters, the recognition time can be significantly shortened, the data processing process is simplified, and the processing speed and efficiency are improved.

[0147] In some embodiments, S620 includes: generating a resistance change curve according to a plurality of resistance values ​​within a preset time period, and extracting slope characteristic data of the resistance change curve.

[0148] Since the battery monitoring parameters include multi-dimensional monitoring parameters, in practical applications, when the gas-sensitive material interacts with a specific gas, the resistance of the gas-sensitive material layer changes more obviously in a short period of time. Therefore, in this embodiment, the resistance in the battery monitoring parameters can be extracted to identify battery leakage. Specifically, the resistance value corresponding to each time point can be extracted, and a resistance change curve can be generated according to the resistance value corresponding to each time point. Then, the resistance slope feature of the resistance change curve can be extracted, and the resistance slope feature can be used as input to call the trained battery leakage classification model for classification and identification processing to obtain the battery leakage identification result.

[0149] When implementing it, Fig. 9 As shown, if within a preset time period, the resistance first drops sharply and then gradually stabilizes, and accordingly, the absolute value of the slope of the resistance change curve first increases sharply and then tends to 0, it can be determined that the battery has leaked and gas leakage has occurred.

[0150] like Fig.10 As shown, if the resistance changes gradually and slowly, or increases or decreases, but the change amplitude is relatively slow, the absolute value of the slope is small, and the change is slow, it is determined that the sensor has aged.

[0151] If the resistance fluctuates but the absolute value of the slope changes irregularly, it is determined that the sensor is vibrating violently.

[0152] If the resistance changes slightly, the absolute value of the slope increases slightly, and then the resistance change becomes smaller and tends to be stable, and the absolute value of the slope changes less and tends to 0. That is, the changes in resistance and slope are similar to those in case of gas leakage, but the changes are much smaller than those in case of gas leakage. In this case, it is determined that battery thermal runaway has occurred.

[0153] In the technical solution of the embodiment of the present application, by extracting the slope characteristics of the resistance change curve and classifying and identifying the resistance slope characteristics, battery leakage identification and classification can be achieved quickly and accurately, and battery leakage conditions can be monitored in real time.

[0154] like Fig.11 As shown, in some embodiments, before S600, the method further includes:

[0155] S500, extracting a plurality of battery monitoring parameters within a preset time period, and performing data preprocessing on the plurality of battery monitoring parameters, wherein the data preprocessing includes at least one of data cleaning, data normalization, and feature selection.

[0156] S622 includes: S624, generating a resistance change curve according to a plurality of resistance values ​​within a preset time period after data preprocessing, and extracting slope change characteristic data of the resistance change curve.

[0157] In practical applications, since the multiple battery monitoring parameters obtained may contain abnormal values ​​and some useless information, the battery monitoring parameters may be preprocessed before extracting the change characteristic data. Specifically, data preprocessing may include but is not limited to data cleaning, data normalization and feature selection. Then, the change characteristic data of the battery monitoring parameters after preprocessing are extracted. Among them, data cleaning includes checking whether there are null values ​​and abnormal values ​​in the data. If there are null values ​​or abnormal values, the null values ​​and abnormal values ​​are deleted, or the abnormal values ​​are replaced with the average value. Data normalization includes determining the actual value range of the battery monitoring data, and then scaling the data to normalized data between 0-1. Feature selection includes selecting features that have a strong correlation with identifying battery leakage from the original battery monitoring data.

[0158] In the technical solution of the embodiment of the present application, by performing data preprocessing on the battery monitoring parameters, the standardization and quality of the data can be improved, the adverse effects of noise data and worthless data on the battery leakage monitoring results can be reduced, and the accuracy of the identification results can be improved.

[0159] In order to make a clearer description of the battery leakage monitoring method provided by the present application, the following is combined with the attached Fig.10 and one A specific embodiment is described, and the specific embodiment includes the following contents:

[0160] S200, obtaining battery monitoring parameters output by the gas sensor under a light-heat combined excitation environment.

[0161] Battery monitoring parameters include current, resistance and impedance.

[0162] S400, when it is detected that the change amplitude of the battery monitoring parameter exceeds the preset change amplitude threshold for the first time, a light switching instruction is sent to the connected light source device to switch the type of light emitted by the light source device, and the wavelength of the light after switching is shorter than the wavelength of the light before switching.

[0163] S500, extracting multiple battery monitoring parameters within a preset time period, wherein the multiple battery monitoring parameters within the preset time period include battery monitoring parameters output by the gas sensor after the wavelength of the light is switched, and performing data preprocessing on the multiple battery monitoring parameters, wherein the data preprocessing includes at least one of data cleaning, data normalization, and feature selection.

