Liquid cooling cooperative intelligent temperature control method and system based on layered PCM

Abstract

The application discloses a liquid cooling cooperative intelligent temperature control method and system based on layered PCM, relates to the technical field of liquid cooling temperature control, and comprises the following steps: S1, collecting liquid cooling temperature control data in real time, and preprocessing the liquid cooling temperature control data; S2, calculating the sensible heat absorption and latent heat absorption of the outer layer and the inner layer PCM, evaluating the thermal imbalance risk of the conductor surface, determining the current thermal state, and outputting a thermal risk warning signal; S3, evaluating the weakening degree of the thermal coupling performance caused by the cable bending state, triggering the layered PCM cooperative temperature control strategy, and executing the hierarchical regulation and control instructions of the power pump, the circulating pump and the three-way valve; and S4, evaluating the deviation between the current heat exchange capacity and the heat load demand, correcting the cooling response coefficient, and then optimizing the PCM cooperative temperature control strategy. The application solves the problem that the existing liquid cooling cable is unstable in thermal coupling between the cooling liquid pipe and the heat conductor, and is prone to causing pipe deformation, contact thermal resistance and local hot spots when frequently bent.

Description

Technical Field

[0001] This invention relates to the field of liquid cooling temperature control technology, specifically to a liquid cooling collaborative intelligent temperature control method and system based on layered PCM. Background Technology

[0002] Currently, with the rapid development of new energy vehicle charging infrastructure and high-power power electronic equipment, liquid-cooled cables are widely used in high-power transmission scenarios. The Joule heat generated by a large current passing through a conductor is proportional to the square of the current, causing a sharp increase in cable heat generation. If this heat cannot be dissipated in time, it will cause a rapid rise in cable temperature, not only accelerating the aging of insulation materials and shortening their lifespan, but also posing serious safety hazards such as melting and fire, becoming a core bottleneck restricting the improvement of charging efficiency. Therefore, the industry has proposed various liquid-cooled temperature control technology solutions.

[0003] For example, the invention disclosed in CN117331387A provides a temperature control method for a liquid-cooled energy storage unit, aiming to address the shortcomings of existing liquid-cooled energy storage units, which suffer from coarse temperature control, resulting in poor consistency and high energy consumption. This invention includes: Step 1, the controller acquires a signal from a temperature sensor; Step 2, when a first preset condition is met, the controller sends a command to the temperature management system to start the circulation pump; Step 3, when a second preset condition is met, the temperature controller starts the compressor and condenser fan or heating resistor to output a preset water temperature; Step 4, when the first preset condition is not met, the temperature controller shuts down the compressor and delays shutting down the condenser fan or heating resistor; Step 5, the cycle of steps 3 and 4 continues until a third preset condition is met, at which point the temperature controller receives a shutdown command from the controller and stops the circulation pump, compressor, and condenser fan; this method can achieve better consistency and energy consumption, maximizing battery energy efficiency.

[0004] For example, invention publication CN118170178A discloses a high-precision liquid-cooled temperature control system and method, including a water-cooled unit. The water-cooled unit supplies liquid to a low-temperature water tank and a high-temperature water tank via an electric three-way valve. The high-temperature water tank supplies liquid to the load. Circulating water passing through the load returns to the high-temperature water tank. Excess cooling water in the high-temperature water tank overflows into the low-temperature water tank, and the cooling water in the low-temperature water tank circulates to the water-cooled unit's refrigeration system. The electric three-way valve is connected to a PID controller, which is connected to a temperature sensor that measures the liquid supply temperature to the load. The PID controller is interlocked with an array database, which stores the temperature zones of the low-temperature water tank and the corresponding opening limits of the electric three-way valve output by the PID controller. This ensures that the liquid supply temperature to the load meets TS±1℃ under wide ambient temperature (-40℃~55℃), high power, and high power density load conditions, achieving precise temperature control.

[0005] However, although the above solutions have achieved certain results in consistent control and precise temperature regulation of liquid cooling heat dissipation, they are mainly aimed at the temperature control scenarios of energy storage units or water-cooled units. They do not consider the dynamic weakening of thermal coupling between the coolant pipe and the conductor under cable bending conditions. Especially under frequent bending conditions, the coolant pipe is prone to deformation or separation, which causes contact thermal resistance and local hot spots, resulting in lagging and uncontrollable heat dissipation performance, making it difficult to meet the requirements of high reliability operation.

[0006] Therefore, in order to address the above problems, there is an urgent need for a liquid cooling collaborative intelligent temperature control method and system based on layered PCM. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a liquid-cooled collaborative intelligent temperature control method and system based on layered PCM, which solves the problems of unstable thermal coupling between the coolant pipe and the heating conductor in existing liquid-cooled cables, and the easy deformation of the pipe and the generation of contact thermal resistance and local hot spots when frequently bent.

[0009] Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solution: a liquid-cooled collaborative intelligent temperature control method based on layered PCM, comprising: S1, real-time acquisition of liquid-cooled temperature control data, and time synchronization, abnormal data removal, smoothing filtering, normalization, and dimensionless processing of the liquid-cooled temperature control data to obtain pre-processed liquid-cooled temperature control data; S2, based on the pre-processed liquid-cooled temperature control data, calculating the sensible heat absorption and latent heat absorption of the outer and inner PCM layers, assessing the risk of thermal imbalance on the conductor surface, determining the current thermal state, and outputting a thermal risk warning signal; S3, after receiving the thermal risk warning signal, the controller extracts the real-time liquid-cooled temperature control data, assesses the degree of weakening of thermal coupling performance by the cable bending state, triggers the layered PCM collaborative temperature control strategy, and executes the graded control commands of the power pump, circulation pump, and three-way valve; S4, after the temperature control strategy is executed, based on the real-time liquid-cooled temperature control data and hot spot risk assessment value, assessing the deviation between the current heat exchange capacity and the heat load demand, and correcting the cooling response coefficient based on the assessment results, thereby optimizing the PCM collaborative temperature control strategy and realizing a closed-loop temperature control.

[0011] Furthermore, real-time acquisition of liquid cooling temperature control data, followed by time synchronization, outlier removal, smoothing filtering, normalization, and dimensionless processing, yields the following specific steps for obtaining preprocessed liquid cooling temperature control data: Real-time acquisition of liquid cooling temperature control data, including conductor surface temperature, PCM material temperature, coolant inlet temperature, coolant outlet temperature, coolant flow rate, coolant density, coolant specific heat capacity, cable bending angle, and ambient temperature; A time series matching method based on dynamic time warping is used to perform unified time indexing on the liquid cooling temperature control data, achieving synchronous alignment of multi-source signals; Z-score statistical methods are used to detect and correct the liquid cooling temperature control data, eliminating outliers caused by noise and sudden disturbances; a polynomial smoothing filtering algorithm is used to extract trends and smooth the liquid cooling temperature control data, thereby preserving curve characteristics and suppressing high-frequency noise; and a min-max normalization method is used to perform cross-dimensional normalization processing on the liquid cooling temperature control data, achieving unified dimensions for the liquid cooling temperature control data.

[0012] Furthermore, based on the pre-processed liquid-cooled temperature control data, the specific steps for calculating the sensible heat absorption and latent heat absorption of the outer and inner PCM layers are as follows: Extract the temperatures of the pre-processed outer and inner PCM materials and compare them with the corresponding phase change initiation and termination temperatures: When the PCM material temperature is lower than the phase change initiation temperature, the corresponding PCM layer is determined to be in the sensible heat zone, and the sensible heat absorption is calculated by extracting the liquid-cooled temperature control data; When the PCM material temperature is between the phase change initiation and termination temperatures, the corresponding PCM layer is determined to be in the paste-like phase change zone, and the sensible heat absorption is calculated while the latent heat absorption is calculated based on the position ratio of the PCM material temperature in the phase change zone; When the PCM material temperature is higher than the phase change termination temperature, the corresponding PCM layer is determined to be in a completely molten state, and only the sensible heat absorption is calculated; Output the sensible heat absorption and latent heat absorption of each PCM layer.

