Abnormal battery positioning method and system, equipment, medium and product

Abstract

The invention relates to an abnormal battery positioning method and system, equipment, a medium and a product. The abnormal battery positioning method comprises the steps of determining operation data of a battery pack; performing abnormal value detection and normalization processing on the operation data to obtain target operation data; performing time sequence analysis on the target operation data, determining time characteristics, and obtaining spatial characteristics according to the position information of each battery and the target operation data; inputting the spatial-temporal characteristics into a thermal imaging model to generate a thermal image of the battery pack according to the spatial-temporal characteristics; and determining that the battery corresponding to the target area is abnormal in response to the fact that the target characteristic value of the target area in the thermal image exceeds a preset threshold value. By adopting the method, the problems that the traditional infrared thermal imaging technology is greatly influenced by the environment, the installation space is limited, the cost is high and the weak signal space-time resolution capability is insufficient can be solved, the thermal state of the battery pack can be accurately reflected, and the abnormal positioning and accurate early warning of the thermal runaway early stage can be realized.

Description

Technical Field

[0001] The present application relates to the field of battery safety monitoring technology, and in particular to a method, system, device, medium, and product for locating abnormal batteries. Background Art

[0002] In battery applications, abnormal conditions such as overheating, overcharging, and short circuits can trigger thermal runaway. This process is often accompanied by a sharp rise in temperature, gas release, and even electrolyte combustion, which can easily lead to catastrophic fires or explosions. Traditional infrared thermal imaging technology relies on surface temperature detection and is susceptible to changes in ambient temperature and humidity, light interference, and the physical barriers of the battery casing. This makes it difficult to accurately reflect the thermal state of the battery pack and requires the installation of a thermal imager, which is space-constrained and expensive. Furthermore, in the early stages of thermal runaway, the subtle local temperature differences and the weak signal of hidden heat accumulation prevent real-time monitoring and imaging. Summary of the Invention

[0003] Based on this, a method and system, equipment, medium and product for locating abnormal batteries are provided to solve the problem in the prior art that traditional thermal imaging technology cannot accurately monitor the thermal status of battery packs.

[0004] In a first aspect, a method for locating an abnormal battery is provided, the method comprising: Determining operating data of a battery pack; wherein the operating data includes surface temperature, internal temperature, gas composition, gas concentration, and shell deformation pressure, and the operating data is obtained based on a sensor array provided on the battery pack; the sensor array includes a flexible thin film temperature sensor, a distributed optical fiber temperature sensor, a gas sensor, a voltage sensor, and a micro-differential pressure sensor; the battery pack includes at least two batteries; performing outlier detection and normalization processing on the operating data to obtain target operating data; Performing a time series analysis on the target operation data to determine a time feature, and obtaining a spatial feature based on the location information of each battery and the target operation data; calculating an attention score of the time feature and the spatial feature through linear transformation, normalizing the attention score into a weight of the time feature and a weight of the spatial feature using a predetermined function, and performing a weighted summation of the time feature and the spatial feature according to the normalized weights to obtain a spatiotemporal feature; Inputting the spatiotemporal features into a thermal imaging model to generate a thermal imaging image of the battery pack according to the spatiotemporal features, wherein the spatiotemporal features include the time features and the spatial features; In response to a target characteristic value of a target area in the thermal imaging image exceeding a preset threshold, a battery abnormality corresponding to the target area is determined; the target characteristic value includes a temperature rise rate, a temperature mutation index, a gas correlation degree, a voltage abnormal change amplitude, and a pressure change rate.

[0005] Optionally, the temperature sensor includes at least one of the following: a flexible thin film temperature sensor, a distributed optical fiber temperature sensor, a thermocouple sensor, and a thermistor; the flexible thin film temperature sensor is embedded in the gaps between the batteries in a matrix form, and the distributed optical fiber temperature sensor is arranged in a serpentine pattern around the battery pack.

[0006] Optionally, the outlier detection method includes at least one of the following: a Z-score method, an IQR method, and an isolation forest algorithm; The normalization process includes at least one of the following: minimum-maximum normalization and Z-score normalization.

[0007] Optionally, the predetermined function includes a Softmax function.

[0008] Optionally, obtaining the spatial feature according to the location information of each battery and the target operation data includes: Constructing a spatial coordinate matrix according to the physical position of each battery in the battery pack; The spatial feature is obtained according to the spatial coordinate matrix and the target operation data.

[0009] Optionally, the positioning method further includes: Calculate the temperature gradient, heat flow direction, and overlap of gas concentration abnormality areas in the image area corresponding to each battery in the thermal imaging image; Determining the risk level of the battery corresponding to the image area based on the temperature gradient, heat flow direction, and overlap of gas concentration abnormal areas; In response to the risk level of any image area exceeding a preset level, it is determined that the battery corresponding to the image area is abnormal.

