Thermal management method and device of micro-nano composite phase change material for energy storage equipment

Abstract

The invention discloses a thermal management method and device for a micro-nano composite phase change material for energy storage equipment, and the method comprises the steps: constructing a phase change management structure layer, the phase change management structure layer comprises a structured composite phase change material with a gradient heat conductivity coefficient, a distributed embedded sensor network and an energy self-regulation interface layer arranged between the phase change material and an external radiator; establishing a thermal state monitoring data set based on the first temperature gradient distribution, the first heat flux density and the phase change state information acquired in real time by the sensor network; based on the thermal state monitoring data set, performing state evaluation through a phase change material state evaluation algorithm to obtain a state evaluation result; and based on a state evaluation result, generating a control instruction through a multi-objective optimization control strategy so as to dynamically adjust the thermal resistance state of the energy self-regulation interface layer. Efficient conduction and uniform distribution of heat are achieved, the optimal comprehensive performance is achieved, and the heat management efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of thermal management technology, and in particular to a thermal management method and apparatus for micro-nano composite phase change materials used in energy storage devices. Background Technology

[0002] With the large-scale grid connection of renewable energy and the rapid development of electric vehicles, electrochemical energy storage systems (such as lithium-ion batteries and supercapacitors) are becoming increasingly important as key energy regulation and buffering units. However, energy storage devices continuously generate heat during charging and discharging, leading to temperature increases. Excessive temperatures can accelerate battery aging, induce thermal runaway, cause permanent damage, or even safety accidents. Furthermore, uneven temperature distribution can lead to internal stress concentration, similarly shortening the equipment's lifespan. Therefore, an efficient and reliable thermal management system is needed to ensure the stable and long-term operation of energy storage systems.

[0003] Phase change materials (PCMs) offer a highly efficient passive temperature control solution for thermal management by utilizing their ability to absorb or release large amounts of latent heat during phase change. Specifically, when applied to the thermal management of energy storage devices, PCMs can absorb heat when the temperature rises, slowing down the temperature increase, and release heat when the temperature decreases, maintaining temperature stability and thus achieving temperature peak shaving and valley filling. However, traditional PCMs have poor thermal conductivity, preventing heat from being quickly conducted and evenly distributed within the material, resulting in localized hotspots and limiting their heat storage and release efficiency. Furthermore, traditional thermal management systems often employ simple on / off control or PID control based on fixed thresholds to start and stop active cooling equipment (such as fans and liquid pumps). However, these control methods suffer from lag, making it difficult to adapt to dynamically changing heat loads and failing to achieve effective synergistic optimization, leading to low thermal management efficiency. Summary of the Invention

[0004] The present invention proposes a thermal management method and device for micro-nano composite phase change materials for energy storage devices, in order to solve the technical problems of inefficient heat conduction and uniform distribution, low overall performance and low thermal management efficiency in related technologies.

[0005] To achieve the above objectives, the present invention provides a thermal management method for micro / nano composite phase change materials used in energy storage devices, the method comprising: A phase change management structure layer is constructed, wherein the phase change management structure layer includes a structured composite phase change material with a gradient thermal conductivity, a distributed embedded sensor network, and an energy self-regulating interface layer disposed between the phase change material and an external heat sink; The sensor network is used to acquire the first temperature gradient distribution, the first heat flux density, and the phase change state information of the phase change material in real time, and a thermal state monitoring dataset is established based on the first temperature gradient distribution, the first heat flux density, and the phase change state information. Based on the aforementioned thermal state monitoring dataset, a state assessment is performed using a phase change material state assessment algorithm to obtain the state assessment results. Based on the state assessment results, control commands are generated through a multi-objective optimization control strategy to dynamically adjust the thermal resistance state of the energy self-regulating interface layer.

[0006] The thermal management method for micro / nano composite phase change materials used in energy storage devices according to embodiments of the present invention may also have the following additional technical features: In one embodiment of the present invention, the energy self-regulating interface layer adopts a smart thermal switch structure based on shape memory alloy, wherein the smart thermal switch structure includes a shape memory alloy spring drive system, a multi-layer composite thermal interface and an elastic support buffer mechanism. The shape memory alloy spring drive system uses a nickel-titanium based alloy material with a two-way shape memory effect; The multilayer composite thermally conductive interface is made of a high thermal conductivity copper-based composite material. The surface of the interface is treated with a micro-nano structure. The two sides of the interface are tightly bonded to the phase change material and the external heat sink, respectively. The elastic support and buffer mechanism is made of a ceramic material with low thermal conductivity and provides a stable reverse support force when the shape memory alloy spring is not activated.

[0007] In one embodiment of the present invention, the sensor network includes an array of fiber optic temperature sensors, a miniature heat flux sensor, and a phase change state detection unit arranged in different functional layers of the phase change material; the step of acquiring the first temperature gradient distribution, the first heat flux density, and the phase change state information of the phase change material in real time through the sensor network includes: The fiber optic temperature sensor array employs wavelength division multiplexing technology to continuously monitor the temperature gradient along the thickness direction of the phase change material by setting multiple measurement points on a single fiber, thereby obtaining the first temperature gradient distribution. Based on the Seebeck effect, the micro heat flux sensor directly measures the first heat flux density through each functional layer using a thin-film thermopile structure. By combining active thermal excitation and passive temperature monitoring, the phase change state detection unit identifies the start and end points of the phase change process by analyzing the transient thermal response characteristics of the material, and determines the start and end points of the phase change process as the phase change state information of the phase change material.

