A pcm thermal storage and dual channel power storage station battery thermal management method and system

Abstract

The present application relates to the technical field of PCM heat storage, and particularly relates to a PCM heat storage and double-flow channel power storage station battery heat management method and system; the method comprises the following steps: a sensor of an energy storage battery unit group collects temperature and pressure data, a data processor analyzes the expansion degree of a PCM composite cold plate, and a control signal is formed; the data processor controls the opening combination of a main flow channel and an auxiliary flow channel according to the control signal, so that cooling water flows through the corresponding channel, and efficient heat exchange is realized; under extreme temperature conditions, the data processor triggers a four-way reversing valve to switch the heat dissipation or heat supply mode, and simultaneously links a heat exchanger to adjust the temperature of a cooling medium, so that the stable working temperature of the energy storage battery unit group is maintained. The system comprises an energy storage battery unit group, a data processor, a four-way reversing valve, an electromagnetic valve, a heat exchanger, a PCM composite cold plate, a PCM composite layer plate, an auxiliary flow channel and a main flow channel. The present application combines the phase change material and the single-phase liquid cooling technology, and can fully exert the synergistic effect of both.

Description

Technical Field

[0001] This invention relates to the field of PCM thermal storage technology, and in particular to a method and system for thermal management of PCM thermal storage and dual-channel energy storage power station batteries. Background Technology

[0002] Phase change materials (PCMs) achieve efficient thermal management through phase changes (solid-liquid / solid-gas) during heat absorption / release, offering unique advantages in heat dissipation. PCMs will be deeply integrated with technologies such as microchannels and heat pipes to form a "passive-active" synergistic heat dissipation system, becoming a standard feature in thermal management for high-power electronics and new energy fields. PCMs (melting point 30-40℃) store waste heat during charging and release heat during cold starts. Relying on the material's inherent properties, they require no active components such as fans / pumps, making them suitable for energy-constrained scenarios. PCMs absorb a large amount of heat during phase change while maintaining almost a constant temperature (isothermal characteristic), resulting in a heat storage density 5-10 times that of sensible heat materials and superior response speed compared to any active cooling system. However, due to the limitations of PCMs' inherent properties, using PCMs alone as the energy storage material in a thermal management system cannot dynamically control different heat dissipation conditions. When energy storage stations operate under constant heat dissipation conditions in a defined environment, a reasonable and effective heat dissipation system combined with PCM devices must be established to achieve effective heat dissipation.

[0003] Prior art 1, Chinese Patent Application No. 202510240093.3, discloses a thermal management method, system, computing device, and medium for a battery energy storage power station, relating to the field of battery cooling control technology, and solving the problems of untimely control and high energy consumption in traditional methods. This method is applied to a battery energy storage power station and specifically includes: acquiring battery temperature data; selecting either a first control strategy or a second control strategy to control the cooling unit to cool the battery based on the temperature data; wherein the first control strategy is based on the MPC algorithm, and the second control strategy is based on the MPC algorithm and its optimal expected cost, which is calculated using the SDP algorithm. Although using the optimal expected cost of the MPC algorithm calculated by the SDP algorithm as a constraint allows the controller to reduce the energy consumption required for temperature control while maintaining temperature control capability when using the second control strategy to cool the battery, significantly improving the economics of the battery energy storage power station; however, the multi-flow design of the dual-channel system increases the complexity of the channel structure, easily leading to gas-liquid stratification or uneven flow distribution.

[0004] Prior art two, Chinese patent application number: 202411048869.3, relates to a battery thermal management system, a charging station, and a control method for a charging station. The battery thermal management system includes a storage tank and a heat exchange tank. The storage tank is adapted to be connected to a first port of the battery heat exchange system and is used to store the cooling medium discharged from the battery heat exchange system. The heat exchange tank is adapted to be connected to a second port of the battery heat exchange system and is used to provide the battery heat exchange system with a pre-stored cooling medium within a preset temperature range. By controlling the connection between the storage tank and the battery heat exchange system, the cooling medium within the battery heat exchange system can be emptied, thereby preventing the original cooling medium in the battery heat exchange system from affecting the temperature of the cooling medium flowing into the battery heat exchange system from the heat exchange tank, thus improving the cooling or heating efficiency of the battery pack. Furthermore, since the cooling medium within the preset temperature range is pre-prepared in the heat exchange tank, the battery pack can rapidly heat up or cool down under the heating or cooling of the cooling medium flowing into the heat exchange tank, improving battery life. However, the collaborative control mechanism of the PCM and the dual-channel system is not yet mature and lacks dynamic adjustment logic.

[0005] Prior art three, Chinese patent application number 202510722513.1, relates to the field of battery swapping technology and discloses a battery thermal management method, device, vehicle, and medium. The method includes: determining a target battery swapping station, where the target battery swapping station is the one the user will visit; predicting the predicted temperature of the depleted battery when the vehicle arrives at the target battery swapping station; predicting the adjustment time to adjust the predicted temperature of the depleted battery to the preset charging temperature range when the predicted temperature of the depleted battery is not within the preset charging temperature range; and activating the battery thermal management system based on the adjustment time to adjust the depleted battery to the preset temperature range. Although this reduces the energy consumption of the vehicle when regulating the battery temperature, the liquid cooling system in the dual-channel system requires a water pump to drive the coolant flow, and the air cooling system requires a fan to provide airflow, both of which increase the system's energy consumption.

[0006] Current technologies 1, 2, and 3 suffer from problems such as complex flow channel structures and uneven flow, imperfect collaborative control strategies, and high system energy consumption. Therefore, this invention provides a method and system for battery thermal management in PCM thermal storage and dual-channel energy storage power stations. Summary of the Invention

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In one aspect, the present invention provides a method for battery thermal management in PCM thermal storage and dual-channel energy storage power stations, comprising the following steps:

[0009] The data processor adjusts the first and second solenoid valves according to the control signals of the energy storage battery unit group, controls the opening combination of the main flow channel and the auxiliary flow channel, so that the cooling water flows through the corresponding channel to achieve efficient heat exchange.

[0010] In one optional implementation, the process of controlling the opening combination of the main flow channel and the auxiliary flow channel includes the following steps:

[0011] The solenoid valve opening command, flow channel switching sequence, and temperature adjustment range are selected from the control signals and coupled to obtain the real-time regional expansion difference. The regional expansion difference is compared with the preset material yield threshold. If it exceeds the material yield threshold, the flow channel reconstruction mechanism is triggered, and the main-auxiliary flow channel parallel mode is started.

[0012] During the construction of the main channel, high heat accumulation areas are identified based on the temperature gradient vector direction in the temperature distribution data, and mapped to high flow rate demand areas. Corresponding flow configuration schemes are generated through nonlinear flow rate allocation. The control strategy of the auxiliary channel calculates the expansion distribution field of deformation stress values ​​in real time, monitors the deformation stress data of the PCM composite cold plate, performs differential calculations with the reference plane, and extracts the spatial coordinates and amplitude information of the positive and negative expansion peak regions. The expansion values ​​are converted into compensation flow values ​​with opposite signs to generate anti-phase compensation flow commands. Negative flow compensation is applied in the positive expansion peak region, and positive flow compensation is applied in the negative expansion peak region. By extracting the spatial coordinates and amplitude information of the expansion peak region, anti-phase compensation flow commands are generated to apply negative flow compensation in the positive expansion peak region and positive flow compensation in the negative expansion peak region, forming an inhibitory flow opposite to thermal deformation.

[0013] The PCM phase change activity is calculated in real time by monitoring the microstate of the phase change material and converted into a branch flow ratio adjustment coefficient. This coefficient is then input to the pulse width modulation controller, which drives the first and second solenoid valves to perform dynamic opening adjustment, thereby achieving precise redistribution of the main and auxiliary flow channels.

[0014] The data processor synchronously accesses the deformation rate monitoring data of the PCM composite cold plate. When it detects that the deformation response of a region of the PCM composite cold plate lags behind the expected value, it triggers the viscosity compensation mechanism. By generating a high-frequency pulse flow sequence, a periodic shear excitation is formed in the lagging region.

[0015] In one optional implementation, the process of obtaining the real-time regional dilatation difference through coupled calculation includes the following steps:

[0016] After receiving the control signal, the data processor first performs control signal component analysis; it then separates three core parameters from the control signal: the duty cycle characteristic value of the solenoid valve opening command, the phase encoding of the flow channel switching sequence, and the scalar data of the temperature regulation amplitude.

[0017] Three core parameters are input into the multimodal data fusion field for coupling calculation. The duty cycle characteristic value of the solenoid valve opening command is convolved with the temperature adjustment amplitude scalar to generate the thermal-fluid coupling strength coefficient. At the same time, the phase encoding of the flow channel switching sequence is interpreted into spatial frequency components through discrete Fourier transform. The spatial frequency components are multiplied with the thermal-fluid coupling strength coefficient to generate a dynamic stress distribution image.

[0018] Based on the dynamic stress distribution image, the stress gradient norm between adjacent regions is calculated. The stress gradient norm values ​​are then weighted and averaged to synthesize a real-time regional expansion difference index, which characterizes the degree of discretization of the expansion behavior between different regions of the PCM composite cold plate.

[0019] In one alternative implementation, the process of generating the thermal-fluid coupling strength coefficient includes the following steps:

[0020] The duty cycle characteristic value of the solenoid valve opening command represents the time-domain pulse density of the expected fluid flux. It is then convolved with the scalar data of the temperature regulation amplitude. The scalar data of the temperature regulation amplitude reflects the intensity of the global heat load. The time-temperature convolution operation integrates and superimposes the fluid pulse density and heat load intensity in the time domain at each calculation time step, and outputs the heat-fluid coupling strength coefficient.

