Battery cell module heat dissipation control method and system

Abstract

The invention relates to the technical field of heat dissipation control, and discloses a battery cell module heat dissipation control method and system, and the method comprises the steps: collecting temperature data, and carrying out the noise removal processing, and obtaining a smooth temperature data set; constructing a three-dimensional thermal field model to obtain a global temperature distribution diagram; a high-heat area is identified and divided through DBSCAN, and an area division result is obtained; a high-heat area boundary is extracted, heat dissipation intensity distribution is calculated, and a cooling parameter value is determined; calculating a temperature response curve, if the temperature difference is reduced, adjusting the cooling parameter value to obtain an optimized heat dissipation scheme, executing and monitoring real-time feedback data, and if the deviation between the real-time feedback data and the temperature response curve is greater than a preset deviation threshold, adjusting the optimized heat dissipation scheme to obtain an updated heat dissipation scheme; and analyzing the long-term thermal field stability to obtain a stability measurement value, and if the stability measurement value meets a preset convergence threshold, obtaining a heat dissipation control scheme. According to the method, the non-uniform thermal field in the battery module can be accurately adapted.

Description

A method and system for heat dissipation control of battery cell modules Technical Field

[0001] This invention relates to the field of heat dissipation control technology, and in particular to a heat dissipation control method and system for battery cell modules. Background Technology

[0002] In the application of rechargeable batteries, especially in new energy vehicles and energy storage systems, efficient heat dissipation management of battery cell modules is crucial to ensuring system safety and performance. With the increase in energy density and the growing demand for fast charging, the heat generated by batteries during operation intensifies. If heat accumulates unevenly, it can easily lead to localized overheating, performance degradation, or even thermal runaway.

[0003] In one existing technology, the heat dissipation system places temperature sensors at several key locations within the battery cell module to collect local temperature data and calculate its average value. When the average temperature exceeds a fixed threshold, the cooling system is activated for overall cooling. However, due to factors such as cell inconsistency and structural layout, there is a significant non-uniform thermal field distribution within the battery module. This holistic control method struggles to accurately identify and address internal hot spots, easily leading to insufficient localized heat dissipation while other areas are over-cooled, resulting in low control accuracy and energy efficiency.

[0004] Therefore, existing technologies have the problem of being unable to accurately adapt to the uneven thermal field inside the battery module. Summary of the Invention

[0005] This invention provides a method for heat dissipation control of battery cell modules to accurately adapt to the uneven thermal field inside the battery module.

[0006] In a first aspect, to address the aforementioned technical problems, this invention provides a method for controlling heat dissipation in a battery cell module, comprising: acquiring real-time temperature data and performing noise reduction processing to obtain a smoothed temperature dataset; constructing a three-dimensional thermal field model based on the smoothed temperature dataset and filling in missing points to obtain a global temperature distribution map; executing the DBSCAN algorithm based on the global temperature distribution map to obtain a set of similar point clusters; if the temperature gradient of the similar point cluster set exceeds a preset gradient threshold, it is marked as a high-heat region, resulting in a region division result; extracting the high-heat region boundary from the region division result, calculating the heat dissipation intensity distribution, and determining cooling parameter values; calculating a temperature response curve based on the cooling parameter values; if the temperature response curve shows a reduction in temperature difference, adjusting the cooling parameter values ​​to obtain an optimized heat dissipation scheme; executing the optimized heat dissipation scheme and monitoring real-time feedback data; if the deviation between the real-time feedback data and the temperature response curve is greater than a preset deviation threshold, adjusting the optimized heat dissipation scheme to obtain an updated heat dissipation scheme; extracting historical execution logs from the updated heat dissipation scheme, analyzing long-term thermal field stability, and obtaining a stability metric value; if the stability metric value meets a preset convergence threshold, obtaining a heat dissipation control scheme.

[0007] Secondly, the present invention provides a heat dissipation control system for a battery cell module, comprising: a data acquisition module for acquiring real-time temperature data and performing noise reduction processing to obtain a smoothed temperature dataset; a thermal field modeling module for constructing a three-dimensional thermal field model based on the smoothed temperature dataset and filling in missing points to obtain a global temperature distribution map; a region division module for executing the DBSCAN algorithm based on the global temperature distribution map to obtain a set of similar point clusters, wherein if the temperature gradient of the set of similar point clusters exceeds a preset gradient threshold, it is marked as a high-heat region, and a region division result is obtained; and a parameter generation module for extracting the high-heat region boundary from the region division result, calculating the heat dissipation intensity distribution, and determining the cooling... The system comprises: a cooling parameter value generation module, used to calculate a temperature response curve based on the cooling parameter value; if the temperature response curve shows a reduction in temperature difference, adjusting the cooling parameter value to obtain an optimized heat dissipation scheme; a simulation optimization module, used to execute the optimized heat dissipation scheme and monitor real-time feedback data; if the deviation between the real-time feedback data and the temperature response curve is greater than a preset deviation threshold, adjusting the optimized heat dissipation scheme to obtain an updated heat dissipation scheme; and a scheme output module, used to extract historical execution logs from the updated heat dissipation scheme, analyze long-term thermal field stability, obtain a stability metric value, and if the stability metric value meets a preset convergence threshold, obtain the final heat dissipation control scheme.

[0008] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention collects temperature data of the entire cell module and models the three-dimensional thermal field, and uses interpolation technology to fill the missing points. It can comprehensively and accurately reflect the complex non-uniform temperature distribution inside the secondary battery module, overcome the problem of thermal field perception blind spot caused by the reliance on a few monitoring points in the prior art, and provide a reliable data basis for precise heat dissipation control.

[0009] (2) Based on the global temperature distribution map, the present invention automatically identifies and divides high-heat areas by executing the DBSCAN algorithm, which can dynamically capture the formation and evolution of "hot spots" of the battery module under different operating conditions, realize the refined and zoned identification of heat dissipation requirements, and improve the pertinence and effectiveness of heat dissipation measures.

[0010] (3) By integrating thermal fluid simulation and real-time feedback mechanism, the present invention dynamically adjusts cooling parameters according to temperature response curve and continuously optimizes heat dissipation scheme, so that heat dissipation system can adapt to changes in the working state of battery cell module. While ensuring heat dissipation effect, it optimizes the flow distribution and energy consumption of cooling medium, and achieves comprehensive improvement of heat dissipation efficiency and system energy efficiency. Attached Figure Description

[0011] Figure 1 is a schematic flowchart of a cell module heat dissipation control method provided in the first embodiment of the present invention; Figure 2 is a schematic structural diagram of a cell module heat dissipation control system provided in the second embodiment of the present invention. Detailed Implementation

[0012] 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.

[0013] Referring to Figure 1, the first embodiment of the present invention provides a heat dissipation control method for a battery cell module, comprising the following steps: S11, collecting real-time temperature data and performing noise reduction processing to obtain a smoothed temperature dataset; S12, constructing a three-dimensional thermal field model based on the smoothed temperature dataset and filling in missing points to obtain a global temperature distribution map; S13, executing the DBSCAN algorithm based on the global temperature distribution map to obtain a set of similar point clusters; if the temperature gradient of the set of similar point clusters exceeds a preset gradient threshold, it is marked as a high-heat region, obtaining a region division result; S14, extracting the high-heat region boundary from the region division result and calculating the heat dissipation intensity allocation. S15. Determine the cooling parameter values; S16. Calculate the temperature response curve based on the cooling parameter values. If the temperature response curve shows a reduction in temperature difference, adjust the cooling parameter values ​​to obtain an optimized heat dissipation scheme; S17. Execute the optimized heat dissipation scheme and monitor real-time feedback data. If the deviation between the real-time feedback data and the temperature response curve is greater than a preset deviation threshold, adjust the optimized heat dissipation scheme to obtain an updated heat dissipation scheme; S18. Extract historical execution logs from the updated heat dissipation scheme, analyze long-term thermal field stability, and obtain a stability metric value. If the stability metric value meets a preset convergence threshold, obtain a heat dissipation control scheme.

