A charging pile immersion liquid cooling heat dissipation system and control method

Abstract

This invention relates to the field of charging pile heat dissipation technology, and discloses a charging pile immersion liquid cooling system and control method. The system includes a coolant tank, a multi-dimensional heat exchange monitoring unit, a dynamic load zoning unit, and a heat dissipation efficiency decision unit. The coolant tank is used to completely immerse the charging pile power module, achieving rapid and uniform heat transfer. The multi-dimensional heat exchange monitoring unit collects real-time data on the coolant flow distribution and heat exchange characteristics of the coolant tank, enabling comprehensive perception of key parameters in the heat dissipation process. The dynamic load zoning unit divides the power load range according to the charging pile's output power value and correlates it with the heat exchange characteristics of the coolant tank, ensuring a close integration of the heat dissipation system with the actual load. The heat dissipation efficiency decision unit generates heat dissipation control commands based on the mapping relationship between the power load range and the heat exchange characteristics, achieving dynamic adjustment of the heat dissipation intensity. This system effectively ensures the stable operation of the charging pile and reduces energy consumption and maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of heat dissipation technology for charging piles, specifically to a liquid cooling system and control method for immersion in a charging pile. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the operational stability and heat dissipation efficiency of charging piles, as core supporting facilities, are becoming increasingly intertwined. Currently, charging pile power modules in the market are constantly upgrading towards higher power density, meeting the demands of high-current fast charging while also facing severe heat dissipation challenges. Traditional air-cooling methods rely on air as the heat exchange medium. However, due to the low thermal conductivity of air, heat is difficult to dissipate quickly when the power module is running under continuous high load, easily leading to temperature accumulation inside the module. This not only causes fluctuations in charging efficiency but may also accelerate the aging of electronic components and shorten the overall lifespan of the equipment.

[0003] While some charging stations have adopted liquid cooling technology, most employ contact-type liquid cooling structures, where the coolant exchanges heat with the surface of the power module through pipes. This structure has significant limitations: firstly, the contact area between the coolant and the power module is limited, and the uneven distribution of liquid flow is easily affected by the pipe layout, leading to untimely heat dissipation in localized areas and the formation of hot spots; secondly, existing liquid cooling systems lack the ability to dynamically adapt to load changes, typically operating with fixed heat dissipation parameters. When the output power of the charging station fluctuates due to changes in charging demand, the cooling system cannot adjust its heat dissipation intensity in time, either resulting in energy waste at low power or failing to meet heat dissipation requirements at high power, thus affecting the operational stability and reliability of the charging station.

[0004] Traditional cooling systems rely on limited monitoring of the heat exchange process, often only monitoring coolant temperature or module surface temperature. They lack real-time sensing of key parameters such as fluid flow distribution and heat exchange efficiency, making it difficult to accurately assess the system's operational status and detect potential heat dissipation problems early. Once a heat dissipation failure occurs, system shutdown for repairs is often necessary, impacting user experience and increasing maintenance costs. These issues are particularly pronounced in high-power charging pile applications, becoming a significant factor hindering the development of charging piles towards higher power and more stable operation. Summary of the Invention

[0005] The purpose of this invention is to provide a charging pile immersion liquid cooling system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a charging pile immersion liquid cooling heat dissipation system, the system comprising:

[0007] Coolant tank for completely immersing the charging pile power module;

[0008] A multi-dimensional heat exchange monitoring unit collects real-time data on the liquid flow distribution and heat exchange characteristics of the coolant tank;

[0009] The dynamic load partitioning unit divides the power load range according to the output power value of the charging pile and associates it with the heat exchange characteristics of the coolant tank.

[0010] The heat dissipation performance decision unit generates heat dissipation control commands based on the mapping relationship between the power load range and the heat exchange characteristics.

[0011] Preferably, the multi-dimensional heat exchange monitoring unit performs the following:

[0012] The physical properties of the coolant are obtained, including the specific heat capacity threshold. A heat capacity accumulation model is established by measuring the coolant density, thermal conductivity and flow rate, and the coolant temperature rise curve is calculated.

[0013] Set standard thermal load test parameters to subject the charging pile power module to stepped power loading over a duration and record the liquid temperature fluctuation frequency;

[0014] The specific heat capacity threshold and liquid temperature fluctuation frequency are compared with preset thresholds. If either result exceeds the preset threshold, the dynamic load partitioning unit is triggered. The preset threshold is determined in advance based on the coolant characteristics and system design. The comparison process is a continuous monitoring activity.

[0015] Preferably, the dynamic load partitioning unit performs:

[0016] The specific heat capacity threshold and liquid temperature fluctuation frequency are normalized, and the heat exchange intensity coefficient is generated by the arithmetic mean method.

