A dual-mode adaptive immersion phase change charging station thermal management system

Abstract

The application discloses a kind of bimodal adaptive immersion phase change charging station heat management systems, it is related to charging station heat management technical field, including, circulation management module, phase change temperature result is cyclically mapped and optimized operation operation, generates circulation scheme;Immersion cooling module, utilize circulation scheme intelligent regulation and control nano phase change slurry in immersion cooling tank adaptive flow, and dynamically switch between single-phase liquid cooling mode and phase change boiling mode, generate heat absorption data;Heat exchange module, heat absorption data are input into plate heat exchanger and carry out heat exchange, generate cooling liquid temperature data, and carry out dry cooler heat dissipation and waste heat recovery operation, generate backflow heat data;Through to charging station preliminary working condition data feature extraction, multidimensional nonlinear coupling analysis, and combined with pre-training model is iteratively optimized, high-precision prediction to dynamic, multimodal temperature change is realized, provides reliable basis for circulation management and cooling mode selection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging station heat management, in particular to a bimodal adaptive immersion phase change charging station heat management system. BACKGROUND

[0002] In recent years, with the rapid development of new energy vehicles and high-speed charging facilities, charging stations face significant heat dissipation and thermal safety challenges during high-power and large-current continuous operation. Existing heat management technologies mainly use single-mode cooling methods such as air cooling and liquid cooling. Cooling circuits are arranged between key components such as power electronic devices, cables, and charging guns to achieve heat conduction and dissipation. Some advanced solutions introduce phase change materials as passive heat storage media to improve heat capacity and buffering capacity. Immersion cooling technology is also gradually applied to high-power electronic devices, which uses insulating liquid to directly contact the heating elements to form an efficient heat dissipation interface, and transfers the waste heat through heat exchangers or dry coolers.

[0003] Under the existing technical conditions, the operation of the heat management system is usually based on preset temperature thresholds or fixed parameter adjustments, which cannot fully combine the coupling changes of external environment and real-time load for intelligent prediction and optimal control. Although some technical solutions attempt to improve heat dissipation performance by adding phase change materials or optimizing cooling circuits, there are still limitations in dynamic mode switching, real-time adjustment of cycle parameters, and multi-modal collaboration, especially in the switching between single-phase liquid cooling and phase change boiling, which lacks precise decision-making mechanisms, resulting in difficulties in balancing response speed and energy efficiency utilization of the heat management system. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a bimodal adaptive immersion phase change charging station heat management system, which solves the problems of slow response and insufficient energy efficiency utilization of the heat management system in the prior art.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] The present application provides a bimodal adaptive immersion phase change charging station heat management system, which includes a data acquisition module that acquires internal and external temperature, humidity, and charging power information of the charging station, performs denoising, normalization, and integration processing, and generates a preliminary working condition data set;

[0008] An intelligent prediction module uses intelligent algorithms to perform real-time prediction and optimization calculation on the preliminary working condition data set, and generates a phase change temperature result;

[0009] A cycle management module performs cycle mapping and optimization operation on the phase change temperature result, and generates a cycle scheme;

[0010] The immersion cooling module intelligently regulates the flow of nano-phase change slurry in the immersion cooling tank by using a circulation scheme, and dynamically switches between single-phase liquid cooling mode and phase change boiling mode to generate heat absorption data;

[0011] The heat exchange module inputs the heat absorption data into the plate heat exchanger for heat exchange to generate cooling liquid temperature data, and performs dry cooler heat dissipation and waste heat recovery operations to generate return heat data.

[0012] The active cooling module performs cable and charging gun active cooling operations on the return heat data to obtain joint temperature data, and performs real-time monitoring and intelligent adjustment, combined with the circulation scheme and the phase change temperature result, to generate a thermal management state.

[0013] As a preferred scheme of the dual-mode adaptive immersion phase change charging station thermal management system, the temperature, humidity and charging power information inside and outside the charging station are collected, denoised, normalized and integrated to generate a preliminary working condition data set, the specific steps are as follows,

[0014] The temperature, humidity and charging power information inside and outside the charging station are collected in real time, and the transient peak and noise are removed by wavelet filtering method to obtain working condition purification data.

[0015] The working condition purification data is normalized by interval scaling to obtain normalized working condition data, and is weighted and integrated to form a preliminary working condition data set.

