Current stability test method and system of energy storage cabinet

Abstract

The invention relates to the technical field of electric power testing, and discloses a current stability testing method and system for an energy storage cabinet, and the method comprises the steps: obtaining current distribution data, and generating a current distribution graph through the combination of a preset node distribution graph; performing current density analysis according to the current distribution map to obtain a current concentration area; acquiring temperature data of the current concentration area, and superposing the temperature data with the current distribution map to obtain a comprehensive distribution map; performing temperature anomaly detection according to the comprehensive distribution map to obtain an overheated point coordinate; performing heat conduction simulation according to the coordinates of the overheat point to obtain a temperature prediction result; and performing current distribution optimization according to the comprehensive distribution map and the temperature prediction result to obtain an optimized current scheme. The method has the following effect that the current stability of the energy storage cabinet can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power testing, and in particular to a current stability testing method and system for an energy storage cabinet. Background Art

[0002] With the development of renewable energy technologies and the widespread adoption of smart grids, the performance stability of energy storage systems, as key components of energy regulation, has become increasingly important. Energy storage cabinets, as the core of energy storage systems, are responsible for storing electrical energy and releasing it when needed. Current stability directly impacts the efficiency of energy storage cabinets and their contribution to grid stability. Therefore, developing an accurate and effective current stability testing method and system is crucial to ensuring the quality and reliability of energy storage cabinets. This is especially critical when integrating highly volatile and intermittent renewable energy sources, such as wind and solar power, into the grid.

[0003] One existing method for testing the current stability of energy storage cabinets utilizes a standard load test procedure. First, the energy storage cabinet is connected to a test platform simulating a power grid under predetermined environmental conditions (including temperature and humidity). The load is then gradually increased or decreased to simulate different power demand scenarios, while the cabinet's output current and its changes are monitored and recorded. During this process, a data acquisition system collects current data in real time and compares it with a preset standard value. If the actual measured value is within the allowable error range, the energy storage cabinet is considered to have met the current stability requirements.

[0004] However, when the energy storage cabinet encounters an inappropriate heat dissipation design or excessively high ambient temperatures during operation, the temperature of certain components can exceed the safe range. As the temperature rises, the chemical reaction rate within the battery accelerates, causing problems such as a decrease in internal resistance and an increase in self-discharge, ultimately leading to unstable output current. Under high-load operating conditions, energy storage components (such as batteries) generate heat. If this heat cannot be dissipated promptly, the internal temperature of the energy storage cabinet will rise, affecting its electrical performance parameters such as internal resistance and capacity. When thermoelectric coupling anomalies occur, such as poor heat dissipation or localized overheating, the lack of a corresponding monitoring mechanism makes it difficult to capture the negative impact of these changes on current stability in real time. Because temperature changes are gradual and have a complex nonlinear relationship with current changes, simply relying on conventional load testing cannot accurately predict or detect problems caused by thermoelectric coupling.

[0005] In summary, the existing technology has the problem of low current stability of the energy storage cabinet. Summary of the Invention

[0006] The present invention provides a current stability testing method and system for an energy storage cabinet, so as to achieve the goal of improving the current stability of the energy storage cabinet.

[0007] In a first aspect, in order to solve the above technical problems, the present invention provides a method for testing the current stability of an energy storage cabinet, comprising: Obtain current distribution data and generate a current distribution map based on a preset node distribution map; Performing current density analysis based on the current distribution map to obtain a current concentration area; Acquiring temperature data of the current concentrated area and superimposing the data with the current distribution map to obtain a comprehensive distribution map; Perform temperature anomaly detection based on the comprehensive distribution map to obtain hotspot coordinates; Perform heat conduction simulation according to the hot spot coordinates to obtain temperature prediction results; Current distribution optimization is performed according to the comprehensive distribution map and the temperature prediction result to obtain an optimized current solution.

[0008] In an optional embodiment, performing current density analysis based on the current distribution map to obtain a current concentration area includes: Calculating the current density according to the current distribution map to obtain current density data; Performing spatial gradient analysis based on the current density data to obtain a current density gradient; When the current density gradient is higher than a preset density gradient threshold, the corresponding position is determined as a current concentration area.

[0009] In an optional embodiment, acquiring the temperature data of the current concentration area and superimposing the temperature data with the current distribution map to obtain a comprehensive distribution map includes: Get temperature data, which includes timestamp, location space coordinates and temperature value; According to the timestamp and the position space coordinates, the temperature data and the current distribution map are temporally and spatially aligned to obtain a fused data set; Data fusion is performed according to the fused data set to obtain a comprehensive distribution map.

[0010] In an optional embodiment, performing data fusion according to the fused data set to obtain a comprehensive distribution map includes: Normalizing the temperature values ​​in the fused data set to obtain a normalized temperature, which serves as the first channel; Normalizing the current distribution data in the fused data set to obtain a normalized current, which serves as a second channel; Multiplying the normalized temperature and the normalized current to obtain a coupling characteristic as a third channel; The first channel, the second channel and the third channel are weightedly superimposed to obtain a comprehensive distribution map.

[0011] In an optional embodiment, performing temperature anomaly detection based on the comprehensive distribution map to obtain hotspot coordinates includes: The temperature distribution threshold is calculated according to the following formula: in, represents the temperature distribution threshold, Indicates the ambient temperature, represents the current density, represents the material resistivity, Indicates the heat dissipation distance of the current path, represents the convection conversion coefficient; Calculating a real-time temperature value according to the first channel in the comprehensive distribution map; When the real-time temperature value is greater than the temperature distribution threshold, the corresponding position is determined as a candidate hotspot; Performing cluster analysis on the candidate hot spots and removing outliers to obtain clustering results; According to the clustering results, the center coordinates of each cluster are used as hotspot coordinates.