[0014] In step S11, real-time temperature data is collected and noise is removed to obtain a smooth temperature dataset, including: S111, collecting real-time temperature data and constructing a state-space equation, using the Kalman gain matrix to correct the preset prior prediction value, and obtaining the optimal posterior temperature estimate; S112, reorganizing the optimal posterior temperature estimate to generate a smooth temperature dataset.

[0015] In step S111, real-time temperature data is collected and a state-space equation is constructed. The Kalman gain matrix is ​​used to correct the preset prior prediction value to obtain the optimal posterior temperature estimate.

[0016] Collect real-time temperature data For each monitoring point, a discrete-time state-space model of its temperature dynamics is constructed, expressed as a state equation. With observation equation .in, Let be the system state vector. ,in This represents the temperature at time k, in °C. This represents the rate of temperature change at time k, in °C / s. These are observed values, in °C. The state transition matrix is Sampling period It can be set to 1 second. For the observation matrix, This indicates that only temperature is observed, and the rate of change is not directly observed. and The covariance matrix is ​​respectively and Process noise and observation noise , For the expectation operator, the process noise covariance matrix Based on historical data statistics, if the noise variance of the temperature component is determined... Noise variance of the rate of change component ,but Observation noise covariance matrix Typically, an approximate value of the variance of the sensor's error range is taken. If the standard deviation of the measurement error of the temperature sensor used within its operating range is approximately... = Then variance The unit is .

[0017] Subsequently, the Kalman filter recursion is performed. First, a prediction is made, calculating the prior state estimate for the current time step based on the posterior estimate from the previous time step. and its covariance In obtaining actual observations Then proceed to the update step, calculating the Kalman gain matrix. This gain matrix automatically balances the weights of state predictions and observations, resolving the unit mixture between the state vector and the unit difference between the observations. Through a linear transformation, it converts the observation residuals (unit °C) into state update quantities (unit °C and °C / s), thus obtaining the optimal posterior state estimate. And update the estimated error covariance. , It is an identity matrix.

[0018] Through the above recursion, each monitoring point outputs the temperature value after noise filtering at each time, thus obtaining the optimal posterior temperature estimate.

[0019] It should be noted that the process noise covariance matrix The determination is made through historical data statistics. Specific steps include collecting historical temperature time-series data, numerically differencing the data, estimating the temperature change rate sequence, calculating the first-order difference sequences of both the temperature sequence and the rate of change sequence, and calculating the sample variance of these two difference sequences as an estimate of the process noise variance. The temperature component noise variance is... The variance of the rate of change component noise is ,but ,in, The unit for the top-left element is (°C)², and the unit for the bottom-right element is (°C / s)². Based on the thermal characteristics of the battery cell module, the temperature change rate is typically between... The variance is between 1°C / s and 1°C / s. =0.0001°C / s corresponds to a standard deviation of approximately 0.01°C / s, which is consistent with the actual noise level.

[0020] It is worth noting that the initial state Take the temperature value observed for the first time at startup as the initial temperature estimate. The initial rate of change is set to 0, that is Initial error covariance It is usually set to a large diagonal matrix, for example ,in It is a squared estimate of the possible maximum value of the rate of change, which, for example, can be set as In step S112, the optimal posterior temperature estimate is recombined to generate a smoothed temperature dataset.

[0021] The optimal posterior temperature estimates for all monitoring points are derived based on their three-dimensional spatial coordinates. The data is then categorized and organized. Each monitoring point corresponds to a unique coordinate, and the optimal posterior estimates for all its time points constitute a time-indexed temperature sequence. This yields a dataset in the form of a three-dimensional array. This dataset can be represented as... Each of them It is a vector containing the timestamp. The optimal posterior temperature estimate for all monitoring points. This dataset is the smoothed temperature dataset.

[0022] In step S12, a three-dimensional thermal field model is constructed based on the smoothed temperature dataset and missing points are filled to obtain a global temperature distribution map, including: S121, mapping the smoothed temperature dataset to a three-dimensional geometric mesh of the battery cell module to obtain sparse three-dimensional point cloud data; S122, identifying void regions in the sparse three-dimensional point cloud data, generating predicted temperature values ​​using the Kriging interpolation algorithm and filling them into the void regions to obtain a global three-dimensional temperature voxel matrix; S123, performing gradient calculation on the global three-dimensional temperature voxel matrix, extracting the temperature change rate vector, and obtaining the heat flow diffusion path; S124, combining the global three-dimensional temperature voxel matrix and the heat flow diffusion path to generate a global temperature distribution map.

[0023] In step S121, the smoothed temperature dataset is mapped to the three-dimensional geometric mesh of the battery cell module to obtain sparse three-dimensional point cloud data.

[0024] First, a corresponding three-dimensional geometric mesh model is established based on the physical structural dimensions, cell arrangement, and casing shape of the battery cell module. This model is typically composed of mesh cells, each representing a tiny physical space region within the module and possessing defined three-dimensional spatial coordinates. A uniform mesh with a resolution of 5 mm is used. Next, the preset three-dimensional spatial coordinates of each monitoring point are read from the smoothed temperature dataset. These coordinates correspond to the actual installation location of the sensor within the battery cell module. The coordinates of each monitoring point are matched against a three-dimensional geometric mesh to find its current or nearest neighboring mesh cell. The smoothed temperature value of this monitoring point at each sampling time is then calculated. The corresponding grid cells are assigned. Due to the limited number of temperature monitoring points, only a few grid cells equipped with sensors receive temperature assignments. At this point, only some cells in the entire 3D grid have temperature values, while the temperature values ​​of the remaining cells are unknown. These grid cells with temperature values, along with their coordinates and temperature values, together constitute a set of discretely distributed data points with temperature attributes in 3D space, i.e., sparse 3D temperature point cloud data.

[0025] In step S122, void regions in the sparse three-dimensional point cloud data are identified, and predicted temperature values ​​are generated using the Kriging interpolation algorithm and filled into the void regions to obtain a global three-dimensional temperature voxel matrix.

[0026] First, by comparing the voxel space coordinates of the 3D geometric mesh with the coordinates of the sparse point cloud, all voxel units not covered by known temperature data are identified and marked as void regions to be filled. Then, based on the known sparse point cloud data, a Kriging interpolation model is established to search for the radius... Usually based on the range of the variation function Settings, take For example, take Minimum number of points Set to 10. The process first calculates the empirical variability function of the known temperature data, that is, calculates the spatial lag distances at different intervals. Temperature semivariance of all point pairs ,in The distance is Number of point pairs For position The temperature value. Next, the theoretical variation function model is fitted with empirical data. This invention uses a spherical model, whose functional form is:

[0027] in, The nugget value represents measurement error and microscopic variation. The partial sill value represents the variation caused by spatial structure. The range is denoted by , representing the maximum effective distance of spatial autocorrelation. For example, the variogram parameter fitting can be as follows: =0.1°C 2 , =1.0°C 2 , =0.1m.