[0010] In a second aspect, a system for locating abnormal batteries is provided, the system comprising: a first determination module, configured to determine operating data of the battery pack; wherein the operating data includes surface temperature, internal temperature, gas composition, gas concentration, and shell deformation pressure, and the operating data is obtained based on a sensor array provided on the battery pack; the sensor array includes a flexible thin film temperature sensor, a distributed optical fiber temperature sensor, a gas sensor, a voltage sensor, and a micro-differential pressure sensor; and the battery pack includes at least two batteries; a processing module, configured to perform outlier detection and normalization processing on the operating data to obtain target operating data; a second determining module, configured to perform time series analysis on the target operating data to determine a time feature, and obtain a spatial feature based on the location information of each battery and the target operating data; An image generation module, configured to input the spatiotemporal features into a thermal imaging model to generate a thermal image of the battery pack according to the spatiotemporal features, wherein the spatiotemporal features include the time features and the spatial features; The third determination module is used to determine the battery abnormality corresponding to the target area in the thermal imaging image in response to the target characteristic value of the target area in the thermal imaging image exceeding a preset threshold; the target characteristic value includes the temperature rise rate, the temperature mutation index, the gas correlation degree, the voltage abnormal change amplitude, and the pressure change rate.

[0011] According to a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and configured to run on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0012] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in the first aspect is implemented.

[0013] In a fifth aspect, a computer program product is provided, comprising a computer program, which implements the method described in the first aspect when executed by a processor.

[0014] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present application.

[0015] The above-mentioned abnormal battery positioning method, system, equipment, medium, and product obtain the target operating data of the battery through a sensor array installed on the battery pack. Based on the target operating data of the battery pack and the position information of each battery in the battery pack, the temporal and spatial characteristics are determined. A thermal image of the battery pack is generated based on the temporal and spatial characteristics. In response to the target characteristic value of the target area in the thermal image exceeding a preset threshold, the battery corresponding to the target area is determined to be abnormal. This solves the problems of traditional infrared thermal imaging technology such as being greatly affected by the environment, limited installation space, high cost, and insufficient spatiotemporal resolution of weak signals. It can accurately reflect the thermal state of the battery pack and achieve early abnormal positioning and precise warning of thermal runaway. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a method for locating an abnormal battery in one embodiment; Figure 2A schematic diagram of sensor connections for a method for locating an abnormal battery in one embodiment; Figure 3 Schematic diagram of a data preprocessing process of a method for locating an abnormal battery in one embodiment; Figure 4 1. A schematic diagram of a spatiotemporal feature extraction process for a method for locating abnormal batteries in one embodiment; Figure 5 A schematic diagram of thermal imaging spatial output of a method for locating abnormal batteries in one embodiment; Figure 6 is a flowchart of an abnormal battery locating method according to an embodiment; Figure 7 Schematic diagram of the structure of a system for locating abnormal batteries in one embodiment; Figure 8 FIG. 1 is a schematic structural diagram of an electronic device in an embodiment. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0018] It should be noted that the diagrams provided in the present embodiment are only schematic illustrations of the basic concept of the present application. The diagrams only show the components related to the present application rather than the number, shape and size of the components when actually implemented. The type, quantity and ratio of each component can be changed at will during actual implementation, and the component layout pattern may also be more complicated. The structures, ratios, sizes, etc. illustrated in the drawings of this specification are only used to match the content disclosed in the specification for people familiar with this technology to understand and read. They are not used to limit the restrictive conditions that can be implemented in this application. Therefore, they have no technical significance. Any modification of the structure, change of the proportional relationship or adjustment of the size should still fall within the scope of the technical content disclosed in this application without affecting the effect and purpose that can be achieved by this application. At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description and are not used to limit the scope of the implementation of this application. The change or adjustment of their relative relationship should also be considered as the scope of the implementation of this application without substantial change in the technical content.

[0019] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places herein does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] As used herein, unless the context clearly indicates otherwise, the terms "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include additional steps or elements.

[0021] The definition of inclusion herein, such as the terms “having”, “may have”, “include” or “may include” as used herein, indicates the existence of the corresponding functions, operations, elements, etc. herein, and does not limit the existence of one or more other functions, operations, elements, etc. In addition, it should be understood that the terms “including” or “having” as used herein indicate the existence of the features, numbers, steps, operations, elements, components or their combination described in the specification, and do not exclude the existence or addition of one or more other features, numbers, steps, operations, elements, components or their combination.

