A method and system for testing the current stability of an energy storage cabinet

Abstract

This invention relates to the field of power testing technology and discloses a method and system for testing the current stability of an energy storage cabinet. The method includes acquiring current distribution data and generating a current distribution map by combining it with a preset node distribution map; performing current density analysis based on the current distribution map to obtain current concentration areas; acquiring temperature data of the current concentration areas and overlaying it with the current distribution map to obtain a comprehensive distribution map; detecting temperature anomalies based on the comprehensive distribution map to obtain hot spot coordinates; performing heat conduction simulation based on the hot spot coordinates to obtain temperature prediction results; and optimizing the current distribution based on the comprehensive distribution map and the temperature prediction results to obtain an optimized current scheme. This method has the following effect: it can improve the current stability of the energy storage cabinet.

Description

Technical Field

[0001] This invention relates to the field of power testing technology, and in particular to a method and system for testing the current stability of an energy storage cabinet. Background Technology

[0002] Currently, 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 particularly important. Energy storage cabinets, as the core of energy storage systems, are responsible for storing electrical energy and releasing it when needed. The stability of the current directly affects the operating efficiency of the energy storage cabinet and its contribution to grid stability. Therefore, developing an accurate and effective current stability testing method and system is crucial for ensuring the quality and reliability of energy storage cabinets. Especially when dealing with the integration of highly volatile and intermittent renewable energy sources (such as wind and solar power) into the grid, ensuring the current stability of the energy storage cabinet under various operating conditions is particularly critical.

[0003] In one existing technology, a method for testing the current stability of an energy storage cabinet employs a standard load testing procedure. First, under set environmental conditions (including temperature and humidity), the energy storage cabinet is connected to a test platform simulating a power grid. Then, different power demand scenarios are simulated by gradually increasing or decreasing the load, while simultaneously monitoring and recording the output current and its changes from the energy storage cabinet. During this process, a data acquisition system collects current data in real time and compares it with preset standard values. If the actual measured value is within the allowable error range, the current stability of the energy storage cabinet is considered to meet the requirements.

[0004] However, when the energy storage cabinet encounters inadequate heat dissipation design or excessively high external ambient temperatures during operation, the temperature of some components may exceed the safe range. As the temperature rises, the rate of chemical reactions inside the battery accelerates, leading to problems such as decreased internal resistance and increased self-discharge rate, ultimately resulting in unstable output current. Under high-load operating conditions, energy storage components (such as batteries) generate heat. If this heat cannot be dissipated in time, 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, it is difficult to capture the negative impact of these changes on current stability in real time without a corresponding monitoring mechanism. Because temperature change is a gradual process with a complex nonlinear relationship with current change, simply relying on conventional load testing cannot accurately predict or detect problems caused by thermoelectric coupling.

[0005] In summary, existing technologies suffer from low current stability in energy storage cabinets. Summary of the Invention

[0006] This invention provides a method and system for testing the current stability of energy storage cabinets, in order to improve the current stability of energy storage cabinets.

[0007] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a method for testing the current stability of an energy storage cabinet, comprising:

[0008] Acquire current distribution data and combine it with a preset node distribution map to generate a current distribution spectrum;

[0009] Based on the current distribution spectrum, current density analysis is performed to obtain the current concentration region;

[0010] Temperature data of the current concentration region is acquired and superimposed with the current distribution map to obtain a comprehensive distribution map.

[0011] Temperature anomaly detection was performed based on the comprehensive distribution map to obtain the coordinates of hotspots.

[0012] Based on the coordinates of the hot spot, heat conduction simulation is performed to obtain temperature prediction results;

[0013] Based on the comprehensive distribution map and the temperature prediction results, the current distribution is optimized to obtain an optimized current scheme.

[0014] In one optional implementation, the step of performing current density analysis based on the current distribution spectrum to obtain the current concentration region includes:

[0015] The current density is calculated based on the current distribution spectrum to obtain current density data.

[0016] Spatial gradient analysis is performed based on the current density data to obtain the current density gradient;

[0017] When the current density gradient is higher than the preset density gradient threshold, the corresponding location is determined to be a current concentration region.

[0018] In one optional implementation, acquiring the temperature data of the current concentration region and overlaying it with the current distribution map to obtain a comprehensive distribution map includes:

[0019] Acquire temperature data, which includes timestamps, spatial coordinates of location, and temperature values;

[0020] Based on the timestamp and the location spatial coordinates, the temperature data and the current distribution spectrum are spatiotemporally aligned to obtain a fused data set;

[0021] Data fusion is performed on the fused dataset to obtain a comprehensive distribution map.

[0022] In one optional implementation, the step of performing data fusion based on the fused data set to obtain a comprehensive distribution map includes:

[0023] The temperature values ​​in the fused data set are normalized to obtain a normalized temperature, which is then used as the first channel.

[0024] The current distribution data in the fused data set is normalized to obtain a normalized current, which is then used as the second channel.

[0025] Multiplying the normalized temperature and the normalized current yields the coupling characteristics, which are then used as the third channel.

[0026] The first channel, the second channel, and the third channel are weighted and superimposed to obtain a comprehensive distribution map.