[0028] Nonlinear least squares fitting is performed using the Levenberg-Marquardt algorithm, minimizing the sum of squared residuals between the theoretical model and the empirical semivariance. Find the optimal parameters Thus, a well-fitted spherical variogram model is obtained. For each void voxel center location to be interpolated Based on the well-fitted model Establish the Kriging equations:

[0029] in, It assigns Kriging weights to n neighboring known temperature points. These are Lagrange multipliers. Solving this system of linear equations yields the optimal weights. Then calculate the position. Temperature forecast:

[0030] and its estimated variance .

[0031] Finally, the above Kriging prediction was performed on each of the vacuoles, and the predicted temperature values ​​were obtained. The corresponding voxels are filled in, while voxels with known point locations retain their original smoothed temperature values. Thus, each voxel in the 3D mesh is assigned a temperature value, generating a global 3D temperature voxel matrix. , where matrix elements Represents the grid index Temperature value of the voxel.

[0032] In step S123, gradient calculation is performed on the global three-dimensional temperature voxel matrix to extract the temperature change rate vector and obtain the heat flow diffusion path.

[0033] First, the global three-dimensional temperature voxel matrix is... Spatial gradient calculation is performed. The central difference method is used to calculate the gradient for each voxel. exist , , Temperature change rates in three spatial directions:

[0034] in , , These represent the physical dimensions of the voxel in three directions. Therefore, a temperature gradient vector is obtained at each voxel location:

[0035] The direction of this vector points in the direction of the fastest temperature increase, while its opposite direction... This represents the local heat flow direction at that point. Subsequently, based on the calculated gradient vector field, a streamline tracing algorithm is used to extract the heat flow diffusion path. Specifically, starting from several identified high-temperature region boundaries or internal feature points, the path is traced along the lines of those points... By integrating along the direction, a continuous path curve is obtained. ,in Let be the arc length parameter. The differential equation of this curve is: .

[0036] The equation is solved using the fourth-order Runge-Kutta method, with an integration step size of 0.5 mm. The process terminates when a path point exceeds the module's geometric boundary or when the number of tracking steps reaches 1000. By repeating this tracking process for multiple seed points, a set of heat flow diffusion paths emanating outward from the high-temperature region can be obtained.

[0037] In step S124, the global temperature voxel matrix and the heat flow diffusion path are combined to generate a global temperature distribution map.

[0038] First, an orthogonal projection transformation is performed, constructing the projection transformation matrix based on the selected observation plane normal vector. The three-dimensional heat flow path point sequence Convert to a two-dimensional coordinate sequence The system fully preserves the associated temperature, temperature gradient vector, and gradient magnitude. Then, it performs two-dimensional visualization attribute mapping, converting the original three-dimensional attributes into two-dimensional graphic codes. The heat flow direction is determined based on the sequence of adjacent points and indicated by arrows. The normalized temperature gradient magnitude is mapped to path line width or color saturation to characterize heat flow intensity. Special markers and numerical annotations are added at characteristic locations such as the starting point of the high-temperature path and key nodes of heat flow to obtain a global temperature distribution map.

[0039] It should be noted that the projection transformation matrix The desired thermal field profile or projection plane needs to be set. For example, it can be set to... .

[0040] In step S13, based on the global temperature distribution map, the DBSCAN algorithm is executed to obtain a set of similar point clusters. If the temperature gradient of the similar point cluster set exceeds a preset gradient threshold, it is marked as a high-heat region, and a region division result is obtained. This includes: S131, extracting temperature feature vectors from the global temperature distribution map, and aggregating the temperature feature vectors using the DBSCAN algorithm to obtain a set of similar point clusters; S132, calculating the intra-cluster temperature gradient of the similar point cluster set, and determining a high-heat region if the intra-cluster temperature gradient exceeds a preset gradient threshold; S133, performing three-dimensional connected component analysis on the high-heat region to obtain a high-heat connected region; and S134, identifying the geometric contour features of the high-heat connected region to obtain a region division result.

[0041] In step S131, the temperature feature vectors in the global temperature distribution map are extracted, and the DBSCAN algorithm is used to aggregate the temperature feature vectors to obtain a set of similar point clusters.

[0042] First, from the three-dimensional temperature voxel matrix corresponding to the global temperature distribution map, a temperature feature vector containing multi-dimensional features is constructed for each voxel unit. This feature vector uses the spatial coordinates of the voxel. and temperature value Based on this, and incorporating the temperature gradient components. This forms a comprehensive feature vector characterizing spatial location, temperature intensity, and direction of thermal change. The eigenvectors of all voxel units constitute the feature space point set to be clustered. Subsequently, the DBSCAN algorithm is used to perform cluster analysis on this feature point set. During algorithm execution, firstly, the Z-score is used to normalize and synthesize the eigenvectors. Standardization is performed, and then a weighted Euclidean distance is defined as the distance metric in the feature space. Different weighting coefficients are assigned to coordinates, temperature values, and gradient components in its calculation to reflect the relative importance of each dimension in the thermal field similarity assessment. Next, the core parameter of the algorithm, the neighborhood radius, is set. The minimum number of points is set by determining the inflection points using a k-distance graph. The size is determined based on the dimension of the feature space and the desired cluster size. The algorithm traverses feature points, identifies core points, and recursively merges points whose density is reachable to form clusters, ultimately dividing the feature space into several sets of density-connected point clusters and discrete noise points.

[0043] Through this process, voxels with similar spatial locations, temperature levels, and thermal change trends are aggregated into the same cluster, resulting in multiple sets of similar point clusters as output. Each set corresponds to a continuous region with relatively uniform physical properties in a three-dimensional thermal field.

[0044] It should be noted that the weights are set as follows: Spatial coordinate weights Based on the characteristic physical scales of each direction of the module Perform inverse proportional normalization settings, for example To ensure strict spatial continuity in smaller physical dimensions (such as the orientation of thin layers in cell stacking), where This is a dimensionless scaling factor. Temperature value weighting. Significant temperature difference as determined by engineering Associated, by The settings are configured to reasonably reflect temperature differences in clustering. Temperature gradient component weights. Then, based on the reference gradient amplitude of a typical heat flow region Set as This is used to capture regions with similar heat flow directions. Global scaling factor for all coefficients. Overall balancing requires experimental calibration based on historical data. For example, the physical dimensions of the battery cell module characteristics are... Significant temperature difference Reference temperature gradient Then, basic normalization can be performed according to the principle that the contribution of each dimension's "unit feature change" to the squared distance is 1, taking... Obtain spatial weights ;Pick Temperature weighting ;Pick Gradient weights .at this time, The contribution of a small directional displacement (0.05 m), a temperature difference of 10°C, or a gradient difference of 200°C / m to the square of the distance is 1.

[0045] In step S132, the intra-cluster temperature gradient of the similar point cluster set is calculated. If the intra-cluster temperature gradient exceeds a preset gradient threshold, a high-heat region is determined.