[0021] The phase code of the flow channel switching sequence is fed into a spectrum decoder, which is a set of digital sequences representing the opening order and position of the flow channel space. The spectrum decoder performs space-frequency decomposition transformation on the phase code, decomposing the complex spatial sequence into a series of basic spatial fluctuation components with different periods and amplitudes. The basic spatial fluctuation components are the spatial frequency components.

[0022] The spatial frequency component is spatially modulated by multiplying it with the thermal-fluid coupling strength coefficient to generate a dynamic stress distribution image that can predict how the internal stress of the cooling plate changes with time and space.

[0023] In one optional implementation, the process of performing spatial modulation dot product operation includes the following steps:

[0024] The thermal-fluid coupling strength coefficient is used as a global weighting factor to scale the amplitude of each fundamental spatial fluctuation component contained in the spatial frequency component.

[0025] The original amplitude of each basic spatial fluctuation component is multiplied by the thermal-fluid coupling strength coefficient to generate a new set of modulated spatial fluctuation components;

[0026] The new spatial fluctuation components are synthesized through spatial recombination calculation; spatial recombination is the inverse process of spatial frequency decomposition transformation, which re-integrates the modulated frequency components into a two-dimensional field distribution map of a cooling plate region; the two-dimensional field distribution map is the dynamic stress distribution image.

[0027] In one optional implementation, the process of synthesizing new spatial fluctuation components through spatial recombination calculations includes the following steps:

[0028] A digital spatial mapping model of the cooling plate area is established, dividing the surface of the cooling plate into regular spatial grid units; each spatial fluctuation component includes its spatial periodicity attribute and modulated amplitude value; spatial reorganization regards each spatial fluctuation component as a basic building unit, and according to the distribution pattern determined by its spatial periodicity attribute, the amplitude value of the spatial fluctuation is allocated to all corresponding spatial grid units in the digital spatial mapping model.

[0029] In the spatial mapping model, each spatial grid cell is given a specific value, representing the strength of the relative stress level at that location, predicted based on the current spatial distribution pattern of the flow channel and the intensity of the heat-flow interaction.

[0030] The numerical spatial mapping model is output as a dynamic stress distribution image, which shows the spatial variation of stress magnitude inside the cooling plate in the form of a two-dimensional field distribution map.

[0031] In one alternative implementation, the process of assigning the amplitude values ​​of spatial fluctuations to all corresponding spatial grid cells in the digital spatial mapping model includes the following steps:

[0032] Read the spatial periodicity attribute of the first spatial fluctuation component. The spatial periodicity attribute determines the spatial interval and direction of the stress fluctuation represented by the spatial fluctuation component recurring on the surface of the cold plate. Based on the spatial interval and direction, in the established digital spatial mapping model, determine all spatial grid cells affected by the spatial fluctuation component, and assign the amplitude value of the spatial fluctuation component to the spatial grid cells according to the weight ratio specified by its inherent spatial distribution pattern; this is the amplitude allocation of a spatial fluctuation component.

[0033] After the first component is assigned, the second spatial fluctuation component is processed sequentially. Its spatial periodicity attribute is read to determine the range of influence, and then the amplitude value is assigned to the corresponding new set of spatial grid cells according to its own mode. This process is repeated until all modulated spatial fluctuation components in the list have completed the assignment of their amplitude values.

[0034] The operation is performed on each independent grid cell in the spatial mapping model: the algebraic summation of all temporary assignments from different spatial fluctuation components in the independent grid cell is performed to obtain a final composite value; the composite value is the predicted relative stress level of the local area of ​​the cold plate represented by the grid cell; after all spatial grid cells have been algebraically summed, the dynamic stress distribution image is obtained.

[0035] In one optional implementation, the system further includes sensors in the energy storage battery cell array that collect temperature and pressure data and transmit them to a data processor. The data processor analyzes the degree of expansion of the PCM composite cold plate and generates a control signal.

[0036] In one optional implementation, the data processor triggers a four-way reversing valve to switch between heat dissipation and heating modes under extreme temperature conditions, while simultaneously coordinating with the heat exchanger to adjust the temperature of the cooling medium and maintain a stable operating temperature for the energy storage battery cell group.

[0037] Another aspect of the present invention provides a PCM thermal storage and dual-channel energy storage power station battery thermal management system, implementing the PCM thermal storage and dual-channel energy storage power station battery thermal management method, comprising: energy storage battery unit group, data processor, four-way reversing valve, second solenoid valve, first solenoid valve, compressor, heat exchanger, one-way valve, PCM composite cold plate, PCM composite layer plate, main channel inlet, auxiliary channel inlet, auxiliary channel, and main channel;

[0038] The energy storage battery unit group is connected to the data processor via a data cable. The data processor is connected to the four-way reversing valve and the first solenoid valve via a data cable. The four-way reversing valve is connected to the second solenoid valve, the compressor, and the heat exchanger. The heat exchanger is connected to the check valve. The check valve is connected to the first solenoid valve and the energy storage battery unit group. The energy storage battery unit group transmits pressure signals to the data processor. The data processor transmits mode switching signals to the four-way reversing valve. The data processor transmits auxiliary flow channel and main flow channel opening and closing signals to the energy storage battery unit group. The data processor transmits flow regulation signals to the first solenoid valve.

[0039] The upper and lower sides of the energy storage battery unit are respectively equipped with PCM composite cold plates and PCM composite layers.

[0040] PCM composite cold plate consists of five layers; the main flow channel inlet is located between the first and fifth layers, and the auxiliary flow channel inlets are located around the main flow channel inlet; the auxiliary flow channel inlets are connected by auxiliary flow channels; the main flow channel inlet has multiple main flow channels.

[0041] This invention uses sensors to collect real-time data on battery temperature and the expansion state of the PCM composite cold plate, which is then analyzed and processed by a data processor to generate precise control signals. Secondly, based on these control signals, the data processor dynamically adjusts the opening and closing states of the first and second solenoid valves, enabling intelligent switching and combined operation of the main flow channel and auxiliary flow channel, thus optimizing the cooling water flow path. Finally, under extreme temperature conditions, the system ensures that the energy storage battery remains within a suitable operating temperature range through mode switching of the four-way reversing valve and temperature regulation of the heat exchanger. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 This is a flowchart of the steps of the PCM thermal storage and dual-channel energy storage power station battery thermal management method provided in Embodiment 1 of the present invention;

[0044] Figure 2 This is a schematic diagram of the PCM thermal storage and dual-channel energy storage power station battery thermal management method provided in Embodiment 1 of the present invention;

[0045] Figure 3 This is a process diagram of forming a control signal provided in Embodiment 2 of the present invention;

[0046] Figure 4 This is a process diagram of controlling the opening combination of the main flow channel and the auxiliary flow channel provided in Embodiment 5 of the present invention;

[0047] Figure 5 This is a process diagram of the data processor triggering the four-way reversing valve to switch between heat dissipation and heating modes, as provided in Embodiment 11 of the present invention.

[0048] Figure 6 This is a schematic diagram of the PCM thermal storage and dual-channel energy storage power station battery thermal management system provided in Embodiment 12 of the present invention;

[0049] Figure 7 This is a structural diagram of the PCM composite cold plate and PCM composite laminate provided in Embodiment 12 of the present invention;

[0050] Figure 8 This is a schematic diagram of the single-layer PCM composite cold plate structure provided in Embodiment 12 of the present invention. Figure 1 ;

[0051] Figure 9 This is a schematic diagram of the single-layer PCM composite cold plate structure provided in Embodiment 12 of the present invention. Figure 2 ;

[0052] Figure 10This is a schematic diagram of the single-layer PCM composite cold plate structure provided in Embodiment 12 of the present invention. Figure 3 ;

[0053] Figure 11 A block diagram of the electronic device provided by the present invention;

[0054] Figure 12 A block diagram of a computer-readable storage medium provided for this invention;

[0055] Reference numerals: 1. Energy storage battery unit; 2. Data processor; 3. Four-way reversing valve; 4. Second solenoid valve; 5. First solenoid valve; 6. Compressor; 7. Heat exchanger; 8. Check valve; 9. PCM composite cold plate; 10. PCM composite layer; 11. Main flow channel inlet; 12. Auxiliary flow channel inlet; 13. Auxiliary flow channel; 14. Main flow channel; 15. Central processing unit / microprocessor / main control chip, etc.; 16. Storage medium; 17. Data bus; 18. Input / output bus / external bus / device bus, etc.; 19. Display; 20. Input / output device; 21. Computer-readable instructions; 22. Non-transitory computer-readable storage medium. Detailed Implementation

[0056] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0057] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0058] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0059] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0060] Example 1: As Figure 1 As shown in the figure, an embodiment of the present invention provides a battery thermal management method for PCM thermal storage and dual-channel energy storage power stations, comprising the following steps:

[0061] Step S100: The sensors of the energy storage battery unit group collect temperature and pressure data and transmit them to the data processor. The data processor analyzes the expansion degree of the PCM composite cold plate and generates a control signal.

[0062] Step S200: The data processor adjusts the first solenoid valve and the second solenoid valve according to the control signal, controls the opening combination of the main flow channel and the auxiliary flow channel, so that the cooling water flows through the corresponding channel to achieve efficient heat exchange;

[0063] Step S300: Under extreme temperature conditions, the data processor triggers the four-way reversing valve to switch between heat dissipation and heating modes, and simultaneously links the heat exchanger to adjust the temperature of the cooling medium to maintain the stable operating temperature of the energy storage battery unit group.