[0164] S624, generating a resistance change curve according to the multiple resistance values ​​within the preset time period after preprocessing, and extracting slope characteristic data of the resistance change curve.

[0165] S822, taking the slope feature data at different time points as input, calling the trained battery leakage recognition model to classify and recognize the slope feature data, and obtaining a battery leakage recognition result, wherein the battery leakage recognition model is trained based on historical battery monitoring parameter change feature data and battery leakage annotation data.

[0166] In specific implementation, the test circuit connected to the gas sensor is turned on, the voltage applied to the heating electrode is controlled by the circuit to adjust the heating temperature, and the gas-sensitive material layer is continuously irradiated with an external light source. When the battery leaks, specific gases, mainly carbonate gases, are released. The released gases interact with the gas-sensitive material, causing the impedance of the gas-sensitive material layer to change dramatically in a short period of time. The impedance and load state of the gas sensor will also change accordingly. The gas sensor will convert the monitored changes into battery monitoring parameters in real time. The battery monitoring parameters can be transmitted to the control end in the form of electrical signals at this time. The battery monitoring parameters include resistance.

[0167] If the resistance first drops sharply, the absolute value of the slope increases sharply, and then the resistance gradually stabilizes. Correspondingly, the absolute value of the slope of the resistance change curve decreases and eventually tends to 0, it can be determined that the battery has leaked and gas has leaked.

[0168] If the resistance changes gradually and slowly, or increases or decreases, but the amplitude of the change is relatively slow, the absolute value of the slope is small, and the change is slow, then it is determined that the sensor has aged.

[0169] If the resistance fluctuates but the absolute value of the slope changes irregularly, it is determined that the sensor is vibrating violently.

[0170] If the resistance changes slightly, the absolute value of the slope increases slightly, and then the resistance change becomes smaller and tends to be stable, and the absolute value of the slope changes less and tends to 0. That is, the changes in resistance and slope are similar to those in case of gas leakage, but the changes are much smaller than those in case of gas leakage. In this case, it is determined that battery thermal runaway has occurred.

[0171] In some embodiments, Fig.12 As shown, the present application also provides a battery leakage monitoring system 800, including a battery 810, a gas sensor 820 and a controller 830 connected in communication;

[0172] The gas sensor 820 includes the gas sensor as described in the above-mentioned gas sensor embodiments, and the controller 830 is used to execute the steps in the above-mentioned battery leakage monitoring method embodiment to monitor whether the battery 810 is leaking.

[0173] Specifically, the gas sensor 820 is externally connected with a test circuit and a light source device. Among them, the external test circuit adopts a constant current design to ensure that the current of the heating electrode and the light source path is constant, and to provide a stable operating temperature and light intensity. The external light source device can be an LED array. In practical applications, the gas sensor and the battery can be placed in the same space, and the external test circuit is connected to the four pins of the gas sensor before testing. The circuit can control the voltage applied to the heating electrode to adjust the heating temperature, and also provide a fixed voltage for the test electrode and detect the current and resistance of the gas-sensitive material layer in real time. During the test, it is only necessary to turn on the test circuit and continuously irradiate the gas-sensitive material layer with an external light source device. When the battery leaks, specific gases will be released, mainly including carbonate gases. The released gas interacts with the gas-sensitive material, which will cause the impedance of the gas-sensitive material layer to change dramatically in a short period of time. The impedance and load state of the gas sensor will also change accordingly. The gas sensor 820 will output the battery monitoring parameters in real time, and the battery monitoring parameters can be transmitted to the controller 830 in the form of electrical signals at this time. After receiving the battery monitoring parameters, the controller 830 may execute steps in any of the above battery leakage monitoring method embodiments to implement battery leakage monitoring and obtain a battery leakage identification result. The specific battery leakage monitoring process can be found in the process described in the above battery leakage monitoring method, which will not be repeated here.

[0174] In the technical solution of the embodiment of the present application, by providing a gas sensor having the above embodiment and a controller capable of executing the above battery monitoring method, on the one hand, accurate battery monitoring parameters can be obtained by relying on a gas sensor with high sensitivity and response rate, and on the other hand, when the change amplitude of the battery monitoring parameter output by the gas sensor is detected for the first time to exceed the preset change amplitude, the wavelength of the light is switched to amplify the change amplitude of the battery monitoring parameter, thereby making the monitoring result obtained by classification and identification based on the change characteristics after the change amplitude is amplified more accurate. The whole process can quickly and accurately realize the identification of battery leakage.

