Charging pile intelligent heat dissipation system based on two-phase cold plate and spraying type

Abstract

The invention relates to the technical field of charging pile heat dissipation, and discloses an intelligent charging pile heat dissipation system based on a two-phase cold plate and a spraying mode. The system comprises a heat dissipation area division unit, a data acquisition unit, a heat dissipation mode analysis unit, an environment heat dissipation area determination unit, a heat dissipation weight construction unit, a heat dissipation sequence generation unit and a driving execution unit. The heat dissipation area dividing unit divides the interior of the charging pile into a plurality of heat dissipation adjusting areas. The data acquisition unit acquires temperature distribution data of a shell surface contact area in real time; the heat dissipation mode analysis unit recognizes a target heat dissipation area and divides a coverage area of the target heat dissipation area into a main heat dissipation area and an auxiliary heat dissipation area; the environment heat dissipation area determination unit determines an environment heat dissipation area in the uncovered area, and the three parts form a heat dissipation processing set. A heat dissipation weight construction unit calculates the heat dissipation adjustment difference degree of adjacent intervals and constructs a weight map, and a heat dissipation sequence generation unit generates a heat dissipation adjustment sequence according to the heat dissipation adjustment difference degree; the driving execution unit drives the two-phase cold plate and the spraying module to dissipate heat in sequence, and targeted heat dissipation is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging pile heat dissipation, in particular to a charging pile intelligent heat dissipation system based on a two-phase cold plate and a spraying type. BACKGROUND

[0002] With the rapid development of the new energy vehicle industry, as the key infrastructure for energy supply, the use frequency and power demand of charging piles are continuously increasing. In high-power charging scenarios, the internal electronic components of the charging pile will continuously generate a large amount of heat, and if the heat cannot be dissipated in time, the internal temperature will rise sharply, thereby affecting the working stability and service life of the electronic components. At present, the common heat dissipation methods for charging piles on the market mainly include natural heat dissipation, forced air cooling, and single liquid cooling heat dissipation.

[0003] The natural heat dissipation method relies on the natural heat exchange between the charging pile shell and the external environment, does not require additional power driving, and has low cost, but the heat dissipation efficiency is greatly affected by the external environment temperature. In high-temperature environments or high-power charging conditions, it is difficult to meet the heat dissipation demand. Forced air cooling drives air flow through a fan to accelerate the discharge of heat inside the charging pile. Compared with natural heat dissipation, the heat dissipation efficiency is improved, but the fan generates noise during operation, and dust is easily accumulated inside the fan after long-term use, which not only further reduces the heat dissipation efficiency, but also may cause equipment failure. The single liquid cooling heat dissipation method usually adopts a cold plate to directly contact the heat generating components for heat exchange. Although the heat dissipation effect is better than air cooling, the traditional liquid cooling system adopts an overall heat dissipation design, i.e., the entire charging pile is cooled, and it is unable to adjust according to the heat generation of different regions.

[0004] In actual application, the heat generation intensity of electronic components at different positions inside the charging pile is obviously different, for example, the heat generation density of power modules and control units is much higher than that of other auxiliary components. When an overall heat dissipation design is adopted, in order to ensure the heat dissipation effect of the region with high heat generation intensity, the operating power of the entire heat dissipation system needs to be increased, which not only causes energy waste, but also may cause excessive heat dissipation in the region with low heat generation intensity, resulting in energy loss and affecting the normal work of some components due to the excessively low local temperature. In addition, the traditional heat dissipation system lacks real-time monitoring of the temperature distribution on the surface of the charging pile shell, and is unable to timely grasp the distribution of internal heat, and is difficult to adjust the heat dissipation strategy according to the actual heat generation condition, resulting in poor flexibility and adaptability of the heat dissipation system, and being unable to meet the efficient heat dissipation demand of the charging pile under different conditions. SUMMARY

[0005] The present application aims to provide a charging pile intelligent heat dissipation system based on a two-phase cold plate and a spraying type to solve the problems raised in the background.

[0006] To achieve the above object, the application provides a two-phase cold plate and spray type charging pile intelligent heat dissipation system, which comprises:

[0007] A heat dissipation area division unit is used for dividing the inside of the charging pile into a plurality of heat dissipation adjustment areas.

[0008] A data acquisition unit is used for acquiring real-time contact area temperature distribution data on the surface of the charging pile shell.

[0009] A heat dissipation mode analysis unit is used for identifying a target heat dissipation area according to the contact area temperature distribution data and dividing the heat dissipation adjustment area covered by the target heat dissipation area into a dominant heat dissipation area and an auxiliary heat dissipation area.

[0010] An environmental heat dissipation area determination unit is used for determining an environmental heat dissipation area in the heat dissipation adjustment area not covered by the target heat dissipation area, and the dominant heat dissipation area, the auxiliary heat dissipation area and the environmental heat dissipation area constitute a heat dissipation processing set.

[0011] A heat dissipation weight construction unit is used for calculating the heat dissipation adjustment difference between any two adjacent heat dissipation adjustment areas in the heat dissipation processing set, and constructing a heat dissipation weight graph with the heat dissipation adjustment areas as vertices and the heat dissipation adjustment difference as edges.

[0012] A heat dissipation sequence generation unit is used for generating a heat dissipation adjustment sequence based on the heat dissipation weight graph.

[0013] A driving execution unit is used for driving the two-phase cold plate module and the spray module to perform heat dissipation operation according to the heat dissipation adjustment sequence.

[0014] Preferably, the heat dissipation mode analysis unit comprises:

[0015] A contact area identification module is used for establishing a standard contact area model according to historical temperature data.

[0016] A region matching module is used for matching the real-time contact area temperature distribution data with the standard contact area model to identify the target heat dissipation area boundary under the current running state of the charging pile.