[0064] In the above embodiments, the present invention achieves efficient thermal management through the synergistic effect of three steps. First, the battery temperature and the expansion state data of the PCM composite cold plate are collected in real time by sensors, and the data is analyzed and processed by a data processor to generate precise control signals. Second, based on the control signals, the data processor dynamically adjusts the opening and closing states of the first and second solenoid valves to achieve intelligent switching and combined operation of the main flow channel and the auxiliary flow channel, optimizing the cooling water flow path. Finally, under extreme temperature conditions, the system ensures that the energy storage battery is always within a suitable operating temperature range through mode switching of the four-way reversing valve and temperature regulation of the heat exchanger.

[0065] In summary, please refer to the appendix for the specific principles. Figure 2 This embodiment achieves real-time temperature monitoring and dynamic response based on PCM phase change characteristics; intelligent switching of the dual-channel structure improves heat dissipation efficiency and temperature uniformity; establishes a thermal management mode adaptable to different ambient temperatures to ensure stable system operation; and realizes optimized energy utilization of the cooling system. It solves the thermal management problem of energy storage batteries during charging and discharging, improving system reliability and energy efficiency.

[0066] Extreme temperature conditions refer to two situations that exceed the normal operating temperature range of the energy storage battery: high-temperature extreme conditions, where the battery temperature exceeds the upper safety threshold, the expansion rate of the PCM composite cold plate reaches the preset warning value, and the conventional heat dissipation mode cannot maintain temperature stability; and low-temperature extreme conditions, where the ambient temperature is lower than the battery's allowable lower operating limit, the battery's self-heating cannot maintain a suitable temperature, and the phase change of the PCM material causes the system temperature to continuously decrease. Extreme conditions are determined by the following technical parameters: the temperature sensor continuously exceeds the set threshold range, the PCM expansion rate exceeds the 75% safety limit, and the cooling medium temperature adjustment reaches the system capacity limit. Under these conditions, the mode switching mechanism is activated, changing the heat conduction path through the four-way reversing valve, and the heat exchanger switches the working medium temperature to ensure that the battery operates within the allowable temperature window. The determination is based entirely on the real-time comparison of sensor data and preset thresholds, without relying on human intervention.

[0067] Example 2: Figure 3 As shown, based on Embodiment 1, the process of forming a control signal in step S100 provided in this embodiment of the invention includes the following steps:

[0068] Step S101: The temperature sensor array collects surface temperature distribution data of the energy storage battery cell, and the pressure sensing module simultaneously acquires the deformation stress value of the phase change material container. The temperature distribution data together constitute the initial feature matrix.

[0069] Step S102: After the temperature distribution data is calculated in three dimensions, a heat flux density distribution map is generated. At the same time, the deformation stress value is converted into a phase change completion curve through the constitutive equation of the phase change material. The data processor performs spatiotemporal registration of the heat flux density distribution map and the phase change completion curve, and uses a feature fusion algorithm to generate a composite thermodynamic state map. The high temperature accumulation region and the phase change hysteresis region in the composite thermodynamic state map are marked as key control regions.

[0070] Step S103: Based on the composite thermodynamic state spectrum, construct a dynamic weighting coefficient matrix, where each weighting factor corresponds to the control priority of the solenoid valve; the weighting factors of the key control area are automatically increased to form a preliminary control strategy table; the preliminary control strategy table is processed by the heat dissipation path optimization program to output control signals containing solenoid valve opening commands, flow channel switching sequences and temperature adjustment amplitudes.

[0071] In the above embodiments, this embodiment establishes a dynamic mapping relationship between heat flux density distribution and phase change completion degree by calculating the three-dimensional gradient of temperature distribution data and converting the material constitutive equation of deformation stress value, realizing the coordinated monitoring of thermo-mechanical coupled field; avoiding the limitations of traditional single-parameter control and improving the accuracy of thermal state assessment. The spatial marking function of composite thermodynamic state spectrum enables the system to accurately identify high-temperature accumulation areas and phase change hysteresis areas, and adjust the control priority of solenoid valves in combination with dynamic weight coefficient matrix; the adaptive allocation strategy based on thermodynamic state ensures that cooling resources are preferentially applied to areas with concentrated heat load, optimizing heat dissipation efficiency. After the preliminary control strategy table is processed by the heat dissipation path optimization program, the output control signal not only includes the preset solenoid valve opening command, but also integrates the comprehensive decision of flow channel switching sequence and temperature adjustment amplitude; during execution, it is fed back to the data acquisition module to form a closed-loop correction mechanism, enabling the system to adapt to the dynamic response characteristics of phase change materials in real time and improve the control stability under extreme conditions.

[0072] In summary, this embodiment achieves precise matching and adaptive optimization of cooling strategies through the generation of thermodynamic state maps and dynamic weight adjustment, effectively improving the thermal management reliability of energy storage batteries.

[0073] Example 3: Based on Example 2, the process of generating a composite thermodynamic state spectrum using a feature fusion algorithm in step S101 of this embodiment of the invention includes the following steps:

[0074] Step S1011: The temperature distribution data is divided into discrete voxel units, and each discrete voxel unit records spatial coordinates and temperature values; heat flux vectors are calculated for adjacent voxel units to establish a three-dimensional heat flux field. After anisotropic filtering, a heat flux density distribution map with directional characteristics is generated, where the length of the heat flux vector reflects the local heat transfer intensity.

[0075] Step S1012: Input the deformation stress value into the material response analysis, establish the constitutive equation of the phase change material based on the nonlinear creep characteristics of the phase change material; decompose the time-domain stress fluctuation into the latent heat release component of phase change and the elastic deformation component through stress relaxation spectrum inversion calculation; fit the evolution curve of each component using the phase change kinetic equation, and output the phase change completion curve characterizing the phase change process of the phase change material, whose curvature change reflects the spatiotemporal difference of the phase change rate.

[0076] Step S1013: The spatial coordinates of the heat flux density distribution map are aligned with the time coordinates of the phase transition completion curve through affine transformation to establish a heat flux-phase transition coupling parameter space; a dynamic matching window is set in the heat flux-phase transition coupling parameter space, and the window size is automatically adjusted as the gradient of the phase transition completion changes; the feature fusion algorithm performs convolution operation within the matching window to extract the covariant features of the heat flux vector and the phase transition rate, and generates a composite thermodynamic state map with local adaptability;

[0077] Step S1014: The identification of key control regions in the composite thermodynamic state spectrum adopts a two-way threshold screening mechanism; the boundary of the high-temperature accumulation region is determined by the extreme point of the second derivative of the heat flux density distribution map, and the phase change lag region is located by the curvature change point of the phase change completion curve; the overlapping part of the high-temperature accumulation region and the phase change lag region is marked as the primary control region, and the non-overlapping part is divided into secondary control regions according to the thermal history record.

[0078] The three-dimensional heat flux vector field is calculated using the temperature difference between discrete voxel elements, where the direction and amplitude of each vector reflect the direction and intensity of heat flux, respectively. Simultaneously, based on the stress-strain hysteresis characteristics of phase change materials, a nonlinear constitutive model is used to decompose the measured stress data into viscoplastic and elastic deformation components related to the latent heat of phase change, and the phase change process curve is obtained by fitting the phase change kinetic equation. The constitutive equation for phase change materials, based on the Schappery nonlinear constitutive model framework in generalized viscoelasticity theory, establishes a stress-strain relationship considering the latent heat effect of phase change by introducing the phase change volume fraction as an internal variable. Inheriting the ability of classical models to describe material creep and relaxation behavior, a new phase change-induced viscoplastic component is added, reflecting the decoupling algorithm between the latent heat release process and the elastic component. The stress relaxation spectrum inversion calculation employs an improved Prony series expansion method. The phase transition kinetic equations adopt the extended Avrami phase transition kinetic equations, introducing stress coupling terms to reflect the influence of mechanical loads on the phase transition rate. The equations retain core parameters such as the pre-exponential factor and phase transition activation energy, but replace the traditional isothermal assumption with a non-equilibrium correction term based on local heat flux density, thus adapting to transient thermo-mechanical coupling scenarios. The mathematical tools used for curvature analysis are derived from Frenet frame theory in differential geometry.

[0079] For high-temperature accumulation zones, the extreme points of the Laplacian operator (i.e., [missing information]) are detected by performing second derivative calculations on the heat flux density distribution map. The extreme points (local maxima) correspond to regions where the heat flow direction changes abruptly, and the lines connecting them naturally form the boundaries of heat accumulation zones. For the phase transition lag region, Frenet frame analysis is performed on the phase transition completion curve. When abrupt changes in curvature K exceeding a threshold, it is determined that there is a phase transition kinetic lag phenomenon at that spatiotemporal coordinate. The set of spatiotemporal coordinates of all abrupt changes constitutes the phase transition lag region. The spatial intersection of the two types of boundaries is the primary control region, and the non-overlapping regions are divided into secondary regions based on historical heat flow accumulation.

[0080] In the above embodiments, this embodiment achieves cross-scale coupling analysis of heat transfer parameters and material phase change dynamics through the reconstruction of the three-dimensional heat flow field of discrete voxel units and the stress component decomposition of the constitutive equation of the phase change material. The spatial vector field provided by the heat flux density distribution map and the temporal evolution characteristics depicted by the phase change completion curve form a complete spatiotemporal correlation mapping in the heat flux-phase change coupling parameter space. The adaptive adjustment characteristic of the dynamic matching window size enables the system to automatically optimize the feature extraction range according to the gradient change of the phase change completion. The capture of the covariant features of heat flux vector and phase change rate during convolution operation ensures that the composite thermodynamic state map can reflect the local thermodynamic state while maintaining the continuity of global parameters. The bidirectional threshold screening mechanism achieves precise spatial positioning of key control regions through the dual discrimination criteria of second derivative extrema points and curvature change points. The hierarchical division of primary and secondary control regions provides a structured decision basis for the dynamic priority allocation of subsequent cooling strategies. The introduction of thermal history records further enhances the temporal correlation of regional hierarchy.