[0013] Further, the specific steps for evaluating the risk of thermal imbalance on the conductor surface, determining the current thermal state, and outputting a thermal risk warning signal are as follows: Calculate the first-order time derivative of the conductor surface temperature to obtain the rate of change of the conductor surface temperature; Multiply the rate of change of the conductor surface temperature, the absolute value of the cable bending angle, and the difference between the conductor surface temperature and the ambient temperature in sequence, and square the obtained result to get the risk surge factor; Add a minterm to the difference between the coolant outlet temperature and the coolant inlet temperature, and multiply the result by the result of subtracting the sum of the latent heat absorption of the inner-layer PCM and the latent heat absorption of the outer-layer PCM from the rated total latent heat of the PCM material and then adding a minterm to get the risk buffer factor; Divide the risk surge factor by the risk buffer factor to obtain the hot spot risk assessment value; Compare the hot spot risk assessment value and the hot spot risk threshold in real time. When the hot spot risk assessment value is less than or equal to the hot spot risk threshold, it is determined that the conductor is in a thermal equilibrium state and the temperature control response is not triggered; When the hot spot risk assessment value is greater than the hot spot risk threshold, it is determined that there is a risk of thermal imbalance and a thermal risk warning signal is output.

[0014] Further, after the controller receives the thermal risk warning signal, the specific steps for extracting real-time liquid cooling temperature control data and evaluating the degree of weakening of the thermal coupling performance by the cable bending state are as follows: After the controller receives the thermal risk warning signal, extract the real-time cable bending angle, conductor surface temperature, coolant outlet temperature, and coolant inlet temperature; Take the natural logarithm of the ratio of the absolute value of the cable bending angle to the critical cable bending angle plus one to get the bending intensity factor; Divide the sum of the latent heat absorption of the inner-layer PCM and the latent heat absorption of the outer-layer PCM by the rated total latent heat of the PCM material, and subtract the obtained ratio from 1 to get the remaining heat capacity ratio factor; Take the negative value of the product of the cooling response coefficient and the coolant flow rate as the exponential power, and calculate the exponential value with the natural constant e as the base to get the cooling weakening correction factor; Multiply the bending intensity factor, the remaining heat capacity ratio factor, and the cooling weakening correction factor in sequence to obtain the bending coupling evaluation value.

[0015] Further, the specific steps for triggering the hierarchical PCM collaborative temperature control strategy and executing the hierarchical control instructions for the power pump, circulation pump, and three-way valve are as follows: Compare the bending coupling evaluation value X with the multi-level coupling weakening thresholds X1 and X2 in real time to trigger the hierarchical PCM collaborative temperature control strategy: When X ≤ X1, execute the first-level response strategy: Maintain the current operating states of the power pump and the circulation pump, keep the three-way valve closed, and control the outer-layer PCM to participate in heat exchange while the inner-layer PCM does not participate in heat exchange; When X1 < X < X2, execute the second-level response strategy: The controller outputs an instruction to accelerate the power pump, and opens a part of the three-way valve. The circulation pump maintains the current rotational speed unchanged, and controls the inner and outer layers of the PCM material to exchange heat in sequence; When X ≥ X2, execute the third-level response strategy: The controller controls both the power pump and the circulation pump to operate at the rated rotational speed, and at the same time opens all the three-way valves to inject the condensate into the heat-conducting metal spring at the maximum flow rate, and the inner and outer layers of the PCM material participate in the heat exchange response simultaneously.

[0016] Furthermore, the specific steps for the controller to output the power pump acceleration command and open part of the three-way valve are as follows: When executing the secondary response strategy, a fuzzy PID control algorithm is adopted, taking the first time derivative of the conductor surface temperature, the conductor surface temperature difference collected by each sensor position, and the temperature difference between the coolant inlet and outlet as input quantities. The adjustment quantities of the power pump speed and the three-way valve opening are generated by reasoning through the fuzzy rule base. The controller dynamically adjusts the power pump speed and the three-way valve opening according to the adjustment quantities.

[0017] Furthermore, after the temperature control strategy is implemented, the specific steps for assessing the deviation between the current heat exchange capacity and the heat load demand based on real-time liquid cooling temperature control data and hot spot risk assessment values ​​are as follows: After the temperature control strategy is implemented, the hot spot risk assessment value is recalculated based on real-time liquid cooling temperature control data. When the hot spot risk assessment value is still greater than the hot spot risk threshold, the cooling regulation response assessment value is calculated in combination with the real-time liquid cooling temperature control data: The difference between the coolant outlet temperature and the coolant inlet temperature is added to a minimum term and multiplied by the absolute value of the difference between the coolant outlet temperature and the ambient temperature to obtain the temperature difference driving factor; the temperature difference driving factor is divided by the product of the coolant flow rate, coolant density, and coolant specific heat capacity to obtain the unit flow heat exchange capacity factor; the difference between the hot spot risk assessment value and the hot spot risk threshold is divided by the hot spot risk threshold, and the absolute value is added to obtain the thermal imbalance regulation factor; the unit flow heat exchange capacity factor is multiplied by the thermal imbalance regulation factor to obtain the cooling regulation response assessment value.

[0018] Furthermore, based on the evaluation results, the cooling response coefficient is corrected, and the PCM collaborative temperature control strategy is optimized to achieve closed-loop temperature control. The specific steps are as follows: Taking the sample set of cooling regulation response evaluation values ​​corresponding to the first drop below the hot spot risk threshold after the implementation of the temperature control strategy in historical data as the basis, the upper and lower quartiles of the sample set are used as the target response interval; the deviation analysis between the current cooling regulation response evaluation value and the target response interval is performed, a nonlinear proportional adjustment model based on the error ratio is constructed, and the historical change trend of the cooling response coefficient is introduced as the weight function. The cooling response coefficient is dynamically corrected using an exponential smoothing iteration method; the corrected cooling response coefficient is used simultaneously to update the bending coupling evaluation value, and the power pump speed, circulation pump speed and three-way valve opening are dynamically adjusted to achieve closed-loop temperature control.

[0019] The second aspect of this invention provides a liquid-cooled collaborative intelligent temperature control system based on layered PCM, comprising: a data acquisition and preprocessing module, a thermal risk modeling and assessment module, a temperature control strategy hierarchical response module, and a closed-loop control adaptive optimization module. The data acquisition and preprocessing module is used to acquire liquid-cooled temperature control data in real time and perform time synchronization, abnormal data removal, smoothing filtering, normalization, and dimensionless processing on the liquid-cooled temperature control data to obtain preprocessed liquid-cooled temperature control data. The thermal risk modeling and assessment module is used to calculate the sensible heat absorption and latent heat absorption of the outer and inner PCM layers based on the preprocessed liquid-cooled temperature control data, and to assess the thermal imbalance on the conductor surface. The system assesses the current thermal state and outputs a thermal risk warning signal. A graded response module for temperature control strategy is used to extract real-time liquid cooling temperature control data after the controller receives the thermal risk warning signal, evaluate the degree of weakening of thermal coupling performance due to cable bending, trigger a layered PCM collaborative temperature control strategy, and execute graded control commands for the power pump, circulation pump, and three-way valve. A closed-loop adaptive optimization module is used to assess the deviation between the current heat exchange capacity and heat load demand based on real-time liquid cooling temperature control data and hotspot risk assessment values ​​after the temperature control strategy is executed. Based on the assessment results, the module corrects the cooling response coefficient, thereby optimizing the PCM collaborative temperature control strategy and achieving closed-loop temperature control.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) The liquid cooling collaborative intelligent temperature control method and system based on layered PCM introduces the independent temperature control mechanism of the layered PCM structure and its inner and outer layers, and combines the influence of cable bending angle on thermal contact performance to perform quantitative modeling, dynamically adjust the heat exchange participation mode and response intensity, so that under flexible operating conditions such as frequent bending and structural deformation, the liquid cooling pipeline and the heat-generating conductor can still maintain a tight and stable thermal coupling relationship, effectively preventing the increase of contact thermal resistance and local hot spot accumulation caused by pipeline detachment or deformation, and ensuring the system's heat dissipation efficiency and operational safety.