[0017] A region mapping module is used for mapping the target heat dissipation area boundary to the spatial coordinates of the heat dissipation adjustment area.

[0018] Preferably, the contact area identification module is specifically used for:

[0019] Constructing a three-dimensional temperature distribution matrix on the surface of the charging pile shell.

[0020] Using K-neighborhood algorithm to perform cluster analysis on the three-dimensional temperature distribution matrix to generate a temperature feature similar region set.

[0021] Screening the effective contact area in the temperature characteristic similar area set according to the temperature change gradient.

[0022] Preferably, the heat dissipation mode analysis unit further comprises:

[0023] A heat source analysis module is configured to extract thermal imaging feature data in the target heat dissipation area.

[0024] A dominant area determination module is configured to mark the heat dissipation adjustment area corresponding to the thermal imaging feature data as a dominant heat dissipation area according to a heat flow density threshold.

[0025] An auxiliary area determination module is configured to mark the heat dissipation adjustment area adjacent to the dominant heat dissipation area as an auxiliary heat dissipation area according to a heat conduction gradient.

[0026] Preferably, the environmental heat dissipation area determination unit is specifically configured to:

[0027] Establish a spatial position relationship atlas of the heat dissipation adjustment area.

[0028] Identify the core influence area of the dominant heat dissipation area using a density peak clustering algorithm.

[0029] Determine the range of the environmental heat dissipation area in the non-target heat dissipation area according to a heat diffusion attenuation model.

[0030] Preferably, the heat dissipation weight construction unit comprises:

[0031] A parameter calculation module is configured to obtain a heat load parameter and a heat dissipation efficiency parameter of each heat dissipation adjustment area.

[0032] A difference degree calculation module is configured to process a heat load parameter difference and a heat dissipation efficiency parameter difference of adjacent heat dissipation adjustment areas using a fuzzy logic algorithm to output a heat dissipation adjustment difference degree.

[0033] Preferably, the heat dissipation sequence generation unit is specifically configured to:

[0034] Convert the heat dissipation weight map into an adjacency matrix.

[0035] Solve an optimal path sequence of the adjacency matrix based on a Hungarian algorithm.

[0036] Optimize the optimal path sequence according to a working state constraint condition of the heat dissipation adjustment area.

[0037] Preferably, the driving execution unit comprises:

[0038] A timing control module is configured to parse an execution node parameter in the heat dissipation adjustment sequence.

[0039] The power prediction module adopts a long short-term memory network model to process historical heat dissipation power data, and generates a phase change triggering parameter of the two-phase cold plate module and a flow control parameter of the spraying module.

[0040] The cooperative driving module synchronously adjusts the refrigerant flow rate and the spraying intensity according to the phase change triggering parameter and the flow control parameter.

[0041] Preferably, the system further comprises:

[0042] The abnormal heat dissipation detection unit is configured to monitor a temperature change rate of the heat dissipation processing set.

[0043] The mode matching unit is configured to compare a real-time temperature change rate curve with a preset abnormal heat dissipation mode library.

[0044] The parameter correction unit is configured to update an edge weight value of the heat dissipation weight graph when a matching degree exceeds a set threshold.

[0045] Preferably, the mode matching unit is specifically configured to:

[0046] Construct a time sequence feature vector of the temperature change rate.

[0047] Calculate a similarity degree of the time sequence feature vector and an abnormal heat dissipation mode template by using a dynamic time warping algorithm.

[0048] Determine an abnormal heat dissipation mode type with the highest matching degree according to a similarity degree sorting result.

[0049] Compared with the prior art, the present application has the following beneficial effects:

[0050] By setting the heat dissipation region division unit, the charging pile is divided into multiple heat dissipation adjustment regions, breaking the limitation of the traditional overall heat dissipation design and providing a basis for targeted heat dissipation. This partition design can accurately correspond to the regions where electronic components with different heating intensities are located, so that subsequent heat dissipation operations can be carried out according to the actual heating conditions of each region, avoiding energy waste caused by indiscriminate heat dissipation of the entire charging pile.

[0051] The data acquisition unit can acquire the temperature distribution data of the contact area on the surface of the charging pile shell in real time. The temperature distribution on the surface of the shell is directly related to the heating condition of the internal components. By collecting this data, the distribution dynamics of the heat in the charging pile can be grasped in a timely and accurate manner, providing a reliable information source for subsequent identification of the target heat dissipation region. Compared with the traditional heat dissipation system which lacks real-time monitoring, the setting of this unit enables the heat dissipation system to dynamically respond to internal heating changes, and no longer relies on preset fixed heat dissipation modes, thereby improving the perception ability of the heat dissipation system to actual working conditions.

[0052] The heat dissipation mode analysis unit identifies a target heat dissipation region according to the contact area temperature distribution data, and divides the heat dissipation adjustment area covered by the target heat dissipation region into a dominant heat dissipation area and an auxiliary heat dissipation area. This division further refines the heat dissipation demand. The dominant heat dissipation area corresponds to a region with high internal heat generation intensity, and the auxiliary heat dissipation area corresponds to a region with relatively low heat generation intensity but still needs to strengthen heat dissipation. By clearly defining the heat dissipation priorities of different regions, the subsequent heat dissipation operation can prioritize the heat dissipation demand of the region with high heat generation intensity, while also considering the auxiliary heat dissipation area, avoiding the problem of insufficient or excessive heat dissipation in some areas due to unreasonable allocation of heat dissipation resources.