[0016] As a preferred scheme of the dual-mode adaptive immersion phase change charging station thermal management system, the preliminary working condition data set is predicted and optimized in real time by using an intelligent algorithm to generate a phase change temperature result, the specific steps are as follows,

[0017] The preliminary working condition data set is feature extracted and time series arranged to obtain working condition feature sequence, and is multi-dimensional feature reorganized and matrix mapped to construct a working condition feature matrix.

[0018] The working condition feature matrix is analyzed by nonlinear coupling to generate a preliminary prediction result.

[0019] The preliminary prediction result is input into the pre-trained nonlinear coupling model for iterative optimization and error correction operation to output the phase change temperature result.

[0020] As a preferred scheme of the dual-mode adaptive immersion phase change charging station thermal management system, the phase change temperature result is cyclically mapped and optimized to generate a circulation scheme, the specific steps are as follows,

[0021] The phase change temperature result is mapped and converted according to the time sequence to generate a control vector set, and combined with nonlinear optimization operation to obtain an optimized control vector set.

[0022] The time series integration and interpolation operation is performed on the optimized control vector set to generate an optimized cycle sequence, and a cycle scheme is formed through time series integration and control mapping operation.

[0023] As a preferred scheme of the dual-mode adaptive immersion phase change charging station thermal management system, the flow of the nano-phase change slurry in the immersion cooling tank is intelligently regulated and dynamically switched between the single-phase liquid cooling mode and the phase change boiling mode to generate heat absorption data, and the specific steps are as follows,

[0024] Control decomposition and time step calculation are performed according to the cycle scheme to obtain the control vector of each time step, and the time series integration is associated with the historical cooling operation data to generate a regulation matrix;

[0025] The flow state and cooling mode of the nano-phase change slurry are intelligently adjusted using the regulation matrix, the heat exchange efficiency critical point is continuously monitored, and dynamic switching is performed between single-phase liquid cooling and phase change boiling to obtain an optimized heat absorption efficiency;

[0026] The optimized heat absorption efficiency is recorded and summarized by time step to generate heat absorption data.

[0027] As a preferred scheme of the dual-mode adaptive immersion phase change charging station thermal management system, the heat absorption data is input into the plate heat exchanger for heat exchange to generate cooling liquid temperature data, and the specific steps are as follows,

[0028] The heat absorption data is input into the plate heat exchanger according to the time sequence, and the flow matching and heat distribution adjustment are performed to form a cold liquid basic temperature set;

[0029] The cold liquid basic temperature set is subjected to nonlinear heat exchange calculation to obtain cooling liquid temperature data.

[0030] As a preferred scheme of the dual-mode adaptive immersion phase change charging station thermal management system, the dry cooler heat dissipation and waste heat recovery operation is performed to generate return heat data, and the specific steps are as follows,

[0031] The cooling liquid temperature data is input into the dry cooler according to the time sequence for temperature comparison and control determination to generate a trigger heat dissipation instruction set, and the cooling liquid flow and heat exchange coupling adjustment operation is performed to obtain a cold liquid residual temperature set;

[0032] The cold liquid residual temperature set is subjected to dry cooler waste heat recovery calculation to obtain a recovered heat sequence, and parameter correction and dynamic optimization operation is performed to generate return heat data.

[0033] As a preferred scheme of the dual-mode adaptive immersion phase change charging station heat management system, the cable and charging gun active cooling operation is performed on the backflow heat data to obtain joint temperature data, and the specific steps are as follows,

[0034] The backflow heat data is input into the charging station cooling channel according to the time sequence, and the air cooling and liquid cooling composite active cooling operation is performed, and the cooling intensity is adjusted according to the heat density and joint structure to obtain joint temperature change information.

[0035] The joint temperature change information is subjected to nonlinear coupling calculation and multidimensional adjustment operation to obtain joint temperature data.

[0036] As a preferred scheme of the dual-mode adaptive immersion phase change charging station heat management system, the cable and charging gun active cooling operation is performed on the backflow heat data to obtain joint temperature data, and the specific steps are as follows,

[0037] The joint temperature data is subjected to real-time monitoring and intelligent adjustment operation to obtain joint temperature sequence.

[0038] The joint temperature sequence is associated with the cycle scheme and the phase change temperature result to perform correlation analysis and state mapping operation to generate a heat management state.

[0039] As a preferred scheme of the dual-mode adaptive immersion phase change charging station heat management system, the nonlinear coupling model is constructed as follows,

[0040] The historical working condition data and corresponding temperature change, flow adjustment and heat recovery information are subjected to normalization processing and time sequence arrangement to form an operable working condition feature sequence.