[0027] In one optional implementation, the step of detecting temperature anomalies based on the comprehensive distribution map to obtain the coordinates of hotspots includes:

[0028] The temperature distribution threshold is calculated using the following formula:

[0029]

[0030] in, Indicates the temperature distribution threshold. Indicates ambient temperature. Indicates current density, Indicates the resistivity of the material. Indicates the heat dissipation distance along the current path. Indicates the convection conversion coefficient;

[0031] The real-time temperature value is calculated based on the first channel in the comprehensive distribution map.

[0032] When the real-time temperature value is greater than the temperature distribution threshold, the corresponding location is identified as a candidate hot spot;

[0033] Cluster analysis was performed on the candidate hotspots, and outliers were removed to obtain the clustering results;

[0034] Based on the clustering results, the center coordinates of each cluster are used as the coordinates of the hot spots.

[0035] In one optional implementation, the heat conduction simulation is performed based on the coordinates of the hot spot to obtain the temperature prediction result.

[0036] The simulation scenario is constructed by calling a pre-established heat conduction model;

[0037] According to the simulation scenario, heat flux density is applied to the position corresponding to the hot spot coordinates. After setting the time step, heat conduction simulation is performed, and the temperature field distribution results are output.

[0038] The temperature field distribution results are input into a pre-trained material aging model, which outputs an aging acceleration factor.

[0039] The temperature prediction results include the temperature field distribution results and the aging acceleration factor.

[0040] In one optional implementation, the step of optimizing the current distribution based on the comprehensive distribution map and the temperature prediction results to obtain an optimized current scheme includes:

[0041] When the aging acceleration factor in the temperature prediction result is greater than the preset aging threshold, it is determined to be a critical risk area.

[0042] The optimization objectives are determined based on the key risk areas and the temperature prediction results.

[0043] Based on the comprehensive distribution map and the optimization objective, gradient descent optimization is performed to generate multiple candidate optimization schemes;

[0044] Thermoelectric coupling simulation was performed on the multiple candidate optimization schemes to obtain the optimized current scheme.

[0045] Secondly, the present invention provides a current stability testing system for an energy storage cabinet, comprising:

[0046] The data acquisition module is used to acquire current distribution data and generate a current distribution map by combining it with a preset node distribution map.

[0047] The current density module is used to perform current density analysis based on the current distribution spectrum to obtain the current concentration region.

[0048] Thermoelectric coupling module is used to acquire temperature data of the current concentration area and overlay it with the current distribution map to obtain a comprehensive distribution map.

[0049] The temperature anomaly module is used to detect temperature anomalies based on the comprehensive distribution map and obtain the coordinates of hot spots.

[0050] The temperature prediction module is used to perform heat conduction simulation based on the coordinates of the hot spot and obtain the temperature prediction result.

[0051] The scheme optimization module is used to optimize the current distribution based on the comprehensive distribution map and the temperature prediction results to obtain an optimized current scheme.

[0052] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the current stability testing method for the energy storage cabinet described in any one of the above.

[0053] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the current stability test method for the energy storage cabinet described in any one of the above-mentioned methods.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] (1) Acquire current distribution data and generate a current distribution map by combining it with a preset node distribution map. The current values ​​of each node in the circuit are collected by a high-precision sensor and matched with the preset node distribution map to construct a visualized current distribution map. This step not only improves the observability of the current state, but also provides a data foundation with a clear structure and accurate spatial correspondence for subsequent analysis, thereby improving the overall system's sensing capability and computational efficiency.

[0056] (2) Current density analysis is performed based on the current distribution spectrum to obtain the current concentration area. Based on the data distribution characteristics in the current distribution spectrum, the current density is calculated and gradient analysis is performed to identify potential risk areas with dense current. This step helps to identify key locations that may cause local overheating or burnout in advance, enhances the system's ability to identify abnormal trends, and improves the accuracy of diagnosis.

[0057] (3) Obtain the temperature data of the current concentration area and overlay it with the current distribution map to obtain a comprehensive distribution map. Based on the identification of the current concentration area, further collect its 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 device's operating status and provides more comprehensive data support for anomaly detection.

[0058] (4) Temperature anomaly detection is performed based on the comprehensive distribution map to obtain the coordinates of overheated hot spots. By continuously monitoring the temperature distribution in the comprehensive distribution map and judging the threshold, high-temperature areas exceeding the safe range, i.e., overheated hot spots, are identified, and their specific coordinate locations are recorded. This step enables a rapid response to abnormal temperature rises, improves the system's early warning capability for potential fault points, and ensures the safety and stability of equipment operation.

[0059] (5) Perform heat conduction simulation based on the coordinates of the hot spot to obtain temperature prediction results. Based on the physical heat conduction model, perform simulation calculations with the hot spot as the initial condition to predict its temperature change trend at different time scales. This step helps to assess the diffusion range and evolution process of the hot spot, providing a scientific basis for subsequent optimization and improving the system's predictability and control capability against thermal risks.