[0046] For each set of similar point clusters obtained after density clustering First, the temperature gradient characterization value within the cluster is calculated. This characterization value is obtained by considering the temperature gradient vector of all voxel units within the cluster. Statistical analysis was performed. The calculation included two key indicators: the magnitude of the average temperature gradient within the cluster. That is, the amplitude of the temperature gradient of all voxel units within the cluster. The arithmetic mean; and the consistency index of temperature gradient direction within the cluster. The average composite vector length of all normalized gradient vectors within the cluster is calculated, with a value between 0 and 1, used to quantify the consistency of heat flow direction within the region. Subsequently, the calculated intra-cluster temperature gradient characterization value is compared with a preset gradient threshold. The preset gradient threshold comprises two dimensions: gradient magnitude threshold... and direction consistency threshold . Based on the average gradient amplitude of the module's entire domain under historical normal operating conditions, for example, the average temperature gradient amplitude during historical normal operation is 15°C / m, with a standard deviation of 5°C / m, applying the 3σ criterion. Set it to 30. This value is set based on the requirement for orderly heat flow, and is typically between 0.7 and 0.9, with 0.8 being a suitable value. If a certain point cluster... Simultaneously satisfy and If so, the cluster is determined to be a high-temperature region.

[0047] In step S133, a three-dimensional connected domain analysis is performed on the high-heat region to obtain the high-heat connected region.

[0048] Collect the various point clusters in the high-heat region As input, the purpose of connected component analysis is to merge multiple physically adjacent (i.e., boundary-touching or overlapping) anomalous point clusters in 3D space into a larger, more complete highly connected region. The analysis process is based on the 6-neighborhood connectivity rule in 3D space. For any two voxel elements belonging to different anomalous point clusters, if they satisfy a set adjacency relationship in the 3D mesh, for example, sharing a 6-neighborhood, then the anomalous regions represented by these two point clusters are considered physically connected. In the algorithm implementation, each anomalous point cluster is first... This is considered as an initial independent sub-region. Next, all anomalous point clusters are traversed, checking if their respective voxel sets have adjacent voxel pairs in 3D space that satisfy connectivity rules. If so, these two point clusters are marked as connected in the region association graph. Finally, through graph connectivity component analysis, all point clusters indirectly connected by adjacency are merged to form several independent highly connected regions. .

[0049] In step S134, the geometric contour features of the high-thermal connectivity region are identified to obtain the region division result.

[0050] First, the basic spatial attributes of the regions are calculated, including the boundary coordinates of their bounding box, equivalent volume, and the location of their centroid. Then, their geometric morphology is analyzed. Principal component analysis is used to obtain the principal directions and eigenvalue ratios of the region's point set to determine its tendency towards a spherical, disk-like, or cylindrical shape, and to estimate its surface area and surface area-to-volume ratio. Simultaneously, the thermophysical properties of the regions are analyzed, calculating the average temperature gradient magnitude, the consistency index of gradient direction distribution, and the statistical distribution of temperature values, such as maximum temperature, average temperature, and temperature standard deviation. Furthermore, by identifying the geometric boundary voxels of the regions, the average temperature gradient at the boundaries, the location of the maximum gradient, and its corresponding normal direction are extracted. Combining all the above geometric contour features, thermal field features, and spatial relationship features, a structured descriptive record is generated for each highly thermally connected region. All the descriptive records of all regions together constitute the final region division result.

[0051] For example, the structured description of the region partitioning result is shown below. The region identifier is R1, triggered by the 2C fast charging condition at 14:30:05 on October 27, 2023; its spatial bounding box range is X[120.5,185.3] mm, Y[45.2,110.7] mm, Z[15.0,18.5] mm, with a volume of 42.7 cm³, and the centroid is located at (152.9,78.0,16.8) mm; the geometric shape is a flattened ellipsoid (eigenvalue ratio 5.2:1.8:1.0, sphericity 0.45, surface area to volume ratio 0.067 mm). - ¹) The thermal field characteristics are an average temperature of 68.5°C, a maximum temperature of 75.2°C, an average gradient of 85.3°C / m with a directional consistency of 0.61; the maximum gradient at the boundary of 152.7°C / m is located at (163.5, 102.1, 17.0) mm, with the normal pointing towards the outer casing; this region is adjacent to the interface of cell 7 / 8 and is 8.5 mm away from the nearest cooling channel.

[0052] In step S14, the high-heat region boundaries are extracted from the region division results, the heat dissipation intensity distribution is calculated, and the cooling parameter values ​​are determined, including: S141, extracting the edge geometric coordinates of the high-heat connected regions in the region division results and constructing a discrete mesh model; S142, calculating the boundary flux and heat load distribution data based on the discrete mesh model to determine the accumulated heat energy; S143, calculating the heat dissipation intensity numerical distribution based on the accumulated heat energy; S144, calculating the flow rate control command value and the medium inlet temperature value based on the heat dissipation intensity numerical distribution to obtain the cooling parameter values.

[0053] In step S141, the edge geometric coordinates of the highly connected regions in the region division results are extracted to construct a discrete mesh model.

[0054] First, the three-dimensional spatial definition of the target high-thermal-connectivity region is obtained from the structured description of the region partitioning results. The bounding box boundary, centroid coordinates, and morphological features of this region are read, and the corresponding three-dimensional voxel labeling data is retrieved. By traversing all voxels in the region, a 6-neighborhood rule is used to identify voxel units with at least one neighboring voxel that does not belong to this region; these units constitute the boundary voxel set of the region. Subsequently, the spatial coordinate center point of each boundary voxel is extracted to form an edge geometric coordinate point cloud describing the geometric contour of the region. Next, based on this edge coordinate point cloud, a discrete mesh model for heat transfer analysis is constructed. This model typically uses a triangular mesh to discretize the continuous boundary surface. For complex geometric contours, Delaunay triangulation can be used to convert the boundary voxel set into a continuous closed surface mesh composed of triangular patches, which is the discrete mesh model.

[0055] In step S142, the boundary flux and heat load distribution data are calculated based on the discrete grid model to determine the cumulative amount of heat energy.

[0056] First, based on the discrete mesh model, the heat transfer characteristics through the boundaries defined by the model are calculated. Using the temperature gradient vectors at the boundary voxels recorded in the region partitioning results, these vectors are mapped to each node or facet of the mesh model using the nearest neighbor interpolation method. Combined with the known thermal conductivity of the battery cell material, the heat flux density through each mesh cell is calculated. The magnitude of this heat flux density vector represents the heat flux intensity per unit area, while its direction defines the heat flux rate through this local boundary. Integrating the heat flux over all boundary grid cells yields the total heat flux through the entire boundary surface of this high-heat region; this is the boundary heat load that needs to be removed by the cooling system. .

[0057] Simultaneously, the cumulative thermal energy within the region is calculated. This amount characterizes the rate of heat generation and storage within the region. The calculation is based on the fundamental law of thermodynamics: the energy change within a region per unit time equals the thermal power generated by internal heat sources minus the thermal power dissipated through the boundaries. Internal heat source power. Approximately ,in, It is an equivalent total internal resistance (Ω) that is related to SOC, temperature, and current magnitude. Ultimately, it represents the amount of heat energy accumulated within the region. Determined by the following formula:

[0058] Among them, positive A positive value indicates a net increase in heat within the region (temperature may rise), while a negative value indicates a net decrease.

[0059] It should be noted that, The internal resistance of a battery was determined using a two-dimensional lookup table based on experimental data. The lookup table was obtained by measuring the DC internal resistance at different states of charge (SOC) and temperatures (T). For example, a certain battery... , hour, The typical value is approximately 5.8 mΩ.