[0017] The pressure drop gradient of the liquid flow is detected by a micro-differential pressure sensor installed in the flow channel of the coolant tank, and the effective cooling flux is generated by combining it with the fluid dynamics turbulence determination model.

[0018] Preferably, the system further includes:

[0019] The heat dissipation attenuation quantification unit standardizes the heat exchange intensity coefficient and the effective cooling flux, and inputs them into the logical decision tree model to generate region selection factors.

[0020] The power module of the charging pile is topologically segmented to obtain each heat dissipation sub-region;

[0021] Based on the heat conduction path length and historical thermal failure frequency of each heat dissipation sub-region, calculate the heat dissipation demand index of the region.

[0022] By calling historical operating data, the ratio of the number of thermal failures to the operating time of each heat dissipation sub-region within the reference cycle is calculated to generate the regional degradation rate.

[0023] Preferably, the heat dissipation attenuation quantization unit further performs:

[0024] The regional heat dissipation demand index and the regional degradation rate are standardized and input into the risk probability model to generate regional activity.

[0025] Sort by region activity in ascending order, and select a set of target heat dissipation sub-regions whose boundary values ​​are less than or equal to the region screening factors by comparing them with preset grouping boundary values.

[0026] Preferably, the multi-dimensional heat exchange monitoring unit further includes:

[0027] An embedded temperature sensor array and an infrared flow field imager are used to monitor the temperature rise rate and flow velocity changes of the target heat dissipation sub-region in real time.

[0028] Collect temperature sampling sequences per unit time, and calculate temperature fluctuations based on moving average and standard deviation analysis models;

[0029] The flow velocity change per unit time is detected, and the difference between the flow velocity at the end time and the flow velocity at the beginning time and the time series ratio are calculated to generate a flow field stability index.

[0030] After standardizing the temperature fluctuation and flow field stability index, the data are input into a linear co-operational model to generate a heat dissipation attenuation influencing factor.

[0031] Preferably, the heat dissipation performance decision unit performs the following:

[0032] Compare the heat dissipation attenuation impact factor with the preset attenuation threshold. If it is greater than the threshold, mark the corresponding target heat dissipation sub-region as a high attenuation region.

[0033] When the charging pile triggers a high-power load, monitor the duration of over-temperature in the high-attenuation region and record the time stamp difference from the moment of power loading to the temperature recovery to a steady state.

[0034] Preferably, the heat dissipation performance decision unit further performs:

[0035] The duration of overheating in the high-attenuation region is standardized in conjunction with the heat dissipation attenuation influencing factor, and then input into the S-shaped function fusion model to generate the regional heat dissipation score.

[0036] A constant thermal load is applied to each high attenuation region, and the thermal resistance is measured.

[0037] The maximum allowable temperature rise is set based on the phase change characteristics of the coolant, and the critical heat flux value of each high attenuation region is calculated.

[0038] Preferably, the heat dissipation performance decision unit ultimately executes:

[0039] After standardizing the thermal resistance and critical heat flux values, they are input into the entropy weight decision model to generate regional weight coefficients.

[0040] By combining the regional heat dissipation scores and regional weighting coefficients of each high-attenuation region, a heat dissipation system performance score is generated through linear weighting.

[0041] Preferably, the present invention further includes a method for controlling the immersion liquid cooling heat dissipation of a charging pile, applied to the aforementioned immersion liquid cooling heat dissipation system for the charging pile, the method comprising:

[0042] Data on the liquid flow distribution and heat exchange characteristics of the coolant tank are obtained through a multi-dimensional heat exchange monitoring unit.

[0043] The dynamic load partitioning unit divides the power load range based on the output power value of the charging pile, associates it with the heat exchange characteristics, and generates a region screening factor.

[0044] The heat dissipation attenuation quantification unit selects the target heat dissipation sub-region based on the region screening factor and calculates the heat dissipation attenuation influence factor.

[0045] The heat dissipation performance decision unit marks high decay areas, monitors the duration of over-temperature, and generates a regional heat dissipation score.

[0046] The regional weighting coefficient is calculated by combining the thermal resistance value and the critical heat flux value, and the weighted output heat dissipation system performance score is then calculated.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] This charging pile immersion liquid cooling system completely submerges the charging pile's power module in a coolant tank. Compared to traditional air cooling and contact liquid cooling methods, this significantly increases the heat exchange contact area, allowing the heat generated by the power module to be quickly and evenly transferred to the coolant. This prevents the formation of localized hot spots and effectively improves the uniformity and timeliness of heat dissipation. The complete immersion design also reduces the contact between air and the power module surface, lowering the risk of oxidation and corrosion, providing a certain degree of protection for the power module, and extending its service life.