[0060] (6) Based on the comprehensive distribution map and the temperature prediction results, the current distribution is optimized to obtain an optimized current scheme. Combining the key risk areas and the temperature prediction results, the gradient descent optimization method is used to generate an optimized scheme, reducing the current density in the key areas, thereby suppressing the generation of hot spots. This method not only improves the uniformity of the current distribution, but also effectively reduces the thermal risk in system operation and improves the stability of the equipment. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of a current stability testing method for an energy storage cabinet provided in the first embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the current stability testing system for an energy storage cabinet provided in the second embodiment of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Reference Figure 1 The first embodiment of the present invention provides a method for testing the current stability of an energy storage cabinet, comprising the following steps:

[0065] S11, acquire current distribution data and generate a current distribution spectrum by combining it with a preset node distribution diagram;

[0066] S12, perform current density analysis based on the current distribution spectrum to obtain the current concentration region;

[0067] S13, acquire the temperature data of the current concentration area, and overlay it with the current distribution map to obtain a comprehensive distribution map;

[0068] S14, Temperature anomaly detection is performed based on the comprehensive distribution map to obtain the coordinates of hotspots;

[0069] S15, Perform heat conduction simulation based on the coordinates of the hot spot to obtain temperature prediction results;

[0070] S16. Based on the comprehensive distribution map and the temperature prediction results, the current distribution is optimized to obtain an optimized current scheme.

[0071] In step S11, current distribution data is acquired and combined with a preset node distribution diagram to generate a current distribution spectrum.

[0072] In one embodiment, the specific steps for acquiring 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 either a shunt or a Hall effect sensor. The shunt is suitable for high-precision scenarios (e.g., accuracy requirements within ±0.1%), while the Hall effect sensor is suitable for non-contact high-current monitoring (e.g., busbar current monitoring). Sensor installation must meet the following requirements: the shunt uses a four-wire Kelvin connection, with a shunt resistance value of 5mΩ~100mΩ, and ensures it shares a common ground with the battery pack; the Hall effect sensor is installed at the busbar bend to avoid magnetic field distortion, and a feedback circuit compensates for hysteresis; a Rogowski coil (suitable for high-current scenarios) is wound around the busbar with a spacing ≤300mm to ensure uniform coverage of the current path. Subsequently, three types of data are synchronously collected through a data acquisition system (such as a PLC or BMS controller): timestamps (using GPS timestamps or main controller clock synchronization to ensure strict alignment of all sensor data in the time dimension), location information, and current values ​​(the raw signals output by the sensors are amplified, filtered, and then converted into digital current values ​​in amperes (A) by an ADC module, and stored as structured data, such as CSV or a database table). Finally, the collected multi-node current data is spatially mapped to a preset node distribution map to generate a current distribution map containing timestamps, location coordinates, and current values. For example, if a node has coordinates (1200mm, 800mm, 500mm) and a current value of 50A at timestamp 2025-05-28 14:33:37.

[0073] It is worth noting that the node distribution map refers to the physical location layout of current sensors in key nodes (such as battery module connection points, busbars, main circuit switches, etc.) in the energy storage cabinet, used to identify the spatial coordinates of data acquisition points; the current distribution map is a visual map generated by combining the current values ​​collected by the sensors in real time with the node distribution map, which 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 areas of concentrated current and abnormal conditions.

[0074] In step S12, current density analysis is performed based on the current distribution spectrum to obtain the current concentration region.

[0075] In one embodiment, current density is calculated based on the current distribution spectrum to obtain current density data;

[0076] Spatial gradient analysis is performed based on the current density data to obtain the current density gradient;

[0077] When the current density gradient is higher than the preset density gradient threshold, the corresponding location is determined to be a current concentration region.

[0078] It is worth noting that the current intensity of each node is extracted based on the current value and location information of each node in the current distribution map. The current density of the node is obtained by calculating the ratio of the current intensity of the node to the cross-sectional area of ​​its corresponding conductor. For example, if the current of 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 circular cables), a uniform distribution model can be directly used for calculation; however, for conductors with complex shapes (such as multi-layer laminates or irregular structures), the conductor needs to be divided into multiple small units, the current density of each unit needs to be calculated separately, and then the overall distribution is obtained through superposition or integration methods. Simulation software (such as COMSOL or ANSYS) is used to simulate the current flow path inside the conductor to verify the accuracy of the theoretical calculation results and correct deviations caused by material inhomogeneities or edge effects. Finally, the current density data covering all nodes is output, forming a quantitative description of the spatial distribution.

[0079] It's worth noting that the uniform distribution model is a mathematical model that assumes all possible values ​​have the same probability or density within a specific range. In current density calculations, this is represented by a uniform distribution of current across the conductor's cross-section. For conductors with regular shapes (such as rectangular busbars or circular cables), if the material is homogeneous and there are no edge effects, the current density can be directly calculated as the ratio of the total current to the cross-sectional area, and the current density value is the same at any location within the conductor. 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's cross-section, without needing to consider positional differences. This model simplifies the calculation process and is suitable for scenarios with strong current distribution regularity and symmetrical geometric structures. However, it requires verification of the actual current flow path using simulation software (such as COMSOL) to correct for deviations caused by material inhomogeneities or complex structures.

[0080] It's worth noting that after obtaining the current density data, spatial gradient analysis is performed based on the differences in current density between adjacent nodes or regions. First, the current density data is divided into multiple spatial grids, and the rate of change of current density within each grid is calculated. For example, if the current density in a certain direction suddenly increases from 10 A / mm² to 20 A / mm² between adjacent grids, its gradient value is 10 A / mm² / unit distance. Then, the gradient values ​​of all grids are compared with a preset density gradient threshold (5 A / mm² / unit distance). When the gradient value of a certain region consistently exceeds the threshold, it indicates a significant current density jump, thus being identified as a current concentration area. Further visualization of the gradient distribution using heatmaps or vector field maps helps locate high-gradient regions. When the gradient value of a certain region is less than or equal to the threshold, it indicates a uniform current density change, which is within the normal operating state.