[0012] In an optional embodiment, heat conduction simulation is performed according to the hot spot coordinates to obtain a temperature prediction result.

[0013] Call the pre-established heat conduction modeling to build a simulation scenario; Applying heat flux density at the position corresponding to the hotspot coordinates according to the simulation scenario, performing heat conduction simulation after setting the time step, and outputting the temperature field distribution result; Inputting the temperature field distribution result into a pre-trained material aging model and outputting an aging acceleration factor; The temperature prediction result includes the temperature field distribution result and the aging acceleration factor.

[0014] In an optional embodiment, the optimizing the current distribution according to the comprehensive distribution map and the temperature prediction result to obtain the optimized current solution includes: When the aging acceleration factor in the temperature prediction result is greater than a preset aging threshold, it is determined to be a critical risk area; determining an optimization target based on the key risk area and the temperature prediction result; Performing gradient descent optimization according to the comprehensive distribution map and the optimization target to generate multiple groups of candidate optimization solutions; The multiple groups of candidate optimization solutions are verified by thermoelectric coupling simulation to obtain an optimized current solution.

[0015] In a second aspect, the present invention provides a current stability testing system for an energy storage cabinet, comprising: The data acquisition module is used to acquire current distribution data and generate a current distribution map based on a preset node distribution map; A current density module, configured to perform current density analysis based on the current distribution map to obtain a current concentration area; a thermoelectric coupling module, configured to obtain temperature data of the current concentration area and superimpose the temperature data with the current distribution map to obtain a comprehensive distribution map; A temperature anomaly module is used to detect temperature anomalies based on the comprehensive distribution map and obtain hot spot coordinates; A temperature prediction module, configured to perform heat conduction simulation based on the hot spot coordinates to obtain a temperature prediction result; The scheme optimization module is used to optimize the current distribution according to the comprehensive distribution map and the temperature prediction result to obtain an optimized current scheme.

[0016] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the current stability testing method of the energy storage cabinet described in any one of the above is implemented.

[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned current stability testing methods for the energy storage cabinet.

[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) Obtain current distribution data and generate a current distribution map based on a preset node distribution map. High-precision sensors are used to collect the current values ​​of each node in the circuit and match them with the preset node distribution map to construct a visual current distribution map. This step not only improves the observability of the current state but also provides a clear structure and accurate spatial correspondence data foundation for subsequent analysis, improving the perception capability and computational efficiency of the overall system.

[0019] (2) Perform current density analysis based on the current distribution map to identify current concentration areas. Based on the data distribution characteristics in the current distribution map, current density is calculated and gradient analyzed to identify potential risk areas with dense current. This step helps to identify key locations that may cause local overheating or burning in advance, enhances the system's ability to identify abnormal trends, and improves diagnostic accuracy.

[0020] (3) Obtaining the temperature data of the current concentration area and superimposing it with the current distribution map to obtain a comprehensive distribution map. Based on the identification of the current concentration area, further collect the corresponding real-time temperature information, and spatially fuse the temperature data with the current distribution map to form a comprehensive distribution map containing both current and temperature information. This multi-dimensional data fusion method significantly enhances the ability to describe the operating status of the device and provides more comprehensive data support for anomaly detection.

[0021] (4) Perform temperature anomaly detection based on the comprehensive distribution map to obtain hotspot coordinates. By continuously monitoring the temperature distribution in the comprehensive distribution map and judging the threshold, high-temperature areas exceeding the safe range, i.e., hotspots, are identified and their specific coordinates are recorded. This step enables a rapid response to abnormal temperature rise, improves the system's early warning capability for potential fault points, and ensures the safety and stability of equipment operation.

[0022] (5) Perform a heat conduction simulation based on the hotspot coordinates to obtain temperature prediction results. Based on the physical heat conduction model, simulation calculations are performed with the hotspot as the initial condition to predict its temperature change trend at different time scales. This step helps to assess the diffusion impact range of the hotspot and its evolution process, providing a scientific basis for subsequent optimization and improving the system's ability to predict and control thermal risks.

[0023] (6) Optimizing the current distribution based on the comprehensive distribution map and the temperature prediction results to obtain an optimized current solution. Combining the key risk areas and the temperature prediction results, a gradient descent optimization method is used to generate an optimization solution, reducing the current density in the key areas and thus suppressing the generation of hot spots. This method not only improves the uniformity of the current distribution, but also effectively reduces the thermal risk during system operation and improves the stability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a current stability testing method for an energy storage cabinet provided by the first embodiment of the present invention; Figure 2 2 is a schematic diagram of the structure of a current stability test system for an energy storage cabinet provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0026] Reference Figure 1 A first embodiment of the present invention provides a method for testing the current stability of an energy storage cabinet, comprising the following steps: S11, obtaining current distribution data and generating a current distribution map in combination with a preset node distribution map; S12, performing current density analysis based on the current distribution map to obtain a current concentration area; S13, acquiring temperature data of the current concentrated area, and superimposing the temperature data with the current distribution map to obtain a comprehensive distribution map; S14, performing temperature anomaly detection based on the comprehensive distribution map to obtain hot spot coordinates; S15, performing heat conduction simulation according to the hot spot coordinates to obtain a temperature prediction result; S16, optimizing the current distribution according to the comprehensive distribution map and the temperature prediction result to obtain an optimized current solution.

[0027] In step S11 , current distribution data is acquired and combined with a preset node distribution map to generate a current distribution map.