[0053] The environmental heat dissipation area determination unit determines an environmental heat dissipation area in the heat dissipation adjustment area not covered by the target heat dissipation region, so that the dominant heat dissipation area, the auxiliary heat dissipation area and the environmental heat dissipation area together constitute a heat dissipation processing set, realizing comprehensive coverage of all regions inside the charging pile. The environmental heat dissipation area corresponds to a region with extremely low internal heat generation intensity, which can meet the demand by relying on natural heat dissipation, without the need for additional activation of the two-phase cold plate module and the spraying module for active heat dissipation, further reducing energy consumption and improving the overall energy efficiency of the heat dissipation system.

[0054] The heat dissipation weight construction unit constructs a heat dissipation weight graph with the heat dissipation adjustment areas as vertices and the heat dissipation adjustment difference degrees between any two adjacent heat dissipation adjustment areas in the heat dissipation processing set as edges. This weight graph can clearly reflect the heat dissipation demand difference between adjacent regions. Based on this weight graph, the heat dissipation sequence generation unit can generate a scientific and reasonable heat dissipation adjustment sequence, ensuring that the heat dissipation operation is orderly carried out according to the heat dissipation demand difference between regions, avoiding the disorder and randomness of the heat dissipation operation, and enabling the heat dissipation resources to be optimally allocated according to the regional demand difference.

[0055] The driving execution unit drives the two-phase cold plate module and the spraying module to perform heat dissipation operation according to the heat dissipation adjustment sequence. The two-phase cold plate module has high heat exchange capacity and can quickly remove a large amount of heat in the dominant heat dissipation area and the auxiliary heat dissipation area, while the spraying module can further enhance the heat dissipation effect by spraying cooling liquid. The cooperative work of the two heat dissipation modules can fully exert their respective heat dissipation advantages and improve the overall heat dissipation efficiency. At the same time, performing heat dissipation operation according to the heat dissipation adjustment sequence enables the two-phase cold plate module and the spraying module to act accurately on different heat dissipation adjustment areas, avoiding energy waste caused by simultaneous global operation of the two modules, and further improving the operation economy and flexibility of the heat dissipation system. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The timing diagram of the intelligent heat dissipation system of the charging pile based on the two-phase cold plate and the spraying type according to the present application;

[0057] Figure 2A flow chart for operation of the heat source analysis unit;

[0058] Figure 3 A flow chart for operation of the heat source analysis unit;

[0059] Figure 4 A flow chart for operation of the heat source analysis unit;

[0060] Figure 5 A flow chart for operation of the heat source analysis unit. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0062] Please refer to Figure 1 The present application provides a two-phase cold plate and spray-based intelligent heat dissipation system for charging piles, which comprises:

[0063] The heat dissipation area division unit divides the internal space of the charging pile into a plurality of mutually independent heat dissipation regulation zones according to geometric structure and heat transfer characteristics. The data acquisition unit acquires real-time temperature distribution data of the contact area through a high-precision infrared temperature sensor array arranged on the surface of the charging pile shell and transmits the data to the central processor in the form of a two-dimensional matrix. After receiving the temperature distribution data, the heat dissipation mode analysis unit identifies the target heat dissipation area through a pattern recognition algorithm and further divides the heat dissipation regulation zone covered by the target heat dissipation area into a dominant heat dissipation zone and an auxiliary heat dissipation zone. The environmental heat dissipation zone determination unit selects the environmental heat dissipation zone that needs to participate in collaborative heat dissipation in the heat dissipation regulation zone not covered by the target heat dissipation area based on the principle of heat diffusion. The heat dissipation weight construction unit calculates the difference in thermodynamic parameters between any two adjacent heat dissipation regulation zones in the heat dissipation processing set and constructs a heat dissipation weight graph in the form of a graph structure. The heat dissipation sequence generation unit processes the heat dissipation weight graph using a path optimization algorithm to generate an optimal heat dissipation regulation sequence. The driving execution unit analyzes the sequence and controls the phase change refrigeration cycle of the two-phase cold plate module and the liquid cooling system of the spray module to perform partitioned heat dissipation operation.

[0064] Embodiment 1: Please refer to Figure 2In the actual operation of the charging pile intelligent cooling system, the cooling mode analysis unit constructs a three-dimensional temperature distribution matrix of the charging pile shell surface through the contact area identification module. The matrix is composed of 256 high-precision infrared temperature sensors, which are evenly arranged on the inner surface of the charging pile shell in the form of an 8x32 array, with a sampling frequency of 10 Hz. The temperature data recorded by each sensor node contains three-dimensional coordinate information (X-axis position, Y-axis position, temperature value), forming a real-time temperature matrix with dimensions of 8x32x1. In the system initialization stage, 72 hours of historical temperature data are continuously collected and stored as a time-sequenced temperature matrix set.

[0065] The contact area identification module uses the K-nearest neighbor algorithm to process the three-dimensional temperature distribution matrix, with a neighborhood radius parameter of 3 sensor spacing units and a temperature difference threshold of ±2.5°C. For each sensor node, calculate the temperature difference with the 8 adjacent nodes in each direction. When the temperature difference of a node with more than half of the adjacent nodes is continuously below the threshold, the node is marked as a core temperature point. All interconnected core temperature points are automatically aggregated into temperature feature similar regions, generating a temperature feature similar region set containing 5-8 independent regions.

[0066] In the region boundary identification stage, the internal temperature gradient of each temperature feature similar region is calculated. Along the boundary line of the region, the temperature change rate of the boundary point and the adjacent point outside is calculated. When the temperature change rate of a certain boundary segment continuously exceeds 3°C / cm and maintains for more than 5 seconds, the boundary segment is marked as an effective contact boundary. Finally, the effective contact region set containing 3-5 effective boundaries is selected, and the boundary coordinates are stored in the standard contact region model database in a linked list structure. When the region matching module is working, the real-time temperature matrix is first normalized. The current matrix is compared with the 20 template matrices in the standard model database in multiple scales: in the first stage, a 16x16 macro window is used to scan the overall temperature distribution, and the Pearson correlation coefficient is calculated; in the second stage, a 5x5 micro window is used to focus on the boundary region, and the local temperature distribution similarity is calculated. When the macro correlation coefficient exceeds 0.85 and the micro similarity is higher than 0.7, it is determined as an effective match. After successful matching, the abnormal temperature rise region in the current temperature matrix is extracted, and the boundary coordinates are smoothed by B-spline curve fitting algorithm to form the target cooling region boundary coordinate sequence.