[0023] (2) This liquid-cooled synergistic intelligent temperature control method and system based on layered PCM divides the PCM material into three state regions according to its temperature and the relative position of the phase change start and end points: sensible heat zone, pasty phase change zone, and fully molten zone. The corresponding sensible heat absorption and latent heat absorption are calculated for each region, thereby characterizing the heat capacity evolution process of different layers of PCM material in real time. This approach avoids the errors caused by treating the heat capacity of the phase change material as a constant in traditional temperature control systems, enhances the ability to identify heat capacity decay trends in advance, and provides a fundamental support for matching heat exchange capacity with heat load.

[0024] (3) This liquid-cooled collaborative intelligent temperature control method and system based on layered PCM constructs a multi-factor coupled model based on conductor temperature change rate, cable bending angle, conductor ambient temperature difference, PCM residual heat capacity, and coolant heat exchange capacity. It designs a quantification method for risk surge factor and risk buffer factor, and outputs hot spot risk assessment value in real time to dynamically determine whether the system has thermal imbalance risk. Compared with the method of judging only by temperature threshold, this mechanism can capture the cumulative effect of temperature rise trend and heat capacity reduction, greatly improving the ability to predict thermal risks and the accuracy of response.

[0025] (4) This liquid-cooled collaborative intelligent temperature control method and system based on hierarchical PCM introduces a fuzzy PID control mechanism, using the conductor surface temperature change rate, spatial temperature difference, and the difference between the coolant outlet temperature and the coolant inlet temperature as control inputs. It then combines fuzzy rule base reasoning to obtain the controller adjustment amount, thereby adjusting the power pump speed and the three-way valve opening in real time. This method balances the accuracy of traditional PID with the adaptability of fuzzy logic, effectively avoiding adjustment oscillations and hysteresis caused by model uncertainty or sensor disturbances, ensuring that the temperature control strategy still has high response rate and accuracy under dynamic operating conditions. Attached Figure Description

[0026] Figure 1 This is a flowchart of a liquid cooling-coordinated intelligent temperature control method based on hierarchical PCM.

[0027] Figure 2 This is a structural diagram of a liquid-cooled collaborative intelligent temperature control system based on layered PCM;

[0028] Figure 3 Diagrams for hotspot risk assessment and thermal state determination under different operating conditions;

[0029] Figure 4 This is a schematic diagram showing the composition and connection relationship of a liquid-cooled temperature control structure.

[0030] Figure 5 This is a cross-sectional view of the internal structure of the cable.

[0031] In the diagram, 1. Charging gun; 2. Thermally conductive metal spring; 3. Coolant pipe; 4. Cable; 5. Power pump; 6. Three-way valve; 7. Circulation pump; 8. Condenser; 9. Boiling chamber; 10. Heating power unit; 11. Charging pile; 12. Protective layer; 13. Outer low-melting-point PCM; 14. Inner high-melting-point PCM; 15. Conductor. Detailed Implementation

[0032] 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.

[0033] Please see Figures 1-5 This invention provides a technical solution: a liquid-cooled collaborative intelligent temperature control method based on layered PCM, comprising: S1, real-time acquisition of liquid-cooled temperature control data, and time synchronization, abnormal data removal, smoothing filtering, normalization, and dimensionless processing of the liquid-cooled temperature control data to obtain pre-processed liquid-cooled temperature control data; S2, based on the pre-processed liquid-cooled temperature control data, calculating the sensible heat absorption and latent heat absorption of the outer and inner PCM layers, assessing the risk of thermal imbalance on the surface of conductor 15, determining the current thermal state, and outputting a thermal risk warning signal; S3, after receiving the thermal risk warning signal, the controller extracts real-time liquid-cooled temperature control data, assesses the degree of weakening of thermal coupling performance by the bending state of cable 4, triggers the layered PCM collaborative temperature control strategy, and executes graded control commands for power pump 5, circulation pump 7, and three-way valve 6; S4, after the temperature control strategy is executed, based on the real-time liquid-cooled temperature control data and hot spot risk assessment value, assessing the deviation between the current heat exchange capacity and heat load demand, and correcting the cooling response coefficient based on the assessment results, thereby optimizing the PCM collaborative temperature control strategy and realizing a closed-loop temperature control.

[0034] Specifically, real-time liquid cooling temperature control data is collected, and the data undergoes time synchronization, outlier removal, smoothing filtering, normalization, and dimensionless processing. The specific steps for obtaining pre-processed liquid cooling temperature control data are as follows: A fixed-width time window is set as one sampling period, and liquid cooling temperature control data is collected periodically. The liquid cooling temperature control data includes conductor surface temperature, PCM material temperature, coolant inlet temperature, coolant outlet temperature, coolant flow rate, coolant density, coolant specific heat capacity, cable bending angle, and ambient temperature. The conductor surface temperature is acquired in real time using a surface-mounted thermocouple sensor attached to the surface of the heating conductor 15. The data is further processed by embedding... The temperature of the PCM material is collected by a miniature thermocouple probe inside the PCM module; the coolant inlet temperature and coolant outlet temperature are measured by high-precision platinum resistance sensors at the inlet and outlet of the flow path, respectively; the coolant flow rate is monitored in real time using a turbine flow meter; the coolant density is obtained by a constant pressure difference method combined with a liquid height and volume ratio measuring device; the coolant specific heat capacity is determined by a specific heat testing instrument before the experiment based on the coolant material type and is used as a known fixed value; the cable bending angle is collected by flexible bending sensors installed at four key parts of the cable and the angle is converted; the ambient temperature is obtained by thermistor elements installed in the cavity around the equipment. Then, a time series matching method based on dynamic time warping is used to create a unified time index for the liquid cooling temperature control data. Various data sources are nonlinearly matched and resampled according to their sampling frequency differences to achieve precise alignment and synchronous fusion of multi-source signals across different time axes. The liquid cooling temperature control data is then detected and corrected using the Z-score statistical method. Z-scores are calculated based on the mean and standard deviation of each data variable. Outliers deviating from the set threshold range are marked and replaced with nearest-neighbor time series interpolation or sliding window averages to eliminate data distortion caused by measurement errors, sensor drift, instantaneous noise, and sudden disturbances, thereby improving data stability. To ensure safety and reliability, a polynomial smoothing filter algorithm is used to extract trends and smooth liquid cooling temperature control data. A polynomial regression model of a set order is used to locally fit the original data within the sliding window. This not only preserves key dynamic features such as the conductor temperature rise curve, coolant temperature difference change, and PCM thermal response, but also effectively suppresses high-frequency interference signals, improving the continuity and physical interpretability of thermal trend modeling. By using the minimum-maximum normalization method combined with the engineering benchmark upper and lower limits of each physical parameter for linear scaling, the liquid cooling temperature control data is mapped from different physical dimensions to a unified standardized range, providing a unified input format for subsequent risk modeling and control strategy optimization.

[0035] This implementation scheme achieves stable acquisition and synchronous fusion of conductor surface temperature, PCM material temperature, coolant inlet temperature, coolant outlet temperature, coolant flow rate, coolant density, coolant specific heat capacity, cable bending angle, and ambient temperature during liquid cooling temperature control by constructing a high-precision data preprocessing mechanism. Dynamic time warping is employed to achieve time alignment of multi-source data, Z-score is used to remove outliers, polynomial smoothing filtering is combined to extract temperature control trend features, and minimum-maximum normalization is used for unified standardization. This scheme improves the accuracy, stability, and consistency of liquid cooling temperature control data, providing reliable data support for subsequent thermal risk assessment and temperature control strategy optimization.