[0028] In one embodiment, the specific steps for obtaining current distribution data are as follows: First, current sensors are deployed at key nodes of the energy storage cabinet (such as battery module connection points, busbars, main circuit switches, etc.). The sensor type can be a shunt or a Hall effect sensor. Shunts are suitable for high-precision scenarios (such as accuracy requirements within ±0.1%), and Hall effect sensors are suitable for non-contact high-current monitoring (such as busbar current monitoring). Sensor installation must meet the following requirements: the shunt uses a four-wire Kelvin connection method, the shunt resistance value is selected from 5mΩ to 100mΩ, and it must be grounded with the battery pack; the Hall effect sensor is installed at the bend of the busbar to avoid magnetic field distortion, while compensating for hysteresis effects through a feedback circuit; the Rogowski coil (suitable for high-current scenarios) is wrapped around the busbar with a spacing of ≤300mm to ensure uniform coverage of the current path. Subsequently, a data acquisition system (such as a PLC or BMS controller) synchronously collects three types of data: timestamps (using GPS timestamps or master controller clock synchronization to ensure that all sensor data is strictly aligned in the time dimension), location information, and current values ​​(the raw signal output by the sensor is amplified and filtered, then converted into a digital current value in A through the ADC module and stored as structured data, such as a CSV or database table). Finally, the collected multi-node current data is spatially mapped with the preset node distribution map to generate a current distribution map containing timestamps, location coordinates, and current values. For example, if the coordinates of a node are (1200mm, 800mm, 500mm), the current value at the timestamp of 2025-05-28 14:33:37 is 50A.

[0029] It is worth noting that the node distribution map refers to the physical location layout of current sensors at key nodes in the energy storage cabinet (such as battery module connection points, busbars, main circuit switches, etc.), which is used to identify the spatial coordinates of data collection points; the current distribution map is a visual map generated by combining the current values ​​collected by the sensor in real time with the node distribution map. It intuitively displays the current intensity and distribution characteristics of each node in the form of a heat map, including timestamps, location coordinates and current values, and is used to analyze current concentration areas and abnormal conditions.

[0030] In step S12, current density analysis is performed based on the current distribution map to obtain a current concentration area.

[0031] In one embodiment, current density calculation is performed based on the current distribution spectrum to obtain current density data; Performing spatial gradient analysis based on the current density data to obtain a current density gradient; When the current density gradient is higher than a preset density gradient threshold, the corresponding position is determined as a current concentration area.

[0032] It is worth noting that the current intensity at each node in the current distribution map is extracted based on the current value and location information of each node. The current density at that node is calculated by calculating the ratio of the node's current intensity to the corresponding conductor cross-sectional area. For example, if the current at a node is 5A and the conductor cross-sectional area is 0.5mm², the current density is 10A / mm². For conductors with regular shapes (such as rectangular busbars or round cables), a uniform distribution model can be used for calculation. However, for conductors with complex shapes (such as multi-layer laminates or irregular structures), the conductor must be divided into multiple small units, and the current density of each unit is calculated separately. The overall distribution is then determined through superposition or integration. Simulation software (such as COMSOL or ANSYS) is used to simulate the current flow path within the conductor to verify the accuracy of the theoretical calculation results and correct for deviations caused by material inhomogeneities or edge effects. The final output is current density data covering all nodes, forming a quantitative description of the spatial distribution.

[0033] It is worth noting that the uniform distribution model is a mathematical model that assumes that the probability or density of all possible values ​​within a specific range is exactly equal. In the current density calculation, it is manifested as a uniform distribution of current on the conductor cross section. For conductors with regular shapes (such as rectangular busbars or round cables), if their materials are uniform and there are no edge effects, the current density can be directly calculated by the ratio of the total current to the cross-sectional area, and the current density value at any position within the conductor is the same. For example, when a 5A current flows through a regular conductor with a cross-sectional area of ​​0.5mm², its current density is 10A / mm², and this value remains constant within the conductor cross section without considering position differences. This model simplifies the calculation process and is suitable for scenarios with strong regularity in current distribution and symmetrical geometric structures. However, the actual current flow path needs to be verified by simulation software (such as COMSOL) to correct deviations caused by material inhomogeneity or complex structures.

[0034] It is worth noting that after obtaining the current density data, spatial gradient analysis is performed through the current density differences between adjacent nodes or regions. First, the current density data is divided into multiple spatial grids, and the rate of change of the current density in each grid is calculated. For example, between adjacent grids, if the current density in a certain direction suddenly increases from 10A / mm² to 20A / mm², its gradient value is 10A / mm² / unit distance. Subsequently, the gradient values ​​of all grids are compared with the preset density gradient threshold (5A / mm² / unit distance). When the gradient value of a certain area is continuously higher than the threshold, it indicates that there is a significant current density jump in the area, and it is thus determined to be a current concentration area. The gradient distribution is further visualized through a heat map or a vector field map to assist in locating high-gradient areas. When the gradient value of a certain area is less than or equal to the threshold, it means that the current density changes evenly and is in a normal working state.

[0035] It's worth noting that when calculating the current density difference between adjacent nodes or grid cells, the difference must be divided by the actual physical distance between the two points (for example, the spacing between adjacent grid centers). This distance is then normalized to a "unit distance" to simplify the calculation. For example, if the spacing between adjacent grid cells is 1 mm, the "unit distance" is 1 mm, and the gradient value represents the change in current density per millimeter (e.g., a change from 10A / mm² to 20A / mm² corresponds to a gradient of 10A / mm² / mm). If the grid spacing is 0.5 mm, the "unit distance" must be adjusted to 0.5 mm, and the gradient value must be scaled accordingly. The choice of unit distance is based on the grid resolution (such as the default grid spacing in the simulation software). Its core purpose is to ensure that gradient values ​​in different regions are comparable by standardizing the scale and can be directly compared to a preset threshold (e.g., 5A / mm² / unit distance) to determine whether current concentration is present.