[0022] In the embodiments of the present application, prefixes such as "first" and "second" are used only to distinguish different description objects and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of prefixes such as ordinal numbers to distinguish description objects in the embodiments of the present application does not constitute a restriction on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and the use of such prefixes should not constitute an unnecessary restriction. In addition, in the description of this embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

[0023] In battery applications, abnormal conditions such as overheating, overcharging, and short circuits can trigger thermal runaway. This process is often accompanied by a sharp rise in temperature, gas release, and even electrolyte combustion, which can easily lead to catastrophic fires or explosions. Traditional infrared thermal imaging technology has three major drawbacks: 1. Physical limitations: It requires direct viewing of the battery surface and cannot penetrate the casing to monitor internal thermal conditions; 2. Environmental interference: Fluctuations in ambient temperature and humidity can lead to surface temperature measurement errors exceeding ±3°C; 3. Inadequate dynamic response: The frame rate is typically ≤10Hz, making it difficult to capture the millisecond-level temperature rise at the beginning of thermal runaway. Early warning systems for battery thermal runaway can accurately identify potential thermal runaway risks by monitoring multimodal characteristics of the battery, such as temperature, voltage, pressure, and gas, at the earliest stages of an event. Feature imaging, as a visualization technique, is widely used in industrial nondestructive testing and is particularly valuable in the perception and identification of complex systems such as lithium-ion battery packs. Therefore, research on thermal imaging methods based on the spatiotemporal characteristics of battery packs is of great significance for energy storage battery pack safety monitoring and early warning of thermal runaway. Based on this, the present application provides a method and system, equipment, medium, and product for locating abnormal batteries, which are used to solve the problems of traditional infrared thermal imaging technology being greatly affected by the environment, limited installation space, high cost, and insufficient temporal and spatial resolution of weak signals. It can accurately reflect the thermal state of the battery pack and realize early abnormal positioning and precise warning of thermal runaway.

[0024] Figure 1 An abnormal battery locating method provided in an embodiment of the present application can be used to locate abnormal batteries in a battery pack, such as abnormal batteries in electric vehicle power batteries and energy storage battery packs in energy storage power stations. The locating method includes: S11. Determine operating data of the battery pack.

[0025] Among them, the operating data includes the surface temperature, internal temperature, gas production composition, gas production concentration, and shell deformation pressure of the battery pack.

[0026] The operating data can be obtained through the sensor array set on the battery pack, which includes flexible temperature sensors, gas sensors, voltage sensors, micro-pressure difference sensors, pressure sensors, etc. Figure 2As shown, the temperature sensor includes at least one of the following: a flexible film temperature sensor, a distributed optical fiber temperature sensor, a thermocouple sensor, or a thermistor. For example, the temperature sensor is installed on the surface or inside the battery pack and is physically connected to the battery pack body to accurately obtain the surface and internal temperatures of the battery. The voltage sensor can be installed between the positive and negative poles of the battery pack or directly connected to the positive and negative poles of the battery, and is electrically connected to the battery pack to measure the terminal voltage of the battery and reflect the battery's charge and discharge status. The micro-differential pressure sensor is installed inside the battery pack to detect and monitor the composition and concentration of the gas released by the battery. The shell deformation pressure indicates the pressure value when the battery shell deforms due to increased internal pressure during operation. The pressure sensor can be installed on the surface of the battery pack and physically connected to the battery pack body to measure the deformation pressure of the battery shell. These multimodal data can comprehensively reflect the state of the battery and provide rich information for subsequent analysis.

[0027] In one embodiment, flexible thin film temperature sensors are embedded in the gaps between the batteries in a matrix format, and distributed optical fiber temperature sensors are arranged in a serpentine pattern around the battery pack.

[0028] Flexible thin-film temperature sensors offer excellent flexibility, allowing them to deform and conform tightly to various curved and irregularly shaped objects. Embedded in a matrix format in the gaps between batteries, they can quickly sense temperature changes and respond quickly, typically outputting accurate temperature signals in seconds or even less. They are sensitive to environmental changes and can accurately reflect ambient temperature variations, making them suitable for temperature monitoring in a variety of complex environments. Distributed fiber optic temperature sensors, on the other hand, are immune to electromagnetic interference and are suitable for use in strong electromagnetic fields. They pose no safety risks such as leakage and electric shock, and exhibit no external electromagnetic interference, making them suitable for use in electromagnetically sensitive environments. They can achieve spatial resolution at the millimeter level or higher. The combined layout of flexible thin-film temperature sensors and distributed fiber optic temperature sensors overcomes the reliance of traditional infrared thermal imaging on the surface temperature of the battery pack.