[0081] In summary, this embodiment integrates the traditionally separate thermal field analysis and material state monitoring into a unified coupled analytical framework. Through spatiotemporal registration and dynamic feature extraction, it achieves a full-dimensional characterization of the thermodynamic state of the phase change energy storage system.

[0082] Example 4: Based on Example 3, the process of automatically adjusting the window size in step S1013 of this embodiment of the invention as the phase transition completion degree changes includes the following steps:

[0083] Step S10131: Normalize the spatial coordinate system of the heat flux density distribution map to obtain a standardized three-dimensional heat flux field spatial grid. At the same time, sample the time series of the phase transition completion curve at equal intervals and convert it into a discrete time grid with the same dimension. The two grids establish a correspondence through space-time mapping. The curvature distribution of the phase transition completion curve is used as an adjustment factor to obtain a finer time grid division in the high curvature region.

[0084] Step S10132: Construct a heat flow-phase change coupled parameter space based on the correspondence. Each node contains three core data layers: the first layer stores the direction cosine and amplitude of the heat flow vector, the second layer records the phase change completion degree and its first derivative at the corresponding time, and the third layer retains the residual information of the original temperature field and stress field. The three layers of data constitute the basic topology of the parameter space.

[0085] Step S10133: At each node, obtain the phase transition completion gradient modulus of the neighborhood surrounding the node, and determine the initial window radius based on the magnitude of the phase transition completion gradient modulus; introduce the heat flux vector divergence as a correction coefficient to nonlinearly scale the initial window radius so that the high heat flux change region automatically shrinks the window.

[0086] Step S10134: The feature fusion algorithm adopts a multi-level convolution strategy: the first-level convolution kernel extracts the change pattern of the phase transition rate along the heat flow vector direction, the second-level convolution kernel captures the hysteresis effect of heat flow-phase transition in the orthogonal direction, and the third-level convolution combines the outputs of the first two levels to generate a covariant feature tensor; the weight coefficients of each level of convolution are dynamically adjusted by the second derivative of the phase transition completion degree of the corresponding node.

[0087] Step S10135: In the composite thermodynamic state map, each pixel contains a feature vector after the above multi-level convolution processing. The feature vector not only retains the spatial distribution characteristics of the original thermal flow field, but also embeds the temporal evolution law of phase change dynamics. The adaptability of the composite thermodynamic state map is reflected in two aspects: first, the deformation of the matching window tracks the movement trajectory of the phase change front; second, the adjustment of the convolution weight reflects the response characteristics of the material at different phase change stages. The dual adaptive mechanism enables the system to accurately capture the nonlinear interaction in the transient thermodynamic coupling process.

[0088] In the above embodiments, this embodiment establishes a parameter space framework with a unified metric standard through preprocessing of spatial grid normalization and time grid equal-interval sampling; it uses the phase transition completion curvature to adjust the grid mapping relationship, ensuring that the spatiotemporal resolution of the phase transition critical region is specifically enhanced. The three-layer data structure of the parameter space realizes the integrated storage of multi-dimensional physical quantities. The direction cosine and amplitude data retain the vector characteristics of the thermal flow field, the phase transition completion and its derivative record the phase transition dynamic process, and the residual information provides a data basis for subsequent correction; the structural design makes the coupling relationship between different physical quantities computable. The dynamic adjustment mechanism of the window size achieves adaptive optimization of the feature extraction scale through the synergistic effect of gradient modulus and thermal divergence. A small window is used in high-gradient regions to ensure local feature accuracy, while thermal divergence correction further enhances the spatial resolution of the heat conduction-dominant region. This dual adjustment avoids information loss or computational redundancy caused by a fixed window size. The multi-level convolution strategy, through directional feature extraction and orthogonal feature complementarity, fully captures the anisotropic characteristics of the heat flow-phase transition interaction. The dynamic adjustment of the weight coefficients enables the feature extraction process to adapt to the material's phase transition state, especially in the critical region where the second derivative of the phase transition completion degree changes significantly, where the system automatically improves feature extraction sensitivity. The generated composite thermodynamic state map achieves an organic unity of spatial distribution characteristics and temporal evolution laws. The window deformation mechanism ensures accurate tracking of the phase transition front motion, while the adaptive weights accurately reflect the state dependence of the material response. The dual adaptive characteristics enable the system to effectively handle the nonlinear coupling problem of thermodynamic parameters, providing a high-precision feature characterization method for the analysis of complex thermodynamic coupling processes.

[0089] Example 5: Figure 4 As shown, based on Embodiment 4, the process of controlling the opening combination of the main flow channel and the auxiliary flow channel in step S200 of this embodiment of the invention includes the following steps:

[0090] Step S201: Select the solenoid valve opening command, flow channel switching sequence and temperature adjustment range from the control signals, and obtain the real-time regional expansion difference through coupling calculation; compare the regional expansion difference with the preset material yield threshold: if it exceeds the material yield threshold, trigger the flow channel reconstruction mechanism and start the main-auxiliary flow channel parallel mode;

[0091] Step S202: During the construction of the main channel, high heat accumulation areas are identified based on the temperature gradient vector direction in the temperature distribution data, and these areas are mapped to high flow rate demand areas. Corresponding flow configuration schemes are generated through nonlinear flow rate allocation. The control strategy of the auxiliary channel calculates the expansion distribution field of deformation stress values ​​in real time. By extracting the spatial coordinates and amplitude information of the expansion peak area, an anti-phase compensation flow command is generated. This applies negative flow compensation in the positive expansion peak area and positive flow compensation in the negative expansion peak area, forming an inhibitory flow that is opposite to thermal deformation.

[0092] Among them, the control strategy of the auxiliary flow channel calculates the expansion distribution field of the deformation stress value in real time, monitors the deformation stress data of the PCM composite cold plate, performs differential operation with the reference plane, and extracts the spatial coordinates and amplitude information of the expansion positive peak region and the expansion negative peak region; converts the expansion value into a compensation flow value with opposite sign, generates an anti-phase compensation flow command, applies negative flow compensation in the expansion positive peak region, and applies positive flow compensation in the expansion negative peak region;

[0093] Step S203: The PCM phase change activity is calculated in real time by monitoring the microstate of the phase change material and converted into a branch flow ratio adjustment coefficient; it is input to the pulse width modulation controller to drive the first solenoid valve and the second solenoid valve to perform dynamic opening adjustment, thereby realizing the precise redistribution of the main and auxiliary flow channels.

[0094] Step S204: The data processor synchronously accesses the deformation rate monitoring data of the PCM composite cold plate. When it detects that the deformation response of the PCM composite cold plate region lags behind the expected value, the viscosity compensation mechanism is triggered. By generating a high-frequency pulse flow sequence, a periodic shear excitation is formed in the lagging region.

[0095] The nonlinear velocity allocation scheme is derived from the calculation of the square root of the temperature gradient vector magnitude. High-temperature regions receive exponentially increasing velocity priority due to the increased magnitude, thus generating a non-uniform flow configuration. The anti-phase compensation command extracts the positive and negative expansion peak regions by performing differential operations between deformation stress monitoring data and a reference plane, then converts the expansion values ​​into compensation flow values ​​with opposite signs for output. The PCM phase change activity detects changes in the material's dielectric properties using a micro-impedance sensor array embedded in the cold plate. The liquid phase ratio is estimated using a Kalman filter, and then converted into a branch flow ratio adjustment coefficient using a linear mapping function. The viscosity compensation mechanism is triggered by the residual signal between the deformation rate monitoring value and the theoretical value. When the residual exceeds the limit, the system generates a pulse waveform sequence with a frequency proportional to the residual amplitude, driving the solenoid valve to open and close at high speed to generate a shear thinning effect in the viscous region.

[0096] In the above embodiments, this embodiment establishes a dynamic balance relationship among temperature field, stress field, phase transition state, and rheological properties; through a graded trigger control strategy, multi-scale regulation from macroscopic flow channel reconstruction to microscopic boundary layer adjustment is achieved, optimizing heat transfer efficiency while ensuring structural integrity. The coupling effect between the subsystems forms a thermal management solution with adaptive characteristics.

[0097] Example 6: Based on Example 5, the process of obtaining the real-time regional dilatation difference through coupled calculation in step S201 of this embodiment of the invention includes the following steps:

[0098] Step S2011: After receiving the control signal, the data processor first performs control signal component analysis; it separates three core parameters from the control signal: the duty cycle characteristic value of the solenoid valve opening command, the phase encoding of the flow channel switching sequence, and the scalar data of the temperature adjustment amplitude.

[0099] Step S2012: The three core parameters are input into the multimodal data fusion field for coupling calculation. The duty cycle characteristic value of the solenoid valve opening command is convolved with the temperature adjustment amplitude scalar to generate the thermal-fluid coupling strength coefficient. At the same time, the phase encoding of the flow channel switching sequence is interpreted into spatial frequency components through discrete Fourier transform. The spatial frequency components are multiplied with the thermal-fluid coupling strength coefficient to generate a dynamic stress distribution image.

[0100] Step S2013: Based on the dynamic stress distribution image, calculate the stress gradient norm between adjacent regions. The stress gradient norm values ​​are processed by weighted averaging to synthesize a real-time regional expansion difference index, which characterizes the degree of discretization of the expansion behavior between different regions of the PCM composite cold plate.