[0036] In step S13 , the temperature data of the current concentration area is obtained and superimposed with the current distribution map to obtain a comprehensive distribution map.

[0037] In one embodiment, temperature data is obtained, which includes a timestamp, a location spatial coordinate, and a temperature value; According to the timestamp and the position space coordinates, the temperature data and the current distribution map are temporally and spatially aligned to obtain a fused data set; Data fusion is performed according to the fused data set to obtain a comprehensive distribution map.

[0038] It's worth noting that temperature data is acquired by deploying temperature sensors (such as NTC thermistors, infrared sensors, or integrated temperature and humidity sensors) at key locations in the energy storage cabinet (such as battery module terminals, busbar connection points, and cooling pipe surfaces). These sensors must maintain high accuracy within ±0.5°C. Data collection frequency can be set to seconds or minutes, depending on requirements. Data is transmitted to the data acquisition host via the 485 bus, CAN bus, or wireless communication (such as LoRa / WiFi). Temperature data storage must be in a file format (such as CSV / Excel) that meets spatiotemporal alignment requirements. The data table structure must include a timestamp (accurate to milliseconds), spatial coordinates, and temperature values. For example: timestamp = 2025-05-28 14:51:14, location = (1200mm, 800mm, 500mm), temperature = 45.3°C. To improve query efficiency, indexes can be created for the timestamp and location fields, and data compression (such as Delta encoding) can be employed.

[0039] It is worth noting that the acquisition clocks of the temperature and current sensors are calibrated using a high-precision time synchronization protocol (such as PTP / gPTP or GPS timestamps) to ensure that the timestamp error between the two is less than 1ms. For example, the temperature sampling time is aligned with the millisecond timestamp of the current data to address time offsets caused by sensor sampling frequency differences. Subsequently, during the spatial calibration phase, a spatial mapping relationship between the temperature and current sensors is established using a pre-set node distribution map. The specific steps include: 1) Single-sensor calibration—assigning unique physical coordinates to each temperature sensor and verifying the spatial consistency of its installation location with the current sensor nodes; 2) Multi-sensor calibration—using a laser rangefinder or total station to measure the relative positions of the sensors and construct a unified spatial coordinate system. After time synchronization and spatial calibration, a data matching algorithm is used to associate the temperature data with the corresponding nodes in the current distribution map. For example, if a temperature sensor has a timestamp of 2025-05-28 14:51:14 and coordinates (1200mm, 800mm, 500mm), it is paired with the current value at that coordinate point at the same timestamp. For timestamps with slight deviations, linear interpolation or nearest neighbor matching can be used to fill in missing data. Ultimately, the matched temperature-current dataset is structured and stored according to the spatiotemporal dimensions (e.g., in a database table or CSV file), forming a fused data set containing timestamps, coordinates, temperature values, and current values, providing a unified data foundation for subsequent analysis.

[0040] It's worth noting that linear interpolation uses the data values ​​at known time points to infer the values ​​at missing time points. First, two valid time points are found in the time series (for example, sampling points 1 minute before and 3 minutes after the missing point) and the corresponding temperature or current values ​​are recorded (e.g., 30°C for the first point and 45°C for the second point). Next, the data change is proportionally distributed based on the time interval between the two time points (e.g., a total interval of 4 minutes) and the location of the missing point (e.g., 2 minutes from the previous point). Assuming the difference between the two data points is 15°C and the missing point is located 2 / 4 of the total interval, the estimated value is 30°C at the previous point plus 2 / 4 of 15°C (i.e., 7.5°C), resulting in a final value of 37.5°C at the missing point. This method is suitable for scenarios with small timestamp deviations and smooth data trends, such as high-frequency temperature or current data. However, be aware that linear interpolation can introduce errors if the actual data exhibits sudden or nonlinear fluctuations (e.g., sudden equipment failure). To verify the rationality, the effect can be evaluated by comparing the overall trends of the data before and after filling, or by calculating statistical indicators such as the mean square error between the filled value and the true value (if any).

[0041] In one embodiment, the temperature values ​​in the fused data set are normalized to obtain a normalized temperature, which is used as the first channel; Normalizing the current distribution data in the fused data set to obtain a normalized current, which serves as a second channel; Multiplying the normalized temperature and the normalized current to obtain a coupling characteristic as a third channel; The first channel, the second channel and the third channel are weightedly superimposed to obtain a comprehensive distribution map.

[0042] It's worth noting that the core of normalization is to convert temperature and current values ​​of different dimensions or magnitudes to a unified numerical range (such as 0 to 1 or -1 to 1) for subsequent analysis. For temperature data, all temperature values ​​are first extracted from the fused data set, and the lowest and highest temperatures are determined as a reference range. Each temperature value is then linearly transformed: the lowest temperature is subtracted from the original temperature value, and then divided by the difference between the highest and lowest temperatures, resulting in a normalized temperature between 0 and 1. For example, if a temperature value is 45°C, the lowest temperature is 30°C, and the highest temperature is 60°C, the normalized temperature is (45-30) / (60-30) = 0.5. Similarly, for current data, all current values ​​are first extracted, the minimum and maximum currents are determined as references, and the same linear transformation is performed on each current value to map it to the 0 to 1 range. For example, if a current value is 5A, the minimum current is 0A, and the maximum current is 10A, then the normalized current is (5-0) / (10-0) = 0.5. In this way, the temperature and current data are compressed to the same scale, forming two independent channels of data, which facilitates subsequent multi-dimensional feature fusion analysis.