[0036] Specifically, based on the pre-treated liquid-cooled temperature control data, the specific steps for calculating the sensible heat absorption, latent heat absorption, and latent heat absorption of the outer and inner PCM layers are as follows: First, extract the temperatures of the pre-treated outer and inner PCM layers and compare them one by one with the corresponding phase change initiation and termination temperatures. When the PCM material temperature is lower than the corresponding phase change initiation temperature, the PCM material is determined to be in the solid-state sensible heat absorption stage. At this time, based on the mass of the PCM material, its specific heat capacity per unit mass, and its current temperature rise, the sensible heat absorption is calculated, and the latent heat absorption is set to zero. When the PCM material temperature is between the corresponding phase change initiation and termination temperatures, the PCM material is determined to be in the paste-like phase change region. During this stage, both sensible heat absorption and latent heat absorption are calculated simultaneously. The calculation method for sensible heat absorption is the same as that for the solid-state sensible heat absorption stage. Latent heat absorption is calculated by multiplying the PCM material's temperature within the phase transition temperature range by its maximum latent heat absorption capacity. When the PCM material temperature is higher than its corresponding phase transition termination temperature, it is considered to be in a fully molten state. In this stage, only the sensible heat absorption is calculated, and the latent heat absorption is directly set to the PCM material's maximum latent heat absorption capacity. Finally, the sensible and latent heat absorption values ​​of the outer and inner PCM layers are output respectively. These serve as the basic input parameters for calculating hotspot risk assessment values ​​and adjusting temperature control response in the liquid cooling temperature control system, enabling dynamic feedback adjustment of the heat exchange capacity and heat load deviation of the heating conductor 15.

[0037] In this implementation scheme, by introducing a range determination mechanism for PCM material temperature and phase transition initiation and termination temperatures, the precise identification of the thermal behavior state of PCM material is achieved. Furthermore, by combining PCM material mass, specific heat capacity per unit mass, current temperature rise, and maximum latent heat absorption capacity, the corresponding sensible heat absorption and latent heat absorption are refined and accurately calculated in the solid-state sensible heat absorption stage, the pasty phase transition region stage, and the complete melting stage. This method can comprehensively reflect the heat absorption process changes of the outer and inner PCM materials in different temperature ranges, effectively improving the dynamic sensing accuracy of the liquid cooling temperature control system for the heat transfer capacity of the heating conductor 15. It also provides a physical dimensionally consistent, clearly defined, and stratified thermal parameter input basis for calculating hotspot risk assessment values, thereby enhancing the closed-loop adaptability of the temperature control response coefficient adjustment strategy and the real-time effectiveness of risk intervention.

[0038] Specifically, the steps for assessing the thermal imbalance risk on the surface of conductor 15, determining the current thermal state, and outputting a thermal risk warning signal are as follows: First, based on the pre-processed liquid cooling temperature control data, the conductor surface temperature data for a continuous time period is extracted, and the first-order time derivative of the conductor surface temperature is calculated using the finite difference method to obtain the conductor surface temperature change rate, which reflects the dynamic intensity of the heat accumulation trend; then, the conductor surface temperature change rate, the absolute value of the cable bending angle, and the difference between the conductor surface temperature and the ambient temperature are multiplied sequentially to comprehensively reflect the combined effect of mechanical stress changes, local heat dissipation efficiency decrease, and temperature rise rate, and the product result is squared to construct a risk surge factor, which is used to measure the risk surge caused by the heat accumulation trend. The degree to which the thermal behavior intensifies and accelerates the trend of thermal imbalance is assessed. Next, the difference between the coolant outlet temperature and the coolant inlet temperature is calculated, and a minima are added. This result is then multiplied by the sum of the latent heat absorption of the inner and outer PCM layers (the rated total latent heat of the PCM material minus the sum of the latent heat absorption of the inner and outer PCM layers, plus the minima) to obtain a risk buffer factor. This factor characterizes the current heat transfer margin and remaining cooling response space of the liquid cooling system. The risk surge factor is divided by the risk buffer factor to obtain the hot spot risk assessment value under the current sampling period, serving as the core indicator characterizing the thermal stability of conductor 15. The minima is a very small but non-zero positive real number used to avoid numerical instability caused by division by zero during calculation; its value range is... arrive Unless otherwise specified, all subsequent minor items shall adopt the definitions and value ranges provided herein. Subsequently, the hotspot risk assessment value calculated in real time is compared with the hotspot risk threshold. When the hotspot risk assessment value is less than or equal to the hotspot risk threshold, the heating conductor 15 is determined to be in thermal equilibrium, maintaining the current operating state of the power pump 5, circulation pump 7, and three-way valve 6, without triggering temperature control intervention. When the hotspot risk assessment value is greater than the hotspot risk threshold, a thermal imbalance risk is determined to exist, and a thermal risk warning signal is immediately output as the prerequisite logic for triggering the temperature control response adjustment mechanism and the closed-loop adjustment of the thermal control strategy, achieving early identification and response control of abnormal hotspot trends.

[0039] The specific formula for calculating the hotspot risk assessment value is as follows:

[0040] ;

[0041] In the formula, This indicates the risk assessment value for hotspots. Indicates the rate of change of temperature on the conductor surface. Indicates the cable bending angle. Indicates the surface temperature of the conductor. Indicates ambient temperature. Indicates the coolant outlet temperature. Indicates the coolant inlet temperature. This indicates the latent heat absorption of the inner PCM layer. This indicates the latent heat absorption of the outer PCM layer. Indicates the rated total latent heat of PCM material. Indicates a minus term.

[0042] In this embodiment, Table 1 is a hotspot risk assessment value data table, listing the variables and corresponding hotspot risk assessment values ​​for five sampling periods. The listed variables include: conductor surface temperature change rate, cable bending angle, conductor surface temperature, ambient temperature, coolant outlet temperature, coolant inlet temperature, latent heat absorption of the inner PCM layer, latent heat absorption of the outer PCM layer, and the rated total latent heat of the PCM material. Specific data are as follows: In sampling period 1, the conductor surface temperature change rate is 0.12, the cable bending angle is 5°C, the conductor surface temperature is 65°C, the ambient temperature is 25°C, the coolant outlet temperature is 50°C, the coolant inlet temperature is 40°C, the latent heat absorption of the inner PCM layer is 300, the latent heat absorption of the outer PCM layer is 200, the rated total latent heat of the PCM material is 1000, and the corresponding hotspot risk assessment value is 0.12; In sampling period 2, the conductor surface temperature change rate is 0.15, the cable bending angle is... 10. Conductor surface temperature is 68°C, ambient temperature is 25°C, coolant outlet temperature is 52°C, coolant inlet temperature is 41°C, inner PCM latent heat absorption is 320 Nm³, outer PCM latent heat absorption is 210 Nm³, rated total latent heat of PCM material is 1000 Nm³, corresponding hot spot risk assessment value is 0.80; In sampling period 3, conductor surface temperature change rate is 0.20, cable bending angle is 20°C, conductor surface temperature is 70°C, ambient temperature is 25°C, coolant outlet temperature is 53°C, coolant inlet temperature is... At a temperature of 41.5°C, the latent heat absorption of the inner PCM layer is 340, the latent heat absorption of the outer PCM layer is 220, and the rated total latent heat of the PCM material is 1000, corresponding to a hot spot risk assessment value of 6.40. During sampling period 4, the conductor surface temperature change rate was 0.25, the cable bending angle was 30°, the conductor surface temperature was 73°C, the ambient temperature was 25°C, the coolant outlet temperature was 54°C, the coolant inlet temperature was 42°C, the latent heat absorption of the inner PCM layer was 360, and the latent heat absorption of the outer PCM layer was 230. The rated total latent heat of the PCM material is 1000, and the corresponding hot spot risk assessment value is 26.34. In sampling period 5, the conductor surface temperature change rate is 0.30, the cable bending angle is 40, the conductor surface temperature is 76, the ambient temperature is 25, the coolant outlet temperature is 55, the coolant inlet temperature is 42.5, the latent heat absorption of the inner PCM is 380, the latent heat absorption of the outer PCM is 240, the rated total latent heat of the PCM material is 1000, and the corresponding hot spot risk assessment value is 78.85.