[0060] In step S143, the numerical distribution of heat dissipation intensity is calculated based on the accumulated amount of heat energy.

[0061] The core of calculating the numerical distribution of heat dissipation intensity is to allocate the total heat load according to the proportion of thermal conductivity. For the first... Each grid cell, heat dissipation intensity The heat flux per unit area, or heat flux density, is expressed in W / m² and can be calculated using the following formula:

[0062] Thermal conductivity of each grid cell The thermal conductivity of this unit is characterized by its material properties and geometric dimensions. It is the target total heat load that needs to be dissipated, usually set as (in Can be directly taken as The unit is W. The denominator is the total thermal conductance of all heat dissipation interface mesh elements. Therefore, each mesh element is assigned a specific heat dissipation intensity value. ,all The set of values ​​constitutes the numerical distribution of heat dissipation intensity. .

[0063] Based on the geometric properties and material characteristics of each mesh element in the discrete mesh model, the thermal conductivity of each element is calculated. For regular cubic mesh elements, the thermal conductivity... The calculation formula is:

[0064] in The thermal conductivity of the material is (W / (m·K)). The thermally conductive cross-sectional area is (m²). This represents the heat conduction path length (m). Subsequently, thermal conduction normalization is performed, converting the thermal conductance of each mesh element into a weighting coefficient:

[0065] This weighting coefficient It is a dimensionless quantity that reflects the relative contribution of each grid cell to the total heat conduction. This represents the thermal conductivity of the j-th grid cell.

[0066] For example, taking typical parameters of a battery cell module as an example, the grid cell size is 5mm × 5mm, and the surface area is... 2.5×10 5 m 2 Aluminum heat dissipation material thermal conductivity i is the heat conduction path length of 237 (W / (m·K)). The target total heat load is 0.005m. Thermal conductivity calculation for 500W =1.185W / K, at which point the total thermal conductivity of 100 similar mesh elements is... =118.5W / K, then the weighting coefficient =0.01, heat flux density calculation 2×10 4 W / m 2 .

[0067] In step S144, based on the heat dissipation intensity distribution, the flow rate control command value and the medium inlet temperature value are calculated to obtain the cooling parameter value.

[0068] First, read in the heat dissipation intensity numerical distribution. and its corresponding grid area Calculate the total heat load And identify the maximum local heat flux density Simultaneously, the inherent parameters of the cooling system are applied: the specific heat capacity of the cooling medium. convective heat transfer coefficient function ,in Given the known calibration coefficients, in this embodiment, , The mass flow rate of the cooling medium and the temperature rise ratio at the hottest point. Safety limit parameter, i.e., maximum allowable wall temperature Upper limit of outlet temperature And actuator physical constraints, and flow range Inlet temperature range .

[0069] Subsequently, an optimization model for the control parameters was constructed and solved. The mass flow rate of the cooling parameter was used as the basis for this optimization. and inlet temperature Given the decision variables, we establish the following constrained optimization problem, with the objective function being to minimize the total energy consumption of the system. Among them, pump power It is a known system constant, the energy consumption of the refrigerator. , These are known constants obtained through calibration using the performance curves of the refrigeration unit. Constraints include: energy balance constraints. Local heat flow constraint, Safety and physical constraints: ; ; .

[0070] The optimal solution to this nonlinear constrained optimization problem is obtained by using a sequential quadratic programming algorithm. Finally, the optimal mass flow rate obtained from the solution will be... Convert to the corresponding pump speed or valve opening command value And combined with the optimal inlet temperature setpoint Together, they constitute the final output cooling parameter values. .

[0071] For example, in this embodiment, the specific heat capacity of the cooling medium is... The value is 3500 J / (kg·K), corresponding to a typical value for a mixture of 50% water and 50% ethylene glycol (volume ratio). The proportionality coefficient for the hottest spot temperature rise. The value is 0.7, based on the fact that the heat load is mainly concentrated downstream, and the cooling medium absorbs 70% of the total temperature rise by the time it reaches the hottest spot. Maximum allowable wall temperature. The maximum safe outlet temperature is 65 °C, based on the temperature resistance limits of the insulation materials, adhesives, or adjacent components within the battery pack. At 55°C, it effectively prevents coolant vaporization or overheating of downstream components. Flow range The values ​​are [0.05, 0.30] kg / s, corresponding to the pump's minimum stable flow rate and maximum design flow rate. The inlet temperature is adjustable within a certain range. The temperature range is [15, 35]°C, determined by the minimum output temperature capacity of the chiller and the ambient temperature of the cooling water. Pump power consumption constant. , ,in Dimensionless Unit: 1 / °C.

[0072] In step S15, a temperature response curve is calculated based on the cooling parameter values. If the temperature response curve shows a reduction in temperature difference, the cooling parameter values ​​are adjusted to obtain an optimized heat dissipation scheme. This includes: S151, performing thermal fluid simulation based on the cooling parameter values ​​to obtain the temperature response curve; S152, analyzing the convergence characteristics of the temperature response curve to obtain the temperature difference gradient distribution; S153, calculating the heat dispersion based on the temperature difference gradient distribution. If the heat dispersion shows a decreasing trend, a temperature deviation correction matrix is ​​constructed, and the temperature deviation correction matrix is ​​converted into a flow rate adjustment increment and an inlet temperature compensation amount. The cooling parameter values ​​are then updated to generate an optimized heat dissipation scheme.

[0073] In step S151, a thermal fluid simulation is performed based on the cooling parameter values ​​to obtain the temperature response curve.

[0074] For example, thermal fluid simulation software can be used. A three-dimensional conjugate heat transfer simulation model of a battery cell module based on ANSYS Fluent can be employed. The coolant inlet boundary conditions, such as mass flow rate and temperature, and the internal heat generation rate of the battery are used as input parameters. Key monitoring points are defined, namely the coordinates of the high-heat region output in step S13. Cooling parameter values ​​and current operating condition data are written to the simulation software via a script interface, driving model updates and initiating transient calculation tasks. After the simulation calculation is completed, the temperature evolution data of each monitoring point over time is read to obtain the temperature response curve.

[0075] In step S152, the convergence characteristics of the temperature response curve are analyzed to obtain the temperature difference gradient distribution.

[0076] First, the instantaneous rate of change of the temperature curve at each monitoring point is calculated using numerical differentiation. Then, the temperature difference gradient value at each monitoring point at each time step is defined and calculated. This value is obtained by calculating the weighted absolute difference between the temperature at that point and the temperatures of its spatial neighbors. It is achieved by analyzing the global average of the temperature difference gradients across all monitoring points. The overall convergence can be judged by the trend of change over time. If the temperature field monotonically decreases over time and eventually approaches zero, it is determined that the temperature field exhibits a convergent trend. Finally, a spatiotemporal dataset is output, representing the temperature difference gradient distribution. In step S153, the heat dispersion is calculated based on the temperature difference gradient distribution. If the heat dispersion shows a decreasing trend, a temperature deviation correction matrix is ​​constructed. The temperature deviation correction matrix is ​​then converted into a flow rate adjustment increment and an inlet temperature compensation amount. The cooling parameter values ​​are updated to generate an optimized heat dissipation scheme.