[0043] It's worth noting that the point-by-point multiplication of the normalized temperature (first channel) and the normalized current (second channel) yields the coupling signature (third channel), whose value reflects the spatial synergistic effect between temperature and current (e.g., the potential risk of combined high temperature and high current). Based on this, the three channels are integrated into a comprehensive distribution map through weighted superposition, with the weights assigned as follows: 40% for the first channel (temperature), 40% for the second channel (current), and 20% for the third channel (coupling signature). This allocation is based on the following rationale: temperature and current, as core physical quantities that directly reflect the operating status of the device, are given equal weight (40%) to reflect their fundamental importance. The coupling signature, through its product, reveals the interactive effects of the two (e.g., the hotspot risk of combined high temperature and high current). However, its extreme values ​​are amplified by the multiplication effect, so its weight is appropriately reduced (20%) to avoid oversensitivity. In practical scenarios, if a factor (e.g., temperature) becomes more decisive for fault prediction, its weight can be dynamically adjusted (e.g., 50% for temperature, 30% for current, and 20% for coupling signature). The coupling signature quantifies the spatial synergy between temperature and current, compensating for the limitations of a single channel. For example, in an energy storage cabinet, local current concentration does not exceed the threshold, but if high temperature is superimposed, it will significantly accelerate the aging of the conductor. The coupling feature can identify such complex risk areas in advance through the product relationship, thereby improving the warning accuracy and system safety.

[0044] In step S14, temperature anomaly detection is performed based on the comprehensive distribution map to obtain hot spot coordinates.

[0045] In one embodiment, the temperature distribution threshold is calculated according to the following formula: in, represents the temperature distribution threshold, Indicates the ambient temperature, represents the current density, represents the material resistivity, Indicates the heat dissipation distance of the current path, represents the convection conversion coefficient; Calculating a real-time temperature value according to the first channel in the comprehensive distribution map; When the real-time temperature value is greater than the temperature distribution threshold, the corresponding position is determined as a candidate hotspot; Performing cluster analysis on the candidate hot spots and removing outliers to obtain clustering results; According to the clustering results, the center coordinates of each cluster are used as hotspot coordinates.

[0046] It's worth noting that this formula establishes a temperature threshold model based on the physical relationship between ambient temperature, current density, material resistivity, heat dissipation distance, and convective heat transfer coefficient. The unit of ambient temperature is Kelvin, the unit of current density is ampere per square meter (A / m²), the unit of material resistivity is ohm-meter (Ω·m), the unit of heat dissipation distance is meter (m), and the unit of convective heat transfer coefficient is watt per square meter·Kelvin (W / (m²·K)). Watt (W) can be expressed as joules per second (J / s) or volts·amperes (V·A).

[0047] It's worth noting that the energy storage cabinet requires real-time ambient temperature monitoring during operation. The dry-bulb temperature method (measured with an unobstructed standard thermometer) is used to obtain the atmospheric temperature. Typical values ​​range from 20-30°C at normal temperatures to over 40°C in high-temperature operating scenarios. To assess the impact of humidity or thermal radiation on the equipment, the wet-bulb temperature method (which reflects humidity saturation) or the black globe temperature method (which quantifies thermal radiation intensity) can be combined to comprehensively assess the energy storage cabinet's heat dissipation requirements in complex environments. For example, in high-temperature and high-humidity environments, rising ambient temperature exacerbates conductor heating, requiring heat dissipation design to compensate for the impact of temperature fluctuations on current stability.

[0048] It is worth noting that the resistivity of the conductor material (such as copper or aluminum) of the energy storage cabinet directly affects the current distribution and stability. For example, the resistivity of copper is about 1.7×10⁻ 8 Ω·m, whose resistance increases linearly with temperature. This leads to increased conductor impedance at high temperatures, which in turn affects current transmission efficiency. During testing, the conductor cross-sectional area must be selected based on material properties to ensure that the temperature rise is manageable at the rated current. For example, the main circuit conductor of a large-capacity energy storage cabinet (e.g., 250kW class) should preferably be made of low-resistivity material and have an optimized cross-sectional design to reduce the risk of current fluctuations caused by resistance changes.

[0049] It's worth noting that the convective heat transfer coefficient determines the efficiency of heat exchange between the energy storage cabinet and its surroundings. Typical values ​​range from 5-25 W / (m²·K) for natural convection, 20-300 W / (m²·K) for forced convection (fan cooling), and 1000-15000 W / (m²·K) for a water-cooled system. During current stability testing, the appropriate cooling method must be selected based on the heat dissipation requirements. For example, high-power energy storage cabinets (e.g., above 1kW) utilize forced air cooling or liquid cooling systems. This improves the convective heat transfer coefficient to quickly dissipate operating heat, preventing sudden changes in conductor resistance or localized overheating due to temperature rise, thereby maintaining stable output current. During testing, the temperature rise curves for different cooling methods can be compared to verify the effectiveness of the cooling strategy in suppressing current fluctuations.

[0050] It should be noted that in the comprehensive distribution map, the first channel is the normalized temperature value. By multiplying the normalized temperature value by the calibration coefficient of the temperature measurement range (for example, mapping the range of 0 - 1 back to the actual temperature range, such as 20 - 80 °C), the real-time temperature value can be restored. This process needs to ensure that the normalization parameters (such as the temperature calibration coefficient) are consistent with the original data to avoid temperature restoration deviation caused by normalization errors.