[0041] According to the physical and process relationship between the working condition feature sequence and the temperature change, a multidimensional nonlinear mapping relationship is established, each dimension of the working condition feature sequence and the temperature output are coupled, the dependence and interaction logic between the variables are clarified, and a nonlinear coupling model is constructed.

[0042] The present application has the following advantages: through feature extraction, multidimensional nonlinear coupling analysis of the preliminary working condition data of the charging station, and iterative optimization combined with the pre-training model, high-precision prediction of dynamic and multi-modal temperature change is realized, which provides a reliable basis for cycle management and cooling mode selection; then the cycle scheme is converted into a time sequence control vector to adaptively regulate the flow of nano-phase change slurry, and dynamically switch between single-phase liquid cooling and phase change boiling mode, the heat absorption efficiency of the cooling medium is maximized, the thermal stability and energy efficiency of the system under different working conditions are ensured, a prediction-driven closed-loop heat management is formed, and the intelligence, reliability and overall efficiency of the charging station heat management system are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0044] Figure 1 A schematic diagram of a dual-mode adaptive immersion phase change charging station thermal management system.

[0045] Figure 2 A flowchart of data acquisition and intelligent prediction.

[0046] Figure 3 A flowchart of immersion cooling dual-mode switching and heat exchange.

[0047] Figure 4 A flowchart of active cooling and thermal management state generation. DETAILED DESCRIPTION

[0048] In order to make the above objectives, features and advantages of the present application more apparent and understandable, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0049] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0050] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0051] Reference Figures 1-4 For one embodiment of the present application, the embodiment provides a dual-mode adaptive immersion phase change charging station thermal management system, as shown in Figure 1 A dual-mode adaptive immersion phase change charging station thermal management system includes a data acquisition module, an intelligent prediction module, a cycle management module, an immersion cooling module, a heat exchange module and an active cooling module, including the following steps:

[0052] The data acquisition module acquires the temperature, humidity and charging power information inside and outside the charging station, performs denoising, normalization and integration processing, and generates a preliminary working condition data set.

[0053] Real-time acquisition of temperature, humidity and charging power information inside and outside the charging station, and removal of transient spikes and noise by wavelet filtering method to obtain clean working condition data.

[0054] Furthermore, real-time acquisition of temperature, humidity and charging power information inside and outside the charging station, and processing of temperature, humidity and charging power information inside and outside the charging station by wavelet filtering method to smooth abnormal fluctuation signals and maintain effective measurement change characteristics without loss, to obtain clean working condition data.

[0055] During processing, the collected signals are segmented according to time series, and each segment of signal is input into the wavelet filtering method for smoothing calculation. The degree of suppression of transient spikes is automatically adjusted by the weight of wavelet coefficient energy distribution, while the dynamic response of temperature, humidity and charging power information is maintained.

[0056] After continuous iteration processing by wavelet decomposition and reconstruction method, the temperature, humidity and charging power information of each time step are updated and corrected by residual correction method to generate a complete set of clean working condition data.

[0057] The clean working condition data is processed by interval scaling and normalization to obtain normalized working condition data, and by weighted integration to form a preliminary working condition data set.

[0058] Further, the clean working condition data is processed by interval scaling and normalization according to its respective dimension range, and the temperature, humidity and charging power information is mapped to the preset standard interval by linear normalization method. In the normalization process, the importance weight of each time step and the relative importance of temperature, humidity and charging power information in the thermal management target is combined to weight and integrate the temperature, humidity and charging power information, to directly generate a preliminary working condition data set.

[0059] It should also be noted that the preset standard interval setting process is based on the historical temperature, humidity and charging power data collected during the operation of the charging station, and the value range under different working conditions is calculated. Combined with the safe operation interval allowed by the equipment and the engineering experience range, the upper and lower limit boundaries of the temperature, humidity and charging power information parameters are determined respectively.

[0060] The determined boundary values are reasonably corrected to ensure that the temperature, humidity and charging power information can still be covered under extreme working conditions, while avoiding distortion of the normalization effect caused by excessively wide intervals. The value range of each type of parameter is mapped to a unified standardized interval.

[0061] The preset strategy combination setting process analyzes the control target and operation characteristics of each time step based on the circulation parameter matrix, the phase change temperature result, the junction temperature sequence and the heat management state, and extracts a key parameter combination that can be used for adjustment, such as a temperature gradient, a flow fluctuation and a heat recovery efficiency.