[0049] The multi-dimensional heat exchange monitoring unit can collect real-time data on the coolant flow distribution and heat exchange characteristics of the coolant tank, breaking through the limitations of traditional heat dissipation systems that only monitor temperature parameters. This enables comprehensive perception of key parameters in the heat dissipation process. By acquiring coolant flow distribution data, uneven coolant flow can be detected promptly, providing a basis for subsequent adjustments. Monitoring heat exchange characteristics accurately reflects the actual operating efficiency of the heat dissipation system, helping maintenance personnel clearly understand the system status, facilitating early identification of potential heat dissipation problems, reducing downtime for maintenance due to heat dissipation failures, lowering maintenance costs, and providing rich practical operational data support for the optimization and improvement of the heat dissipation system.

[0050] The dynamic load zoning unit divides the power load range based on the charging pile's output power value and correlates it with the heat exchange characteristics of the coolant tank, enabling the cooling system to closely integrate with the actual load conditions of the charging pile. Different power load ranges correspond to different amounts of heat generation. By associating the load range with the heat exchange characteristics, the required heat dissipation intensity under different loads can be clearly defined, avoiding energy waste or insufficient heat dissipation problems caused by traditional systems using fixed heat dissipation parameters. When the charging pile's output power changes, the system can quickly identify its current load range, providing a precise basis for generating subsequent heat dissipation control commands and achieving dynamic response to load changes.

[0051] The heat dissipation efficiency decision unit generates heat dissipation control commands based on the mapping relationship between power load range and heat exchange characteristics, enabling the heat dissipation system to dynamically adjust. In low-power load ranges, lower-intensity heat dissipation control commands are generated based on the associated heat exchange characteristics, reducing energy consumption; in high-power load ranges, higher-intensity heat dissipation control commands are generated to ensure timely heat dissipation and meet cooling requirements. This dynamic control method based on actual load and heat exchange status not only ensures the heat dissipation needs of the charging pile under different operating conditions but also achieves rational energy utilization, improving the overall system's operational economy and reliability. Attached Figure Description

[0052] Figure 1 This is a timing diagram of the immersion liquid cooling system for the charging pile described in this invention.

[0053] Figure 2 A flowchart illustrating the workflow of the multi-dimensional heat exchange monitoring unit;

[0054] Figure 3 A flowchart illustrating the workflow of a dynamic load partitioning unit;

[0055] Figure 4 A flowchart of the heat dissipation attenuation quantization unit. Detailed Implementation

[0056] 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.

[0057] Please see Figure 1 The present invention provides a charging pile immersion liquid cooling heat dissipation system and control method, the system comprising: a coolant tank, a multi-dimensional heat exchange monitoring unit, a dynamic load partitioning unit, and a heat dissipation performance decision unit.

[0058] The coolant tank contains coolant with specific physical properties, completely immersing the charging pile's power module within it. A multi-dimensional heat exchange monitoring unit collects real-time data on the coolant flow distribution and heat exchange characteristics within the tank, including temperature, flow rate, and pressure parameters. A dynamic load zoning unit receives the charging pile's real-time output power value, divides it into different power load zones based on preset power thresholds, and dynamically maps each zone to the monitored heat exchange characteristics. Based on the established mapping relationship, a heat dissipation efficiency decision unit generates control commands for the coolant circulation pump, external heat exchanger, or valve components to adjust the heat dissipation intensity.

[0059] Example 1: See Figure 2 This document describes the specific operation procedures and data processing methods of a multi-dimensional heat exchange monitoring unit. The core function of this unit is to acquire real-time data on the liquid flow distribution and heat exchange characteristics within the coolant tank, and to provide decision-making support for the system through a series of model calculations. Upon startup, the multi-dimensional heat exchange monitoring unit first acquires the physical properties of the coolant. These parameters include the coolant's density, thermal conductivity, and dynamic viscosity, which are continuously measured by online sensors installed in the circulation pipeline. Simultaneously, the coolant flow rate is monitored in real-time using an electromagnetic flowmeter. Based on these real-time measurements, the system establishes a heat capacity accumulation model. The core of this model lies in utilizing the coolant's specific heat capacity threshold, a key parameter value pre-set based on the coolant type and concentration. By integrating flow rate, density, and specific heat capacity, the model calculates the amount of heat absorbed by the coolant flowing across the power module surface per unit time, and further derives the expected temperature rise curve of the coolant under a given heat load. This curve describes the theoretical trajectory of coolant temperature change over time under ideal conditions.

[0060] To verify the model's accuracy and obtain actual operational characteristics, the system performs a standard thermal load test. This test sets a rigorous power loading program, controlling the charging pile's power module to apply stepped power over a duration. For example, the test might start at a low power level, increasing the output power in preset steps at fixed time intervals until the rated maximum power is reached. Throughout the test, a high-precision temperature sensor records the coolant's temperature changes at an extremely high sampling frequency. This continuous temperature data is sent to a signal processing module, where a spectral analysis algorithm identifies and extracts the frequency characteristics of the coolant temperature fluctuations. These frequency characteristics reflect the periodic temperature changes during heat exchange.