[0029] S12. Perform outlier detection and normalization processing on the operating data to obtain target operating data.

[0030] The collected operating data is preprocessed, including outlier detection and normalization, to obtain the target operating data. Outlier detection removes noise and erroneous data from the operating data, ensuring its reliability. Normalization converts data of different dimensions into a unified dimension, facilitating subsequent analysis and processing.

[0031] In one embodiment, the outlier detection method includes at least one of the following: a Z-score method, an IQR method, and an isolation forest algorithm; The normalization process includes at least one of the following: minimum-maximum normalization and Z-score normalization.

[0032] Outlier detection can use statistical or machine learning-based methods, such as the Z-score method, the IQR (Interquartile Range Method), or the Isolation Forest algorithm. Normalization can use minimum-maximum normalization or Z-score normalization to adapt to different data characteristics.

[0033] For example, the target operating data is multimodal data including the surface temperature, internal temperature, gas composition, gas concentration, and shell deformation pressure of the battery pack. Figure 3 As shown in the figure, the collected multimodal data is subjected to outlier detection and normalization. Outlier detection uses the statistically based Z-score method to calculate the deviation of each data point from the mean. Data points exceeding a certain standard deviation are considered outliers and corrected or eliminated. Normalization uses the minimum-maximum normalization method to convert the data to the [0,1] interval. The formula is: ; Among them, X n is the normalized data, X is the original data, and X min and X max are the minimum and maximum values of the original data respectively.

[0034] S13. Perform time series analysis on the target operation data to determine the time characteristics, and obtain the spatial characteristics based on the location information of each battery and the target operation data.

[0035] Time series analysis can use autoregressive model (AR), moving average model (MA), autoregressive moving average model (ARMA), autoregressive integrated moving average model (ARIMA), or long short-term memory network (LSTM). Choose the appropriate analysis method based on the temporal characteristics of the data.

[0036] The spatiotemporal characteristics of thermal runaway of the battery pack are extracted through time series analysis and spatial feature construction. Time series analysis can explore the changing patterns of data in the time dimension, and spatial feature construction can consider the physical position relationship of each battery in the battery pack, thereby comprehensively considering the characteristics of both time and space dimensions.

[0037] In one embodiment, obtaining the spatial features according to the position information of each battery and the target operation data includes: constructing a spatial coordinate matrix according to the physical position of each battery in the battery pack; and obtaining the spatial features according to the spatial coordinate matrix and the target operation data.

[0038] like Figure 4 As shown, time series analysis of the target operating data obtained after preprocessing can be performed using a long short-term memory (LSTM) network to extract temporal features. LSTM can capture dependencies in long time series and is suitable for processing temporal variations in battery status data. Furthermore, a spatial coordinate matrix is constructed based on the physical location of each battery in the battery pack, with each battery position represented by (x, y, z) coordinates. Combined with the target operating data, a spatial feature vector is generated. For example, temperature, voltage, pressure, and gas data are integrated with the spatial coordinates to obtain a feature vector containing spatial position information, also known as a spatial feature.

[0039] In one embodiment, after performing time series analysis on the target operating data to determine the time characteristics and obtaining the spatial characteristics based on the location information of each battery and the target operating data, the following steps are further included: Based on the attention mechanism, weights are assigned to temporal features and spatial features respectively to obtain spatiotemporal features.

[0040] After determining the temporal and spatial features, the attention scores of the temporal and spatial features can be calculated through linear transformation. The Softmax function is used to normalize the attention scores into the weights of the temporal and spatial features. Finally, the temporal and spatial features are weighted and summed according to the normalized weights to obtain the spatiotemporal features.

[0041] S14. Input the spatiotemporal features into the thermal imaging model to generate a thermal imaging image of the battery pack according to the spatiotemporal features.

[0042] Among them, spatiotemporal features include time features and space features.

[0043] The thermal imaging model can be pre-trained and then built using a machine learning algorithm with spatiotemporal features as input, mapping the spatiotemporal features to the thermal imaging space. Machine learning algorithms have powerful nonlinear mapping capabilities and can accurately establish the relationship between spatiotemporal features and thermal imaging. Model training methods include: 1) Construction of training dataset Data source: Multimodal data collected from lithium battery packs under normal operation, overcharge, overheating, short circuit and other operating conditions, including several sets of labeled samples (label definitions: normal state / warning state / thermal runaway state) Enhancement strategy: Add Gaussian noise (σ=0.05, σ indicates the standard deviation) to the time series data and randomly flip / rotate the spatial grid data to improve the generalization ability of the model. 2) Loss function design Pixel-level regression loss: The mean squared error (MSE) is used to calculate the pixel difference between the predicted thermal image and the real thermal image captured by the infrared camera.