[0062] According to the optimization of heat absorption efficiency, reflux heat distribution and cold liquid residual temperature change, the control parameter combinations of different time steps are classified and matched to form a preliminary strategy set; a multi-dimensional linear mapping method is used to map the preliminary strategy set to a preset standard interval, to ensure that each strategy is feasible within the operation range of the corresponding time step, while taking into account the dynamic switching requirements of the single-phase liquid cooling, phase change boiling and composite cooling modes; the mapped strategy set is combined and optimized, the strategy priority is arranged and adjusted in time sequence, and a complete preset strategy combination is formed.

[0063] The intelligent prediction module uses intelligent algorithms to perform real-time prediction and optimization calculation on the preliminary working condition data set to generate a phase change temperature result, as shown in Figure 2 .

[0064] The preliminary working condition data set is subjected to feature extraction and time sequence arrangement to obtain a working condition feature sequence, and a working condition feature matrix is constructed through multi-dimensional feature reorganization and matrix mapping operations.

[0065] Furthermore, by analyzing the change trend of temperature, humidity and charging power at adjacent time points, calculating the fluctuation amplitude and identifying the periodic characteristics, the continuous change of each working condition parameter is converted into a numerical description, thereby forming a working condition feature sequence.

[0066] The working condition feature sequence is reorganized in a multi-dimensional space, and each working condition parameter is arranged and combined according to time steps and feature dimensions, and the reorganized multi-dimensional features are organized into a working condition feature matrix through matrix mapping operations, so that the temperature, humidity and charging power information in the matrix not only reflects the time sequence dynamics, but also embodies the correlation between multi-dimensional parameters, while maintaining the original logical order of working condition parameters and the integrity of working condition feature sequence.

[0067] The working condition feature matrix is subjected to nonlinear coupling analysis to generate a preliminary prediction result.

[0068] Furthermore, the temperature, humidity and charging power information in the working condition feature matrix is subjected to nonlinear correlation analysis in the time sequence and multi-dimensional parameter space, the coupling relationship and mutual influence law between parameters are identified, and the possible temperature response and energy transfer state of each time step are calculated in combination with historical working condition features and heat change trend, to form a preliminary prediction result.

[0069] The preliminary prediction result is input into a pre-trained nonlinear coupling model for iterative optimization and error correction operation, and a phase change temperature result is output.

[0070] Further, the preliminary prediction result is input into the pre-trained nonlinear coupling model according to a time sequence, and the temperature, humidity and charging power information are iteratively optimized and calculated through the internal multi-dimensional nonlinear mapping relationship of the model. In each iteration, the prediction error is corrected, and the actual working condition characteristics are compared and adjusted with the prediction result to minimize the prediction deviation and enhance the response capability to dynamic working conditions. After continuous iterative optimization processing, a complete phase change temperature result is output.

[0071] It should be noted that the nonlinear coupling model is constructed as follows:

[0072] The historical working condition data and corresponding temperature change, flow regulation and heat recovery information are normalized and time series arranged to form an operable working condition characteristic sequence.

[0073] According to the physical and process relationship between the working condition characteristic sequence and the temperature change, a multi-dimensional nonlinear mapping relationship is established to couple the working condition characteristic sequence and the temperature output, and the dependence and interaction logic between the variables are determined to construct the nonlinear coupling model.

[0074] It should be noted that the intelligent algorithm refers to a prediction and optimization closed loop from the working condition data to the phase change temperature. The intelligent algorithm first extracts and digitizes the time sequence characteristics of the temperature, humidity and charging power, and forms a working condition characteristic matrix through multi-dimensional reorganization and matrix mapping. Through nonlinear coupling analysis, the preliminary prediction of temperature response and energy transfer at each time step is obtained. Then, the preliminary prediction is input into the pre-trained nonlinear coupling model according to the time sequence for iterative optimization and error correction, so that the prediction and the actual working condition are continuously compared and adjusted to minimize the deviation, and a stable and reliable phase change temperature result is output.

[0075] The cycle management module performs cycle mapping and optimization operation on the phase change temperature result to generate a cycle scheme.

[0076] The phase change temperature result is mapped and converted according to the time sequence to generate a control vector set, and combined with nonlinear optimization operation to obtain an optimized control vector set.

[0077] Further, the phase change temperature result is processed according to the time sequence, and the temperature value at each time step is converted into a corresponding control parameter vector through mapping rules to form a preliminary control vector set.