[0043] Table 1. Hotspot Risk Assessment Data Table

[0044]

[0045] like Figure 3As shown, the figure displays the hotspot risk assessment values ​​and thermal state determination results under five typical operating conditions, used to identify the degradation trend and potential thermal imbalance risk of cable 4's thermal coupling performance under typical thermal conditions. The horizontal axis represents the sampling period, the vertical axis represents the hotspot risk assessment value, the black dashed line represents the hotspot risk threshold, red markers indicate thermal imbalance risk states where the hotspot risk assessment value is higher than the hotspot risk threshold, and green markers indicate thermal equilibrium states where the hotspot risk assessment value is lower than or equal to the hotspot risk threshold. It can be seen from the figure that in sampling periods 1 to 3, the hotspot risk assessment value is lower than the hotspot risk threshold, and is determined to be in a thermal equilibrium state; while in sampling periods 4 and 5, the hotspot risk assessment value rises significantly, exceeding the hotspot risk threshold, and is determined to be in a thermal imbalance risk state. Figure 3 This intuitively demonstrates the impact of multivariate coupling changes on the thermal stability of the system, and verifies the effectiveness of the risk quantification and temperature control strategies in this invention.

[0046] In this implementation scheme, a hotspot risk assessment value calculation mechanism is constructed that can simultaneously reflect the heat source intensity and cold source margin by introducing the conductor surface temperature change rate, cable bending angle, difference between conductor surface temperature and ambient temperature, difference between coolant outlet temperature and coolant inlet temperature, rated total latent heat of PCM material, latent heat absorption of inner PCM material, and latent heat absorption of outer PCM material, thus accurately capturing the thermal state evolution characteristics of conductor 15. By constructing risk surge factor and risk buffer factor, the sensitivity to thermal imbalance risk is enhanced; based on the real-time comparison of hotspot risk assessment value and hotspot risk threshold, the thermal balance state and thermal imbalance risk state are accurately classified, and the closed-loop output of thermal risk early warning signal is realized. This provides a unified and quantifiable judgment basis for dynamic adjustment response, active thermal risk early warning, and hierarchical control of cooling strategy in liquid cooling temperature control system, significantly improving the temperature control stability and safety robustness of energy storage system under high heat load conditions.

[0047] Specifically, after receiving the thermal risk warning signal, the controller extracts real-time liquid cooling temperature control data and evaluates the degree of weakening of the thermal coupling performance of cable 4 due to its bending state. The specific steps are as follows: After receiving the thermal risk warning signal, the controller first extracts data from the current sampling period, including cable bending angle, conductor surface temperature, coolant outlet temperature, and coolant inlet temperature. The ratio between the absolute value of the cable bending angle and the critical bending angle is increased by a constant and then the natural logarithm is taken to obtain the bending severity factor of cable 4 at the current moment, which reflects the potential destructive ability of the mechanical state of cable 4 to the thermal coupling channel. The sum of the latent heat absorption of the inner PCM and the latent heat absorption of the outer PCM in the current period is calculated and divided by the rated total latent heat of the PCM material. The remaining heat capacity ratio factor is obtained by subtracting the obtained ratio from the constant, which is used to quantify the remaining heat transfer capacity of the phase change material. The current coolant flow rate data is obtained from the liquid cooling temperature control data, and the product of the cooling response coefficient and the coolant flow rate is negatively taken as the exponential term. The natural constant e is used as the base for exponential calculation to obtain the cooling weakening correction factor, which is used to characterize the attenuation effect of the cooling capacity caused by the change in flow rate. The cooling response coefficient is obtained by using a least squares fitting algorithm based on the correlation between coolant flow rate and conductor surface temperature change rate in historical liquid cooling temperature control data. The value range of the cooling response coefficient is [0,1]. Finally, the bending severity factor, the residual heat capacity ratio factor, and the cooling attenuation correction factor are multiplied sequentially to output the cable bending coupling evaluation value for the current period. This value is used to characterize the degree to which the bending state weakens the stability of the thermal coupling path and to provide a physical layer basis for the dynamic adjustment of the temperature control response strategy.

[0048] The specific formula for calculating the bending coupling evaluation value is as follows:

[0049] ;

[0050] In the formula, This represents the bending coupling evaluation value. Indicates the cable bending angle. Indicates the critical angle for cable bending. This indicates the latent heat absorption of the inner PCM layer. This indicates the latent heat absorption of the outer PCM layer. Indicates the rated total latent heat of PCM material. Indicates the cooling response coefficient. This indicates the coolant flow rate.

[0051] In this implementation scheme, a cable bending coupling evaluation model based on liquid-cooled temperature control data is constructed. This model enables multi-dimensional fusion calculation of cable bending angle, conductor surface temperature, coolant outlet temperature, coolant inlet temperature, latent heat absorption of inner PCM layer, latent heat absorption of outer PCM layer, rated total latent heat of PCM material, and coolant flow rate. This allows for accurate quantification of the degree to which the cable bending state weakens the thermal coupling performance of the liquid-cooled temperature control system. Based on real-time extraction of liquid-cooled temperature control data, this method constructs bending severity factors, residual heat capacity ratio factors, and cooling weakening correction factors, and outputs bending coupling evaluation values. This achieves dynamic evaluation and feedback of the heat conduction path stability, effectively enhancing the temperature control strategy's ability to perceive and control thermal imbalance risks, and improving the intelligent response capability and system safety margin of the liquid-cooled temperature control system under complex operating conditions.

[0052] Specifically, the steps for triggering the layered PCM collaborative temperature control strategy and executing the graded control commands of power pump 5, circulation pump 7, and three-way valve 6 are as follows: Based on the bending coupling evaluation value X, the controller compares the bending coupling evaluation value X with the first-level coupling weakening threshold X1 and the second-level coupling weakening threshold X2 in real time to form the basis for initiating the layered response and triggering the layered PCM collaborative temperature control strategy. Specifically, this includes: When the bending coupling evaluation value X is less than or equal to the first-level coupling weakening threshold X1, the first-level response strategy is executed: This stage represents a small bending impact and no significant weakening of the system's thermal coupling performance. The controller maintains the current operating state of power pump 5 and circulation pump 7 unchanged, and the three-way valve 6 remains closed, controlling only the outer layer PCM material to participate in cooling heat exchange; the inner layer PCM material does not participate in the temperature control response for the time being. When the bending coupling evaluation value X is greater than the first-level coupling weakening threshold X1 and less than the second-level coupling weakening threshold X2, the second-level response strategy is executed: In this stage, the degree of system coupling weakening intensifies, and the controller issues an acceleration command to power pump 5 to improve cooling capacity, while simultaneously opening part of the three-way valve 6 to adjust... The flow direction of the condensate is controlled, the speed of the circulating pump remains constant, and the outer PCM material is prioritized for heat exchange while the inner PCM material is gradually guided to participate in the temperature control process. When the bending coupling evaluation value X is greater than or equal to the secondary coupling weakening threshold X2, a three-level response strategy is executed: In this stage, the thermal coupling performance of the system is severely reduced. The controller controls the power pump 5 and the circulating pump 7 to run at their rated speeds simultaneously and opens all three-way valves 6, allowing the condensate to be injected into the cavity of the thermally conductive metal spring 2 at maximum flow rate. This drives the heat exchange medium to fully cover the heat exchange interface, while simultaneously activating the outer and inner PCM materials to participate in the temperature control response, achieving multi-level cooling linkage regulation, enhancing temperature control efficiency, and curbing the spread of hot spots.

[0053] In this implementation scheme, by comparing the bending coupling evaluation value with the multi-level coupling weakening threshold in real time, a multi-level response mechanism is dynamically triggered to realize the graded linkage control of the power pump 5, the circulation pump 7 and the three-way valve 6. It can accurately control the condensate injection path and flow distribution according to the degree of thermal coupling performance weakening, and dynamically adjust the heat exchange sequence and participation of the outer PCM material and the inner PCM material. This effectively improves the response sensitivity and cooling stability of the liquid cooling temperature control system under the complex bending state of the cable 4, and provides a highly adaptable temperature control capability for suppressing thermal imbalance and preventing hot spot risks of conductor 15 under high heat load conditions.