[0081] It's worth noting that when calculating the current density difference between adjacent nodes or grids, the difference must be divided by the actual physical distance between the two points (e.g., the distance between the center points of adjacent grids). This distance is standardized to a "unit distance" to simplify calculations. For example, if the spacing between adjacent grids is 1 mm, then the "unit distance" is 1 mm, and the gradient value represents the change in current density per millimeter (e.g., a change from 10 A / mm² to 20 A / mm² corresponds to a gradient value of 10 A / mm² / mm). If the grid spacing is 0.5 mm, then the "unit distance" needs to be adjusted to 0.5 mm, and the gradient value needs to be proportionally converted. The choice of unit distance is based on the precision of the grid division (e.g., the default grid spacing in the simulation software). Its core purpose is to ensure the comparability of gradient values ​​in different regions by standardizing the units, and to allow direct comparison with a preset threshold (e.g., 5 A / mm² / unit distance) to determine whether current concentration exists.

[0082] 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.

[0083] In one implementation, temperature data is acquired, which includes a timestamp, location spatial coordinates, and temperature value;

[0084] Based on the timestamp and the location spatial coordinates, the temperature data and the current distribution spectrum are spatiotemporally aligned to obtain a fused data set;

[0085] Data fusion is performed on the fused dataset to obtain a comprehensive distribution map.

[0086] It is 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, bus connection points, and cooling pipe surfaces). These sensors must meet high accuracy requirements within ±0.5°C. The data acquisition frequency can be set to the second or minute level as needed, and data is transmitted to the data acquisition host via a 485 bus, CAN bus, or wireless communication (such as LoRa / WiFi). The storage format of the temperature data must meet spatiotemporal alignment requirements, using a file format (such as CSV / Excel). 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 used.

[0087] It is worth noting that the acquisition clocks of the temperature and current sensors are calibrated using high-precision time synchronization protocols (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-level timestamp of the current data to solve the time offset problem caused by the difference in sensor sampling frequency. Subsequently, in the spatial calibration stage, the spatial mapping relationship between the temperature and current sensors is established through a preset node distribution map. The specific steps include: 1) Single sensor calibration—assigning a unique physical coordinate to each temperature sensor and verifying the spatial consistency between its installation position and the current sensor node; 2) Multi-sensor calibration—using a laser rangefinder or total station to measure the relative positions between sensors and construct a unified spatial coordinate system. After completing time synchronization and spatial calibration, the temperature data is associated with the corresponding nodes in the current distribution map through a data matching algorithm. For example, if the timestamp of a temperature sensor is 2025-05-28 14:51:14 and the coordinates are (1200mm, 800mm, 500mm), then it is paired with the current value at the same coordinate point at the same timestamp. For cases with minor discrepancies in timestamps, linear interpolation or nearest neighbor matching methods can be used to fill in the missing data. Finally, the matched temperature-current dataset is stored in a structured manner according to the spatiotemporal dimensions (such as 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.

[0088] It's worth noting that linear interpolation extrapolates the value of a missing time point from data values ​​at known time points. First, two valid time points before and after the missing time point are found in the time series (e.g., sampling points 1 minute before and 3 minutes after the missing point), and the corresponding temperature or current values ​​at these two time points are recorded (e.g., 30°C for the former and 45°C for the latter). Then, based on the time interval between these two time points (e.g., a total interval of 4 minutes) and the location of the missing point (e.g., 2 minutes from the former), the data variation is proportionally allocated. Assuming the difference between the preceding and following data is 15°C, and the missing point is located at 2 / 4 of the total interval, the estimated value is 30°C for the former point plus 2 / 4 of 15°C (i.e., 7.5°C), ultimately yielding 37.5°C for the missing point. This method is suitable for scenarios with small timestamp deviations and smooth data trends, such as high-frequency sampled temperature or current data. However, it should be noted that if the actual data exhibits abrupt changes or non-linear fluctuations (e.g., sudden equipment failure), linear estimation will introduce errors. To verify its rationality, the effect can be evaluated by comparing the overall trend of the data before and after filling, or by calculating the mean square error between the filled value and the true value (if any).

[0089] In one embodiment, the temperature values ​​in the fused data set are normalized to obtain a normalized temperature, which is then used as the first channel.

[0090] The current distribution data in the fused data set is normalized to obtain a normalized current, which is then used as the second channel.

[0091] Multiplying the normalized temperature and the normalized current yields the coupling characteristics, which are then used as the third channel.

[0092] The first channel, the second channel, and the third channel are weighted and superimposed to obtain a comprehensive distribution map.

[0093] 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 dataset, and the lowest and highest temperatures are determined as reference ranges. Then, a linear transformation is performed on each temperature value: the original temperature value is subtracted from the lowest temperature, and then divided by the difference between the highest and lowest temperatures, ultimately yielding a normalized temperature between 0 and 1. For example, if a temperature is 45°C, the lowest is 30°C, and the highest is 60°C, then 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 benchmarks, and then the same linear transformation is performed on each current value to map it to the 0-1 interval. 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, temperature and current data are compressed to the same scale, forming two independent channels of data, which facilitates subsequent multidimensional feature fusion analysis.