[0044] State classification loss: The warning state classification results are optimized in combination with the cross entropy loss function, with weight coefficients α = 0.7 (α indicates regression loss); β = 0.3 (β indicates classification loss). 3) Optimization algorithm The AdamW (Adam with Weight Decay) optimizer (learning rate 1e-4, weight decay 0.01) is used with a cosine annealing learning rate scheduling strategy, and the training period is set to 50 epochs.

[0045] Among them, machine learning algorithms may include: Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN) or Generative Adversarial Network (GAN), etc.

[0046] After the thermal imaging model is constructed, the spatiotemporal features are input into the thermal imaging model to generate a thermal image of the battery pack from the thermal imaging model, including the following steps: (1) Spatiotemporal feature encoding layer Input data format: Convert the spatiotemporal feature vector into a three-dimensional tensor [number of batteries, time series length, feature dimension], where the feature dimension contains temperature / voltage / pressure / gas concentration data and their spatial coordinates (x, y, z).

[0047] Position embedding technology: The battery's physical position (x, y, z) is encoded using sine and cosine functions to generate a position embedding vector, which is then added element-by-element to the sensor data features to achieve deep fusion of spatial position information and status data.

[0048] (2) Temporal feature extraction network Main network structure: A bidirectional long short-term memory network (Bi-LSTM) is stacked in 2 layers, the number of hidden layer neurons is set to 128, and a dropout layer (retention rate 0.5) is configured to prevent overfitting.

[0049] Output features: Extract dynamic features of time series, including temperature change rate (dT / dt), voltage fluctuation coefficient , pressure rise gradient (dP / dt) and other derived characteristics.

[0050] (3) Spatial Feature Convolutional Network Two-dimensional convolution module: Converts the physical layout of the battery pack into an N×M grid matrix (N is the number of rows and M is the number of columns), and each grid node is loaded with the preprocessed data of the corresponding battery.

[0051] Convolution kernel design: A 3×3 local receptive field convolution kernel is used in conjunction with dilated convolution (dilation rate = 2) to expand the spatial perception range and extract the heat conduction correlation features between adjacent batteries.

[0052] Pooling strategy: Alternate between maximum pooling (preserving spatial anomaly features) and average pooling (capturing global heat distribution trends).

[0053] (4) Feature fusion and mapping layer Cross-modal fusion: Through tensor concatenation, the temporal features (dimension 1×256) output by the Bidirectional Long Short-Term Memory (Bidirectional Long Short-Term Memory) network are aligned with the spatial features (dimension N×M×64) extracted by the CNN to form a joint spatiotemporal feature tensor.

[0054] Nonlinear mapping: A multi-layer fully connected network (2 layers, 256 neurons per layer, ReLU (Rectified Linear Unit) activation function) is used to map the joint features to the thermal imaging space, outputting an RGB pixel matrix of dimension N × M × 3 (corresponding to the red, green, and blue channel values of the thermal image).

[0055] For example, Figure 5 As shown, a convolutional neural network (CNN) can be used to build a thermal imaging model, taking spatiotemporal features as input. CNNs have powerful image processing and feature extraction capabilities, mapping spatiotemporal features to the thermal imaging space. The spatiotemporal feature vectors are converted into a matrix suitable for CNN input. Through convolutional, pooling, and fully connected layers, the output is a matrix of thermal image pixel values.

[0056] After generating the thermal image of the battery pack, you can visually see the thermal distribution of the battery pack and promptly detect potential thermal runaway risks, such as Figure 6 As shown: (1) Thermal imaging generation technology 1) Pseudo-color mapping algorithm The HSV color space conversion model is used to implement temperature-color mapping.

[0057] Hue (H): 0°-180° corresponds to a gradient from blue (cold) to red (hot); Saturation (S): fixed at 90% to enhance color distinction; Brightness (V): Dynamically adjusted according to temperature, V=30% in low temperature area, V=90% in high temperature area.

[0058] 2) Abnormal area marking rules Warning area (temperature rise rate ≥ 2°C / s): add a yellow dotted frame on the edge; Early thermal runaway area (multiple features exceeded): displayed with a red checkerboard texture overlay.

[0059] 3) Multi-dimensional visualization output 2D Plan View: The physical layout of the battery pack is displayed as a grid, with each cell displaying the corresponding battery's thermal status value (temperature / voltage / pressure composite value) and color mapping results; 3D Stereo View: Constructs a 3D coordinate system with xy position axes and z temperature axis, generates a thermal distribution surface map through surface interpolation algorithm, and supports mouse dragging and rotation to view from any angle.