[0054] Specifically, when executing the secondary response strategy, the controller outputs an acceleration command for the power pump 5 and opens part of the three-way valve 6. The specific steps are as follows: After receiving the judgment result that the bending coupling evaluation value is greater than the first coupling weakening threshold and less than the second coupling weakening threshold, the controller uses a fuzzy PID control algorithm to jointly regulate and control the power pump speed and the three-way valve opening. The fuzzy PID control algorithm uses the first-order time derivative of the conductor surface temperature as the dynamic response rate input, the temperature difference of the conductor surface collected by temperature sensors placed at different locations as the spatial thermal imbalance input, and the temperature difference between the coolant inlet temperature and the coolant outlet temperature as the cooling heat transfer efficiency input. All three inputs are extracted and standardized in real time based on the pre-processed liquid cooling temperature control data. The controller enters the fuzzy rule base based on the inputs to perform fuzzy inference and obtain the fuzzy control output; the fuzzy rule base consists of a multi-dimensional rule table constructed based on historical liquid cooling temperature control data. Subsequently, the fuzzy control output is converted into specific adjustment quantities through a defuzzification algorithm, corresponding to the increment of the power pump speed and the adjustment angle of the three-way valve opening, respectively. The controller dynamically updates the control command of the power pump 5 according to the adjustment amount, increases the speed of the power pump to enhance the driving force of the liquid flow, and at the same time controls the three-way valve 6 to open at the adjusted opening angle, guiding the condensate into the heat exchange structure at the required flow rate, thereby optimizing the heat exchange sequence between the outer layer PCM material and the inner layer PCM material, and improving the response accuracy and thermal risk suppression capability of the liquid cooling temperature control system under bending conditions.

[0055] In this implementation scheme, a fuzzy PID control algorithm is introduced when executing the secondary response strategy to achieve joint regulation and control of the power pump speed and the three-way valve opening. Based on three standardized input quantities—the first-order time derivative of the conductor surface temperature, the conductor surface temperature difference, and the temperature difference between the coolant inlet temperature and the coolant outlet temperature—it can accurately reflect the dynamic response rate, spatial thermal imbalance, and cooling heat transfer efficiency of the liquid cooling temperature control system. This drives the controller to complete fuzzy inference and defuzzification output based on a fuzzy rule base constructed from historical liquid cooling temperature control data. This enables precise linkage control of the power pump speed increment and the three-way valve opening adjustment angle, thereby improving the condensate injection path regulation capability when cable 4 is in a moderately bent state, optimizing the heat exchange sequence between the outer and inner PCM materials, and enhancing the active response speed and suppression stability of the liquid cooling temperature control system to hot spot risks.

[0056] Specifically, after the temperature control strategy is implemented, the following steps are taken to assess the deviation between the current heat exchange capacity and the heat load demand based on real-time liquid cooling temperature control data and hot spot risk assessment values: After the temperature control strategy is implemented, the controller recalculates the current hot spot risk assessment value based on the real-time liquid cooling temperature control data collected in the latest cycle. When the latest calculated hot spot risk assessment value is still greater than the hot spot risk threshold, it indicates that the current thermal imbalance has not been sufficiently alleviated. The cooling regulation response assessment value is further calculated in conjunction with the current liquid cooling temperature control data to quantify the regulation efficiency of the current heat exchange response effect. The specific calculation steps are as follows: First, a minimum term is added to the difference between the coolant outlet temperature and the coolant inlet temperature, and the result is multiplied by the absolute value of the difference between the coolant outlet temperature and the ambient temperature to construct a temperature difference driving factor, which reflects the heat transfer driving force generated by the superposition of the coolant temperature gradient and environmental influences during the heat exchange process. Then, the temperature difference driving factor is divided by the product of the coolant flow rate, coolant density, and coolant specific heat capacity to obtain a unit flow rate heat exchange capacity factor, which measures the heat removal capacity that a unit volume of coolant can handle. Based on this, the difference between the hotspot risk assessment value and the hotspot risk threshold is divided by the hotspot risk threshold, and the absolute value is taken. One is then added to this absolute value to obtain the thermal imbalance adjustment factor, which describes the gain of the current thermal imbalance level on the cooling response requirement. Finally, the unit flow heat exchange capacity factor is multiplied by the thermal imbalance adjustment factor to obtain the cooling regulation response assessment value, which serves as a key criterion for evaluating the heat exchange effectiveness of the current temperature control strategy and the degree of matching with the heat load demand.

[0057] The specific formula for calculating the cooling regulation response evaluation value is as follows:

[0058] ;

[0059] In the formula, This represents the evaluation value of the cooling regulation response. Indicates the coolant inlet temperature. Indicates the coolant outlet temperature. Indicates ambient temperature. Indicates coolant flow rate. Indicates the density of the coolant. This indicates the specific heat capacity of the coolant. This indicates the risk assessment value for hotspots. Indicates the hotspot risk threshold. Indicates a minus term.

[0060] In this implementation plan, a cooling regulation response assessment value is constructed based on real-time liquid cooling temperature control data and the latest hotspot risk assessment value, effectively establishing a quantitative correspondence between heat exchange capacity and heat load demand. This assessment method, through the step-by-step calculation of the temperature difference driving factor, the unit flow heat exchange capacity factor, and the thermal imbalance regulation factor, effectively reflects the deviation between the actual regulation effect of the cooling response and the current thermal imbalance state, improving the feedback regulation accuracy after the temperature control strategy is implemented and the reliability of the controller's response to dynamic changes in thermal risk.

[0061] Specifically, the steps for correcting the cooling response coefficient based on the evaluation results and optimizing the PCM collaborative temperature control strategy to achieve closed-loop temperature control are as follows: Based on a sample set of cooling regulation response evaluation values ​​from historical liquid cooling temperature control data, where the hotspot risk assessment value first falls below the hotspot risk threshold after the implementation of the temperature control strategy, the upper and lower quartiles of the sample set are set as the target response interval. This forms a reference standard to measure the difference between the current system response performance and the historical best state. A point-by-point deviation analysis is performed between the current cooling regulation response evaluation value and the target response interval to obtain the error ratio between the absolute value of the deviation and the median of the target interval. A nonlinear proportional adjustment model is constructed based on this error ratio. The model design fully considers the nonlinear coupling characteristics between the hotspot risk assessment value and the cooling response, improving the accuracy and sensitivity of the cooling response coefficient correction. Furthermore, the recent trend of the cooling response coefficient is introduced as a historical weighting function. An exponential smoothing iteration method is used to perform a weighted average of the original correction values, weakening short-term fluctuations caused by sudden disturbances and enhancing the dynamic stability and adjustment continuity of the cooling response coefficient as the heat load evolves. The final corrected cooling response coefficient is synchronized in real time to the bending coupling evaluation value calculation module, replacing the original cooling response coefficient. The bending coupling evaluation value is recalculated, and the combined control of the power pump speed adjustment command, the circulation pump speed adjustment command, and the three-way valve opening angle adjustment command is driven based on the updated bending coupling evaluation value. This enables dynamic adjustment of the thermal response of the inner and outer PCM materials, forming a closed control loop. This further enhances the adaptability, temperature control accuracy, and thermal risk suppression effectiveness of the PCM collaborative temperature control strategy under multivariable coupling.

[0062] In this implementation plan, a nonlinear control mechanism matching the actual heat load changes is constructed based on the dynamic deviation correction of the cooling regulation response evaluation value. By introducing the historical cooling response coefficient change trend to construct a weight function, the stability and accuracy of the cooling response coefficient adjustment are improved, and closed-loop linkage control of the power pump speed, circulation pump speed and three-way valve opening is realized. This ensures that the liquid cooling temperature control system maintains stable heat exchange capacity when the cable is bent, effectively mitigates hot spot risk, and enhances the timeliness, adaptability and safety redundancy of the temperature control strategy.