[0175] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0176] Based on the same inventive concept, the embodiment of the present application also provides a battery leakage monitoring device for implementing the battery leakage monitoring method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more battery leakage monitoring device embodiments provided below can refer to the limitations of the battery leakage monitoring method above, and will not be repeated here.

[0177] In some embodiments, Fig.13 As shown, a battery leakage monitoring device 900 is provided, including: a data acquisition module 910, an instruction sending module 920, a data extraction module 930 and a leakage identification module 940, wherein:

[0178] The data acquisition module 910 is used to acquire the battery monitoring parameters output by the gas sensor in the light-thermal combined excitation environment, and the gas sensor includes the gas sensor in the above-mentioned gas sensor embodiments.

[0179] The instruction sending module 920 is used to send a light switching instruction to the connected light source device when it is detected that the change amplitude of the battery monitoring parameter exceeds the preset change amplitude threshold for the first time, so that the light source device switches the type of light emitted, and the wavelength of the light after switching is shorter than the wavelength of the light before switching.

[0180] The data extraction module 930 is used to extract the change characteristic data of multiple battery monitoring parameters within a preset time period, and the multiple battery monitoring parameters within the preset time period include the battery monitoring parameters output by the gas sensor after the wavelength of the light is switched.

[0181] The leakage identification module 940 is used to classify and identify the change feature data to obtain a battery leakage identification result.

[0182] In the technical solution of the embodiment of the present application, by conducting a combined photothermal excitation test on the precious metal-loaded metal oxide gas sensor, the sensitivity of the gas sensor can be greatly improved, and the stability of the gas sensor is greatly improved while ensuring the sensitivity and detection limit. When the change amplitude of the battery monitoring parameter exceeds the preset change amplitude threshold for the first time, a light switching instruction is issued to switch the light wavelength to amplify the change amplitude of the battery monitoring parameter, thereby making it possible to classify and identify the change characteristics of the battery monitoring parameter after the change amplitude is amplified, and the monitoring result obtained is more accurate.

[0183] In some embodiments, the data extraction module 940 is further used to extract the variation characteristics of multiple battery monitoring parameters within a first preset time period, and the starting point of the first preset time period is the moment when the light source device switches the wavelength of the light.

[0184] In some embodiments, the data extraction module 940 is also used to extract the change characteristics of multiple battery monitoring parameters within a second preset time period, the starting point of the second preset time period is before the moment when the light source device switches the wavelength of the light, and the end point of the second time period is after the moment when the light source device switches the wavelength of the light.

[0185] In some embodiments, the leakage identification module 940 is also used to take the change characteristic data as input, call the trained battery leakage identification model to classify and identify the change characteristic data, and obtain a battery leakage identification result, wherein the battery leakage identification model is trained based on historical battery monitoring parameter change characteristic data and battery leakage annotation data.

[0186] In some embodiments, the data extraction module 930 is further used to generate a battery monitoring parameter change curve according to multiple battery monitoring parameters within a preset time period, and extract slope characteristic data of the battery monitoring parameter change curve, wherein the change characteristic data includes slope characteristic data.

[0187] In some embodiments, the data extraction module 930 is further configured to generate a resistance change curve according to a plurality of resistance values ​​within a preset time period, and the battery monitoring parameter change curve includes a resistance change curve.

[0188] like Fig.14As shown, in some embodiments, the device also includes a data preprocessing module 922, which is used to perform data preprocessing on multiple battery monitoring parameters, and the data preprocessing includes at least one of data cleaning, data normalization, and feature selection; the data extraction module 930 is also used to extract the change characteristic data of multiple battery monitoring parameters within a preset time period after data preprocessing.

[0189] Each module in the above battery leakage monitoring device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0190] In some embodiments, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.15 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a battery leakage monitoring method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0191] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0192] In some embodiments, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned battery leakage monitoring method when executing the computer program.

[0193] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned battery leakage monitoring method are implemented.

[0194] In some embodiments, a computer program product is provided, including a computer program, which implements the steps in the above-mentioned battery leakage monitoring method when executed by a processor.

[0195] It should be noted that the data involved in this application (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0196] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A gas sensor, characterized in that: include: A gas-sensitive material layer, a test electrode, a heating electrode and a heating material layer are stacked in sequence; The gas-sensitive material layer includes metal oxide and noble metal coated on the test electrode; The heating material layer includes a metal oxide coated on the heating electrode.

2. The gas sensor according to claim 1, characterized in that The gas-sensitive material layer includes tungsten trioxide doped with palladium.

3. The gas sensor according to claim 1, characterized in that: The gas-sensitive material layer includes palladium and tungsten trioxide which are superimposed and coated.

4. The gas sensor according to claim 2 or 3, characterized in that: The molar ratio of the palladium to the tungsten trioxide is (0.5-2):

100.