[0061] After obtaining the calculated value of the specific heat capacity threshold and the measured value of the liquid temperature fluctuation frequency, the system compares these values ​​with preset safe operating thresholds. These preset thresholds are determined in advance based on coolant characteristics, system design, and safety specifications. The comparison process is a continuous monitoring activity; once any value is found to exceed its corresponding threshold range, the system immediately generates a trigger signal. This signal is sent to the dynamic load zoning unit, informing it that the current heat exchange status may be abnormal and that the corresponding zoning management mechanism needs to be activated.

[0062] Another crucial component of the multi-dimensional heat exchange monitoring unit is the embedded temperature sensor array and infrared flow field imager. The temperature sensor array employs a distributed layout, with numerous sensor nodes strategically placed on the inner surface of the coolant tank, the power module housing, and key flow channel areas. These sensors acquire temperature data at millisecond intervals, forming high spatiotemporal resolution temperature field information. By analyzing the temperature data over a continuous time series, the system calculates the temperature rise rate at each monitoring point, i.e., the amount of temperature change per unit time.

[0063] Infrared flow field imagers provide non-contact visualization and monitoring of the flow field. This device acquires spatial images of the flow velocity distribution by scanning the coolant flow field with infrared light. These image data are processed and converted into quantitative information on the flow velocity, displaying the flow pattern of the coolant in different areas of the tank. Infrared imaging can reveal phenomena such as eddies, stagnant zones, or uneven flow in the flow field. The system further processes and analyzes the acquired temperature data. After acquiring the temperature sampling sequence per unit time, a moving average algorithm is first used to smooth the raw data, eliminating random noise interference. Then, a standard deviation analysis model is applied to calculate the degree of fluctuation in the temperature sequence, obtaining a quantified temperature fluctuation index. This index reflects the stability and uniformity of temperature changes.

[0064] The system processes flow velocity monitoring data, calculating the change in flow velocity by comparing the flow velocity values ​​at different times within a unit of time. Specifically, it calculates the difference between the flow velocity at the end and the flow velocity at the beginning, divides this difference by the time interval, and obtains a time-series ratio. This ratio is defined as a flow field stability index, used to quantify the severity and regularity of flow velocity changes. The system standardizes both temperature fluctuations and the flow field stability index to eliminate the influence of differences in units and orders of magnitude. The standardized data is input into a linear co-operational model, which integrates the two indices into a comprehensive heat dissipation attenuation factor through weighting coefficients. This factor serves as an important intermediate parameter, providing a quantitative basis for subsequent heat dissipation performance decisions. The entire processing is continuous and automated, ensuring real-time and comprehensive monitoring of the system's heat exchange status.

[0065] The multi-dimensional heat exchange monitoring unit enables precise monitoring of the heat exchange process within the coolant tank. From acquiring basic physical properties to extracting features under standard test conditions, and then to the fusion analysis of multi-sensor data, this unit constructs a complete thermal state sensing system. Each processing step aims to accurately capture the thermal behavior characteristics of the system, providing a reliable data foundation for the intelligent control of the entire cooling system. This implementation demonstrates deep monitoring and intelligent analysis capabilities for the thermal management process.

[0066] Example 2: See Figure 3 This study focuses on the specific data processing flow and decision-making mechanism of the dynamic load zoning unit. The dynamic load zoning unit receives real-time data input from the upstream monitoring unit, primarily including two key parameters: specific heat capacity threshold and liquid temperature fluctuation frequency. The specific heat capacity threshold reflects the heat capacity characteristics of the coolant under current operating conditions, while the liquid temperature fluctuation frequency characterizes the dynamic stability of the system's heat exchange process. Since these two parameters have different physical meanings and units of measurement, the unit first performs data normalization. This process uses a linear transformation method to map the original data to a unified [0,1] numerical range. Specifically, the system determines the maximum and minimum value ranges of each parameter based on historical operating data, and then uses a scaling formula to transform the real-time measured values ​​into this standardized range. This process eliminates the interference of different units of measurement on subsequent analysis.

[0067] After normalization, the unit uses an arithmetic mean method to fuse the data of the two standardized parameters. This method assigns equal weights to the two parameters, calculates their simple arithmetic mean, and generates a single heat exchange intensity coefficient. This coefficient serves as a comprehensive indicator, quantifying the overall heat exchange capacity of the current system. A higher coefficient value indicates a more ideal heat exchange efficiency; a lower coefficient value suggests a potential risk of decreased heat dissipation efficiency. Simultaneously, the unit acquires fluid pressure data through a micro-differential pressure sensor array deployed within the coolant tank's internal flow channel network. These high-precision sensors are arranged at specific spatial intervals in straight pipe sections, bends, and variable cross-section areas of the flow channel, forming a distributed monitoring network for fluid pressure. The sensors measure the pressure difference between upstream and downstream points in the flow channel in real time, and the system constructs a pressure drop gradient distribution map throughout the flow channel using a spatial interpolation algorithm. This gradient map clearly shows the pressure loss variation characteristics of the coolant as it flows through different structural regions.