[0101] After generating a dynamic stress distribution image, the system defines a computational grid on the image. For each element in the grid, it calculates the stress difference between the element and all its neighboring elements. The norm of these difference vectors is calculated (i.e., the magnitude of the difference is calculated), thus obtaining a set of gradient norm values ​​that characterize the severity of local stress changes. The discrete gradient norms are smoothed and averaged by a weighted average that considers the importance of the region, and finally synthesized into a single, quantifiable real-time regional expansion difference index.

[0102] In the above embodiments, this embodiment establishes a correlation mapping mechanism between the dynamic adjustment of the solenoid valve, the timing of the flow channel switching, and the temperature field changes through multi-parameter analysis of the control signal (duty cycle characteristic value, phase encoding, scalar data), providing an accurate input benchmark for multi-physics coupling; the solenoid valve opening characteristics and temperature adjustment parameters are nonlinearly coupled using convolution operation, and the generated thermal-fluid coupling strength coefficient quantifies the interaction strength between thermal load and fluid dynamics; at the same time, the time-series signal is converted into spatial frequency components through discrete Fourier transform, realizing the coupling conversion between the time and space domains; the dynamic stress distribution image constructed by dot product operation combines the thermal-fluid coupling effect with the spatial frequency characteristics through tensor synthesis, fully characterizing the dynamic response characteristics of the phase change material under non-uniform thermal load; the calculation and weighting of the stress gradient norm transforms the local stress field difference into a scalar expansion difference index, accurately reflecting the regional deformation discrete characteristics of the composite cold plate under the synergistic effect of multiple parameters, providing a quantitative basis for thermomechanical reliability assessment. The entire process realizes a closed-loop calculation chain of control signals, multi-physics coupling, stress field reconstruction, and deformation discretization, forming a dynamic online monitoring capability for the expansion behavior of cold plates of phase change materials.

[0103] Example 7: Based on Example 6, the process of generating the thermal-fluid coupling strength coefficient in step S2012 of this embodiment of the invention includes the following steps:

[0104] Step S20121: The duty cycle characteristic value of the solenoid valve opening command, which represents the time-domain pulse density of the expected fluid flux, is convolved with the scalar data of the temperature regulation amplitude. The scalar data of the temperature regulation amplitude reflects the intensity of the global heat load. The time-temperature convolution operation integrates and superimposes the fluid pulse density and heat load intensity in the time domain at each calculation time step, and outputs the heat-fluid coupling strength coefficient.

[0105] The temperature regulation amplitude scalar data is derived from the assessment of the overall thermal state of the battery cell group, and its value directly reflects the intensity of the total heat load that the system needs to handle. Time-temperature convolution is a special type of fusion calculation that synchronously correlates and deeply superimposes parameters from two different physical domains—the duty cycle characteristic value of the solenoid valve opening command in the time domain and the temperature regulation amplitude scalar representing the heat load intensity. Specifically, within each unified time calculation step, the pulse density value representing the instantaneous fluid flux intention is multiplied by the current heat load intensity value to obtain an instantaneous interaction intensity value. Subsequently, the system accumulates and integrates all these instantaneous interaction intensity values ​​from the current moment and several consecutive time steps prior to it. The result of the integral is finally output as the heat-fluid coupling intensity coefficient. This coefficient is a dimensionless scalar, and its magnitude comprehensively characterizes the cumulative intensity and potential impact of the thermal interaction effect brought about by fluid flow under the continuous action of historical and current heat loads, providing a key fusion parameter for subsequent judgment of the overall thermodynamic state of the system.

[0106] Step S20122: The phase code of the flow channel switching sequence is sent to a spectrum decoder, which is a set of digital sequences representing the opening order and position of the flow channel space; the spectrum decoder performs space-frequency decomposition transformation on the phase code, decomposing the complex spatial sequence into a series of basic spatial fluctuation components with different periods and amplitudes, which are the spatial frequency components; the spatial frequency components reveal the spatial non-uniformity and periodicity characteristics of the expected distribution of coolant flow in the flow channel network;

[0107] The phase encoding of the flow channel switching sequence received by the spectrum decoder is essentially a set of digital instruction sequences arranged in a specific order, indicating the opening priority of different flow channel spatial positions. Space-frequency decomposition transform (SFD) is an analytical method for this spatial sequence, aiming to deconstruct the spatial distribution patterns hidden within it. This transform treats the complex original spatial sequence as a signal composed of multiple superimposed basic spatial waveforms with different periods and amplitudes. Through a series of calculations that match and filter the input sequence with internal reference waveforms, SFD can decompose the complex input sequence and extract several of its most basic and regular spatial fluctuation patterns. The extracted basic fluctuation pattern components are the spatial frequency components. Each spatial frequency component contains two key pieces of information: period (the spatial distance of the repeated fluctuations) and amplitude (the significance of the fluctuation pattern). This combination reveals the expected uneven distribution of coolant flow in various parts of the flow channel network over a future period (reflected by amplitude) and its possible periodic patterns in space (reflected by period).

[0108] Step S20123: Perform a spatial frequency component and a thermal-fluid coupling strength coefficient to perform a spatial modulation dot product operation; so that the spatial frequency component representing the spatial distribution characteristics is weighted and modulated by the coefficient representing the thermal-fluid interaction strength, generating a dynamic stress distribution image that can predict how the internal stress of the cooling plate changes with time and space.

[0109] In the above embodiments, this embodiment forms a complete dynamic stress prediction system for thermal-fluid coupling; it integrates the time-domain control characteristics or duty cycle feature value of the solenoid valve with the thermal load scalar through time-temperature convolution operation; it establishes a time-dynamic model of the thermal-fluid interaction intensity; and it transforms the channel switching sequence into spatial frequency components through space-frequency decomposition to construct a frequency domain representation of the spatial distribution of the channel network; the two together form a complete parametric description of the time-space dual-domain coupling. The dot product operation realizes the directional modulation of the thermal-fluid coupling intensity coefficient or time-domain integration result with the spatial frequency component or spatial decomposition result, so that the spatial distribution characteristics of the channel network are dynamically corrected by the real-time thermal-fluid interaction intensity; the operation is essentially a tensor condensation of the time-varying thermal boundary conditions and spatial flow field characteristics. The finally generated dynamic stress distribution image has the following technical characteristics: it inherits the time resolution of time-temperature convolution, reflecting the valve control response delay and thermal inertia; it retains the spatial resolution of space-frequency decomposition, characterizing the channel geometric constraints; and it realizes the nonlinear superposition of thermal-fluid coupling effects through dot product operation, and the output result is a time-varying tensor field, which also contains the spatiotemporal evolution information of stress amplitude and distribution mode characteristics.

[0110] In summary, this embodiment achieves closed-loop mapping of fluid control parameters, thermal-fluid coupling strength, and spatial stress distribution. Its core innovation lies in coupling the traditionally separate time-domain control signal analysis and spatial channel characteristic analysis through tensor operations.

[0111] Example 8: Based on Example 7, the process of performing spatial modulation dot multiplication in step S20123 of this embodiment includes the following steps:

[0112] Step S201231: Use the thermal-fluid coupling strength coefficient as a global weighting factor to scale the amplitude of each fundamental spatial fluctuation component contained in the spatial frequency component;

[0113] Step S201232: Multiply the original amplitude of each basic spatial fluctuation component by the thermal-fluid coupling strength coefficient to generate a new set of modulated spatial fluctuation components;

[0114] Step S201233: The new spatial fluctuation components are synthesized through spatial domain reconstruction calculation; spatial domain reconstruction is the inverse process of spatial frequency decomposition transformation, which re-integrates the modulated frequency components into a two-dimensional field distribution map of a cooling plate region; the two-dimensional field distribution map is the dynamic stress distribution image.

[0115] In the above embodiments, the thermal-fluid coupling effect is accurately modulated in the spatial domain. The thermal-fluid coupling strength coefficient, as a global weighting factor, is used to linearly modulate each spatial frequency component through scalar multiplication. This operation converts the thermodynamic energy parameters or coupling strength coefficients obtained by time-domain integration into amplitude control variables of spatial distribution, establishing a time-space parameter transfer channel. Multiplying the amplitude of each basic spatial fluctuation component by the coupling coefficient redistributes energy to the modal responses of each order of the flow channel network. The modulated new spatial fluctuation component simultaneously includes: the original flow channel geometric constraints and the real-time thermal-fluid coupling strength. The spatial reconstruction calculation, as the inverse transformation of spatial-frequency decomposition, has the following technical characteristics: maintaining the orthogonality of each modulated frequency component, reconstructing the two-dimensional field distribution through linear superposition, and the grid resolution of the output result is determined by the highest-order spatial frequency component. The final generated two-dimensional field distribution map is essentially a linear combination of weighted spatial-frequency basis functions, and its technical characteristics include: the field distribution gradient reflects the spatial imbalance of the thermal-fluid coupling strength, local extreme points correspond to the phase abrupt change region of flow channel switching, and the contour topology maintains a homotopic relationship with the original flow channel network.

[0116] In summary, this embodiment realizes closed-loop calculation from frequency domain modulation to spatial domain reconstruction, transforming time-varying thermodynamic parameters into measurable spatial stress distribution through frequency domain amplitude modulation.