5. The gas sensor according to any one of claims 1 to 3, characterized in that: The gas-sensitive material layer has a porous structure.

6. The gas sensor according to any one of claims 1 to 3, characterized in that: The test electrodes include arc-shaped interdigital electrodes.

7. A battery leakage monitoring method, characterized in that: The method comprises: Obtaining battery monitoring parameters output by a gas sensor in a combined photothermal excitation environment, wherein the gas sensor comprises a gas sensor according to any one of claims 1 to 6; When it is detected that the variation amplitude of the battery monitoring parameter exceeds a preset variation amplitude threshold for the first time, a light switching instruction is sent to the connected light source device, so that the light source device switches the type of light emitted, and the wavelength of the light after switching is shorter than the wavelength of the light before switching; Extracting characteristic data of changes in a plurality of battery monitoring parameters within a preset time period, wherein the plurality of battery monitoring parameters within the preset time period include battery monitoring parameters output by the gas sensor after the wavelength of the light is switched; The change characteristic data is classified and identified to obtain a battery leakage identification result.

8. The method according to claim 7, characterized in that The step of extracting the change characteristic data of multiple battery monitoring parameters within a preset time period includes: Extracting the variation characteristics of a plurality of battery monitoring parameters within a first preset time period, wherein the starting point of the first preset time period is the moment when the light source device switches the wavelength of the light.

9. The method according to claim 7, characterized in that: The step of extracting the change characteristic data of multiple battery monitoring parameters within a preset time period includes: Extracting the variation characteristics of multiple battery monitoring parameters within a second preset time period, the starting point of the second preset time period is before the moment when the light source device switches the wavelength of the light, and the end point of the second time period is after the moment when the light source device switches the wavelength of the light.

10. The method according to any one of claims 7 to 9, characterized in that: The classifying and identifying the change characteristic data to obtain a battery leakage identification result includes: Taking the change feature data as input, calling the trained battery leakage recognition model to classify and recognize the change feature data, and obtaining a battery leakage recognition result; The battery leakage identification model is trained based on historical battery monitoring parameter change characteristic data and battery leakage annotation data.

11. The method according to any one of claims 7 to 9, characterized in that: The step of extracting the change characteristic data of multiple battery monitoring parameters within a preset time period includes: Generating a battery monitoring parameter change curve according to the multiple battery monitoring parameters within the preset time period; Extract slope characteristic data of the battery monitoring parameter change curve, wherein the change characteristic data includes slope characteristic data.

12. The method according to claim 11, characterized in that The battery monitoring parameters include resistance value; The generating a battery monitoring parameter variation curve according to the plurality of battery monitoring parameters within the preset time period comprises: A resistance variation curve is generated according to the plurality of resistance values ​​within the preset time period, and the battery monitoring parameter variation curve includes a resistance variation curve.

13. The method according to any one of claims 7 to 9, characterized in that: Before extracting the change characteristic data of multiple battery monitoring parameters within a preset time period, the method further includes: Performing data preprocessing on a plurality of battery monitoring parameters within a preset time period, wherein the data preprocessing includes at least one of data cleaning, data normalization, and feature selection; Extracting the change characteristic data of multiple battery monitoring parameters within a preset time period includes: Extract the change characteristic data of multiple battery monitoring parameters within a preset time period after data preprocessing.

14. A battery leakage monitoring system, characterized in that: include: Batteries, communication-connected gas sensors and controllers; The gas sensor comprises the gas sensor as claimed in any one of claims 1 to 6, and the controller is used to execute the battery leakage monitoring method as claimed in any one of claims 7 to 13 to monitor whether the battery is leaking.

15. A battery leakage monitoring device, characterized in that: The device comprises: A data acquisition module, used to acquire battery monitoring parameters output by a gas sensor in a light-thermal combined excitation environment, wherein the gas sensor comprises a gas sensor according to any one of claims 1 to 6; An instruction sending module is used to send a light switching instruction to a connected light source device when it is detected that the change amplitude of the battery monitoring parameter exceeds a preset change amplitude threshold for the first time, so that the light source device switches the type of light emitted, and the wavelength of the light after switching is shorter than the wavelength of the light before switching; A data extraction module, used to extract characteristic data of changes in a plurality of battery monitoring parameters within a preset time period, wherein the plurality of battery monitoring parameters within the preset time period include battery monitoring parameters output by the gas sensor after the wavelength of the light is switched; The leakage identification module is used to classify and identify the change feature data to obtain a battery leakage identification result.

16. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 7 to 13 are implemented.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 7 to 13 are implemented.

18. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 7 to 13 are implemented.

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