[0063] like Figure 2 As shown, the second aspect of this invention provides a liquid-cooled collaborative intelligent temperature control system based on layered PCM, comprising: a data acquisition and preprocessing module, a thermal risk modeling and assessment module, a temperature control strategy hierarchical response module, and a closed-loop control adaptive optimization module, wherein: the data acquisition and preprocessing module is used to acquire liquid-cooled temperature control data in real time, and perform time synchronization, abnormal data removal, smoothing filtering, normalization, and dimensionless processing on the liquid-cooled temperature control data to obtain preprocessed liquid-cooled temperature control data; the thermal risk modeling and assessment module is used to calculate the sensible heat absorption and latent heat absorption of the outer and inner PCM layers based on the preprocessed liquid-cooled temperature control data, and to assess the thermal imbalance on the surface of conductor 15. The system assesses the current thermal state and outputs a thermal risk warning signal. A graded response module for temperature control strategy is used to extract real-time liquid cooling temperature control data after the controller receives the thermal risk warning signal, evaluate the degree to which the bending state of cable 4 weakens the thermal coupling performance, trigger a layered PCM collaborative temperature control strategy, and execute graded control commands for power pump 5, circulation pump 7, and three-way valve 6. A closed-loop adaptive optimization module is used to evaluate the deviation between the current heat exchange capacity and the heat load demand based on real-time liquid cooling temperature control data and hotspot risk assessment values ​​after the temperature control strategy is executed, and corrects the cooling response coefficient based on the evaluation results, thereby optimizing the PCM collaborative temperature control strategy and achieving closed-loop temperature control.

[0064] like Figure 4The diagram illustrates the composition and connection relationships of a liquid-cooled temperature control structure. The charging gun 1, via an embedded thermally conductive metal spring 2, forms a thermally coupled conduction path with the coolant pipe 3, and is integrated with the cable 4 to create a heat exchange channel with a flexible structure and high thermal conductivity. The cable 4 contains a circulating coolant channel. Driven by the power pump 5, the coolant is drawn from the cooling circuit and enters the liquid-cooled temperature control system. A three-way valve 6 controls the flow path of the coolant, and a closed-loop circulation is achieved under the action of the circulating pump 7. The coolant first flows through the condenser 8, where a phase change condensation process occurs to release the carried heat. It then flows into the boiling chamber 9, where it exchanges heat with the heating power unit 10 arranged therein, absorbing the heat load generated by the heating power unit 10. After heat exchange, the coolant returns along a multi-layered heat conduction and exchange path constructed within the charging pile 11's shell structure, forming a complete liquid-cooled temperature control path from the charging gun 1 to the charging pile 11. Figure 4 It not only fully reflects the closed-loop structure of the liquid cooling cycle from the coolant drive source to the high heat load components and then to the return path, but also clarifies the functional connection relationship between the power pump 5, the three-way valve 6, the circulation pump 7 and the condenser 8. This provides structural support for the implementation of the temperature control strategy graded response mechanism and closed-loop control algorithm based on thermal risk modeling in this invention, and realizes the dynamic identification and adaptive temperature control response of the thermal imbalance state on the surface of the heat-generating conductor 15.

[0065] like Figure 5 The diagram shows the internal structural cross-section of cable 4, further refining the embedding method of the liquid-cooled temperature control structure and the arrangement scheme of the layered heat exchange materials within cable 4. As shown in the diagram: conductor 15 is located in the innermost layer of cable 4, serving as a heat source; the conductor surface temperature is a key input for hotspot risk assessment and temperature control response. The inner high-melting-point PCM 14 covers the conductor 15, addressing heat load absorption during high-temperature stages and participating in regulation as a deep heat exchange unit in the temperature control strategy. The outer low-melting-point PCM 13 is distributed outside the inner high-melting-point PCM 14, preferentially responding to medium- and low-temperature thermal disturbances, forming the first response barrier of the layered PCM collaborative temperature control strategy. The coolant pipe 3 and the thermally conductive metal spring 2 are embedded together in the outer structure of cable 4, serving as a high thermal conductivity flexible heat exchange channel, and... Figure 4 The liquid cooling temperature control system shown forms a closed loop; the protective layer 12 covers the outermost layer and is used for the overall protection of the cable 4 structure and the stable maintenance of the thermal control environment. Figure 5 This clearly reflects the collaborative embedding method of the layered PCM temperature control material, liquid cooling channel and heating conductor 15 inside the cable 4 in this invention.

[0066] In this implementation scheme, a multi-functional collaborative architecture is constructed, including a data acquisition and preprocessing module, a thermal risk modeling and assessment module, a temperature control strategy hierarchical response module, and a closed-loop control adaptive optimization module. This architecture enables high-precision real-time acquisition and standardized processing of liquid cooling temperature control data, dynamic modeling of the sensible and latent heat absorption of the outer and inner PCM materials, accurate determination of the risk of thermal imbalance on the surface of conductor 15, real-time assessment of the degree of weakening of thermal coupling performance by the bending state of cable 4, and closed-loop strategy updates based on feedback correction of the cooling response coefficient using hot spot risk assessment value and cooling adjustment response assessment value. This significantly improves the response sensitivity, control accuracy, and thermal safety assurance capability of the liquid cooling temperature control system under complex thermal conditions.

[0067] 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.

[0068] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A liquid-cooled synergistic intelligent temperature control method based on layered PCM, characterized in that, It includes the following steps: S1. Collect the liquid cooling temperature control data in real time, and perform time synchronization, abnormal data elimination, smoothing filtering, normalization and dimensionless processing on the liquid cooling temperature control data to obtain the preprocessed liquid cooling temperature control data; S2. Based on the preprocessed liquid cooling temperature control data, calculate the sensible heat absorption and latent heat absorption of the outer and inner PCMs, evaluate the thermal imbalance risk on the surface of the conductor (15), determine the current thermal state, and output a thermal risk warning signal; S3. After receiving the thermal risk warning signal, the controller extracts the real-time liquid cooling temperature control data, evaluates the degree of weakening of the thermal coupling performance by the bending state of the cable (4), triggers the hierarchical PCM collaborative temperature control strategy, and executes the hierarchical control instructions for the power pump (5), circulation pump (7) and three-way valve (6); The specific steps for the controller to extract the real-time liquid cooling temperature control data and evaluate the degree of weakening of the thermal coupling performance by the bending state of the cable (4) after receiving the thermal risk warning signal are as follows: After receiving the thermal risk warning signal, the controller extracts the real-time cable bending angle, conductor surface temperature, coolant outlet temperature and coolant inlet temperature; add 1 to the ratio of the absolute value of the cable bending angle to the critical cable bending angle and then take the natural logarithm to obtain the bending intensity factor; divide the sum of the latent heat absorption of the inner PCM and the latent heat absorption of the outer PCM by the rated total latent heat of the PCM material, and subtract the obtained ratio from 1 to get the remaining heat capacity ratio factor; take the negative value of the product of the cooling response coefficient and the coolant flow rate as the exponential power, and calculate the exponential value with the natural constant e as the base to obtain the cooling weakening correction factor; multiply the bending intensity factor, the remaining heat capacity ratio factor and the cooling weakening correction factor in sequence to obtain the bending coupling evaluation value; The specific steps for triggering the hierarchical PCM collaborative temperature control strategy and executing the hierarchical control instructions for the power pump (5), circulation pump (7) and three-way valve (6) are as follows: Compare the bending coupling evaluation value X with the multi-level coupling weakening thresholds X1 and X2 in real time, and trigger the hierarchical PCM collaborative temperature control strategy: When X ≤ X1, execute the first-level response strategy: maintain the current operating states of the power pump (5) and the circulation pump (7), keep the three-way valve (6) closed, and control the outer PCM to participate in heat exchange while the inner PCM does not participate in heat exchange; When X1 < X < X2, execute the second-level response strategy: the controller outputs an acceleration instruction for the power pump (5), opens a part of the three-way valve (6), keeps the rotation speed of the circulation pump (7) unchanged, and controls the inner and outer layers of the PCM material to exchange heat in sequence; When X ≥ X2, execute the third-level response strategy: the controller controls both the power pump (5) and the circulation pump (7) to operate at the rated speed, and at the same time opens all the three-way valves (6) to inject the condensate into the heat-conducting metal spring (2) at the maximum flow rate, and the inner and outer layers of the PCM material participate in the heat exchange response simultaneously; S4. After the temperature control strategy is executed, based on the real-time liquid cooling temperature control data and the hot spot risk assessment value, evaluate the deviation between the current heat exchange capacity and the heat load demand, and modify the cooling response coefficient based on the evaluation result, so as to optimize the PCM collaborative temperature control strategy and achieve a temperature control closed loop.