[0117] Example 9: Based on Example 8, the process of synthesizing the new spatial fluctuation component through spatial recombination calculation in step S201233 of this embodiment of the invention includes the following steps:

[0118] Step S2012331: Establish a digital spatial mapping model of the cooling plate area, dividing the surface of the cooling plate into regular spatial grid units; each spatial fluctuation component includes its spatial periodicity attribute and modulated amplitude value; spatial reorganization treats each spatial fluctuation component as a basic building unit, and according to the distribution pattern determined by its spatial periodicity attribute, distributes the amplitude value of the spatial fluctuation to all corresponding spatial grid units in the digital spatial mapping model; the principle of amplitude value allocation is that the final value of a spatial grid unit is determined by the algebraic sum of the values ​​allocated to all spatial fluctuation components at that location;

[0119] Step S2012332: Each spatial grid cell in the spatial mapping model obtains a specific value, representing the strength of the relative stress level at that location predicted based on the current flow channel spatial distribution pattern and the intensity of heat-flow interaction;

[0120] Step S2012333: The numerical spatial mapping model is output as a dynamic stress distribution image, which shows the spatial variation of the stress magnitude inside the cooling plate in the form of a two-dimensional field distribution map.

[0121] In the above embodiments, this embodiment achieves accurate reconstruction from frequency domain modulation to spatial stress field; the digital spatial mapping model serves as the reconstruction carrier, and its grid resolution determines the spatial sampling density of the field distribution. Each spatial fluctuation component serves as an independent basis function, and its projection weight on the grid cell is determined by its spatial periodicity. The amplitude allocation process is essentially a linear expansion of the basis functions in Hilbert space; the value of each grid cell is the result of the interference superposition of all fluctuation components at that point. The algebraic summation operation preserves the orthogonality of each frequency component. High-frequency components contribute to local gradient changes, while low-frequency components determine the global distribution trend; the numerical mapping result reflects the flow channel network. The convolution effect of the periodicity of the network space and the heat flow intensity results in a discrete scalar field composed of grid cell values. The gradient direction corresponds to the energy transfer path of the heat flow coupling. The extreme positions of the field distribution and the abrupt phase transition regions of the flow channel form a topological correspondence. The two-dimensional field distribution map is essentially a normalized visualization of the discrete scalar field. The color / contour line encoding corresponds to the continuous spatial differentiation of the stress level. The spatial resolution is limited by the Nyquist sampling theorem of the highest effective frequency component. Through basis function projection and linear superposition, the frequency domain modulation result is transformed into a spatial stress distribution with clear physical meaning. Its mathematical essence is to complete the inverse transformation from Fourier space to real space.

[0122] Example 10: Based on Example 9, the process of allocating the amplitude value of spatial fluctuations to all corresponding spatial grid cells in the digital spatial mapping model in step S2012331 of this embodiment of the invention includes the following steps:

[0123] Step S20123311: Read the spatial periodicity attribute of the first spatial fluctuation component. The spatial periodicity attribute determines the spatial interval and direction of the stress fluctuation represented by the spatial fluctuation component recurring on the surface of the cold plate. Based on the spatial interval and direction, in the established digital spatial mapping model, determine all spatial grid cells affected by the spatial fluctuation component, and assign the amplitude value of the spatial fluctuation component to the spatial grid cells according to the weight ratio specified by its inherent spatial distribution pattern; this is the amplitude allocation of a spatial fluctuation component.

[0124] Based on the physical dimensions and geometric topology of the cold plate, a two-dimensional matrix composed of equally spaced virtual grid points is defined, with each grid point corresponding to a specific position on the cold plate, thereby constructing a spatial coordinate framework in the digital domain that completely corresponds to the physical cold plate.

[0125] Step S20123312: After completing the allocation of the first component, process the second spatial fluctuation component in sequence, read its spatial periodicity attribute to determine the range of influence, and then allocate the amplitude value to the corresponding new set of spatial grid cells according to its own mode; this process is repeated until all modulated spatial fluctuation components in the list have completed the allocation of their amplitude values.

[0126] Step S20123313: Perform the following operation on each independent grid cell in the spatial mapping model: perform algebraic summation on all temporary assignments from different spatial fluctuation components within the independent grid cell to obtain a final composite value; the composite value is the predicted relative stress level of the local area of ​​the cold plate represented by the grid cell; after all spatial grid cells have completed algebraic summation, the dynamic stress distribution image is obtained.

[0127] In the above embodiments, this embodiment achieves accurate projection and synthesis of multi-scale spatial fluctuation components onto a discrete grid field; spatial periodic attributes generate directional projection templates to determine the radiation influence range of each component in the grid domain; amplitude allocation is essentially discrete sampling of basis functions in the local support domain, with weight ratios determined by the analytical expression of the basis functions; each component independently completes projection, maintaining modal orthogonality. Iterative processing ensures that components of each frequency band are superimposed in descending order of scale, with high-frequency components generating abrupt gradients in small-scale grid groups and low-frequency components contributing to a wide-area gradual trend. During the loop, the temporary values ​​of grid cells constitute intermediate tensors, recording part of the summation results. Algebraic summation is equivalent to the spatial implementation of frequency domain convolution, satisfying the superposition principle. The final value reflects the interference results of each mode at that location, with extreme points appearing in the region of multi-component in-phase superposition, corresponding to discontinuities in the flow channel structure. The processing order does not affect the final synthesis result, the grid cell resolution must satisfy the sampling theorem for the highest frequency component, and the topology of the output image faithfully preserves the original frequency domain energy distribution characteristics.

[0128] Example 11: As Figure 5 As shown, based on Embodiment 1, the process by which the data processor triggers the four-way reversing valve to switch between heat dissipation and heating modes, as provided in this embodiment of the invention, includes the following steps:

[0129] Step S301: Under extreme temperature conditions, the data processor integrates the acquired PCM composite cold plate expansion degree and real-time temperature data, calculates the deviation between the two to generate a thermal management demand polarity criterion; the thermal management demand polarity criterion is the relative positional relationship between the surface temperature distribution of the energy storage battery cell and the phase transition critical point of the PCM, which is normalized to form a thermal demand polarity index.

[0130] Step S302: When the absolute value of the heat demand polarity index exceeds the preset critical threshold, the data processor starts the extreme operating condition control sequence; the working mode is determined according to the positive and negative sign characteristics of the heat demand polarity index: when the index is positive, it is determined that the system has heat dissipation demand; when the index is negative, it is determined that the system has heat supply demand; the determination result is converted into the directional control command of the four-way reversing valve.

[0131] Step S303: After receiving the orientation control command, the four-way reversing valve performs flow channel reconfiguration, switching the medium flow direction by changing the connection topology of its internal channels. In heat dissipation mode, the cooling medium is guided to the external circulation loop; in heating mode, the cooling medium is introduced into the internal circulation loop. This flow channel reconfiguration process is achieved through the precise rotational movement of the valve core. Simultaneously, the data processor generates a heat exchanger adjustment coefficient based on the absolute value of the heat demand polarity index. The heat exchanger adjustment coefficient is mapped to the power output of the heat exchanger, and precise control of the cooling medium temperature is achieved by adjusting the energy exchange intensity of the heat exchanger.

[0132] In the above embodiment, the entire control process forms a closed-loop feedback: the cooling medium temperature data after the four-way reversing valve switches is collected in real time, compared with the target temperature value, and a temperature deviation signal is generated. This signal corrects the heat exchanger regulation coefficient through an adaptive calibration algorithm, ultimately stabilizing the cooling medium temperature within a preset range.

[0133] Example 12: As Figures 6-10 As shown, based on Embodiments 1-11, the PCM thermal storage and dual-channel energy storage power station battery thermal management system provided by this embodiment of the invention includes: energy storage battery unit group 1, data processor 2, four-way reversing valve 3, second solenoid valve 4, first solenoid valve 5, compressor 6, heat exchanger 7, one-way valve 8, PCM composite cold plate 9, PCM composite layer plate 10, main channel inlet 11, auxiliary channel inlet 12, auxiliary channel 13, and main channel 14;

[0134] The energy storage battery unit 1 is connected to the data processor 2 via a data cable. The data processor 2 is connected to the four-way reversing valve 3 and the first solenoid valve 5 via a data cable. The four-way reversing valve 3 is connected to the second solenoid valve 4, the compressor 6, and the heat exchanger 7. The heat exchanger 7 is connected to the one-way valve 8. The one-way valve 8 is connected to the first solenoid valve 5 and the energy storage battery unit 1. The energy storage battery unit 1 transmits pressure signals to the data processor 2. The data processor 2 transmits mode switching signals to the four-way reversing valve 3. The data processor 2 transmits main and auxiliary flow channel opening and closing signals to the energy storage battery unit 1. The data processor 2 transmits flow regulation signals to the first solenoid valve 5.

[0135] like Figure 7As shown, PCM composite cold plate 9 and PCM composite layer plate 10 are respectively provided on the upper and lower sides of the energy storage battery unit group 1.

[0136] like Figures 8-10 As shown, the PCM composite cold-rolled sheet 9 comprises five layers; as Figure 8 The diagram shows the structure of the first and fifth layers. The main flow channel inlet 11 is located in the middle of the first and fifth layers, and the auxiliary flow channel inlet 12 is located around the main flow channel inlet 11. Figure 9 The structure of the second and fourth layers is shown, with the auxiliary flow channel inlets 12 connected by auxiliary flow channel 13; as shown Figure 9 The structure of the third layer plate is shown, with multiple main channels 14 provided at the main channel inlet 11.

[0137] In the above embodiments, this embodiment utilizes PCM phase change material heat storage and dual-channel heat exchange technology to achieve efficient temperature management of the energy storage battery, ensuring its stable operation under different operating conditions. Its working principle can be divided into heat dissipation mode and heat supply mode, and the data processor 2 intelligently regulates the working status of each component.

[0138] 1. Heat Dissipation Mode: When the battery overheats, and the energy storage battery unit 1 generates a large amount of heat during charging and discharging, the system enters heat dissipation mode.