[0067] The area mapping module calls a preset space mapping parameter table when performing coordinate conversion. The parameter table contains the correspondence between the physical coordinates of 256 sensor nodes and the heat dissipation adjustment areas. A three-dimensional coordinate system is established with the lower left corner of the charging pile bottom surface as the origin. Every 4 sensor nodes correspond to one heat dissipation adjustment area (a total of 8 areas) in the X-axis direction, and every 8 sensor nodes correspond to one heat dissipation adjustment area (a total of 4 areas) in the Y-axis direction. When the boundary coordinates of the target heat dissipation area are input, the module performs the following mapping process: first, convert the boundary coordinates to the nearest sensor node number, and then query the heat dissipation adjustment area number according to the node number. For boundary coordinates that span multiple heat dissipation adjustment areas, the area ratio method is used for attribution determination - calculate the projection area of the boundary polygon in each heat dissipation adjustment area, and include the heat dissipation adjustment area with an area ratio of more than 15% in the target area.

[0068] In the actual operation example of the charging pile, when local temperature rise is detected in the charging gun interface area: the contact area identification module identifies a high-temperature cluster (average temperature 58.3°C) formed by the 6x4 sensor area near the charging gun interface through K-nearest neighbor clustering, and the temperature difference with the surrounding area is up to 7.2°C. The area matching module successfully matches this area with the "gun mouth load" template in the standard model (macro correlation coefficient 0.91), and the boundary recognition accuracy reaches ±2mm. The area mapping module maps this area to four heat dissipation adjustment areas numbered A3, A4, B3, and B4, of which A3 area contains 63% of the high-temperature area and is determined as the core mapping area. The final output of the target heat dissipation area boundary data contains 24 key coordinate points, accurately covering a 15cmx10cm rectangular area around the charging gun interface.

[0069] During the continuous operation of the system, the contact area identification module updates the three-dimensional temperature matrix every 30 seconds, and when a newly appearing temperature feature similar area is detected: if the area exists for more than 3 sampling periods (30 seconds) and the temperature gradient change rate exceeds 0.5°C / s, the standard model update process is automatically triggered. The update process retains 80% of the original model's basic data, and the new area data is incorporated into the model library after 3 verifications, ensuring that the system adapts to the aging and working condition changes of the charging pile.

[0070] Example 2: see Figure 3In the operation process of the charging pile, the heat source analysis module of the heat dissipation mode analysis unit receives real-time thermal imaging data from the infrared thermal imager. The thermal imager collects the temperature distribution of the surface of the charging pile shell at a frequency of 5 frames per second, generating a gray image matrix with a resolution of 256x256 pixels. The gray value of each pixel corresponds to a specific temperature, and the conversion relationship follows the pre-calibrated temperature-gray curve. When the charging pile is in a high-power charging state, the heat source analysis module starts the feature extraction process: first, the original thermal image is preprocessed, and the bilateral filtering algorithm is used to eliminate sensor noise while retaining the edge information of temperature discontinuity. Subsequently, the connected component analysis technique is used to identify the high-temperature region in the thermal image, and the gray threshold value of 180 (corresponding to 65°C) is set as the region segmentation reference to mark all the mutually connected pixel blocks that exceed the threshold value.

[0071] In the heat source feature extraction stage, morphological features are calculated for each high-temperature pixel block, the equivalent elliptical long-axis direction of the block is extracted as the main direction of heat diffusion, the standard deviation of the pixel gray values in the block is calculated as the temperature uniformity index, and the perimeter-to-area ratio of the block is calculated as the edge sharpness coefficient. For the typical hot spot appearing in the charging gun interface region, the characteristic data shows that the long-axis direction is consistent with the charging gun insertion angle (about 15° inclination), the temperature uniformity index is in the interval of 0.18-0.25, and the edge sharpness coefficient maintains at about 0.35. These characteristic parameters are stored in the heat feature database in a structured data format.

[0072] When the dominant region determination module is working, the material parameter table of the heat dissipation adjustment region is called, which records the thermal conductivity of each heat dissipation adjustment region shell material, such as aluminum alloy (thermal conductivity 117 W / (m·K)) for the charging gun interface region and engineering plastic (thermal conductivity 0.2 W / (m·K)) for the control cabinet region. When calculating the heat flux density value of each heat dissipation adjustment region, the real-time temperature gradient data is combined: 9 temperature measurement points are arranged in the A3 heat dissipation adjustment region (charging gun interface core region), and the X / Y bidirectional temperature gradient is calculated according to the point spacing and temperature difference. The heat flux density calculation uses the Fourier law, taking the product of the thermal conductivity of aluminum alloy and the temperature gradient. When it is detected that the heat flux density of the southeast quadrant of A3 region continuously exceeds 8kW / m² for more than 10 seconds, this region is marked as the dominant heat dissipation region, and the marking signal triggers the supercharging instruction of the two-phase cold plate module.