[0078] After forming the control vector set, a nonlinear optimization operation is performed on each control vector. By calculating the variation difference and correlation between each time step control vector and the adjacent time step and related control parameters, the coupling relationship and mutual influence law between parameters are identified. Then, combined with the cooling flow characteristics and heat transfer efficiency, an iterative algorithm is used to adjust the control parameters, so that the temperature regulation response of each time step can match the expected thermal management target.

[0079] During the optimization process, the deviation of the control parameters is continuously corrected to ensure that the control vector is continuous in time and the logic is clear. Finally, an optimized control vector set is generated.

[0080] The optimized control vector set is integrated and interpolated in time sequence to generate an optimized cycle sequence. Through time sequence integration and control mapping operation, a cycle scheme is formed.

[0081] Further, the optimized control vector set is integrated in time sequence. First, the interval or missing value between consecutive time steps is supplemented using the interpolation method to smooth the connection of the control parameters at each time step, forming an optimized cycle sequence.

[0082] After generating the optimized cycle sequence, each time step control vector in the sequence is mapped to the cycle control strategy to analyze the response relationship of the control parameters to the flow and heat conduction of the nano-phase change slurry in the immersion cooling tank (such as flow rate change, local temperature peak, and heat transfer efficiency change). Combined with historical cooling operation data, the control strength and distribution of each time step are adjusted to make the optimized cycle sequence continuous in time and logically consistent, forming a cycle scheme that can be directly used for immersion cooling tank operation.

[0083] The immersion cooling module uses the cycle scheme to intelligently regulate the flow of nano-phase change slurry in the immersion cooling tank and dynamically switches between single-phase liquid cooling mode and phase change boiling mode to generate heat absorption data.

[0084] According to the cycle scheme, control decomposition and time step calculation are performed to obtain the control vector of each time step. Time sequence integration and historical cooling operation data correlation are performed to generate a regulation matrix.

[0085] Further, the cycle scheme is executed to decompose the control and calculate the time step. The control vector corresponding to each time step is calculated based on the control parameters, and the expression is:

[0086]

[0087] wherein, represents the control vector of time step , t represents the time step index, represents the temperature recorded in the immersion cooling tank at time step , and temperature information at time step temperature information at time step temperature information at time step temperature information at time step temperature information at time step temperature information at time step

[0088] temperature information at time step

[0089] temperature information at time step

[0090] temperature information at time step

[0091] temperature information at time step

[0092] temperature information at time step

[0093] The control results under the same or similar temperature gradient, flow speed and heat absorption efficiency in historical operation are compared with the state corresponding to the current control vector, and the past operation experience is combined with the real-time working condition to adjust the switching time and duration, so that the nano-phase change slurry obtains continuous, stable and efficient heat absorption performance in the whole cycle process, and the optimized heat absorption efficiency is obtained.

[0094] The optimized heat absorption efficiency is recorded and summarized according to time steps to generate heat absorption data.

[0095] Furthermore, the optimized heat absorption efficiency is recorded according to time steps, and the temperature gradient, flow characteristics and corresponding heat absorption information of each time step are saved in sequence to form a continuous time sequence record.

[0096] The records of each time step are summarized to integrate the change of the optimized heat absorption efficiency during the whole cycle period, calculate the average heat absorption level, peak heat absorption point and flow characteristic change trend of each time period, and generate complete heat absorption data.

[0097] The heat exchange module inputs the heat absorption data into the plate heat exchanger for heat exchange to generate cooling liquid temperature data, and performs dry cooler heat dissipation and waste heat recovery operation to generate backflow heat data, as shown in Figure 3 .

[0098] The heat absorption data is input into the plate heat exchanger according to the time sequence, and the flow matching and heat distribution adjustment are performed to form a cooling liquid basic temperature set.

[0099] Furthermore, the heat absorption data is input into the plate heat exchanger according to the time sequence, and the cooling liquid flow is matched at each time step, and the heat distribution is adjusted according to the heat absorption of the nano-phase change slurry to make the heat uniformly transmitted among the channels of the plate heat exchanger.

[0100] At the same time, the flow and temperature relationship of different time steps is analyzed, the heat exchange path is adjusted, the cooling liquid temperature of each time step is kept stable and matched with the heat absorption input, and finally a continuous cooling liquid basic temperature set is formed.

[0101] The cooling liquid basic temperature set is subjected to nonlinear heat exchange calculation to obtain cooling liquid temperature data.