[0139] PCM composite cold plate 9 and layer 10 absorb heat, and the internal paraffin-expanded graphite composite material undergoes a phase change, absorbing heat and expanding in volume, thus buffering the instantaneous heat load.

[0140] A capacitive pressure sensor detects the degree of PCM expansion and transmits the signal to a data processor 2;

[0141] First stage expansion rate <30%: Only the main channel 14 is opened, and cooling water enters from the main channel inlet 11, flows longitudinally along the multi-layer plate, and carries away heat; at this time, the first solenoid valve 5 is opened and the second solenoid valve 4 is closed.

[0142] Second stage expansion rate ≥ 50%: If the heat continues to increase, the data processor 2 simultaneously opens the auxiliary flow channel 13, and the cooling water enters from the auxiliary flow channel inlet 12 and flows horizontally in a ring to accelerate heat dissipation; at this time, the main flow channel 14 and the auxiliary flow channel 13 operate in parallel to improve heat exchange efficiency.

[0143] In extreme cases with an expansion rate ≥85%, if the temperature still cannot be controlled, the system will activate emergency protection, cut off the battery power, and activate the emergency cooling device.

[0144] After the cooling water dissipates heat through the heat exchanger 7, it returns to the system via the check valve 8, forming a closed-loop circulation.

[0145] 2. Heating mode in low-temperature environments:

[0146] When the ambient temperature is too low, the battery cannot maintain its optimal operating temperature:

[0147] The temperature sensor detects a low temperature signal, the data processor 2 sends a command, and the four-way reversing valve 3 switches the flow direction, so that the system enters the heating mode.

[0148] Compressor 6 drives the refrigerant to circulate in reverse, and heat exchanger 7 provides high-temperature water to flow into PCM composite cold plate 9 to heat the battery;

[0149] The battery's own heat generation and external heating work together to maintain the optimal operating temperature.

[0150] 3. Collaborative control and optimization:

[0151] The data processor 2 monitors the temperature and PCM expansion status in real time, and dynamically adjusts the four-way reversing valve 3, the first solenoid valve 5, and the second solenoid valve 4 to optimize the opening and closing combination of the main / auxiliary flow channels; the phase change characteristics of the PCM composite cold plate 9 can store the instantaneous heat during charging and discharging, reduce the frequent start and stop of the compressor 6, and reduce energy consumption; the dual flow channel design makes the main flow channel longitudinal and the auxiliary flow channel annular, making the heat exchange more uniform, ensuring that the battery surface temperature difference is ≤2℃, and improving safety.

[0152] The five-layer plate structure and dual-channel design of this embodiment significantly improve the heat exchange area and flexibility; the data processor 2 automatically switches modes according to different operating conditions to optimize energy utilization; the PCM buffers thermal shock and prevents thermal runaway; the emergency protection mechanism ensures system safety under extreme conditions; through the combination of PCM heat storage and liquid cooling dual channels and intelligent control, efficient temperature management of the energy storage battery is achieved, taking into account functions such as heat dissipation, heat preservation, and energy recovery, and is suitable for long-term stable operation under complex operating conditions.

[0153] This embodiment mainly consists of several parts, including a PCM composite cold plate 9, an embedded capacitive pressure sensor, a data processor 2, and a flow regulation device. The PCM composite cold plate 9 is composed of five layers of single plates, each of which is directly filled with PCM. The five layers of cold plates form one main flow channel 14 and two auxiliary flow channels 13. The interior is filled with paraffin-expanded graphite composite material. Sensors are placed in the gaps between the PCM filling layers. When the PCM absorbs heat and expands in volume, the main flow channel 14 opens. Cooling water flows through the main flow channel 14 and the auxiliary flow channels 13. The cooling water absorbs heat and removes heat, while the PCM material releases heat, ensuring that the PCM material maintains a reasonable reaction change.

[0154] When a large amount of heat is generated during charging and discharging, the main flow channel 14 cannot dissipate the heat in time. Under the action of the sensor, the auxiliary flow channel 13 opens. The auxiliary flow channel 13 and the main flow channel 14 do not intersect, and cooling water flows into the auxiliary flow channel 13 to accelerate heat dissipation. The main flow channel 14 and the auxiliary flow channel 13 work together to coordinate heat dissipation. The energy storage battery unit group 1 is externally connected to an external heat exchanger 7 and a four-way reversing valve 3 to adapt to different operating conditions. When the ambient temperature is high and heat is generated during charging and discharging, the composite cooling system is in heat dissipation mode. Cold water enters the composite heat exchanger 7, while hot water is connected to the external heat exchanger 7, supplying heat to the living area. When the ambient temperature is low, the four-way reversing valve 3 automatically switches to the heating mode, allowing high-temperature water to combine with the heat generated by the energy storage battery unit group 1 to keep the energy storage battery at a suitable operating temperature.

[0155] The PCM composite cold plate 9 is filled with PCM. After absorbing heat, the temperature of the paraffin-expanded graphite composite material remains unchanged, but its volume increases. Taking the paraffin-expanded graphite composite material (10% EG) as an example, the volume expansion rate is 3%-8%. Each cold plate is sealed and filled with PCM material with reserved expansion space. A capacitive pressure sensor is embedded in the PCM composite cold plate 9. The capacitive sensor is more sensitive and reacts quickly. When the PCM material absorbs heat and undergoes a slight expansion, the pressure changes the distance between the capacitor plates, which is transmitted to the data processor 2 through an electrical signal. The data processor 2 makes a judgment based on the received signal. In the first stage, if the actual expansion rate is less than 30% of the expansion threshold, the first solenoid valve 5 of the main flow channel 14 is opened and the second solenoid valve 4 of the auxiliary flow channel 13 is closed. If the actual expansion rate reaches the expansion threshold of 50%, the first solenoid valve 5 and the second solenoid valve 4 control the auxiliary flow channel 13 and the main flow channel 14 to open simultaneously. If the pressure expansion rate does not decrease within the theoretical time after the auxiliary flow channel 13 and the main flow channel 14 are opened, the second stage begins. The first solenoid valve 5 quickly responds and adjusts the flow rate of the main flow channel 14. If the actual expansion rate continues to increase to 75% after the second stage response, the data processor adjusts the heat exchanger 7 to lower the cooling water temperature and simultaneously cuts off the power supply to the energy storage battery. When the heat exchanger 7 cannot suppress the expansion trend after adjusting the cooling water temperature, and the actual expansion rate reaches 85% of the threshold, the emergency protection program is activated, cutting off all external power supplies and simultaneously activating the emergency device inside the energy storage battery to quickly absorb excess heat and ensure the overall safety of the system. The front-end solenoid valves of the auxiliary flow channel 13 and the main flow channel 14 are jointly controlled by the data processor 2. The number of auxiliary flow channels 13 that are open and whether the main flow channel 14 is open are directly determined by the heat absorption degree of the PCM board.

[0156] When the ambient temperature is too low, the heat generated by the energy storage battery cannot maintain the ambient temperature within the optimal operating range. A temperature sensor sends an electrical signal to the data processing center, and the data processor 2 sends a mode-switching electrical signal to control the four-way reversing valve 3, simultaneously switching the system from heat dissipation mode to heat supply mode. This increases the cooling water temperature, supplying heat to the battery and maintaining a stable operating temperature for the energy storage battery.

[0157] This embodiment combines phase change material (PCM) with single-phase liquid cooling technology, fully leveraging their synergistic effect. It is particularly suitable for solving the heat dissipation challenges of simultaneous transient high heat flux and long-term temperature control. PCM stores waste heat during charging and releases it during cold starts, reducing PTC heating energy consumption. PCM replaces part of the metal cold plate and has a higher heat storage density, resulting in a more compact design and a 20-30% reduction in system volume. This simplifies the system. The multi-layer composite cold plate's main and auxiliary flow channels and temperature sensing device work together to achieve dynamic adjustment and rapid response of the system, ensuring temperature stability, reducing energy waste, saving costs, and lowering the risk of thermal runaway.

[0158] Figure 11 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0159] The electronic device may include a central processing unit / microprocessor / main control chip, etc. 15; and a storage medium 16 coupled to the central processing unit / microprocessor / main control chip, etc. 15, and storing computer-executable instructions therein for performing the steps of various methods of embodiments of the present invention when executed by the processor.

[0160] The central processing unit / microprocessor / main control chip, etc., 15 may include, but are not limited to, one or more processors or microprocessors.

[0161] Storage medium 16 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0162] In addition, the electronic device may also include (but is not limited to) a data bus 17, an input / output bus / external bus / device bus 18, a display 19, and input / output devices 20 (e.g., keyboard, mouse, speaker, etc.).

[0163] The central processing unit / microprocessor / main control chip, etc. 15 can communicate with external devices (19, 20, etc.) via wired or wireless networks (not shown) through input / output buses / external buses / device buses, etc. 18.

[0164] Storage medium 16 may also store at least one computer-executable instruction for performing steps of various functions and / or methods in the embodiments described herein when the central processing unit / microprocessor / main control chip, etc., is running at runtime.

[0165] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0166] Figure 12 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0167] like Figure 12 As shown, instructions, such as computer-readable instructions 21, are stored on the non-transitory computer-readable storage medium 22. When the computer-readable instructions 21 are executed by a processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium 22 can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions 21 stored on the computer-readable storage medium 22, the various methods described above can be performed.