[0077] Firstly, based on the temperature difference gradient distribution Calculate the scalar index characterizing the overall heat distribution uniformity of the module, namely heat dispersion. This index is defined as the standard deviation of the temperature difference gradient values ​​across all monitoring points. .analyze During the simulation period The trend of change within: if its derivative It is negative 70% of the time and If the final value is less than 70% of the initial value, it indicates that the heat dispersion is decreasing, meaning that the current cooling scheme effectively promotes thermal field homogenization. Once the decreasing trend is confirmed, fine-tuning is performed to accelerate convergence or optimize the final state. The core of fine-tuning is to construct a temperature deviation correction matrix associated with the cooling channel or heat dissipation interface mesh. Each element of the matrix For a corresponding local control area, its value is determined by the final temperature of the high-temperature monitoring point above or near that area. Average temperature of the target area The deviation is calculated using proportional-integral adjustment, i.e. ,in This involves identifying the temperature deviation at the associated monitoring points. Subsequently, this spatial temperature correction requirement is converted into adjustments to the global control parameters of the cooling system. A preset mapping relationship is used to map the temperature deviation correction matrix. Converted into the corresponding flow rate adjustment increment vector and a global inlet temperature compensation amount Finally, the calculated adjustment increment and compensation amount are added to the current cooling parameter values ​​to obtain the updated cooling parameter values: the new flow rate control command. ,in: and new inlet temperature setpoint The solution comprised of this set of updated parameters constitutes the optimized heat dissipation solution.

[0078] It should be noted that the target average temperature The setting is based on the desired global average temperature of the module, which can be consistent with the temperature setpoint of the battery management system, and can be set to 25 °C. Proportional gain With integral gain Typically, this is obtained by tuning parameters on historical data using the Ziegler-Nichols method. For example, 0.5; =0.05. Dimensionless Since the unit of the integral term is °C·s, the unit is 1 / s.

[0079] The preset mapping relationship uses a linear model to correct the temperature deviation vector. Converted into cooling control parameter adjustment amounts, this model is expressed as: .in, for The flow velocity sensitivity matrix, its elements Indicates the first The unit flow velocity increment in the flow channel causes the first Temperature changes in each monitoring area; for The inlet temperature sensitivity vector, whose elements This indicates that a change in inlet temperature causes the first... Temperature changes in each monitoring area; for The velocity increment vector, This is the inlet temperature compensation amount. The negative sign indicates that increasing the flow rate or decreasing the inlet temperature will cause the temperature at the monitoring point to decrease. This is for solving... and Constructing an augmented matrix The dimension is M×(N+1), and the augmented control vector is... The dimension is (N+1)×1, and the equation can be written as The optimal adjustment amount is obtained by using the weighted least squares method. ,in This is a diagonal weight matrix used to allocate correction priorities based on the importance of each monitoring area, set according to the average deviation between historical sensor readings and model predictions. The solution obtained from this is... This includes the required flow rate adjustment increment and inlet temperature compensation.

[0080] In step S16, the optimized heat dissipation scheme is executed, and real-time feedback data is monitored. If the deviation between the real-time feedback data and the temperature response curve is greater than a preset deviation threshold, the optimized heat dissipation scheme is adjusted to obtain an updated heat dissipation scheme, including: S161, extracting the flow rate adjustment command from the optimized heat dissipation scheme, converting the flow rate adjustment command into a solenoid valve opening signal, and driving the actuator array; S162, acquiring the real-time feedback data of the actuator array, and constructing real-time temperature field time series data based on the real-time feedback data; S163, aligning the real-time temperature field time series data with the temperature response curve to generate a deviation vector; S164, if the deviation vector exceeds a preset deviation threshold, calculating a flow distribution correction factor to obtain an updated heat dissipation scheme.

[0081] In step S161, the flow rate adjustment command in the optimized heat dissipation scheme is extracted, and the flow rate adjustment command is converted into an electromagnetic valve opening signal to drive the actuator array.

[0082] The specific implementation is based on a pre-generated control lookup table. This lookup table is established through offline experimental calibration: under typical system operating conditions, different control commands, such as opening percentage, are applied at fixed intervals to the actuator of each controllable flow channel throughout its entire operating range. After the system stabilizes, the corresponding stable flow values ​​under each command are recorded, forming a mapping data pair of "control command - measured flow".

[0083] During online execution, the target flow rate command for each flow channel in the optimization scheme is read. This is then used as the query key. The corresponding initial opening instruction is then searched in the control lookup table. Then, the final opening command for each flow channel is given. The command is sent to the corresponding solenoid valve driver.

[0084] In step S162, real-time feedback data of the actuator array is obtained, and real-time temperature field time series data is constructed based on the real-time feedback data.

[0085] Two types of core real-time feedback data are collected synchronously through a sensor network: actuator status data and temperature field data. The actuator status data mainly includes the real-time flow rate of each controllable channel. and inlet temperature Temperature field data is acquired through an array of temperature sensors deployed inside and on the surface of the battery cell module. Each sensor periodically returns the temperature reading at its location. Subsequently, all collected raw data were aligned, time-synchronized, and validated based on a unified timestamp, outliers were removed, and moving average filtering was applied. The processed data was then organized into real-time temperature field time-series data. Each record unit in this data stream contains the following information: timestamp. Reading vectors of all temperature sensors Measured flow vectors of each channel and measured inlet temperature .

[0086] In step S163, the real-time temperature field time series data is aligned with the temperature response curve to generate a deviation vector.

[0087] Using the start time of the optimization scheme execution as the time origin, the time axis of the real-time acquired temperature field data stream is strictly synchronized and aligned with the time axis of the temperature response curve. For each monitoring point... At each alignment time Read the measured temperature Compared with the simulated predicted temperature Calculate the instantaneous temperature difference at that point. Subsequently, the temperature differences at all monitoring points at the same time are combined into a multi-dimensional vector, namely the temperature deviation vector. .

[0088] In step S164, if the deviation vector exceeds a preset deviation threshold, a flow distribution correction factor is calculated to obtain an updated heat dissipation scheme.

[0089] Set the preset deviation threshold vector and the real-time deviation vector The absolute values ​​of each component are compared with it. When any component is detected... After 30 seconds, a correction process is triggered. First, the set of monitoring points exceeding the standard is identified, and the correction intensity for each point is calculated. ,right Perform maximum absolute value normalization to quantify its demand.

[0090] The influence matrix F is transformed into a normalized sensitivity matrix, i.e., a dimensionless matrix with dimensions M×N (M is the number of monitoring points, and N is the number of flow channels). Elements This represents the influence coefficient of the unit normalized flow rate change in the j-th channel on the normalized temperature at the i-th monitoring point. The negative sign indicates that an increase in flow rate leads to a decrease in temperature, consistent with physical laws.

[0091] F is the normalized sensitivity matrix, and its elements are calculated using the following formula:

[0092] in This is the temperature change (in °C) at the i-th monitoring point, measured experimentally. The experimental method involves increasing the flow rate of the j-th channel individually under baseline operating conditions. (Unit: kg / s), record after stabilization. . It is the change in flow rate of the j-th channel (in kg / s), usually taken as a small increment such as 0.01 kg / s to avoid nonlinear effects. For temperature reference values, a typical operating temperature such as 25°C is used. / Dimensionless. For flow rate reference value, take the rated flow rate, such as 0.1 kg / s, and make... / Dimensionless.