[0102] Furthermore, the cooling liquid basic temperature set is subjected to nonlinear heat exchange calculation, the cooling liquid basic temperature set and the heat absorption data are coupled and operated by analyzing the temperature difference, flow distribution and heat transfer efficiency of each channel in the plate heat exchanger, the changes of fluid flow resistance and heat transfer coefficient in different time steps are considered, the cooling liquid temperature of each time step is accurately calculated and adjusted, and continuous cooling liquid temperature data is generated.

[0103] The cooling liquid temperature data is input into the dry cooler in time sequence to generate a set of heat dissipation instructions and to perform cooling liquid flow and heat exchange coupling adjustment operation to obtain a set of cooling liquid residual temperature.

[0104] Further, the cooling liquid temperature data is input into the dry cooler in time sequence to monitor and compare the cooling liquid temperature at each time step in real time, to perform control determination on the monitored temperature and preset temperature threshold and operating conditions (such as cooling liquid inlet pressure, ambient temperature and wind speed) to generate a set of heat dissipation instructions to clearly define the heat dissipation requirement of each channel and time step.

[0105] The cooling liquid flow is adjusted according to the set of heat dissipation instructions to realize coupling control of the cooling liquid and the dry cooler heat exchange by adjusting the flow distribution, inlet pressure and flow rate to evenly distribute the heat in each channel; the cooling liquid residual heat at each time step is continuously recorded to integrate the temperature change, residual heat and flow state at each time step to form a set of cooling liquid residual temperature.

[0106] It should be noted that the preset temperature threshold is set by organizing historical working condition data, analyzing the temperature change, humidity fluctuation and charging power information at each time step, extracting the temperature peak value, valley value and average trend; the upper and lower temperature limits are usually determined according to the safety operation requirement and heat absorption efficiency target, and the value range is taken as an example, which is 20-60℃.

[0107] The upper and lower temperature limits are mapped to the cycle parameters and control vectors of each time step, and are fine-tuned in combination with the preset strategy combination to make the preset temperature threshold adapt to different working condition changes, realize on-demand, time-sharing and adaptive mode switching, and ensure that the nano-phase change paste obtains continuous, stable and efficient heat absorption.

[0108] The operating conditions are obtained by real-time acquisition and recording of the ambient temperature, humidity and wind speed around the dry cooler, the cooling liquid inlet pressure, flow rate, flow speed and load condition, and are organized in time sequence to form the operating conditions corresponding to each time step.

[0109] The set of cooling liquid residual temperature is input into the dry cooler waste heat recovery calculation program in time sequence to perform energy conversion analysis on the cooling liquid residual temperature at each time step, to calculate the recoverable heat and form a sequence of recovered heat.

[0110] Further, the set of cooling liquid residual temperature is input into the dry cooler waste heat recovery calculation program in time sequence to perform energy conversion analysis on the cooling liquid residual temperature at each time step, to calculate the recoverable heat and form a sequence of recovered heat.

[0111] The temperature gradient, flow distribution and heat exchange efficiency are parameter corrected according to the sequence of recovered heat to adjust the recovery path and energy distribution to realize dynamic optimization operation of waste heat recovery; the recovered heat at each time step after optimization is summarized and integrated to form the data of returned heat.

[0112] actively cooling module, actively cooling operation of the backflow heat data through the cable and the charging gun, obtaining the joint temperature data, and performing real-time monitoring and intelligent adjustment, combining the circulation scheme and the phase change temperature result, generating the thermal management state, as shown in Figure 4 .

[0113] The backflow heat data is input into the cooling passage of the charging station according to the time sequence, and the air cooling and liquid cooling composite active cooling operation is performed, and the cooling intensity is adjusted according to the heat density and the joint structure, and the joint temperature change information is obtained.

[0114] Further, the backflow heat data is input into the cooling passage of the charging station according to the time sequence, and the air cooling and liquid cooling composite active cooling operation is performed, and the cooling intensity is adjusted according to the heat density and the joint structure, and the joint temperature change information is obtained.

[0115] During the execution of the air cooling and liquid cooling composite active cooling operation, according to the deviation of the joint temperature and the target temperature of each time step, the heat density distribution and the real-time flow rate of the cooling passage, the increase and decrease of the air cooling wind volume and the liquid cooling flow volume are dynamically adjusted, and at the same time, the joint geometry and the material thermal conductivity are combined to determine the required local cooling intensity of each joint, so as to perform real-time fine tuning and optimization of the cooling parameters (including air cooling wind volume, liquid cooling flow volume and cooling distribution order), so that the joint temperature gradually approaches the expected target and keeps continuous and smooth, ensuring that the temperature change of each joint is stable and controllable under different loads, ambient temperatures and working conditions, and generating continuous, complete and time-sequenced joint temperature change information.