[0073] The auxiliary area determination module sets a virtual detection path along the main direction of heat diffusion based on the heat conduction gradient analysis, starting from the boundary of the dominant heat dissipation area. In the B3 heat dissipation adjustment area (adjacent to the A3 area), three radial detection lines are arranged, and each detection line is provided with 10 equally spaced virtual temperature measurement points. The theoretical temperature values of each point are calculated through the heat conduction equation, and the heat conduction efficiency is obtained by comparing with the actual measured values. When the heat conduction gradient (temperature attenuation rate per unit distance) of the west region of the B3 area remains in the range of 0.15-0.2℃ / cm, and the temperature difference with the dominant heat dissipation area is less than 12℃, the region is marked as an auxiliary heat dissipation area. In the marking process, the region growing algorithm is used: taking the boundary pixels of the dominant heat dissipation area as seed points, expanding to the adjacent heat dissipation adjustment area, and stopping growing when the heat flux density of the expanded region decreases to 30%-60% of the dominant area.

[0074] In the actual operation case in the summer high temperature environment, after the charging pile continuously performs three fast charging operations: the heat source analysis module detects that an elliptical heat spot (maximum temperature 71.3℃) of 98x65 pixels appears in the charging gun interface area, and the long axis direction coincides with the charging gun axis. The dominant area determination module calculates the heat flux density distribution of the A3 area, finds that a peak area of 12.3kW / m² appears in the southeast quadrant, and immediately marks the quadrant as the dominant heat dissipation area. The auxiliary area determination module traces the heat diffusion path and finds that heat is conducted along the metal components of the charging gun support to the B3 area, forming a transition area with a gradient of 0.18℃ / cm in the west side of the B3 area, and immediately marking the area as an auxiliary heat dissipation area. The system driving unit responds to the marking result: starting the two-phase cold plate secondary refrigeration mode for the A3 area, and starting the directional atomization cooling of the spraying module for the B3 area.

[0075] During the continuous monitoring of the system, the determination of the auxiliary heat dissipation area has dynamic adjustment characteristics. When the charging pile ends the charging operation, the temperature of the dominant heat dissipation area decreases at a rate of 1.2℃ / s, and the auxiliary area determination module updates the heat conduction gradient data in real time. If the temperature drop rate of the auxiliary heat dissipation area lags behind the dominant area by more than 15%, the system automatically increases the spraying intensity of the area; otherwise, when the temperature of the auxiliary area drops to the threshold of ambient temperature +5℃, the auxiliary marking is automatically removed. This dynamic marking mechanism effectively avoids the waste of energy caused by excessive cooling in the intermittent working mode of the charging pile.

[0076] For special working conditions, when multiple discrete hot spots are detected: the system runs multiple decision threads in parallel. In a certain double-gun simultaneous charging working condition, the heat source analysis module identifies two independent hot spots (located in A3 and C2 regions, respectively). The dominant region determination module establishes an independent heat flux density distribution map for each hot spot, marking two dominant heat dissipation regions. The auxiliary region determination module calculates the heat diffusion path of each dominant region, and when it detects that the two auxiliary regions overlap in D1 region, it starts the conflict resolution protocol - recalculates the integrated thermal gradient of the overlapping region according to the heat flux superposition principle, marks the region as a double auxiliary heat dissipation region, and triggers the cooperative heat dissipation strategy.

[0077] Example 3: refer to Figure 4 The environmental heat dissipation region determination unit establishes a spatial position relationship map of the heat dissipation adjustment regions during system operation. This map uses a graph structure, where the vertices correspond to the geometric center coordinates of the 36 heat dissipation adjustment regions, and the edges represent the connectivity relationship between adjacent regions. Each vertex attribute includes region number, three-dimensional coordinates, and material code; each edge attribute includes connectivity type (directly adjacent / indirectly adjacent), heat transfer coefficient, and distance weight. The Delaunay triangulation algorithm is used to ensure the accuracy of the spatial relationship during graph construction, forming a topological network containing 78 connected edges.

[0078] When the density peak clustering algorithm identifies the core influence area of the dominant heat dissipation region, it first calculates the local density index of each heat dissipation adjustment region. For any region , its local density is defined by the following formula:

[0079]

[0080] Where: represents the Euclidean distance between region and , and is the cutoff distance parameter (taking 1.5 times the average inter-regional distance). The relative distance of each region is also calculated, which is the minimum distance from the region to any higher density region. Regions with a local density multiplied by the relative distance exceeding a certain threshold are selected as seed points of the core influence area. In actual operation of the charging pile, when A3 region is marked as the dominant heat dissipation region, the algorithm calculates its local density as 3.28 and the relative distance as 2.1 (normalized value), which is significantly higher than other regions, so it is determined as the core seed point.

[0081] In the region growing phase, the seed point is expanded along the edges of the spatial position relationship map. The expansion condition considers the heat transfer efficiency factor The factor is determined by the material combination between regions: 0.8 for metal-metal connection, 0.3 for metal-plastic connection, and 0.1 for plastic-plastic connection. The growth stops when the cumulative heat transfer efficiency product of the extended path is lower than 0.15. The final generated core influence region contains 6-8 heat dissipation regulation zones, forming a heat influence cluster centered on the dominant heat dissipation zone.

[0082] The heat diffusion attenuation model is used to determine the range of the environmental heat dissipation zone. The unsteady heat conduction equation is established:

[0083]

[0084] where: is the temperature distribution function, is the time variable, is the material thermal diffusion coefficient, is the surface heat dissipation coefficient, is the ambient temperature. The equation is solved by finite difference method to predict the temperature field evolution in the next 180 seconds. In the non-target heat dissipation region, the region with a temperature rise rate prediction value exceeding 0.4℃ / min is selected as the environmental heat dissipation zone candidate range.

[0085] The parameter calculation module of the heat dissipation weight construction unit obtains the thermal load parameters of each heat dissipation regulation zone in real time. The thermal load parameters are determined by the current temperature , the rated temperature , and the heat capacity : The heat dissipation efficiency parameter considers the surface area of the heat sink , the surface emissivity , and the cooling medium flow rate : where and are weight coefficients adjusted according to the type of heat sink.