[0093] Calculate the initial flow allocation correction factor vector This vector This reflects the relative adjustment required for each flow channel to correct the current temperature deviation. Perform maximum absolute value normalization to obtain the final flow allocation correction factor vector. :

[0094] The value range is [-1, 1], to avoid excessive flow rate adjustment. If all If the value is zero, the original flow rate remains unchanged. It should be noted that... The initial flow rate is assigned a correction factor vector, which is a dimensionless intermediate variable with a dimension of N×1 (N is the number of flow channels). For vectors The k-th component represents the initial correction factor value of the k-th flow channel (k is the flow channel index, with a value range of 1 to N). The j-th component of the final correction factor vector c is obtained by normalizing c' (j is also the channel index).

[0095] Updated target flow rate .Will Replace the optimized heat dissipation solution in An updated heat dissipation solution was obtained. This refers to the original target flow rate (in kg / s) in the optimized heat dissipation solution. The updated flow rate... Units should be kept constant in kg / s, ensuring dimensional consistency. In practical applications, actuator constraints (such as flow velocity range) must be considered. )right Perform amplitude limiting. For example, if the calculated amplitude is... Below the minimum allowed flow Then force set to .

[0096] In step S17, historical execution logs are extracted from the updated heat dissipation scheme, long-term thermal field stability is analyzed, and a stability metric value is obtained. If the stability metric value meets a preset convergence threshold, a heat dissipation control scheme is obtained, including: S171, extracting a multidimensional temperature control variable sequence from the historical execution logs of the updated heat dissipation scheme; S172, calculating the fluctuation variance value within a preset iteration period based on the multidimensional temperature control variable sequence; S173, normalizing the fluctuation variance value and evaluating the thermal field stability to obtain a stability metric value; S174, if the stability metric value meets a preset convergence threshold, outputting the updated heat dissipation scheme as a heat dissipation control scheme.

[0097] In step S171, the multidimensional temperature control variable sequence is extracted from the historical execution log of the updated heat dissipation scheme.

[0098] Extract continuous historical records associated with the current updated cooling scheme from the historical execution log database. The extracted data constitutes a multidimensional time series, the core dimension of which includes the sequence of control variables, namely the sequence of target flow rate setpoints for each cooling channel in each iteration. with inlet temperature setpoint sequence The state variable sequence, namely the key temperature sequence measured after the scheme has stabilized, corresponding to the above control settings, such as the average temperature at each monitoring point. Maximum temperature ; and a sequence of performance metrics, such as the time required for each execution of the scheme to reach a steady state. and total system power consumption Sequences. These are sequences of multidimensional variables arranged in the order of execution.

[0099] In step S172, the fluctuation variance value within a preset iteration period is calculated based on the multidimensional temperature control variable sequence.

[0100] First, define an iteration period length for evaluating long-term stability. For example, recently The record of this scheme execution. Next, from the multidimensional temperature control variable sequence, the most recent... Data for each cycle. For each individual temperature control variable, such as the target flow rate of channel 1. Average temperature Maximum temperature Wait, calculate its in The sample variance of values ​​taken within a period. For a specific variable Its fluctuation variance The calculation formula is: ,in, Is this variable in the th... The value of each period, This is the sample mean of the variable within the period. The variance of the fluctuations of all variables of interest is calculated in parallel, forming a variance vector. .

[0101] In step S173, the fluctuation variance value is normalized and the thermal field stability is evaluated to obtain a stability metric value.

[0102] First, the variance of the fluctuation for each temperature control variable. Set a reference variance baseline value This benchmark value represents the typical volatility level of the variable under ideal stable conditions or within the design allowable range, and can be obtained through statistical analysis of historical long-term stable operating data. Subsequently, the normalized volatility exponent for each variable is calculated. Next, the normalized volatility exponents of all variables are weighted and aggregated to calculate the comprehensive stability measure. The weighted geometric mean is used, and its formula is as follows:

[0103] in, It is to assign to variables weights ( This reflects the importance of the variable to overall stability; for example, the highest temperature typically has a higher weight than the average flow rate. The weighted aggregation yields... It is a single scalar. Finally, this scalar is scaled to map to the range of 0 to 100. The mapping function is: ,in, and The settings combine historical operational data. Within long-term historical data, all time periods manually determined to be "extremely stable and without anomalies" are used to calculate the timeframes within these periods. average and standard deviation , Calculate the data for the 10 cycles preceding each historical thermal alarm and performance degradation. average value , It can be Set it to 1.0, Set it to 2.5.

[0104] It should be noted that weight The specific values ​​are set based on the relative importance of each variable to system safety, performance, and operating efficiency. A directly applicable example is as follows: for variables determined by the highest temperature... Temperature standard deviation Average temperature Total system power consumption and the flow rate setpoints for each channel and inlet temperature setpoint The weights of the multidimensional variable set can be assigned as follows: , , , The weight set for the flow rate of each channel Inlet temperature setting weight .

[0105] In step S174, if the stability metric value meets the preset convergence threshold, the updated heat dissipation scheme is output as a heat dissipation control scheme.

[0106] Set a preset convergence threshold ,For example Points. Reaching the preset convergence threshold indicates that the heat dissipation scheme is sufficiently stable and can be adopted long-term. The stability metric value mentioned in step [of the text]... If the current updated heat dissipation scheme is determined to meet the long-term stability requirements, the updated heat dissipation scheme will be output as a heat dissipation control scheme.

[0107] In summary, this invention discloses a heat dissipation control method for battery cell modules, which achieves precise adaptation to the non-uniform thermal field inside the battery module.

[0108] Referring to Figure 2, a second embodiment of the present invention provides a heat dissipation control system for a battery cell module, comprising: a data acquisition module for acquiring real-time temperature data and performing noise reduction processing to obtain a smoothed temperature dataset; a thermal field modeling module for constructing a three-dimensional thermal field model based on the smoothed temperature dataset and filling in missing points to obtain a global temperature distribution map; a region division module for executing the DBSCAN algorithm based on the global temperature distribution map to obtain a set of similar point clusters; if the temperature gradient of the set of similar point clusters exceeds a preset gradient threshold, it is marked as a high-heat region, thus obtaining a region division result; and a parameter generation module for extracting the boundaries of the high-heat regions from the region division result and calculating the heat dissipation intensity allocation. The system comprises the following modules: a cooling parameter value determination module; a scheme generation module, used to calculate a temperature response curve based on the cooling parameter value; if the temperature response curve shows a reduction in temperature difference, adjusting the cooling parameter value to obtain an optimized heat dissipation scheme; a simulation optimization module, used to execute the optimized heat dissipation scheme and monitor real-time feedback data; if the deviation between the real-time feedback data and the temperature response curve is greater than a preset deviation threshold, adjusting the optimized heat dissipation scheme to obtain an updated heat dissipation scheme; and a scheme output module, used to extract historical execution logs from the updated heat dissipation scheme, analyze long-term thermal field stability, obtain a stability metric value, and if the stability metric value meets a preset convergence threshold, obtaining the final heat dissipation control scheme.

[0109] It should be noted that the cell module heat dissipation control system provided in this embodiment of the invention is used to execute all the process steps of the cell module heat dissipation control method in the above embodiment. The working principle and beneficial effect of the two are one-to-one, so they will not be described again.