[0068] After acquiring the pressure drop gradient data, the unit inputs it into a fluid dynamics turbulence determination model. This model, built on classical fluid mechanics principles, primarily analyzes the intrinsic relationship between fluid flow state and pressure drop characteristics. The model first calculates dimensionless parameters characterizing the flow state based on channel geometry, coolant properties, and average velocity. By comparing these parameter values ​​with critical reference values, it determines whether the current flow regime is laminar, transitional, or fully developed turbulent. In turbulent flow, the model further analyzes the relationship between the pressure drop gradient and turbulence intensity. Based on the flow regime determination results, the model calculates the effective cooling flux index. This index differs from simple volumetric flow rate; it emphasizes the portion of coolant actually participating in effective heat exchange. In laminar flow, due to fluid stratification, the low-velocity layer near the pipe wall contributes less to heat exchange; while in turbulent flow, intense mixing allows more coolant to contact the heat-generating surface. By quantifying the scale and intensity of turbulent eddies and combining this with the energy dissipation characteristics reflected by the pressure drop gradient, the model calculates the effective coolant flux truly used for heat transport in the current flow regime. This value directly reflects the actual heat transfer capacity of the heat dissipation system.

[0069] The entire data processing flow forms a closed loop: from the normalization and fusion of multi-source heterogeneous data to generate the heat exchange intensity coefficient, to the pressure drop gradient processing based on fluid dynamics analysis to generate the effective cooling flux. These two output indicators together constitute a quantitative description of the system's heat dissipation state. The heat exchange intensity coefficient reflects the overall system performance from a thermodynamic perspective, while the effective cooling flux reveals the microscopic mechanism of heat dissipation efficiency from a fluid dynamics perspective. These two indicators are transmitted in real time to the downstream heat dissipation attenuation quantification unit, providing data support for the selection and evaluation of heat dissipation sub-regions.

[0070] In specific operational scenarios, such as when the charging pile is operating at medium power output, the monitoring unit may detect an increase in the frequency of liquid temperature fluctuations. This signal, after normalization, leads to a decrease in the heat exchange intensity coefficient. Simultaneously, the micro-differential pressure sensor detects an abnormally large increase in the pressure drop gradient in a specific flow channel region. Turbulence analysis indicates flow separation in this region, resulting in a decrease in effective cooling flux. This data linkage accurately identifies local bottleneck areas in the heat dissipation system. The entire process is fully automated, requiring no manual intervention, and achieves real-time perception and quantitative assessment of the heat dissipation system's status.

[0071] Example 3: See Figure 4This involves the operation of the heat dissipation attenuation quantification unit, which is responsible for performing refined zoning evaluation of the system. The unit receives heat exchange intensity coefficient and effective cooling flux data from the dynamic load zoning unit. These two parameters are first standardized to eliminate dimensional differences. The standardization process uses extreme value normalization to transform each parameter value to the [0,1] interval. The processed data is then input into a pre-built logical decision tree model. This model contains multiple decision nodes, each with a set threshold condition. Based on the numerical magnitude and combination characteristics of the input parameters, the model makes logical judgments along the branch paths of the tree structure, ultimately outputting a region selection factor. This factor is a dimensionless numerical value used to determine the range of heat dissipation areas that require focused monitoring.

[0072] The system simultaneously performs topology analysis on the charging pile power modules, dividing them into several independent heat dissipation sub-regions based on the physical structure layout and electrical connections of the power modules. The division is based on factors including component density, heat generation distribution, and coolant flow channel design. Each sub-region is assigned a unique identifier, and its spatial location information is recorded. For each heat dissipation sub-region, the system calculates two key indicators. The regional heat dissipation requirement indicator is calculated based on the heat conduction path length and historical thermal failure frequency. The heat conduction path length refers to the shortest heat transfer distance from the heat-generating element to the main coolant flow, which is obtained through measurement using a three-dimensional model. The historical thermal failure frequency is extracted from the system's operation and maintenance database, recording the number of times temperature exceedance events have occurred in this region in the past. These two parameters are weighted to obtain the regional heat dissipation requirement indicator, calculated using the following formula:

[0073]

[0074] in: Indicates the regional heat dissipation demand index. Indicates the length of the heat conduction path. Indicates the historical thermal failure frequency. and These are the corresponding weighting coefficients.