[0060] S15 . In response to a target characteristic value of the target area in the thermal imaging image exceeding a preset threshold, determining that a battery corresponding to the target area is abnormal.

[0061] Using the constructed thermal imaging model, a thermal image of the battery pack is generated. Each pixel in the thermal image corresponds to a location in the battery pack, and the pixel's color indicates the thermal state at that location. Based on preset early warning thresholds for thermal runaway, such as temperature rise rate, temperature mutation index, gas correlation, abnormal voltage change amplitude, and pressure change rate, abnormal cells in the battery pack are located and their thermal state assessed. If characteristics detected in certain areas exceed thresholds, an alarm is triggered for the abnormal cell, prompting personnel to take appropriate action.

[0062] After the thermal image is generated, the abnormality of the battery can be judged, such as setting the temperature threshold T th =60℃, temperature change rate threshold ΔT / Δt =5℃ / s, ΔT indicates temperature change, Δt indicates time change, when a battery cell in the reconstructed thermal image exceeds the threshold for three consecutive time points, and the gas concentration at the corresponding position is abnormal ( ), it is determined to be an abnormal battery, and the coordinates and risk level are output.

[0063] In one embodiment, the positioning method further includes: Calculate the temperature gradient, heat flow direction, and overlap of gas concentration abnormality areas in the image area corresponding to each battery in the thermal imaging image; Determine the risk level of the battery corresponding to the image area based on the temperature gradient, heat flow direction, and overlap of gas concentration abnormal areas; In response to a risk level of any image region exceeding a preset level, it is determined that a battery corresponding to the image region is abnormal.

[0064] Using the constructed thermal imaging model, a thermal image of the battery pack is generated. Each pixel in the thermal image corresponds to a location in the battery pack, and the color of the pixel represents the thermal state of that location. Based on the preset early warning thresholds for thermal runaway, such as the temperature rise rate, temperature mutation index, gas correlation, voltage abnormal change amplitude, pressure change rate, etc., the thermal state of abnormal batteries in the battery pack is located and evaluated. The temperature gradient, heat flow direction, and overlap of gas concentration abnormal areas in the image area corresponding to each battery in the thermal image are calculated. Based on the temperature gradient, heat flow direction, and overlap of gas concentration abnormal areas, the risk level of the battery corresponding to the image area is determined. When the risk level of any image area exceeds the preset level, an alarm is issued for the abnormal battery, reminding relevant personnel to take measures.

[0065] Among them, the temperature rise rate indicates the speed at which the temperature increases over time, which can be measured in ℃ / min or ℃ / s; the temperature mutation index indicates the degree to which the temperature changes drastically in a short period of time; the gas correlation indicates the ratio or change relationship between the concentrations of different characteristic gases. When an abnormal chemical reaction occurs inside the battery, trace characteristic gases will be produced first, such as small molecular gases such as H2, CO, and C2H4. The gas correlation can include the H2 / CO ratio, Therefore, compared with single gas concentration information, gas correlation can amplify the tiny chemical reactions generated inside the battery and provide clearer and earlier battery abnormality signals; the amplitude of abnormal voltage change indicates the degree to which the voltage deviates from the normal range in a short period of time, which can be determined by measuring the voltage fluctuation range; the pressure change rate indicates the speed at which the pressure changes over time.

[0066] The temperature gradient indicates the rate of temperature change in space, representing the degree of temperature variation per unit distance. Batteries generate heat during charging and discharging, and the temperature gradient can help identify uneven heat distribution within the battery and on the surface of the battery pack. In thermal imaging, the temperature gradient can be demonstrated by the intensity of the color change; the more intense the color change, the greater the temperature gradient. The direction of heat flow indicates the direction of heat transfer. In thermal imaging, this direction can be visually represented by isotherms (lines connecting points of the same temperature). The denser the isotherms, the more distinct the heat flow direction. The overlap of abnormal gas concentration areas indicates the degree of spatial overlap between abnormal gas concentration areas and abnormal temperature areas in thermal imaging. In practical applications, thermal imaging technology and gas sensors can be combined to monitor battery status. Thermal imaging provides temperature distribution information, while gas sensors detect gas concentration. By spatially aligning the data from these two and calculating the overlap, a more comprehensive assessment of battery safety status can be achieved.

[0067] This application dynamically analyzes thermal images to accurately locate battery cells with abnormal temperatures or risk of thermal runaway. This application addresses issues such as environmental interference, physical limitations, high installation costs, and insufficient temporal and spatial resolution associated with traditional infrared thermal imaging, providing an efficient and reliable technical solution for the safety monitoring of energy storage battery packs.