[0094] It is worth noting that by multiplying the normalized temperature (first channel) and normalized current (second channel) point by point, the coupling feature (third channel) is obtained. Its value reflects the spatial synergistic effect of temperature and current (such as the potential risk of high temperature and high current superposition). Based on this, the three channels are integrated into a comprehensive distribution map through weighted superposition, with the weight allocation as follows: first channel (temperature) 40%, second channel (current) 40%, and third channel (coupling feature) 20%. This allocation is based on the following rationale: temperature and current, as core physical quantities, directly reflect the operating status of the equipment, and are given the same weight (40%) to reflect their fundamental importance; the coupling feature reveals the interaction between the two through a product (such as the hotspot risk of high temperature and high current superposition), but its extreme values ​​are amplified due to the product effect, so its weight is appropriately reduced (20%) to avoid oversensitivity; in practical scenarios, if a certain factor (such as temperature) is more decisive for fault prediction, the weight can be dynamically adjusted (e.g., temperature 50%, current 30%, coupling feature 20%). The role of the coupling feature is to quantify the spatial synergy of temperature and current, compensating for the limitations of a single channel. For example, in an energy storage cabinet, local current concentration may not exceed the threshold, but if high temperature is added, it will significantly accelerate conductor aging. Coupling characteristics can identify such complex risk areas in advance through the product relationship, thereby improving the accuracy of early warning and system safety.

[0095] In step S14, temperature anomaly detection is performed based on the comprehensive distribution map to obtain the coordinates of hot spots.

[0096] In one implementation, the temperature distribution threshold is calculated according to the following formula:

[0097]

[0098] in, Indicates the temperature distribution threshold. Indicates ambient temperature. Indicates current density, Indicates the resistivity of the material. Indicates the heat dissipation distance along the current path. Indicates the convection conversion coefficient;

[0099] The real-time temperature value is calculated based on the first channel in the comprehensive distribution map.

[0100] When the real-time temperature value is greater than the temperature distribution threshold, the corresponding location is identified as a candidate hot spot;

[0101] Cluster analysis was performed on the candidate hotspots, and outliers were removed to obtain the clustering results;

[0102] Based on the clustering results, the center coordinates of each cluster are used as the coordinates of the hot spots.

[0103] It is worth noting that this formula establishes a temperature threshold model through the physical relationships 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 amperes 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 watts per square meter·Kelvin (W / (m²·K)), where watts (W) can be expressed as joules per second (J / s) or volts-amperes (V·A).

[0104] It is worth noting that the ambient temperature needs to be monitored in real time during the operation of the energy storage cabinet. The atmospheric temperature is obtained using the dry-bulb temperature method (measured with an unobstructed standard thermometer), with typical values ​​of 20-30°C at room temperature or above 40°C in high-temperature operating scenarios. If it is necessary to assess the impact of humidity or thermal radiation on the equipment, the wet-bulb temperature method (reflecting humidity saturation) or the black-bulb temperature method (quantifying thermal radiation intensity) can be combined to comprehensively determine the heat dissipation requirements of the energy storage cabinet in complex environments. For example, in high-temperature and high-humidity environments, the increase in ambient temperature exacerbates conductor heating, requiring heat dissipation design to compensate for the impact of temperature fluctuations on current stability.

[0105] It is worth noting that the resistivity of the conductor material (such as copper or aluminum) in the energy storage cabinet directly affects the current distribution and stability. For example, the resistivity of copper is approximately 1.7 × 10⁻⁻⁻⁻⁶. 8The resistivity of a conductor increases linearly with temperature (Ω·m), leading to increased conductor impedance at high temperatures and consequently affecting current transmission efficiency. During testing, the conductor cross-sectional area must be selected based on material properties to ensure controllable temperature rise under rated current. For example, the main circuit conductor of a large-capacity energy storage cabinet (such as a 250kW unit) should preferably use low-resistivity materials and have an optimized cross-sectional design to reduce the risk of current fluctuations caused by resistance changes.

[0106] It is worth noting that the convective heat transfer coefficient determines the heat exchange efficiency between the energy storage cabinet and its surrounding environment. Typical values ​​are 5-25 W / (m²·K) for natural air convection, 20-300 W / (m²·K) for forced convection (fan cooling), and 1000-15000 W / (m²·K) for water-cooled systems. In current stability testing, a suitable cooling method must be selected based on heat dissipation requirements. For example, high-power energy storage cabinets (e.g., above 1kW) employ forced air cooling or liquid cooling systems. By increasing the convective heat transfer coefficient, operating heat is quickly dissipated, preventing sudden changes in conductor resistance or localized overheating due to temperature rise, thereby maintaining stable output current. During testing, the temperature rise curves under different cooling methods can be compared to verify the effectiveness of the heat dissipation strategy in suppressing current fluctuations.

[0107] It is worth noting that in the comprehensive distribution map, the first channel represents the normalized temperature value. Multiplying the normalized temperature value by the calibration coefficient of the temperature measurement range (such as mapping the 0-1 range back to the actual temperature range, such as 20-80°C) will restore the real-time temperature value. This process requires ensuring that the normalization parameters (such as the temperature calibration coefficient) are consistent with the original data to avoid temperature restoration deviations due to normalization errors.