[0075] The regional degradation rate is obtained through statistical analysis. The system retrieves historical operating data and counts the number of thermal failures occurring in each heat dissipation sub-region within a set reference period. Dividing this number by the total operating time of the region yields the probability of thermal failure per unit time, i.e., the regional degradation rate. This indicator reflects the reliability degradation trend of the region. The heat dissipation degradation quantification unit further performs optimization and screening. The regional heat dissipation demand indicator and the regional degradation rate are standardized using the same extreme value normalization method as described above. The standardized data is input into a risk probability model, which is trained based on historical data and outputs a regional activity value between 0 and 1. The lower the value, the higher the potential risk of the region, requiring priority attention. The system sorts all heat dissipation sub-regions in ascending order of regional activity value, forming a region sequence from high risk to low risk. The sorted sequence is compared with a preset grouping boundary value. The grouping boundary value is a dynamically adjusted parameter; its initial value is set based on the system design specifications and is subsequently optimized based on actual operating data. The system selects heat dissipation sub-regions whose group boundary values ​​are less than or equal to the regional screening factor. These selected regions constitute the target heat dissipation sub-region set, which serves as the key objects for subsequent refined monitoring and management.

[0076] The entire implementation process embodies an analytical approach that moves from the overall system to the local level: first, filtering factors are generated at the system level using a logical decision tree; then, the system is divided into manageable sub-regions based on topological analysis; next, the status of each sub-region is evaluated through multi-indicator calculations; and finally, priority is given to identifying target areas requiring immediate attention through risk ranking. This hierarchical and progressive analytical method ensures that system resources are concentrated in the areas requiring the most attention, improving the efficiency and accuracy of heat dissipation management. The standardization methods used in data processing guarantee that parameters of different properties and dimensions can be compared on the same scale, while the application of risk probability models makes the decision-making process more objective and quantifiable.

[0077] Example 4: This describes the process of identifying and monitoring high-attenuation regions using a heat dissipation performance decision unit. This unit receives heat dissipation attenuation impact factor data from upstream units. This factor quantifies the degree of heat dissipation performance attenuation in each target heat dissipation sub-region. The unit internally presets an attenuation threshold, which is set based on system design parameters and historical operating data. The system compares the heat dissipation attenuation impact factor of each target heat dissipation sub-region with this threshold in real time. When the factor value of a certain region continuously exceeds the threshold and reaches a preset duration requirement, the system automatically marks that region as a high-attenuation region. The marking operation includes setting a status identifier in the system database and visually indicating it with a specific color on the monitoring interface.

[0078] When the charging pile's operating status changes, especially when a high-power load range is triggered, the system initiates a dedicated monitoring process for the high-power attenuation area. The determination of a high-power load is based on whether the charging pile's output power exceeds a preset power threshold. The monitoring process focuses on dynamic temperature response characteristics. The system collects temperature data at a millisecond-level sampling frequency using an embedded temperature sensor array deployed in the high-power attenuation area. Monitoring begins at the instant power loading, precisely marked by a synchronization signal provided by the power monitoring unit. The system records this moment as the start of the timestamp.

[0079] Temperature sensors continuously monitor regional temperature changes and transmit the data to the decision unit in real time. The unit internally sets a steady-state temperature range, determined based on historical normal operating temperature data for the area. The system continuously analyzes the incoming temperature data stream, and when it detects that a temperature value has re-entered and stabilized within the steady-state temperature range, it records this moment as the end of a timestamp. The system calculates the difference between the end and beginning of the timestamp, which is defined as the over-temperature duration. This duration directly reflects the time required for the high-temperature decay area to recover to a safe temperature state after experiencing a sudden heat load, and is a key indicator for evaluating its heat dissipation performance.

[0080] The entire monitoring process is fully automated. The system independently records the duration of over-temperature for each marked high-attenuation area. This data, along with corresponding power load levels, ambient temperature, and other operating condition information, is stored in the system database to form historical performance records. See Table 1 for an example snippet of the monitoring data recording.

[0081] Table 1: Record of Overheating Duration in High Attenuation Region

[0082]

[0083] This table records the duration of over-temperature in different high-attenuation zones under specific time points, power loads, and ambient temperature conditions. Each zone number uniquely identifies a different high-attenuation zone. The power loading timestamp precisely records the moment the power is increased to the target value. The temperature recovery steady-state timestamp records the starting point when the temperature sensor reading in this zone becomes consistently stable within the preset steady-state temperature range. The over-temperature duration column calculates and displays the number of milliseconds elapsed from power loading to temperature recovery steady-state. The trigger power column records the output power value of the charging pile that triggered this monitoring. The ambient temperature column records the ambient temperature data during the monitoring period.

[0084] The system continuously records this data, creating a performance history archive for each high-degradation area. The duration of over-temperature is a crucial basis for subsequent assessment of the area's heat dissipation performance, prediction of potential risks, and formulation of maintenance strategies. A longer over-temperature duration indicates insufficient heat dissipation capacity in that area, resulting in a lag in responding to sudden thermal loads, and may require priority maintenance or adjustment of the heat dissipation strategy. By analyzing the trend of over-temperature duration under different operating conditions, the system can further assess the rate of heat dissipation degradation.