[0086] The difference calculation module uses a fuzzy logic system to process the parameter differences between adjacent regions. The system inputs are the thermal load parameter difference and the heat dissipation efficiency parameter difference , and the output is the heat dissipation regulation difference . In the fuzzy stage, the is divided into "low", "medium", and "high" fuzzy sets, with the division threshold being [200, 500] J / ℃; is divided into "small", "medium", and "large" fuzzy sets, with the division threshold being [0.3, 0.6]. The fuzzy rule base contains 9 rules, for example: "if is high and is large, then "great". De-fuzzification adopts the barycentric method to calculate the accurate output value, and finally obtains the normalized difference degree in the range of 0-1.

[0087] In the actual running example, when the charging pile performs fast charging: the environmental heat dissipation area determination unit detects that the temperature rising rate of the B2 area (close to the charging gun interface but not the target area) is 0.47 ℃ / min, and it is included in the environmental heat dissipation area. The parameter calculation module measures that the thermal load parameter of the B2 area is 385 J / ℃, the thermal load parameter of the adjacent A3 area is 820 J / ℃, and the heat dissipation efficiency parameters are 0.55 and 0.72, respectively. The difference degree calculation module calculates that J / ℃ (moderate to high), (smaller), according to the fuzzy rule, the difference degree The difference degree value is added to the corresponding edge of the heat dissipation weight graph, which affects the generation of the subsequent heat dissipation sequence. The system updates the determination result of the environmental heat dissipation area every 5 minutes. When the temperature of the dominant heat dissipation area decreases or the charging power changes, the core influence area range is adjusted accordingly, and the division of the environmental heat dissipation area is dynamically updated. This dynamic adjustment mechanism enables the system to adapt to the heat dissipation needs of the charging pile under different working states.

[0088] Example 4: refer to Figure 5 , the heat dissipation sequence generation unit receives the heat dissipation weight graph and converts it into a 36x36 adjacency matrix. The row and column indices of the matrix correspond to the heat dissipation adjustment area numbers, and the matrix element values store the heat dissipation adjustment difference degrees between the areas. For non-adjacent areas, the matrix element values are set to infinity. During the conversion process, sparse matrix storage technology is used to retain only 128 valid connection relationships, reducing the computational complexity. When solving the optimal path sequence based on the Hungarian algorithm, a cost matrix is first constructed. Take the reciprocal of each element of the adjacency matrix, so that the smaller the difference degree, the smaller the cost. The algorithm performs row reduction operation: subtract the minimum value of each row from the elements of the row, so that there is at least one zero element in each row. Then perform column reduction operation: subtract the minimum value of each column from the elements of the column, so that there is at least one zero element in each column. Cover all zero elements by drawing lines, and when the number of lines drawn is equal to the order of the matrix, the optimal solution is found. In actual operation, this algorithm can complete the solution of a 36-order matrix within 200 milliseconds, and output the optimal access sequence containing 36 areas.

[0089] Three types of constraints are considered in the system: minimum running time constraint (each cold plate module runs at least 30 seconds), power ramp constraint (the power change of adjacent regions does not exceed 40% of the rated value), and device recovery time constraint (the interval between two cooling operations in the same region is at least 60 seconds). The optimization process uses a constraint propagation algorithm, which first detects the node pairs that violate the constraints in the initial sequence, and then adjusts them through node exchange and insertion of null operations. In one optimization process, it was found that the power difference between region No. 12 and region No. 13 in the sequence reached 52%, violating the power ramp constraint. By inserting a null operation after region No. 12, the power change was reduced to 38%.

[0090] The timing control module of the drive execution unit parses the optimized cooling regulation sequence, which is stored in XML format and contains parameters such as the start timestamp, duration, refrigeration power level, and spray intensity level of each cooling regulation region. The module generates an execution schedule accurate to the millisecond based on the system clock and triggers the cooling operations of each region through a hardware timer. The power prediction module uses a long short-term memory network model to process historical cooling power data. The network input is the temperature, power, and environmental humidity time series of the past 30 minutes (sampling interval of 10 seconds), and the output is the power prediction value for the next 5 minutes. The network structure contains 3 hidden layers, each with 64 neurons, and uses a tanh activation function. The training data comes from the operation records of the charging pile for three months, including different seasons and different charging power cooling power patterns. When the two-phase cold plate module needs to trigger phase change, the output phase change trigger parameters include: refrigerant flow set value, evaporator pressure target value, and superheat control range; the spray module's flow control parameters include: water pump speed, nozzle opening degree, and atomization particle size distribution.

[0091] The collaborative driving module executes control instructions based on prediction parameters. The two-phase cold plate module uses a PID controller to adjust the refrigerant flow rate, aiming to control the evaporator superheat within the range of 4-6°C. The spray module controls the electromagnetic valve opening and closing frequency through pulse width modulation, maintaining the spray intensity within the range of 0.8-1.2 L / min·m². The module realizes synchronous control of two cooling modes: when a sharp temperature rise is detected in a region, the cold plate module starts phase change cooling first, and 300 milliseconds later the spray module starts auxiliary cooling; when the temperature tends to be stable, the spray module gradually reduces the intensity, and the cold plate module maintains the basic refrigeration power. In one actual operation data record, refer to Table 1.

[0092] Table 1: Cooling sequence execution record

[0093] During execution, the timing control module monitors the completion status of each node, and when the cooling effect of a certain area does not meet the expected target (temperature drop rate is less than 70% of the set value), the module automatically extends the duration of that area by up to 10 seconds and adjusts the timestamps of the subsequent sequence. At the same time, the power prediction module updates the prediction parameters every 30 seconds, adjusting the refrigerant flow and spray intensity according to real-time temperature changes. This dynamic adjustment mechanism ensures that the cooling operation always matches the actual heat load. The cooperative driving module also handles special conditions: when the ambient humidity exceeds 85%, it automatically reduces the spray intensity by 20% to prevent dew formation; when an abnormal refrigerant pressure is detected, it starts the safety mode, limiting the cold plate power to within 60% of the rated value. All control parameters and operating states are written to the system log in real time for subsequent analysis and optimization.