[0108] It is worth noting that after the initial screening of candidate overheating points, cluster analysis is needed to further identify the actual overheating areas and eliminate noise interference. First, the coordinate data of the candidate overheating points are input into a clustering algorithm (such as DBSCAN or K-means). By calculating the spatial distance between points, nearby points with high density are grouped into the same cluster. For example, if a certain area within the energy storage cabinet has multiple adjacent candidate points with a significantly higher distribution density than other areas, it is identified as a cluster; isolated or sparsely distributed points are considered outliers and eliminated. Subsequently, the geometric center coordinates of each cluster are calculated (e.g., taking the average of the x and y coordinates of all points within the cluster), serving as the representative location of the overheating point for that cluster. This process requires adjusting clustering parameters (such as neighborhood radius and minimum number of points) based on the actual scenario to ensure that both real hotspots (such as concentrated heat at conductor connections) are captured and false signals caused by sensor errors or instantaneous fluctuations are filtered out. Finally, the cluster center coordinates provide a direct visual location of the key overheating areas within the energy storage cabinet, providing a basis for subsequent fault diagnosis or heat dissipation optimization.

[0109] 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, two key parameters are defined - the neighborhood radius ε (which determines 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, all points within its neighborhood (including other core points and border points) are recursively expanded and added 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 that cluster. This process continues until all core points have been processed. Finally, points that are not assigned to any cluster are marked as noise. This algorithm does not require presetting 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.

[0110] In step S15, a heat conduction simulation is performed based on the coordinates of the overheated point to obtain a temperature prediction result.

[0111] In one implementation, a pre-established heat conduction model is called to construct a simulation scenario;

[0112] According to the simulation scenario, a heat flux density is applied at the position corresponding to the coordinates of the overheated point. After setting the time step, a heat conduction simulation is performed, and the temperature field distribution result is output;

[0113] The temperature field distribution result is input into a pre-trained material aging model to output an aging acceleration factor;

[0114] The temperature prediction result includes the temperature field distribution result and the aging acceleration factor.

[0115] It should be noted that the purpose of setting the time step is to balance the calculation accuracy and efficiency in the heat conduction simulation and ensure the accurate capture of the dynamic changes of the temperature field. A smaller time step can more precisely reflect the rapid changes of temperature over time (such as transient thermal shock or periodic thermal fluctuations), thereby improving the local accuracy of the simulation result; while an overly large time step causes key temperature jumps to be missed, affecting the accurate calculation of the material aging acceleration factor. In this embodiment, a high-precision simulation is set to 0.1 seconds. For high-efficiency simulation in engineering applications, it is set to 2 seconds.

[0116] It is worth noting that in predicting the overheating temperature of the energy storage cabinet, a heat conduction model can be constructed and simulated using professional thermal simulation software (such as COMSOL Multiphysics or ANSYS Fluent). First, import the three-dimensional geometric model of the energy storage cabinet (such as the STEP file format) into the heat transfer module of COMSOL Multiphysics, and accurately define key areas (such as conductor connections or PCB traces) based on the coordinates of the overheating hotspot. Then, assign parameters such as thermal conductivity, specific heat capacity, and density to different components in the model (e.g., the thermal conductivity of copper is 401 W / (m·K), and the thermal conductivity of polyethylene is 0.33 W / (m·K)). Apply heat flux density (such as the power loss value calculated by current density) at the overheating hotspot location, and set the ambient temperature (such as 25°C) and the convective heat transfer coefficient (such as 25 W / (m²·K) for natural convection). Next, the time step (0.1-1 second) is configured using the transient solver, and non-uniform mesh generation is enabled (to refine the overheated hotspot region and improve accuracy). After running the simulation, the temperature field distribution results (such as temperature-time curves and spatial contour maps) are output, visually presenting the local temperature rise process (e.g., a hotspot experiencing a temperature rise exceeding 80°C due to current concentration). After the simulation is complete, the temperature field data (such as the maximum temperature value and duration) is exported as a CSV or MATLAB format file and input into the material aging model based on the Arrhenius equation.

[0117]

[0118] in, For activation energy, Boltzmann's constant, As the reference temperature, To predict temperature, As an aging acceleration factor, calculate the aging acceleration factor (for example, when the temperature of a certain hot spot increases by 10°C from the reference temperature of 60°C, the aging rate increases by 2-3 times).

[0119] In step S16, the current distribution is optimized based on the comprehensive distribution map and the temperature prediction results to obtain an optimized current scheme.

[0120] 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.

[0121] The optimization objectives are determined based on the key risk areas and the temperature prediction results.

[0122] Based on the comprehensive distribution map and the optimization objective, gradient descent optimization is performed to generate multiple candidate optimization schemes;

[0123] Thermoelectric coupling simulation was performed on the multiple candidate optimization schemes to obtain the optimized current scheme.

[0124] It is worth noting that in optimizing the current distribution of energy storage cabinets, the preset threshold for the aging acceleration factor needs to be determined based on material characteristics and the principle of safety redundancy. The aging acceleration factor threshold can be set to 2.5 times, meaning that when the temperature in a certain area rises above the reference temperature (e.g., 60°C) to the point that the aging rate exceeds 2.5 times the reference value, it is considered a critical risk area. The selection of this threshold needs to consider both material life requirements (e.g., the rated life of capacitors or insulating materials) and the tolerance range of actual operating conditions.