[0085] Example 5: The process of in-depth evaluation and comprehensive system performance calculation of high-attenuation regions by a heat dissipation performance decision unit. This unit first preprocesses the recorded over-temperature duration data. A min-max normalization method is used to transform the original time values ​​to a standard numerical range. Simultaneously, the heat dissipation attenuation influence factor values ​​for the corresponding high-attenuation regions are obtained. These two parameters are input into a S-shaped function fusion model. This model uses a mathematical function form, and its output value is constrained within a fixed range. The model performs nonlinear transformation and weighted fusion on the input parameters, ultimately outputting a regional heat dissipation score between 0 and 1. The lower the score, the less ideal the heat dissipation performance of the region, indicating a higher risk.

[0086] The system performs thermal resistance characteristic tests on various high-attenuation regions. During the test, a constant and known power heat load is applied to the target region. This heat load is precisely output by controlling the conduction state of specific switching devices in the power module. Before the heat load is applied, the system records the initial steady-state temperature of the region. After the temperature reaches a new steady state, the steady-state temperature value is recorded. The difference between the two steady-state temperatures is calculated. Combined with the known heat load power value, the thermal resistance of the region is calculated based on fundamental thermodynamic principles. This thermal resistance value comprehensively reflects the overall efficiency of the heat transfer path from the heat source to the coolant, including multiple factors such as contact thermal resistance, material thermal conductivity, and convective heat transfer resistance.

[0087] Based on the physical properties of the coolant, especially its phase change characteristics, a maximum allowable temperature rise is set for the system. This value is determined based on the coolant's saturation temperature or boiling point characteristics at a specific pressure, taking into account a safety margin to prevent unintended phase changes. Based on this maximum allowable temperature rise, the measured thermal resistance, and the effective heat dissipation area of ​​the high-attenuation region, the system calculates the critical heat flux. This value represents the maximum heat flow that can be transferred per unit area of ​​heat dissipation surface while ensuring the coolant does not fail. The calculation process considers the coupling effect of heat conduction and convection heat transfer.

[0088] The heat dissipation performance decision unit performs a final comprehensive performance evaluation, standardizing both the measured thermal resistance and the calculated critical heat flux to eliminate dimensional differences. The standardized data is then input into an entropy-weighted decision model. This model, based on the principle of information entropy, analyzes the numerical distribution characteristics of each input parameter. The model automatically calculates the dispersion of each parameter; parameters with higher dispersion are assigned higher weights because they contain more differential information. Through entropy weighting, the model outputs a region weight coefficient for each high-attenuation region. This coefficient is a dimensionless value that quantifies the importance and influence of that region on the overall heat dissipation performance of the current system.

[0089] The system integrates the evaluation results of all marked high-attenuation areas. For each high-attenuation area, it obtains its regional heat dissipation score and regional weight coefficient. A linear weighted summation method is used to multiply the heat dissipation score of each area by its corresponding weight coefficient to obtain the weighted score value for that area. Then, the weighted score values ​​of all high-attenuation areas are summed. This summation result is the heat dissipation system performance score, a comprehensive quantitative indicator reflecting the current health status and performance level of the entire immersion liquid cooling system. The score result is displayed in real time on the system monitoring interface and stored in a historical database for trend analysis. This score can serve as the basis for system maintenance decisions, heat dissipation strategy adjustments, or early warning triggers. The entire evaluation process is executed automatically and periodically in the system background, ensuring continuous monitoring and quantitative evaluation of the heat dissipation system status.

[0090] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0091] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A charging pile immersion liquid cooling heat dissipation system, characterized in that, The application relates to a charging pile power module cooling system, which comprises the following: a cooling liquid tank for completely immersing the charging pile power module; a multi-dimensional heat exchange monitoring unit for collecting liquid flow distribution data and heat exchange characteristics of the cooling liquid tank in real time; the multi-dimensional heat exchange monitoring unit performs the following: obtaining cooling liquid physical property parameters, wherein the cooling liquid physical property parameters include a specific heat capacity threshold value, a heat capacity accumulation model is established by measuring the density, thermal conductivity and flow rate of the cooling liquid, and a cooling liquid temperature rise curve is calculated; setting standard heat load test parameters, so that the charging pile power module is subjected to step power loading within a time duration, and liquid temperature fluctuation frequency is recorded; comparing the specific heat capacity threshold value and the liquid temperature fluctuation frequency with preset threshold values respectively, and triggering a dynamic load partition unit if any result exceeds the preset threshold value; the preset threshold values are determined in advance based on the cooling liquid characteristics and system design, and the comparison process is a continuous monitoring activity; a dynamic load partition unit divides power load intervals according to the charging pile output power value and associates the heat exchange characteristics of the cooling liquid tank; a heat dissipation efficiency decision unit generates a heat dissipation control instruction based on the mapping relationship between the power load intervals and the heat exchange characteristics.