[0051] It should be noted that after the preliminary screening of candidate overheating points, clustering analysis needs to be carried out to further identify the real overheating area and eliminate noise interference. First, input the coordinate data of candidate overheating points into a clustering algorithm (such as DBSCAN or K-means). By calculating the spatial distance between each point, points that are adjacent and have a high density are grouped into the same cluster. For example, if there are multiple adjacent candidate points in a certain area inside the energy storage cabinet and their distribution density is significantly higher than other areas, it is determined as a cluster; while isolated or sparsely distributed points are regarded as outliers and eliminated. Subsequently, calculate the geometric center coordinates of each cluster (such as taking the average of the horizontal and vertical coordinates of all points within the cluster) as the representative position of the overheating point of the cluster. This process needs to adjust the clustering parameters (such as the neighborhood radius and the minimum number of points) in combination with the actual scenario to ensure that both real hot spots (such as concentrated heat generation at the conductor connection) can be captured and false signals caused by sensor errors or instantaneous fluctuations can be filtered. Finally, the key overheating areas inside the energy storage cabinet can be visually located through the cluster center coordinates, providing a basis for subsequent fault diagnosis or heat dissipation optimization.

[0052] It should be noted that DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identifies clusters and noise through density distribution, and its core steps are as follows: First, define two key parameters - the neighborhood radius ε (determining the spatial range around a point) and the minimum number of samples MinPts (the threshold for judging whether the density is sufficient). The algorithm starts from any unvisited point and calculates the number of points within its ε neighborhood: if the number of points in the neighborhood ≥ MinPts, it is marked as a core point, and a new cluster is created based on this; if the number of points in the neighborhood < MinPts, it is marked as a noise point and temporarily skipped. Subsequently, starting from the core point, recursively expand all points within its neighborhood (including other core points and border points) and add them to the current cluster. Although border points do not meet the core point conditions, if they are within the neighborhood of a certain core point, they are included in the cluster. This process continues until all core points have been processed. Finally, points that have not been included in any cluster are marked as noise. This algorithm does not require pre-setting the number of clusters, can adapt to clusters of any shape, and effectively filters noise, but its effect highly depends on the reasonable selection of ε and MinPts.

[0053] In step S15, a heat conduction simulation is performed according to the hot spot coordinates to obtain a temperature prediction result.

[0054] In one embodiment, a pre-established heat conduction model is called to construct a simulation scenario; Applying heat flux density at the position corresponding to the hotspot coordinates according to the simulation scenario, performing heat conduction simulation after setting the time step, and outputting the temperature field distribution result; Inputting the temperature field distribution result into a pre-trained material aging model and outputting an aging acceleration factor; The temperature prediction result includes the temperature field distribution result and the aging acceleration factor.

[0055] It's worth noting that the purpose of setting the time step is to balance computational accuracy and efficiency in heat conduction simulations and ensure accurate capture of dynamic changes in the temperature field. A smaller time step can more precisely reflect rapid temperature changes over time (such as transient thermal shocks or periodic thermal fluctuations), thereby improving the local accuracy of the simulation results. However, an excessively large time step can cause key temperature transitions to be missed, affecting the accurate calculation of the material aging acceleration factor. In this example, a simulation time step of 0.1 seconds is used for high-precision simulations. For high-efficiency simulations suitable for engineering applications, a time step of 2 seconds is used.

[0056] It's worth noting that to predict the hotspot temperature of an energy storage cabinet, a heat conduction model can be constructed and simulated using specialized thermal simulation software (such as COMSOL Multiphysics or ANSYS Fluent). First, import the energy storage cabinet's 3D geometry (e.g., in STEP format) into the Heat Transfer Module within COMSOL Multiphysics. Critical regions (such as conductor connections or PCB traces) are accurately defined based on the hotspot coordinates. Subsequently, parameters such as thermal conductivity, specific heat capacity, and density are assigned to various model components (e.g., copper conductors and insulation materials). For example, the thermal conductivity of copper is 401 W / (m·K), while the thermal conductivity of polyethylene is 0.33 W / (m·K)). A heat flux (e.g., power loss calculated from current density) is applied to the hotspot location. The ambient temperature (e.g., 25°C) and the convective heat transfer coefficient (e.g., 25 W / (m²·K) for natural convection) are also set. Next, use the transient solver to configure the time step (0.1-1 second) and enable non-uniform meshing (to refine the hotspot area for improved accuracy). After running the simulation, output the temperature field distribution results (such as temperature-time curves and spatial cloud maps), which intuitively present the local temperature rise process (for example, a hotspot temperature rise exceeding 80°C due to current concentration). After the simulation is complete, export the temperature field data (such as maximum temperature value and duration) to a CSV or MATLAB format file and input it into the material aging model based on the Arrhenius equation: in, is the activation energy, is the Boltzmann constant, is the reference temperature, To predict temperature, Calculate the aging acceleration factor (for example, when the temperature of a hotspot rises by 10°C compared to the base temperature of 60°C, the aging rate accelerates by 2-3 times).

[0057] In step S16, current distribution optimization is performed based on the comprehensive distribution map and the temperature prediction result to obtain an optimized current solution.

[0058] In one embodiment, when the aging acceleration factor in the temperature prediction result is greater than a preset aging threshold, it is determined to be a critical risk area; determining an optimization target based on the key risk area and the temperature prediction result; Performing gradient descent optimization according to the comprehensive distribution map and the optimization target to generate multiple groups of candidate optimization solutions; The multiple groups of candidate optimization solutions are verified by thermoelectric coupling simulation to obtain an optimized current solution.