[0110] 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 cell module heat dissipation control program. When the processor executes the computer program, it implements the steps in the various cell module heat dissipation control method embodiments described above, such as step S11 shown in FIG1. ​​Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the various device embodiments described above, such as a scheme output module.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 controlling heat dissipation in a battery cell module, characterized in that, include: Real-time temperature data is collected and noise is removed to obtain a smoothed temperature dataset. A three-dimensional thermal field model is constructed based on the smoothed temperature dataset and missing points are filled to obtain a global temperature distribution map. Based on the global temperature distribution map, the DBSCAN algorithm is executed to obtain a set of similar point clusters. If the temperature gradient of the set of similar point clusters exceeds a preset gradient threshold, it is marked as a high-heat region, and the region division result is obtained. Extract the high-heat region boundaries from the region division results, calculate the heat dissipation intensity distribution, and determine the cooling parameter values; Based on the cooling parameter values, a temperature response curve is calculated. If the temperature response curve shows a reduction in temperature difference, the cooling parameter values ​​are adjusted to obtain an optimized heat dissipation scheme. The optimized heat dissipation scheme is executed, and real-time feedback data is monitored. If the deviation between the real-time feedback data and the temperature response curve is greater than a preset deviation threshold, the optimized heat dissipation scheme is adjusted to obtain an updated heat dissipation scheme. Historical execution logs are extracted from the updated heat dissipation scheme, and long-term thermal field stability is analyzed to obtain a stability metric value. If the stability metric value meets a preset convergence threshold, a heat dissipation control scheme is obtained.

2. The cell module heat dissipation control method according to claim 1, characterized in that, The process of collecting real-time temperature data and performing noise reduction to obtain a smoothed temperature dataset includes: collecting real-time temperature data and constructing a state-space equation; using the Kalman gain matrix to correct the preset prior prediction value to obtain the optimal posterior temperature estimate; and reorganizing the optimal posterior temperature estimate to generate a smoothed temperature dataset.

3. The cell module heat dissipation control method according to claim 1, characterized in that, The step of constructing a three-dimensional thermal field model based on the smoothed temperature dataset and filling in missing points to obtain a global temperature distribution map includes: mapping the smoothed temperature dataset to a three-dimensional geometric mesh of a battery cell module to obtain sparse three-dimensional point cloud data; identifying void regions in the sparse three-dimensional point cloud data, generating predicted temperature values ​​using the Kriging interpolation algorithm and filling them into the void regions to obtain a global three-dimensional temperature voxel matrix; performing gradient calculation on the global three-dimensional temperature voxel matrix, extracting the temperature change rate vector, and obtaining the heat flow diffusion path; and combining the global three-dimensional temperature voxel matrix with the heat flow diffusion path to generate a global temperature distribution map.

4. The cell module heat dissipation control method according to claim 1, characterized in that, The process involves: executing the DBSCAN algorithm based on the global temperature distribution map to obtain a set of similar point clusters; marking a high-heat region as such if the temperature gradient of the similar point cluster exceeds a preset gradient threshold, thus obtaining a region division result. This includes: extracting temperature feature vectors from the global temperature distribution map; aggregating the temperature feature vectors using the DBSCAN algorithm to obtain a set of similar point clusters; calculating the intra-cluster temperature gradient of the similar point cluster; determining a high-heat region if the intra-cluster temperature gradient exceeds a preset gradient threshold; performing three-dimensional connected component analysis on the high-heat region to obtain a high-heat connected region; and identifying the geometric contour features of the high-heat connected region to obtain a region division result.

5. The cell module heat dissipation control method according to claim 1, characterized in that, The step of extracting the high-heat region boundary from the region division result, calculating the heat dissipation intensity distribution, and determining the cooling parameter values ​​includes: extracting the edge geometric coordinates of the high-heat connected regions in the region division result and constructing a discrete mesh model; calculating the boundary flux and heat load distribution data based on the discrete mesh model to determine the accumulated heat energy; calculating the heat dissipation intensity numerical distribution based on the accumulated heat energy; and calculating the flow rate control command value and the medium inlet temperature value based on the heat dissipation intensity numerical distribution to obtain the cooling parameter values.

6. The cell module heat dissipation control method according to claim 1, characterized in that, The step of calculating the temperature response curve based on the cooling parameter values, and adjusting the cooling parameter values ​​to obtain an optimized heat dissipation scheme if the temperature response curve shows a reduction in temperature difference, includes: performing thermal fluid simulation based on the cooling parameter values ​​to obtain the temperature response curve; analyzing the convergence characteristics of the temperature response curve to obtain the temperature difference gradient distribution; calculating the heat dispersion based on the temperature difference gradient distribution; if the heat dispersion shows a decreasing trend, constructing a temperature deviation correction matrix; converting the temperature deviation correction matrix into a flow rate adjustment increment and an inlet temperature compensation amount; updating the cooling parameter values; and generating an optimized heat dissipation scheme.

7. The cell module heat dissipation control method according to claim 1, characterized in that, The process of executing the optimized heat dissipation scheme and monitoring real-time feedback data includes adjusting the optimized heat dissipation scheme to obtain an updated heat dissipation scheme if the deviation between the real-time feedback data and the temperature response curve is greater than a preset deviation threshold. This includes: extracting the flow rate adjustment command from the optimized heat dissipation scheme and converting the flow rate adjustment command into a solenoid valve opening signal to drive the actuator array; acquiring the real-time feedback data of the actuator array and constructing real-time temperature field time series data based on the real-time feedback data; aligning the real-time temperature field time series data with the temperature response curve to generate a deviation vector; and calculating a flow distribution correction factor if the deviation vector exceeds a preset deviation threshold to obtain an updated heat dissipation scheme.

8. The cell module heat dissipation control method according to claim 1, characterized in that, The process of extracting historical execution logs from the updated heat dissipation scheme, analyzing long-term thermal field stability, and obtaining a stability metric value, and obtaining a heat dissipation control scheme if the stability metric value meets a preset convergence threshold, includes: extracting a multidimensional temperature control variable sequence from the historical execution logs of the updated heat dissipation scheme; calculating the fluctuation variance value within a preset iteration period based on the multidimensional temperature control variable sequence; normalizing the fluctuation variance value and evaluating the thermal field stability to obtain a stability metric value; and outputting the updated heat dissipation scheme as a heat dissipation control scheme if the stability metric value meets a preset convergence threshold.

9. A heat dissipation control system for a battery cell module, characterized in that, include: The data acquisition module is used to collect real-time temperature data and perform noise reduction processing to obtain a smoothed temperature dataset; The thermal field modeling module is used to construct a three-dimensional thermal field model based on the smoothed temperature dataset and fill in the missing points to obtain a global temperature distribution map. The region division module is used to execute the DBSCAN algorithm based on the global temperature distribution map to obtain a set of similar point clusters. If the temperature gradient of the set of similar point clusters exceeds a preset gradient threshold, it is marked as a high-heat region, and the region division result is obtained. The parameter generation module is used to extract the high-heat region boundary from the region division result, calculate the heat dissipation intensity distribution, and determine the cooling parameter value. The scheme generation module is used to calculate the temperature response curve based on the cooling parameter values. If the temperature response curve shows a reduction in temperature difference, the cooling parameter values ​​are adjusted to obtain an optimized heat dissipation scheme. The simulation optimization module is used to execute the optimized heat dissipation scheme and monitor real-time feedback data. If the deviation between the real-time feedback data and the temperature response curve is greater than a preset deviation threshold, the optimized heat dissipation scheme is adjusted to obtain an updated heat dissipation scheme. The scheme output module is used to extract historical execution logs from the updated heat dissipation scheme, analyze the long-term thermal field stability, obtain a stability metric value, and if the stability metric value meets a preset convergence threshold, obtain the final heat dissipation control scheme.

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