2. The charging pile immersion liquid cooling heat dissipation system according to claim 1, characterized in that, The dynamic load partition unit performs the following: normalizing the specific heat capacity threshold value and the liquid temperature fluctuation frequency, and generating a heat exchange intensity coefficient by using an arithmetic average method; detecting the liquid flow pressure drop gradient by using a micro pressure difference sensor arranged in the flow channel of the cooling liquid tank, and generating an effective cooling flux by combining a fluid dynamics turbulent flow determination model.

3. The charging pile immersion liquid cooling heat dissipation system according to claim 2, characterized in that, The application further comprises the following: a heat dissipation attenuation quantification unit, which standardizes the heat exchange intensity coefficient and the effective cooling flux, inputs a logical decision tree model to generate a region screening factor; topologically segmenting the charging pile power module to obtain each heat dissipation subregion; calculating a region heat dissipation demand index based on the heat conduction path length and the historical thermal failure frequency of each heat dissipation subregion; generating a region degradation rate by calling historical operation data to count the number of thermal failures and the running time ratio of each heat dissipation subregion within a reference period.

4. The charging pile immersion liquid cooling heat dissipation system according to claim 3, characterized in that, The heat dissipation attenuation quantification unit further performs the following: standardizing the region heat dissipation demand index and the region degradation rate, inputs a risk probability model to generate a region activity level; arranging the region activity levels in ascending order, comparing the preset grouping boundary value with the region screening factor, and selecting a target heat dissipation subregion set with a boundary value less than or equal to the region screening factor.

5. The charging pile immersion liquid cooling heat dissipation system according to claim 4, characterized in that, The multi-dimensional heat exchange monitoring unit further comprises the following: an embedded temperature sensor array and an infrared flow field imager for monitoring the temperature rise rate and flow rate change of the target heat dissipation subregion in real time; collecting temperature sampling sequences within a unit time, calculating a temperature fluctuation amount based on a moving average and a standard deviation analysis model; detecting the flow rate change amount within a unit time, calculating the difference and time sequence ratio between the flow rate at the end time and the flow rate at the start time, and generating a flow field stability index; standardizing the temperature fluctuation amount and the flow field stability index and inputting a linear coordination model to generate a heat dissipation attenuation influence factor.

6. The charging pile immersion liquid cooling heat dissipation system according to claim 5, characterized in that, The heat dissipation efficiency decision unit performs the following: comparing the heat dissipation attenuation influence factor with a preset attenuation threshold value, and marking the corresponding target heat dissipation subregion as a high attenuation region if the attenuation influence factor is greater than the threshold value. When the charging pile triggers a high-power load, the over-temperature duration of the high-attenuation area is monitored, and the timestamp difference from the power loading moment to the temperature recovery steady state is recorded.

7. The charging pile immersion liquid cooling heat dissipation system according to claim 6, characterized in that, The heat dissipation performance decision unit also performs: The over-temperature duration of the high-attenuation area is standardized in combination with the heat dissipation attenuation influence factor, and the regional heat dissipation score is generated by inputting the S-type function fusion model; A constant thermal load is applied to each high-attenuation area, and the thermal resistance value is measured; In combination with the maximum allowed temperature rise set by the phase change characteristics of the cooling liquid, the critical heat flux value of each high-attenuation area is calculated.

8. The charging pile immersion liquid cooling heat dissipation system according to claim 7, characterized in that, The heat dissipation performance decision unit finally performs: After standardizing the thermal resistance value and the critical heat flux value, the entropy weight decision model is inputted to generate the regional weight coefficient; The regional heat dissipation score and the regional weight coefficient of each high-attenuation area are integrated, and the heat dissipation system performance score is generated by linear weighting.

9. A charging pile immersion liquid cooling heat dissipation control method, characterized in that, Applied to the charging pile immersion liquid cooling heat dissipation system of any one of claims 1-8, comprising: Obtain the liquid flow distribution data and heat exchange characteristics of the cooling liquid tank through the multi-dimensional heat exchange monitoring unit; The dynamic load partition unit divides the power load interval based on the charging pile output power value, correlates the heat exchange characteristics, and generates the regional screening factor; The heat dissipation attenuation quantification unit selects the target heat dissipation sub-area according to the regional screening factor and calculates the heat dissipation attenuation influence factor; The heat dissipation performance decision unit marks the high-attenuation area, monitors the over-temperature duration, and generates the regional heat dissipation score; In combination with the thermal resistance value and the critical heat flux value, the regional weight coefficient is calculated, and the heat dissipation system performance score is outputted by weighting.

Images