[0059] It's worth noting that when optimizing current distribution in energy storage cabinets, the preset threshold for the aging acceleration factor must be determined based on material properties and safety redundancy principles. The aging acceleration factor threshold can be set at 2.5 times. This means that when the temperature in a certain area rises above the baseline temperature (e.g., 60°C) to a level that causes the aging rate to exceed 2.5 times the baseline value, it is identified as a critical risk area. The selection of this threshold should be based on material life requirements (e.g., the rated life of capacitors or insulation materials) and the fault tolerance range of actual operating conditions.

[0060] It's worth noting that the specific steps for determining optimization objectives based on key risk areas and temperature prediction results are as follows: First, using an aging acceleration factor threshold (e.g., 1.5) as a screening condition, all sub-areas exceeding this threshold are extracted. For each sub-area, the peak temperature, temperature gradient (e.g., the temperature difference between adjacent grid cells), and time-accumulated aging integral (e.g., the aging integral calculated using the Arrhenius model) are calculated. For each sub-area, the optimization objective must meet two constraints: 1) the peak temperature must be reduced to less than 90% of the threshold temperature (e.g., 120°C) (i.e., 110°C), and the temperature gradient must be kept within a safe range (e.g., ≤5°C / mm); and 2) the aging acceleration factor must be reduced to less than 80% of the threshold (e.g., 1.2). For example, if the peak temperature in a subregion is 120°C and the aging acceleration factor is 1.8, the objective function must include two optimization items: 1. Reducing the weight of the peak temperature (e.g., a weight of 0.6), with the goal of reducing the peak temperature from 120°C to 110°C, with an allowable error of ±2°C; 2. Reducing the weight of the aging acceleration factor (e.g., a weight of 0.4), with the goal of reducing it from 1.8 to 1.2, with an allowable error of ±0.1. Ultimately, the optimization objectives of all subregions are weighted and summed according to their spatial location weights (e.g., closer to key components, higher weights), forming a comprehensive optimization function. Constraints are then added (e.g., total energy consumption does not exceed 90% of the original system, and the current adjustment range is limited to ±15%) to ensure that the optimization solution balances local risk control with overall system performance.

[0061] It's worth noting that gradient descent optimization reduces the aging rate in critical risk areas by adjusting the current distribution. Its core process includes objective function design, parameter initialization, gradient update, and convergence assessment. First, a multi-objective loss function is designed that comprehensively considers the temperature field energy in critical areas (e.g., heat accumulation in high-temperature concentrated areas) and the total current density loss (e.g., additional energy consumption due to uneven current distribution). Weight coefficients are adjusted to balance thermal effects and electrical losses. Subsequently, initial parameter values ​​are set based on the initial current distribution (e.g., normalized sensor data). The learning rate (set between 0.01 and 0.1) and maximum number of iterations (e.g., 100) are also determined. During the gradient calculation and update phase, a mini-batch gradient descent method is used. Each iteration, the current parameters of a random subset of regions are randomly selected. The gradient of the loss function with respect to the current distribution is calculated using mathematical methods, and the parameters are adjusted in the direction of the negative gradient. For example, if the temperature field energy in a region significantly increases due to excessively high current density, the current distribution in that region is reduced for optimization. Finally, when the change in the loss function is lower than a preset threshold (such as 1e-5) or the maximum number of iterations is reached, the optimization is stopped and multiple sets of candidate optimization solutions (such as current distribution configurations corresponding to different weight coefficients) are output to provide a basis for subsequent screening.

[0062] It's worth noting that thermoelectric coupling simulation verifies the feasibility of the optimized current scheme. The process includes setting up the simulation environment, setting materials and boundary conditions, meshing and solving, and verifying and screening the results. First, the 3D geometry of the energy storage cabinet is imported into COMSOL Multiphysics or ANSYS Icepak. Heat sources are defined based on the optimized current distribution scheme (for example, calculating the power loss in each region by multiplying the square of the current and the resistance). Subsequently, physical parameters such as thermal conductivity and specific heat capacity are assigned to the conductors (e.g., copper busbars) and insulating materials (e.g., polyimide film) in the model. The ambient temperature (e.g., 25°C) and convective heat transfer coefficient (e.g., 25 W / (m²·K) for natural convection) are also set. During the meshing phase, a locally refined mesh (e.g., 0.1 mm × 0.1 mm) is used in critical risk areas, while a coarser mesh is used in non-critical areas to improve computational efficiency. A transient solver (with a time step of 0.1-1 seconds) is used to iteratively calculate the dynamic interaction between the temperature field distribution and the current density. Finally, by comparing simulation results with predicted values ​​from the material aging model, the optimization scheme is verified to meet the following criteria: whether the temperature field energy in critical areas is below the preset aging acceleration factor threshold (e.g., 2.5 times the baseline value), whether the total current distribution loss (e.g., the product of current density and resistance) does not exceed the system design limit, and whether the electrical performance (e.g., voltage drop and impedance) meets relevant standards (e.g., GB / T11024.2-2019). Based on this, a comparative analysis of multiple candidate schemes (e.g., convergence speed, thermal stress distribution uniformity, etc.) is conducted to comprehensively evaluate and select the current optimization scheme with the best performance as the final output.

[0063] In summary, the present invention discloses a current stability testing method for an energy storage cabinet. By considering the current concentration area and thermoelectric coupling, the key area is optimized, thereby improving the stability of the energy storage cabinet.