[0094] In Example 5, the abnormal cooling detection unit continuously monitors the temperature change rate of the cooling treatment set, collecting data through 144 temperature sensors arranged in 36 cooling adjustment areas. The sensors collect temperature values every 4 seconds, and the system calculates the instantaneous temperature change rate of each cooling adjustment area as the difference between the current temperature value and the temperature value 4 seconds ago divided by the time interval. These change rate data are stored in time series, with each cooling adjustment area independently recording the change rate data of the last 300 sampling points, forming a circular buffer with a length of 300. When the temperature change rate of a certain cooling adjustment area exceeds the set threshold (positive threshold + 0.8°C / s, negative threshold - 1.2°C / s) for 3 consecutive sampling points, the system triggers the abnormal detection process.

[0095] When the pattern matching unit constructs the time series feature vector of the temperature change rate, it extracts the data of the last 30 sampling points from the circular buffer. The feature vector contains 10 dimensions: the first 5 dimensions are statistical features of the change rate (mean, variance, skewness, kurtosis, range), and the last 5 dimensions are time domain features (zero-crossing rate, autocorrelation coefficient, trend slope, fluctuation frequency, number of abrupt points). After normalization, these features form a standardized 30-dimensional feature vector. The system maintains an abnormal cooling pattern library containing 12 pre-defined abnormal pattern templates, each corresponding to a typical cooling system failure such as blocked heat sink, refrigerant leakage, clogged spray nozzle, sensor failure, etc.

[0096] The dynamic time warping algorithm is used to calculate the similarity between the real-time feature vector and the abnormal pattern template. The algorithm first constructs a cumulative distance matrix, with rows corresponding to time points of the real-time feature vector and columns corresponding to time points of the template feature vector. By finding an optimal bending path that minimizes the cumulative distance, the path allows for non-linear stretching of the time axis. The similarity score is calculated based on the final cumulative distance, with a score range of 0-1, where a higher value indicates greater similarity. The system sets a similarity threshold of 0.75, and when the similarity of a certain template exceeds this threshold, it is determined to be a successful match.

[0097] When determining the abnormal pattern type according to the similarity ranking result, the system selects the top 3 highest scoring templates for comprehensive judgment. If the score of the highest scoring template exceeds the second highest scoring template by more than 0.15, the abnormal type corresponding to the highest scoring template is directly adopted; if the score difference between the top three templates is less than 0.1, a secondary discrimination mechanism is started, historical fault records and current operating environment parameters are considered, and the final abnormal type is determined through weighted voting.

[0098] The parameter correction unit updates the edge weight values of the heat dissipation weight graph when the matching is successful. The update process is based on the influence factor of the abnormal pattern. Each type of abnormal pattern corresponds to an influence coefficient matrix, which defines the influence degree of this type of abnormality on the heat transfer efficiency between each type of heat dissipation regulation area. For example, the influence coefficient of the radiator blockage abnormality is 0.6-0.8, indicating that the heat transfer efficiency is reduced by 20%-40%; while the influence coefficient of the sensor failure abnormality is 1.2-1.5, indicating that the heat dissipation regulation strength needs to be enhanced. The system selects the corresponding influence coefficient matrix according to the determined abnormal type, and adjusts the weight values of all edges in the heat dissipation weight graph by weighting.

[0099] In an actual running example, the system detects that the temperature change rate of the B3 heat dissipation regulation area is abnormal: the change rate of the last 5 sampling points remains between +0.9°C / s to +1.1°C / s, which is significantly higher than the positive threshold. The pattern matching unit extracts the feature vector of this area and calculates the similarity with the abnormal pattern library. The dynamic time warping algorithm calculation result shows that the similarity with the "radiator local blockage" template is 0.83, the similarity with the "insufficient refrigerant flow" template is 0.71, and the similarity with the "environmental temperature mutation" template is 0.62. According to the score difference, the system determines that it is a radiator local blockage abnormality.

[0100] The parameter correction unit calls the influence coefficient matrix corresponding to the radiator blockage, which defines the heat transfer influence coefficient of the blocked area on the adjacent area as 0.7 and the influence coefficient of the area separated by one area as 0.9. The system updates the weight values of all edges connected to the B3 area in the heat dissipation weight graph: the edge weight between B3 and A3 is multiplied by 0.7, the edge weight between B3 and C3 is multiplied by 0.7, the edge weight between B3 and B2 is multiplied by 0.7, and the edge weight between B3 and B4 is multiplied by 0.7. The edge weights of indirectly connected areas are also updated, such as the edge weight between A3 and C3 is multiplied by 0.9. These adjustments make the heat dissipation sequence generation unit preferentially process the B3 area and its surrounding areas in subsequent calculations.

[0101] The system continuously monitors the abnormal handling effect, and re-evaluates the abnormal state every 2 minutes. When the temperature change rate of B3 area returns to the normal range (-0.3℃ / s to +0.4℃ / s) and maintains for 1 minute, the abnormal state is automatically released, and the edge weight of the heat dissipation weight map is gradually restored to the normal value. The recovery process adopts gradual adjustment, and adjusts 20% of the weight difference every 30 seconds to avoid system parameter mutation leading to unstable operation. All abnormal events and processing processes are recorded in the system log, which is used to optimize the abnormal mode library and influence coefficient matrix.

[0102] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0103] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, alternatives, and variations can be made in the embodiments without departing from the spirit and scope of the present application as defined by the appended claims and their equivalents.