[0116] The joint temperature change information is calculated and adjusted by a nonlinear coupling method, and the joint temperature data is obtained.

[0117] Further, the joint temperature change information of each time step is comprehensively analyzed, the heat transfer relationship between the joints, the local temperature difference and the influence of the cooling liquid or the phase change slurry flow on the temperature are considered at the same time, these factors are related to each other through matrix mapping method, the interaction between the joints is evaluated, and the cooling intensity, flow rate and heat exchange parameters are adjusted accordingly, so that the temperature change of each joint not only reflects its own conditions, but also reflects the coupling relationship with other joints, so that the joint temperature data is coordinated and consistent in the whole time sequence.

[0118] The joint temperature data is monitored and adjusted in real time, and the joint temperature sequence is obtained.

[0119] Further, the joint temperature data is monitored in real time according to the time sequence, intelligent adjustment operations are performed by analyzing the temperature change trend, gradient fluctuation and environmental influence factors of each joint, including adjusting the cooling liquid flow, cooling strength and local heat exchange efficiency, so that the joint temperature is kept in dynamic balance and optimal distribution at each time step, and finally a continuous joint temperature sequence arranged in time sequence is obtained.

[0120] The joint temperature sequence is combined with the cycle scheme and the phase change temperature result for correlation analysis and state mapping operation to generate a thermal management state.

[0121] Further, the joint temperature sequence is combined with the cycle scheme and the phase change temperature result for corresponding correlation according to the time sequence, state mapping operation is performed by analyzing the temperature change, cycle parameter adjustment and phase change temperature response at each time step, the thermal management characteristics at each time step are comprehensively evaluated, and a continuous thermal management state arranged in time sequence is generated.

[0122] To sum up, the present application realizes high-precision prediction of dynamic and multi-modal temperature change by feature extraction, multi-dimensional nonlinear coupling analysis of preliminary working condition data of the charging station, and iterative optimization combined with a pre-trained model, providing a reliable basis for cycle management and cooling mode selection; then the cycle scheme is converted into a time sequence control vector to adaptively control the flow of nano-phase change slurry and dynamically switch between single-phase liquid cooling and phase change boiling mode, realizing maximum thermal absorption efficiency of the cooling medium, ensuring the thermal stability and energy efficiency of the system under different working conditions, forming a prediction-driven closed-loop thermal management, thereby significantly improving the intelligence, reliability and overall efficiency of the charging station thermal management system.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A dual-modal adaptive immersion phase change charging station thermal management system, characterized in that: include, The data acquisition module collects temperature, humidity and charging power information inside and outside the charging station, performs noise reduction, normalization and integration processing to generate a preliminary operating condition dataset. The intelligent prediction module uses intelligent algorithms to perform real-time prediction and optimization calculations on the preliminary working condition dataset to generate phase change temperature results. The loop management module performs loop mapping and optimization operations on the phase transition temperature results to generate loop schemes. The immersion cooling module uses a circulation scheme to intelligently regulate the flow of nano-phase change slurry in the immersion cooling tank and dynamically switches between single-phase liquid cooling mode and phase change boiling mode to generate heat absorption data. The heat exchange module inputs heat absorption data into the plate heat exchanger for heat exchange, generates coolant temperature data, and performs heat dissipation and waste heat recovery operations for the dry cooler, generating return heat data. The active cooling module performs active cooling operations on the cable and charging gun based on the return heat data, obtains the connector temperature data, and performs real-time monitoring and intelligent adjustment. Combined with the circulation scheme and phase change temperature results, it generates a thermal management status.

2. The dual-modal adaptive immersion phase change charging station thermal management system as described in claim 1, characterized in that: The collected temperature, humidity, and charging power information inside and outside the charging station are then processed through noise reduction, normalization, and integration to generate a preliminary operating condition dataset. The specific steps are as follows. Real-time collection of temperature, humidity and charging power information inside and outside the charging station; removal of transient spikes and noise by wavelet filtering method to obtain operating condition purification data. The working condition purification data is processed by range scaling and normalization to obtain normalized working condition data, which is then weighted and integrated to form a preliminary working condition dataset.