[0068] This application does not require the installation of a thermal imager, but can utilize the existing sensor signals of the BMS (Battery Management System). It solves the problems of traditional infrared thermal imaging technology being greatly affected by the environment, limited installation space, high cost, and insufficient temporal and spatial resolution of weak signals. It can accurately reflect the thermal state of the battery pack and achieve early abnormal location and precise warning of thermal runaway.

[0069] During the specific implementation process, the installation position and number of sensors can be adjusted according to the actual battery pack structure and application scenarios, appropriate outlier detection and normalization methods can be selected, and the parameters of time series analysis and imaging models can be optimized to improve the accuracy of thermal imaging and the reliability of early warning.

[0070] It should be understood that although Figure 1-6 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1-6At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0071] This application also provides a positioning system for abnormal batteries, such as Figure 7 As shown, the positioning system includes: A first determining module 71 is configured to determine operating data of the battery pack; wherein the operating data includes surface temperature, internal temperature, gas composition, gas concentration, and shell deformation pressure, and the operating data is obtained based on a sensor array provided on the battery pack; the sensor array includes a temperature sensor, a gas sensor, a voltage sensor, and a micro-differential pressure sensor; the battery pack includes at least two batteries; A processing module 72 is configured to perform outlier detection and normalization processing on the operating data to obtain target operating data; a second determining module 73 for performing a time series analysis on the target operating data to determine a time feature, and obtaining a spatial feature based on the location information of each battery and the target operating data; An image generation module 74 is configured to input the spatiotemporal features into a thermal imaging model to generate a thermal image of the battery pack according to the spatiotemporal features; the spatiotemporal features include the time features and the spatial features; The third determination module 75 is used to determine the battery abnormality corresponding to the target area in the thermal imaging image in response to the target characteristic value of the target area in the thermal imaging image exceeding a preset threshold; the target characteristic value includes temperature rise rate, temperature mutation index, gas correlation, voltage abnormal change amplitude, and pressure change rate.

[0072] Optionally, the temperature sensor includes at least one of the following: a flexible thin film temperature sensor, a distributed optical fiber temperature sensor, a thermocouple sensor, and a thermistor; the flexible thin film temperature sensor is embedded in the gaps between the batteries in a matrix form, and the distributed optical fiber temperature sensor is arranged in a serpentine pattern around the battery pack.

[0073] Optionally, the outlier detection method includes at least one of the following: a Z-score method, an IQR method, and an isolation forest algorithm; The normalization process includes at least one of the following: minimum-maximum normalization and Z-score normalization.

[0074] Optionally, the second determining module is further configured to: Based on the attention mechanism, weights are assigned to the temporal feature and the spatial feature respectively to obtain the spatiotemporal feature.

[0075] Optionally, the second determining module is further configured to: Constructing a spatial coordinate matrix according to the physical position of each battery in the battery pack; The spatial feature is obtained according to the spatial coordinate matrix and the target operation data.

[0076] Optionally, the positioning system further includes a calculation module for calculating the temperature gradient, heat flow direction, and overlap of gas concentration abnormality areas in the image area corresponding to each battery in the thermal imaging image; Determining the risk level of the battery corresponding to the image area based on the temperature gradient, heat flow direction, and overlap of gas concentration abnormal areas; In response to the risk level of any image area exceeding a preset level, it is determined that the battery corresponding to the image area is abnormal.

[0077] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only illustrative, in which the units described as separate components may or may not be physically separated, and the components of the units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present application solution.

[0078] Figure 8 This is a structural diagram of an electronic device shown in an example embodiment of the present application. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, the method described in any of the above embodiments is implemented. Figure 8 The electronic device 80 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0079] like Figure 8 As shown, electronic device 80 may be implemented as a general-purpose computing device, such as a server device. Components of electronic device 80 may include, but are not limited to, at least one processor 81, at least one memory 82, and a bus 83 connecting various system components (including memory 82 and processor 81).

[0080] The bus 83 includes a data bus, an address bus, and a control bus.

[0081] The memory 82 may include a volatile memory, such as a random access memory (RAM) 821 and / or a cache memory 822 , and may further include a read-only memory (ROM) 823 .

[0082] The memory 82 may also include a program tool 825 (or utility) having a set (at least one) of program modules 824, such program modules 824 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0083] The processor 81 executes various functional applications and data processing by running the computer program stored in the memory 82, such as the method provided in any of the above embodiments.