2. The liquid cooling synergistic intelligent temperature control method based on layered PCM according to claim 1, characterized in that: The specific steps for acquiring real-time liquid cooling temperature control data, and performing time synchronization, outlier removal, smoothing filtering, normalization, and dimensionless processing on the liquid cooling temperature control data to obtain preprocessed liquid cooling temperature control data are as follows: Real-time acquisition of liquid cooling temperature control data, including conductor surface temperature, PCM material temperature, coolant inlet temperature, coolant outlet temperature, coolant flow rate, coolant density, coolant specific heat capacity, cable bending angle, and ambient temperature; A time series matching method based on dynamic time warping is adopted to perform unified time indexing on liquid cooling temperature control data, achieving synchronous alignment of multi-source signals. The Z-score statistical method is used to detect and correct liquid cooling temperature control data, eliminating outliers caused by noise and sudden disturbances. A polynomial smoothing filtering algorithm is used to extract trends and smooth the liquid cooling temperature control data, thereby preserving curve characteristics and suppressing high-frequency noise. The min-max normalization method is used to perform cross-dimensional normalization on the liquid cooling temperature control data, achieving a unified dimension for the liquid cooling temperature control data.

3. The liquid cooling synergistic intelligent temperature control method based on layered PCM according to claim 1, characterized in that: The specific steps for calculating the sensible heat absorption and latent heat absorption of the outer and inner PCM layers based on the pre-processed liquid cooling temperature control data are as follows: The temperatures of the pretreated outer and inner PCM materials are extracted and compared with their corresponding phase change initiation and termination temperatures. When the PCM material temperature is lower than the phase change initiation temperature, the corresponding PCM layer is determined to be in the sensible heat zone, and the sensible heat absorption is calculated by extracting liquid cooling temperature control data. When the PCM material temperature is between the phase change initiation and termination temperatures, the corresponding PCM layer is determined to be in the paste phase change zone, and the latent heat absorption is calculated based on the proportion of the PCM material temperature within the phase change zone. When the PCM material temperature is higher than the phase change termination temperature, the corresponding PCM layer is determined to be in a fully molten state, and only the sensible heat absorption is calculated. The sensible heat absorption and latent heat absorption of each PCM layer are output.

4. The liquid cooling synergistic intelligent temperature control method based on layered PCM according to claim 1, characterized in that: The specific steps for assessing the risk of thermal imbalance on the surface of the conductor (15), determining the current thermal state, and outputting a thermal risk warning signal are as follows: Calculate the first time derivative of the conductor surface temperature to obtain the conductor surface temperature change rate; multiply the conductor surface temperature change rate, the absolute value of the cable bending angle, and the difference between the conductor surface temperature and the ambient temperature in sequence, and square the result to obtain the risk surge factor; add a minimum term to the difference between the coolant outlet temperature and the coolant inlet temperature, and multiply the result by the rated total latent heat of the PCM material minus the sum of the latent heat absorption of the inner PCM and the outer PCM, plus the minimum term, to obtain the risk buffer factor; divide the risk surge factor by the risk buffer factor to obtain the hot spot risk assessment value. Real-time comparison of hot spot risk assessment value and hot spot risk threshold. When the hot spot risk assessment value is less than or equal to the hot spot risk threshold, the conductor (15) is determined to be in thermal equilibrium and the temperature control response is not triggered. When the hot spot risk assessment value is greater than the hot spot risk threshold, it is determined that there is a risk of hot imbalance and a hot spot risk warning signal is output.

5. The liquid cooling synergistic intelligent temperature control method based on layered PCM according to claim 1, characterized in that: The specific steps for the controller to output an acceleration command to the power pump (5) and open part of the three-way valve (6) are as follows: When executing the secondary response strategy, a fuzzy PID control algorithm is adopted. The first time derivative of the conductor surface temperature, the conductor surface temperature difference collected by each sensor position, and the temperature difference between the coolant inlet and outlet are used as input quantities. The adjustment quantities of the power pump speed and the three-way valve opening are generated by reasoning through the fuzzy rule base. The controller dynamically adjusts the power pump speed and the three-way valve opening according to the adjustment quantities.

6. The liquid cooling synergistic intelligent temperature control method based on layered PCM according to claim 1, characterized in that: After the temperature control strategy is implemented, the specific steps for assessing the deviation between the current heat exchange capacity and the heat load demand based on real-time liquid cooling temperature control data and hot spot risk assessment values ​​are as follows: After the temperature control strategy is implemented, the hot spot risk assessment value is recalculated based on real-time liquid cooling temperature control data. When the hot spot risk assessment value is still greater than the hot spot risk threshold, the cooling regulation response assessment value is calculated in combination with the real-time liquid cooling temperature control data: the difference between the coolant outlet temperature and the coolant inlet temperature is added to a minimum term and then multiplied by the absolute value of the difference between the coolant outlet temperature and the ambient temperature to obtain the temperature difference driving factor; the temperature difference driving factor is divided by the product of the coolant flow rate, coolant density, and coolant specific heat capacity to obtain the heat transfer capacity factor per unit flow rate. Divide the difference between the hot spot risk assessment value and the hot spot risk threshold by the hot spot risk threshold, take the absolute value, and add one to obtain the thermal imbalance adjustment factor; multiply the unit flow heat exchange capacity factor and the thermal imbalance adjustment factor to obtain the cooling adjustment response assessment value.

7. The liquid cooling synergistic intelligent temperature control method based on layered PCM according to claim 1, characterized in that: The specific steps for correcting the cooling response coefficient based on the evaluation results, thereby optimizing the PCM collaborative temperature control strategy and achieving closed-loop temperature control are as follows: Based on a sample set of cooling regulation response assessment values ​​from historical data, where the hotspot risk assessment value first falls below the hotspot risk threshold after the implementation of a temperature control strategy, the upper and lower quartiles of the sample set are used as the target response interval. A deviation analysis is performed between the current cooling regulation response assessment value and the target response interval to construct a nonlinear proportional adjustment model based on the error ratio. The historical trend of the cooling response coefficient is introduced as a weighting function, and the cooling response coefficient is dynamically corrected using an exponential smoothing iterative method. The corrected cooling response coefficient is then used to update the bending coupling assessment value, dynamically adjusting the power pump speed, circulation pump speed, and three-way valve opening to achieve closed-loop temperature control.

8. A liquid-cooled synergistic intelligent temperature control system based on layered PCM, employing the liquid-cooled synergistic intelligent temperature control method based on layered PCM as described in any one of claims 1-7, characterized in that: include: The module comprises a data acquisition and preprocessing module, a thermal risk modeling and assessment module, a temperature control strategy hierarchical response module, and a closed-loop control adaptive optimization module, among which: The data acquisition and preprocessing module is used to acquire liquid cooling temperature control data in real time, and to perform time synchronization, abnormal data removal, smoothing filtering, normalization and dimensionless processing on the liquid cooling temperature control data to obtain preprocessed liquid cooling temperature control data. The thermal risk modeling and assessment module is used to calculate the sensible heat absorption and latent heat absorption of the outer and inner PCM layers based on the pre-processed liquid cooling temperature control data, assess the thermal imbalance risk of the conductor (15) surface, determine the current thermal state, and output a thermal risk warning signal. The temperature control strategy hierarchical response module is used to extract real-time liquid cooling temperature control data after the controller receives the thermal risk warning signal, evaluate the degree of weakening of thermal coupling performance by the bending state of the cable (4), trigger the hierarchical PCM collaborative temperature control strategy, and execute the hierarchical control instructions of the power pump (5), circulation pump (7) and three-way valve (6). The closed-loop control adaptive optimization module is used to evaluate the deviation between the current heat exchange capacity and the heat load demand based on real-time liquid cooling temperature control data and hot spot risk assessment value after the temperature control strategy is executed, and to correct the cooling response coefficient based on the evaluation results, thereby optimizing the PCM collaborative temperature control strategy and realizing closed-loop temperature control.

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