[0125] It is worth noting that the specific steps for determining the optimization target based on the key risk areas and temperature prediction results are as follows: First, using the aging acceleration factor threshold (e.g., 1.5) as a screening criterion, extract all sub-regions exceeding this threshold, and calculate the temperature peak, temperature gradient (e.g., temperature difference between adjacent grids), and time-accumulated aging integral (e.g., the aging integral value calculated by the Arrhenius model) for each sub-region. For each sub-region, the optimization target must satisfy two constraints: 1) The temperature peak must be reduced to below 90% of the threshold temperature (e.g., 120℃) (i.e., 110℃), and the temperature gradient must be controlled within a safe range (e.g., ≤5℃ / mm); 2) The aging acceleration factor must be reduced to below 80% of the threshold (e.g., 1.2). For example, if the peak temperature of a certain sub-region is 120℃ and the aging acceleration factor is 1.8, then the objective function needs to include two optimization terms: ① A weighted term for reducing the peak temperature (e.g., weight 0.6), with the objective of reducing the peak temperature from 120℃ to 110℃, allowing an error of ±2℃; ② A weighted term for reducing the aging acceleration factor (e.g., weight 0.4), with the objective of reducing it from 1.8 to 1.2, allowing an error of ±0.1. Finally, the optimization objectives of all sub-regions are weighted and summed according to spatial location weights (e.g., the closer to the key component, the higher the weight), forming a comprehensive optimization function, and constraints are 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 scheme satisfies local risk control while taking into account the overall system performance balance.

[0126] It's worth noting that gradient descent optimization reduces the aging rate of 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, comprehensively considering the temperature field energy (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) in critical areas. The weighting coefficients are adjusted to balance the demands of thermal effects and electrical losses. Then, initial parameter values ​​are set based on the initial current distribution (e.g., normalized data collected by sensors), while the learning rate (set between 0.01 and 0.1) and the maximum number of iterations (e.g., 100) are determined. In the gradient calculation and update phase, a mini-batch gradient descent method is used. In each iteration, current parameters in a subset of areas are randomly selected, and the gradient of the loss function with respect to the current distribution is calculated using mathematical methods. The parameters are then adjusted along the negative gradient direction. For example, if a region experiences a significant increase in temperature field energy due to excessively high current density, optimization is achieved by reducing the current distribution in that region. Finally, when the change in the loss function is lower than a preset threshold (e.g., 1e-5) or the maximum number of iterations is reached, the optimization stops and multiple candidate optimization schemes (e.g., current distribution configurations corresponding to different weight coefficients) are output to provide a basis for subsequent screening.

[0127] It is worth noting that thermoelectric coupling simulation verifies the feasibility of the optimized current scheme. The process includes simulation environment setup, material and boundary condition settings, mesh generation and solving, and result verification and selection. First, the 3D geometric model of the energy storage cabinet is imported into COMSOL Multiphysics or ANSYS Icepak, and heat sources are defined according to the optimized current distribution scheme (e.g., calculating power loss in each region by multiplying the square of the current by the resistance). Then, 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, while the ambient temperature (e.g., 25°C) and convective heat transfer coefficient (e.g., 25 W / (m²·K) for natural convection) are set. During the mesh generation stage, locally refined meshes (e.g., 0.1mm × 0.1mm) are used for critical risk areas, while non-critical areas are coarsened to improve computational efficiency. A transient solver (time step set to 0.1-1 seconds) is enabled to iteratively calculate the dynamic interaction between the temperature field distribution and current density. Finally, by comparing the simulation results with the predicted values ​​of the material aging model, the optimization scheme was verified to meet the following conditions: whether the temperature field energy in the key area is lower than the preset aging acceleration factor threshold (e.g., 2.5 times the benchmark value), whether the total loss of the current distribution (e.g., the sum of the product of current density and resistance) does not exceed the upper limit of the system design, and whether the electrical performance (e.g., voltage drop and impedance) meets the relevant standards (e.g., GB / T11024.2-2019). Based on this, through comparative analysis of multiple candidate schemes (e.g., convergence speed, uniformity of thermal stress distribution, etc.), the optimal current optimization scheme was comprehensively evaluated and selected as the final output.

[0128] In summary, this invention discloses a current stability testing method for energy storage cabinets. By considering the current concentration area and thermoelectric coupling, the method optimizes key areas and improves the stability of the energy storage cabinet.

[0129] Reference Figure 2 The second embodiment of the present invention provides a current stability testing system for an energy storage cabinet, comprising:

[0130] The data acquisition module is used to acquire current distribution data and generate a current distribution map by combining it with a preset node distribution map.

[0131] The current density module is used to perform current density analysis based on the current distribution spectrum to obtain the current concentration region.

[0132] Thermoelectric coupling module is used to acquire temperature data of the current concentration area and overlay it with the current distribution map to obtain a comprehensive distribution map.

[0133] The temperature anomaly module is used to detect temperature anomalies based on the comprehensive distribution map and obtain the coordinates of hot spots.

[0134] The temperature prediction module is used to perform heat conduction simulation based on the coordinates of the hot spot and obtain the temperature prediction result.

[0135] The scheme optimization module is used to optimize the current distribution based on the comprehensive distribution map and the temperature prediction results to obtain an optimized current scheme.