Claims

1. A smart heat dissipation system for charging piles based on a two-phase cold plate and a spray system, characterized in that, The system includes: The heat dissipation area division unit is used to divide the interior of the charging pile into multiple heat dissipation adjustment zones; The data acquisition unit is used to collect real-time temperature distribution data of the contact area on the surface of the charging pile casing. The heat dissipation mode analysis unit is used to identify the target heat dissipation area based on the temperature distribution data of the contact area, and to divide the heat dissipation adjustment area covered by the target heat dissipation area into the main heat dissipation area and the auxiliary heat dissipation area. An environmental heat dissipation zone determination unit is used to determine an environmental heat dissipation zone in a heat dissipation adjustment zone not covered by the target heat dissipation zone. The main heat dissipation zone, the auxiliary heat dissipation zone and the environmental heat dissipation zone constitute a heat dissipation treatment set. A heat dissipation weight construction unit is used to calculate the heat dissipation adjustment difference between any two adjacent heat dissipation adjustment zones in the heat dissipation treatment set, and construct a heat dissipation weight graph with the heat dissipation adjustment zone as the vertex and the heat dissipation adjustment difference as the edge. A heat dissipation sequence generation unit is used to generate a heat dissipation adjustment sequence based on the heat dissipation weight map; The drive execution unit is used to drive the two-phase cold plate module and the spray module to perform heat dissipation operations according to the heat dissipation adjustment sequence.

2. The intelligent heat dissipation system for charging piles based on two-phase cold plates and spray system as described in claim 1, characterized in that, The heat dissipation mode analysis unit includes: The contact area identification module is used to build a standard contact area model based on historical temperature data; The area matching module is used to match the real-time collected temperature distribution data of the contact area with the standard contact area model to identify the boundary of the target heat dissipation area under the current operating state of the charging pile. The region mapping module is used to map the boundary of the target heat dissipation area to the spatial coordinates of the heat dissipation adjustment area.

3. The intelligent heat dissipation system for charging piles based on two-phase cold plates and spray system according to claim 2, characterized in that, The contact area identification module is specifically used for: Construct a three-dimensional temperature distribution matrix on the surface of the charging pile casing; The K-nearest neighbor algorithm is used to perform cluster analysis on the three-dimensional temperature distribution matrix to generate a set of regions with similar temperature characteristics; Effective contact areas are selected from the set of temperature-characteristic similar regions based on the temperature change gradient.

4. The intelligent heat dissipation system for charging piles based on two-phase cold plates and spray system according to claim 1, characterized in that, The heat dissipation mode analysis unit also includes: The heat source analysis module is used to extract thermal imaging feature data within the target heat dissipation area; The dominant region determination module marks the heat dissipation adjustment area corresponding to the thermal imaging feature data as the dominant heat dissipation area based on the heat flux density threshold. The auxiliary area determination module marks the heat dissipation adjustment area adjacent to the main heat dissipation area as the auxiliary heat dissipation area based on the heat conduction gradient.

5. The intelligent heat dissipation system for charging piles based on two-phase cold plates and spray system according to claim 1, characterized in that, The environmental heat dissipation zone determination unit is specifically used for: Establish a spatial relationship map of the heat dissipation regulation zones; Density peak clustering algorithm is used to identify the core influence area of ​​the dominant heat dissipation zone; The range of the environmental heat dissipation zone is determined in the non-target heat dissipation zone based on the thermal diffusion attenuation model.

6. The intelligent heat dissipation system for charging piles based on two-phase cold plates and spray system according to claim 1, characterized in that, The heat dissipation weight construction unit includes: The parameter calculation module is used to obtain the heat load parameters and heat dissipation efficiency parameters of each heat dissipation adjustment zone; The difference calculation module uses a fuzzy logic algorithm to process the difference between the heat load parameters and the heat dissipation efficiency parameters of adjacent heat dissipation adjustment zones, and outputs the difference in heat dissipation adjustment.

7. The intelligent heat dissipation system for charging piles based on two-phase cold plates and spray system according to claim 6, characterized in that, The heat dissipation sequence generation unit is specifically used for: Convert the heat dissipation weight graph into an adjacency matrix; The optimal path sequence of the adjacency matrix is ​​obtained based on the Hungarian algorithm; The optimal path sequence is optimized based on the working state constraints of the heat dissipation adjustment zone.

8. The intelligent heat dissipation system for charging piles based on two-phase cold plates and spray system according to claim 1, characterized in that, The drive execution unit includes: The timing control module is used to parse the execution node parameters in the heat dissipation adjustment sequence; The power prediction module uses a long short-term memory network model to process historical heat dissipation power data and generate phase change triggering parameters for the two-phase cold plate module and flow control parameters for the spray module. The collaborative drive module synchronously adjusts the refrigerant flow rate and spray intensity based on the phase change trigger parameters and flow control parameters.

9. The intelligent heat dissipation system for charging piles based on two-phase cold plates and spray system according to claim 1, characterized in that, The system also includes: An abnormal heat dissipation detection unit is used to monitor the temperature change rate of the heat dissipation processing assembly; The pattern matching unit compares the real-time temperature change rate curve with a preset abnormal heat dissipation pattern library; The parameter correction unit updates the edge weight values ​​of the heat dissipation weight graph when the matching degree exceeds a set threshold.

10. The intelligent heat dissipation system for charging piles based on two-phase cold plates and spray system according to claim 9, characterized in that, The pattern matching unit is specifically used for: Construct a time-series feature vector of the temperature change rate; The similarity between the time series feature vector and the abnormal heat dissipation pattern template is calculated using a dynamic time warping algorithm. The abnormal heat dissipation pattern type with the highest matching degree is determined based on the similarity ranking results.

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