3. The dual-modal adaptive immersion phase change charging station thermal management system as described in claim 2, characterized in that: The process of using intelligent algorithms to perform real-time prediction and optimization calculations on the preliminary working condition dataset to generate phase transition temperature results involves the following specific steps. Feature extraction and time series processing are performed on the preliminary working condition dataset to obtain the working condition feature sequence, and a working condition feature matrix is ​​constructed through multi-dimensional feature recombination and matrix mapping operations. Nonlinear coupling analysis is performed on the operating condition characteristic matrix to generate preliminary prediction results; The preliminary prediction results are input into the pre-trained nonlinear coupled model for iterative optimization and error correction, and the phase transition temperature results are output.

4. The dual-modal adaptive immersion phase change charging station thermal management system as described in claim 3, characterized in that: The process of performing cyclic mapping and optimization operations on the phase transition temperature results to generate a cyclic scheme involves the following steps: The phase transition temperature results are mapped and transformed according to the time series to generate a control vector set, and combined with nonlinear optimization calculations to obtain an optimized control vector set; The optimized control vector set is integrated and interpolated over time to generate an optimized cyclic sequence. The cyclic scheme is then formed through time series integration and control mapping operations.

5. The dual-modal adaptive immersion phase change charging station thermal management system as described in claim 4, characterized in that: The method of intelligently controlling the flow of nano-phase change slurry in an immersion cooling tank using a circulation scheme, and dynamically switching between single-phase liquid cooling mode and phase change boiling mode to generate heat absorption data, is as follows: Based on the cyclic scheme, control decomposition and time step calculation are performed to obtain the control vector for each time step. The time series is then integrated and correlated with historical cooling operation data to generate a control matrix. The flow state and cooling mode of nano-phase change slurry are intelligently adjusted by using a control matrix, the critical point of heat exchange efficiency is continuously monitored, and dynamic switching is performed between single-phase liquid cooling and phase change boiling to obtain optimized heat absorption efficiency. The optimization of heat absorption efficiency is recorded and summarized on a time-step basis to generate heat absorption data.

6. The dual-modal adaptive immersion phase change charging station thermal management system as described in claim 5, characterized in that: The process of inputting heat absorption data into a plate heat exchanger for heat exchange to generate coolant temperature data involves the following steps: The heat absorption data is input into the plate heat exchanger according to the time series to perform flow matching and heat distribution regulation, forming a cold liquid base temperature set. Nonlinear heat exchange calculations were performed on the base temperature set of the coolant to obtain coolant temperature data.

7. The dual-modal adaptive immersion phase change charging station thermal management system as described in claim 6, characterized in that: The process of performing heat dissipation and waste heat recovery operations in the dry cooler to generate reflux heat data is described in the following steps. The coolant temperature data is input into the dry cooler according to the time series for temperature comparison and control judgment, generating a set of trigger heat dissipation commands, and performing coupled regulation of coolant flow and heat exchange to obtain the coolant residual temperature set. Waste heat recovery calculations were performed on the residual heat collection of the cold liquid to obtain the recovered heat sequence. Parameter correction and dynamic optimization were then performed to generate reflux heat data.

8. The dual-modal adaptive immersion phase change charging station thermal management system as described in claim 7, characterized in that: The process of actively cooling the cable and charging gun based on the return heat data to obtain connector temperature data involves the following steps: The return heat data is input into the charging station cooling path according to the time series, and active cooling operation is carried out by a combination of air cooling and liquid cooling. The cooling intensity is adjusted according to the heat density and connector structure to obtain connector temperature change information. Nonlinear coupling calculations and multidimensional adjustment operations are performed on the joint temperature change information to obtain joint temperature data.

9. The dual-modal adaptive immersion phase change charging station thermal management system as described in claim 1, characterized in that: The process involves real-time monitoring and intelligent adjustment, combining the circulation scheme and phase change temperature results to generate a thermal management status. The specific steps are as follows: Real-time monitoring and intelligent adjustment of joint temperature data are performed to obtain a joint temperature sequence; By combining the joint temperature sequence with the cyclic scheme and phase change temperature results, correlation analysis and state mapping operations are performed to generate the thermal management state.

10. The dual-modal adaptive immersion phase change charging station thermal management system as described in claim 3, characterized in that: The nonlinear coupling model is constructed as follows. Historical operating condition data and corresponding temperature changes, flow regulation and heat recovery information are normalized and time series processed to form an operable operating condition characteristic sequence. Based on the physical and technological relationship between the operating condition characteristic sequence and temperature change, a multidimensional nonlinear mapping relationship is established, the operating condition characteristic sequence of each dimension is coupled with the temperature output, the dependency and interaction logic between each variable is clarified, and a nonlinear coupling model is constructed.

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