[0064] Reference Figure 2 A second embodiment of the present invention provides a current stability testing system for an energy storage cabinet, comprising: The data acquisition module is used to acquire current distribution data and generate a current distribution map based on a preset node distribution map; A current density module, configured to perform current density analysis based on the current distribution map to obtain a current concentration area; a thermoelectric coupling module, configured to obtain temperature data of the current concentration area and superimpose the temperature data with the current distribution map to obtain a comprehensive distribution map; A temperature anomaly module is used to detect temperature anomalies based on the comprehensive distribution map and obtain hot spot coordinates; A temperature prediction module, configured to perform heat conduction simulation based on the hot spot coordinates to obtain a temperature prediction result; The scheme optimization module is used to optimize the current distribution according to the comprehensive distribution map and the temperature prediction result to obtain an optimized current scheme.

[0065] It should be noted that the current stability testing system for an energy storage cabinet provided in an embodiment of the present invention is used to execute all the process steps of the current stability testing method for an energy storage cabinet in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0066] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, the steps of the current stability test method of each energy storage cabinet described above are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0067] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0068] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0069] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0070] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0071] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0072] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0073] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for testing the current stability of an energy storage cabinet, characterized in that: include: Obtain current distribution data and generate a current distribution map based on a preset node distribution map; Performing current density analysis based on the current distribution map to obtain a current concentration area; Acquiring temperature data of the current concentrated area and superimposing the data with the current distribution map to obtain a comprehensive distribution map; Perform temperature anomaly detection based on the comprehensive distribution map to obtain hotspot coordinates; Perform heat conduction simulation according to the hot spot coordinates to obtain temperature prediction results; Current distribution optimization is performed according to the comprehensive distribution map and the temperature prediction result to obtain an optimized current solution.

2. The current stability testing method of the energy storage cabinet according to claim 1, characterized in that: The performing current density analysis according to the current distribution map to obtain a current concentration area includes: Calculating the current density according to the current distribution map to obtain current density data; Performing spatial gradient analysis based on the current density data to obtain a current density gradient; When the current density gradient is higher than a preset density gradient threshold, the corresponding position is determined as a current concentration area.

3. The current stability testing method of the energy storage cabinet according to claim 1, characterized in that: The step of acquiring the temperature data of the current concentration area and superimposing the temperature data with the current distribution map to obtain a comprehensive distribution map includes: Get temperature data, which includes timestamp, location space coordinates and temperature value; According to the timestamp and the position space coordinates, the temperature data and the current distribution map are temporally and spatially aligned to obtain a fused data set; Data fusion is performed according to the fused data set to obtain a comprehensive distribution map.

4. The current stability testing method of the energy storage cabinet according to claim 3, characterized in that: The step of performing data fusion according to the fused data set to obtain a comprehensive distribution map includes: Normalizing the temperature values ​​in the fused data set to obtain a normalized temperature, which serves as the first channel; Normalizing the current distribution data in the fused data set to obtain a normalized current, which serves as a second channel; Multiplying the normalized temperature and the normalized current to obtain a coupling characteristic as a third channel; The first channel, the second channel and the third channel are weightedly superimposed to obtain a comprehensive distribution map.

5. The current stability testing method of the energy storage cabinet according to claim 4, characterized in that: The performing temperature anomaly detection based on the comprehensive distribution map to obtain hotspot coordinates includes: The temperature distribution threshold is calculated according to the following formula: in, represents the temperature distribution threshold, Indicates the ambient temperature, represents the current density, represents the material resistivity, Indicates the heat dissipation distance of the current path, represents the convection conversion coefficient; Calculating a real-time temperature value according to the first channel in the comprehensive distribution map; When the real-time temperature value is greater than the temperature distribution threshold, the corresponding position is determined as a candidate hotspot; Performing cluster analysis on the candidate hot spots and removing outliers to obtain clustering results; According to the clustering results, the center coordinates of each cluster are used as hotspot coordinates.

6. The current stability testing method of the energy storage cabinet according to claim 1, characterized in that: The heat conduction simulation is performed according to the hot spot coordinates to obtain a temperature prediction result, including: Call the pre-established heat conduction modeling to build a simulation scenario; Applying heat flux density at the position corresponding to the hotspot coordinates according to the simulation scenario, performing heat conduction simulation after setting the time step, and outputting the temperature field distribution result; Inputting the temperature field distribution result into a pre-trained material aging model and outputting an aging acceleration factor; The temperature prediction result includes the temperature field distribution result and the aging acceleration factor.

7. The current stability testing method of the energy storage cabinet according to claim 1, characterized in that: The current distribution optimization is performed according to the comprehensive distribution map and the temperature prediction result to obtain an optimized current solution, including: When the aging acceleration factor in the temperature prediction result is greater than a preset aging threshold, it is determined to be a critical risk area; determining an optimization target based on the key risk area and the temperature prediction result; Performing gradient descent optimization according to the comprehensive distribution map and the optimization target to generate multiple groups of candidate optimization solutions; The multiple groups of candidate optimization solutions are verified by thermoelectric coupling simulation to obtain an optimized current solution.

8. A current stability test system for an energy storage cabinet, characterized in that: include: The data acquisition module is used to obtain current distribution data and generate a current distribution map based on a preset node distribution map; A current density module, configured to perform current density analysis based on the current distribution map to obtain a current concentration area; a thermoelectric coupling module, configured to obtain temperature data of the current concentration area and superimpose the temperature data with the current distribution map to obtain a comprehensive distribution map; A temperature anomaly module is used to detect temperature anomalies based on the comprehensive distribution map and obtain hot spot coordinates; A temperature prediction module, configured to perform heat conduction simulation based on the hot spot coordinates to obtain a temperature prediction result; The scheme optimization module is used to optimize the current distribution according to the comprehensive distribution map and the temperature prediction result to obtain an optimized current scheme.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for testing the current stability of the energy storage cabinet according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the current stability testing method for the energy storage cabinet according to any one of claims 1 to 7.

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