[0084] The electronic device 80 can also communicate with one or more external devices 84 (e.g., a keyboard, pointing device, etc.). This communication can occur via an input / output (I / O) interface 85. Furthermore, the electronic device 80 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 86. As shown, the network adapter 86 communicates with other modules of the electronic device 80 via a bus 83. It should be understood that, although not shown, other hardware and / or software modules can be used in conjunction with the electronic device 80, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0085] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, depending on the embodiment of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0086] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the method provided in any of the above embodiments when the program is executed by a processor.

[0087] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0088] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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 memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0089] An embodiment of the present application further provides a computer program product, including a computer program, which implements any of the above methods when executed by a processor.

[0090] The program code for executing the computer program product of the present application may be written in any combination of one or more programming languages, and the program code may be executed entirely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0091] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for locating an abnormal battery, characterized in that: The positioning method includes: Determining operating data of a battery pack; wherein the operating data includes surface temperature, internal temperature, gas composition, gas concentration, and shell deformation pressure, and the operating data is obtained based on a sensor array provided on the battery pack; the sensor array includes a temperature sensor, a gas sensor, a voltage sensor, and a micro-differential pressure sensor; the battery pack includes at least two batteries; Performing outlier detection and normalization processing on the operating data to obtain target operating data; Performing a time series analysis on the target operation data to determine a time feature, and obtaining a spatial feature based on the location information of each battery and the target operation data; calculating an attention score of the time feature and the spatial feature through linear transformation, normalizing the attention score into a weight of the time feature and a weight of the spatial feature using a predetermined function, and performing a weighted summation of the time feature and the spatial feature according to the normalized weights to obtain a spatiotemporal feature; Inputting the spatiotemporal features into a thermal imaging model to generate a thermal imaging image of the battery pack according to the spatiotemporal features, wherein the spatiotemporal features include the time features and the spatial features; In response to a target characteristic value of a target area in the thermal imaging image exceeding a preset threshold, a battery abnormality corresponding to the target area is determined; the target characteristic value includes a temperature rise rate, a temperature mutation index, a gas correlation degree, a voltage abnormal change amplitude, and a pressure change rate.

2. The positioning method according to claim 1, wherein: The temperature sensor includes at least one of the following: a flexible thin film temperature sensor, a distributed optical fiber temperature sensor, a thermocouple sensor, and a thermistor; the flexible thin film temperature sensor is embedded in the gaps between the batteries in a matrix form, and the distributed optical fiber temperature sensor is arranged in a serpentine shape around the battery pack.

3. The positioning method according to claim 1, wherein: The outlier detection method includes at least one of the following: Z-score method, IQR method, isolation forest algorithm; The normalization process includes at least one of the following: minimum-maximum normalization and Z-score normalization.

4. The positioning method according to claim 1, wherein: The predetermined function includes a Softmax function.

5. The positioning method according to claim 1, wherein: The obtaining of the spatial features according to the location information of each battery and the target operation data includes: Constructing a spatial coordinate matrix according to the physical position of each battery in the battery pack; The spatial feature is obtained according to the spatial coordinate matrix and the target operation data.

6. The positioning method according to claim 1, wherein: The positioning method further includes: Calculate the temperature gradient, heat flow direction, and overlap of gas concentration abnormality areas in the image area corresponding to each battery in the thermal imaging image; Determining the risk level of the battery corresponding to the image area based on the temperature gradient, heat flow direction, and overlap of gas concentration abnormal areas; In response to the risk level of any image area exceeding a preset level, it is determined that the battery corresponding to the image area is abnormal.

7. A system for locating abnormal batteries, characterized in that: The positioning system comprises: a first determination module, configured to determine operating data of the battery pack; wherein the operating data includes surface temperature, internal temperature, gas composition, gas concentration, and shell deformation pressure, and the operating data is obtained based on a sensor array provided on the battery pack; the sensor array includes a temperature sensor, a gas sensor, a voltage sensor, and a micro-differential pressure sensor; and the battery pack includes at least two batteries; a processing module, configured to perform outlier detection and normalization processing on the operating data to obtain target operating data; a second determining module, configured to perform time series analysis on the target operating data to determine a time feature, and obtain a spatial feature based on the location information of each battery and the target operating data; An image generation module, configured to input the spatiotemporal features into a thermal imaging model to generate a thermal image of the battery pack according to the spatiotemporal features, wherein the spatiotemporal features include the time features and the spatial features; The third determination module is used to determine the battery abnormality corresponding to the target area in the thermal imaging image in response to the target characteristic value of the target area in the thermal imaging image exceeding a preset threshold; the target characteristic value includes the temperature rise rate, the temperature mutation index, the gas correlation degree, the voltage abnormal change amplitude, and the pressure change rate.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and configured to run on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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