[0136] It should be noted that the current stability testing system for an energy storage cabinet provided in this embodiment of the 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 are one-to-one, so they will not be described again.

[0137] This invention also 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, it implements the steps in the embodiments of the current stability testing methods for the various energy storage cabinets described above, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the data acquisition module.

[0138] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0139] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0140] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0141] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0142] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0143] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as 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 to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0144] The specific embodiments described above further illustrate the purpose, technical solution, 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 current stability of an energy storage cabinet, characterized in that, The method comprises the following steps: obtaining current distribution data and combining a preset node distribution map to generate a current distribution atlas; conducting current density analysis according to the current distribution atlas to obtain a current concentration area; obtaining temperature data of the current concentration area and superimposing the temperature data on the current distribution atlas to obtain a comprehensive distribution atlas; conducting temperature anomaly detection according to the comprehensive distribution atlas to obtain an overheating point coordinate; conducting heat conduction simulation according to the overheating point coordinate to obtain a temperature prediction result; conducting current distribution optimization according to the comprehensive distribution atlas and the temperature prediction result to obtain an optimized current scheme; wherein the heat conduction simulation according to the overheating point coordinate to obtain the temperature prediction result comprises: calling a pre-established heat conduction modeling to construct a simulation scene; applying a heat flow density at a position corresponding to the overheating point coordinate according to the simulation scene, conducting heat conduction simulation after setting a time step, and outputting a temperature field distribution result; inputting the temperature field distribution result into a pre-trained material aging model to output an aging acceleration factor; the temperature prediction result comprises the temperature field distribution result and the aging acceleration factor; wherein the current distribution optimization according to the comprehensive distribution atlas and the temperature prediction result to obtain the optimized current scheme comprises: when the aging acceleration factor in the temperature prediction result is greater than a preset aging threshold, determining that it is a critical risk area; determining an optimization target according to the critical risk area and the temperature prediction result; conducting gradient descent optimization according to the comprehensive distribution atlas and the optimization target to generate a plurality of groups of candidate optimization schemes; conducting thermoelectric coupling simulation verification on the plurality of groups of candidate optimization schemes to obtain the optimized current scheme.

2. The method of claim 1, wherein, The current density analysis according to the current distribution atlas to obtain the current concentration area comprises: conducting current density calculation according to the current distribution atlas to obtain current density data; conducting spatial gradient analysis according to the current density data to obtain a current density gradient; when the current density gradient is higher than a preset density gradient threshold, determining the corresponding position as the current concentration area.

3. The method of claim 1, wherein, The temperature data of the current concentration area is obtained and superimposed on the current distribution atlas to obtain the comprehensive distribution atlas, which comprises: obtaining temperature data, which includes a timestamp, a position spatial coordinate and a temperature value; spatiotemporally aligning the temperature data and the current distribution atlas according to the timestamp and the position spatial coordinate to obtain a fusion data set; conducting data fusion according to the fusion data set to obtain the comprehensive distribution atlas.

4. The method of claim 3, wherein, The data fusion according to the fusion data set to obtain the comprehensive distribution atlas comprises: normalizing the temperature value in the fusion data set to obtain a normalized temperature, which is used as a first channel; normalizing the current distribution data in the fusion data set to obtain a normalized current, which is used as a second channel; multiplying the normalized temperature and the normalized current to obtain a coupling feature, which is used as a third channel; weighting and superimposing the first channel, the second channel and the third channel to obtain the comprehensive distribution atlas.

5. The method of claim 4, wherein, The temperature anomaly detection according to the comprehensive distribution map obtains overheat point coordinates, and the temperature anomaly detection according to the comprehensive distribution map comprises the following steps: The temperature distribution threshold is calculated according to the following formula: ； wherein, represents a temperature distribution threshold, represents an ambient temperature, represents a current density, represents a material resistivity, represents a heat dissipation distance of the current path, represents a convection transfer coefficient; The real-time temperature value is calculated 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 overheat point; After the cluster analysis of the candidate overheat point and the elimination of outliers, the clustering result is obtained; According to the clustering result, the center coordinates of each cluster are taken as the overheat point coordinates.

6. A current stability test system for an energy storage cabinet, comprising: The current stability test method for realizing the energy storage cabinet as claimed in any one of claims 1 to 5 comprises the following steps: A data acquisition module is configured to acquire current distribution data and generate a current distribution map in combination with a preset node distribution map; A current density module is configured to perform current density analysis according to the current distribution map to obtain a current concentration area; A thermoelectric coupling module is configured to acquire temperature data of the current concentration area and superimpose the temperature data on the current distribution map to obtain a comprehensive distribution map; A temperature anomaly module is configured to perform temperature anomaly detection according to the comprehensive distribution map to obtain overheat point coordinates; A temperature prediction module is configured to perform heat conduction simulation according to the overheat point coordinates to obtain a temperature prediction result; A scheme optimization module is configured to perform current distribution optimization according to the comprehensive distribution map and the temperature prediction result to obtain an optimized current scheme.

7. An electronic device, comprising: The computer program stored in the memory and configured to be executed by the processor, when the processor executes the computer program, realizes the current stability test method for the energy storage cabinet as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the current stability test method for the energy storage cabinet as claimed in any one of claims 1 to 5.

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