[0168] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0169] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0171] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0172] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for battery thermal management in a PCM thermal storage and dual-channel energy storage power station, characterized in that, Includes the following steps: The data processor adjusts the first and second solenoid valves according to the control signals of the energy storage battery unit group. It selects the solenoid valve opening command, flow channel switching sequence and temperature adjustment range from the control signals and calculates the real-time regional expansion difference through coupling. The regional expansion difference is compared with the preset material yield threshold. If it exceeds the material yield threshold, the flow channel reconstruction mechanism is triggered and the main-auxiliary flow channel parallel mode is started. The opening combination of the main flow channel and the auxiliary flow channel is controlled so that the cooling water flows through the corresponding channel to achieve efficient heat exchange. The process of controlling the opening and combination of the main flow channel and the auxiliary flow channel includes the following steps: During the construction of the main channel, high heat accumulation areas are identified based on the temperature gradient vector direction in the temperature distribution data, and these areas are mapped to high flow rate demand areas. Corresponding flow configuration schemes are generated through nonlinear flow rate allocation. The control strategy of the auxiliary channel calculates the expansion distribution field of deformation stress values ​​in real time, monitors the deformation stress data of the PCM composite cold plate, performs differential calculations with the reference plane, and extracts the spatial coordinates and amplitude information of the positive and negative expansion peak areas. The expansion value is converted into a compensation flow value with the opposite sign to generate an anti-phase compensation flow command. Negative flow compensation is applied in the positive peak expansion region and positive flow compensation is applied in the negative peak expansion region. By extracting the spatial coordinates and amplitude information of the expansion peak region, an anti-phase compensation flow command is generated to apply negative flow compensation in the positive peak expansion region and positive flow compensation in the negative peak expansion region, forming an inhibitory flow that is opposite to the thermal deformation. The PCM phase change activity is calculated in real time by monitoring the microstate of the phase change material and converted into a branch flow ratio adjustment coefficient. This coefficient is then input to the pulse width modulation controller, which drives the first and second solenoid valves to perform dynamic opening adjustment, thereby achieving precise redistribution of the main and auxiliary flow channels. The data processor synchronously accesses the deformation rate monitoring data of the PCM composite cold plate. When it detects that the deformation response of a region of the PCM composite cold plate lags behind the expected value, it triggers the viscosity compensation mechanism. By generating a high-frequency pulse flow sequence, a periodic shear excitation is formed in the lagging region.

2. The PCM thermal storage and dual-channel energy storage power station battery thermal management method as described in claim 1, characterized in that, The process of obtaining the real-time regional dilatation difference through coupled calculation includes the following steps: After receiving the control signal, the data processor first performs control signal component analysis; it then separates three core parameters from the control signal: the duty cycle characteristic value of the solenoid valve opening command, the phase encoding of the flow channel switching sequence, and the scalar data of the temperature regulation amplitude. Three core parameters are input into the multimodal data fusion field for coupling calculation. The duty cycle characteristic value of the solenoid valve opening command is convolved with the temperature adjustment amplitude scalar to generate the thermal-fluid coupling strength coefficient. At the same time, the phase encoding of the flow channel switching sequence is interpreted into spatial frequency components through discrete Fourier transform. The spatial frequency components are multiplied with the thermal-fluid coupling strength coefficient to generate a dynamic stress distribution image. Based on the dynamic stress distribution image, the stress gradient norm between adjacent regions is calculated. The stress gradient norm values ​​are then weighted and averaged to synthesize a real-time regional expansion difference index, which characterizes the degree of discretization of the expansion behavior between different regions of the PCM composite cold plate.

3. The PCM thermal storage and dual-channel energy storage power station battery thermal management method as described in claim 2, characterized in that, The process of generating the thermal-fluid coupling strength coefficient includes the following steps: The duty cycle characteristic value of the solenoid valve opening command represents the time-domain pulse density of the expected fluid flux. It is then convolved with the scalar data of the temperature regulation amplitude. The scalar data of the temperature regulation amplitude reflects the intensity of the global heat load. The time-temperature convolution operation integrates and superimposes the fluid pulse density and heat load intensity in the time domain at each calculation time step, and outputs the heat-fluid coupling strength coefficient. The phase code of the flow channel switching sequence is fed into a spectrum decoder, which is a set of digital sequences representing the opening order and position of the flow channel space; The spectrum decoder performs a space-frequency decomposition transformation on the phase code, decomposing the complex spatial sequence into a series of basic spatial fluctuation components with different periods and amplitudes. These basic spatial fluctuation components are the spatial frequency components. The spatial frequency component is spatially modulated by multiplying it with the thermal-fluid coupling strength coefficient to generate a dynamic stress distribution image that can predict how the internal stress of the cooling plate changes with time and space.

4. The PCM thermal storage and dual-channel energy storage power station battery thermal management method as described in claim 3, characterized in that, The process of performing dot product operations for spatial modulation includes the following steps: The thermal-fluid coupling strength coefficient is used as a global weighting factor to scale the amplitude of each fundamental spatial fluctuation component contained in the spatial frequency component. The original amplitude of each basic spatial fluctuation component is multiplied by the thermal-fluid coupling strength coefficient to generate a new set of modulated spatial fluctuation components; The new spatial fluctuation components are synthesized through spatial recombination calculations; Spatial reconstruction is the inverse process of spatial frequency decomposition transformation, which re-integrates the modulated frequency components into a two-dimensional field distribution map of a cooling plate region; the two-dimensional field distribution map is the dynamic stress distribution image.

5. The PCM thermal storage and dual-channel energy storage power station battery thermal management method as described in claim 4, characterized in that, The process of synthesizing new spatial fluctuation components through spatial recombination calculations includes the following steps: A digital spatial mapping model of the cooling plate area is established, dividing the surface of the cooling plate into regular spatial grid units; each spatial fluctuation component includes its spatial periodicity attribute and modulated amplitude value; spatial reorganization regards each spatial fluctuation component as a basic building unit, and according to the distribution pattern determined by its spatial periodicity attribute, the amplitude value of the spatial fluctuation is allocated to all corresponding spatial grid units in the digital spatial mapping model. In the spatial mapping model, each spatial grid cell is given a specific value, representing the strength of the relative stress level at that location, predicted based on the current spatial distribution pattern of the flow channel and the intensity of the heat-flow interaction. The numerical spatial mapping model is output as a dynamic stress distribution image, which shows the spatial variation of stress magnitude inside the cooling plate in the form of a two-dimensional field distribution map.

6. The PCM thermal storage and dual-channel energy storage power station battery thermal management method as described in claim 5, characterized in that, The process of assigning the amplitude values ​​of spatial fluctuations to all corresponding spatial grid cells in the digital spatial mapping model includes the following steps: Read the spatial periodicity attribute of the first spatial fluctuation component. The spatial periodicity attribute determines the spatial interval and direction of the stress fluctuation represented by the spatial fluctuation component recurring on the surface of the cold plate. Based on the spatial interval and direction, in the established digital spatial mapping model, determine all spatial grid cells affected by the spatial fluctuation component, and assign the amplitude value of the spatial fluctuation component to the spatial grid cells according to the weight ratio specified by its inherent spatial distribution pattern; this is the amplitude allocation of a spatial fluctuation component. After the first component is assigned, the second spatial fluctuation component is processed sequentially, its spatial periodicity is read to determine the range of influence, and then the amplitude value is assigned to the corresponding new set of spatial grid cells according to its own pattern. This process is repeated until all modulated spatial fluctuation components in the list have completed the allocation of their amplitude values. The operation is performed on each independent grid cell in the spatial mapping model: the algebraic summation of all temporary assignments from different spatial fluctuation components in the independent grid cell is performed to obtain a final composite value; the composite value is the predicted relative stress level of the local area of ​​the cold plate represented by the grid cell; after all spatial grid cells have been algebraically summed, the dynamic stress distribution image is obtained.

7. The PCM thermal storage and dual-channel energy storage power station battery thermal management method as described in claim 1, characterized in that, It also includes sensors for the energy storage battery unit to collect temperature and pressure data, which are transmitted to a data processor. The data processor analyzes the expansion degree of the PCM composite cold plate and generates control signals.

8. The PCM thermal storage and dual-channel energy storage power station battery thermal management method as described in claim 1, characterized in that, It also includes: under extreme temperature conditions, the data processor triggers a four-way reversing valve to switch between heat dissipation and heating modes, while simultaneously coordinating with the heat exchanger to adjust the temperature of the cooling medium and maintain the stable operating temperature of the energy storage battery cell group.

9. A PCM thermal storage and dual-channel energy storage power station battery thermal management system, implementing the PCM thermal storage and dual-channel energy storage power station battery thermal management method as described in any one of claims 1 to 8, characterized in that, include: Energy storage battery unit group, data processor, four-way reversing valve, second solenoid valve, first solenoid valve, compressor, heat exchanger, one-way valve, PCM composite cold plate, PCM composite layer plate, main flow channel inlet, auxiliary flow channel inlet, auxiliary flow channel, main flow channel; The energy storage battery unit group is connected to the data processor via a data cable. The data processor is connected to the four-way reversing valve and the first solenoid valve via a data cable. The four-way reversing valve is connected to the second solenoid valve, the compressor, and the heat exchanger. The heat exchanger is connected to the check valve. The check valve is connected to the first solenoid valve and the energy storage battery unit group. The energy storage battery unit group transmits pressure signals to the data processor. The data processor transmits mode switching signals to the four-way reversing valve. The data processor transmits auxiliary flow channel and main flow channel opening and closing signals to the energy storage battery unit group. The data processor transmits flow regulation signals to the first solenoid valve. The upper and lower sides of the energy storage battery unit are respectively equipped with PCM composite cold plates and PCM composite layers. The PCM composite cold plate consists of five layers; the main flow channel inlet is located between the first and fifth layers, and the auxiliary flow channel inlet is located around the main flow channel inlet. The inlets of the auxiliary channels are connected by auxiliary channels; the inlets of the main channel are equipped with multiple main channels.

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