[0008] In one embodiment of the present invention, establishing a thermal state monitoring dataset based on the first temperature gradient distribution, the first heat flux density, and the phase transition state information includes: Abnormal data points in the first temperature gradient distribution and the first heat flux density are identified by cross-validation and filled in using a data reconstruction algorithm to form a complete second temperature gradient distribution and second heat flux density. The second temperature gradient distribution and the second heat flux density are denoised and filtered using a wavelet transform algorithm to obtain the third temperature gradient distribution and the third heat flux density. Time series analysis was performed on the third temperature gradient distribution to extract thermal state characteristic parameters, including phase change plateau characteristics, temperature rise rate characteristics, and spatial temperature distribution characteristics. A thermal state monitoring dataset is established based on the third temperature gradient distribution, the third heat flux density, the phase transition state information, and the thermal state characteristic parameters.

[0009] In one embodiment of the present invention, the step of performing state assessment based on the thermal state monitoring dataset using a phase change material state assessment algorithm to obtain the state assessment result includes: Based on the third temperature gradient distribution in the thermal state monitoring dataset and the thermal conductivity parameters of each functional layer of the phase change material, a heat flow distribution calculation model is constructed. The heat flux distribution calculation model is stabilized by a regularization optimization algorithm to obtain the heat flux density vector field at any time in the entire phase change material, and a heat flux distribution cloud map is generated based on the heat flux density vector field. Based on the temperature time series data in the distribution cloud map and the start and end points of the phase change process in the thermal state monitoring dataset, as well as the characteristics of the phase change platform, a calculation model for the phase change interface propagation rate is established. In the phase change interface propulsion rate calculation model, the evolution trajectory of the phase change interface is tracked by the level set method, and the latent heat release effect in the phase change process is handled by the enthalpy method model. The propulsion rate of the phase change interface is inverted in real time by using the numerical solution method of the moving boundary problem. Based on the heat flow distribution in the distribution cloud map and the propagation rate of the phase change interface, a model for assessing the remaining thermal storage capacity is constructed. Based on the remaining thermal storage capacity assessment model, an estimated value of the remaining thermal storage capacity is obtained; The heat flow distribution cloud map, the advancement rate of the phase change interface, and the estimated value of the remaining thermal storage capacity are determined as the state assessment results.

[0010] In one embodiment of the present invention, the step of generating control commands based on the state assessment results through a multi-objective optimization control strategy to dynamically adjust the thermal resistance state of the energy self-regulating interface layer includes: A multi-objective optimization problem is constructed, with the thermal resistance state of the energy self-regulating interface layer as the control variable, and the optimization objectives being the uniformity of heat flow distribution, the stability of the phase change interface propagation rate, and the availability of remaining thermal storage capacity, and including temperature constraints, energy consumption constraints, and equipment life constraints. Based on the state assessment results, the thermal uniformity control requirement level, energy consumption control requirement level, and lifetime protection requirement level are obtained by quantification through fuzzy reasoning mechanism. Based on the working mode of the energy storage device and the various demand levels, an adaptive weight allocation method is used to dynamically adjust the weight coefficients of each optimization objective in the multi-objective optimization problem. Based on the model predictive control framework, using the current state evaluation results and the dynamically adjusted weight coefficients, the multi-objective optimization problem is solved through a rolling optimization algorithm to obtain the optimal control sequence for the thermal resistance state of the energy self-regulating interface layer in the future time domain, and to generate the optimal control command for the current moment.

[0011] To achieve the above objectives, another aspect of the present invention provides a thermal management device for micro / nano composite phase change materials used in energy storage devices, the device comprising: A construction module is used to construct a phase change management structure layer, wherein the phase change management structure layer includes a structured composite phase change material with a gradient thermal conductivity, a distributed embedded sensor network, and an energy self-regulating interface layer disposed between the phase change material and an external heat sink. A module is established to acquire, in real time, the first temperature gradient distribution, the first heat flux density, and the phase change state information of the phase change material through the sensor network, and to establish a thermal state monitoring dataset based on the first temperature gradient distribution, the first heat flux density, and the phase change state information. The state assessment module is used to perform state assessment based on the thermal state monitoring dataset using a phase change material state assessment algorithm to obtain the state assessment result. The control module is used to generate control commands based on the state assessment results through a multi-objective optimization control strategy, so as to dynamically adjust the thermal resistance state of the energy self-regulating interface layer.

[0012] Another object of the present invention is to provide an electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of the first aspects above.

[0013] Another object of the present invention is to provide a computer storage medium storing computer-executable instructions; the computer-executable instructions, when executed by a processor, cause the computer to perform the method described in any one of the first aspects above.

[0014] This invention discloses a thermal management method and apparatus for energy storage devices using micro / nano composite phase change materials. The method constructs a phase change management structure layer, which includes a structured composite phase change material with gradient thermal conductivity, a distributed embedded sensor network, and an energy self-regulating interface layer disposed between the phase change material and an external heat sink. The sensor network acquires real-time information on a first temperature gradient distribution, a first heat flux density, and the phase change state of the phase change material. A thermal state monitoring dataset is established based on these data. A phase change material state assessment algorithm is used to evaluate the state based on the thermal state monitoring dataset, yielding the evaluation result. Based on the evaluation result, a multi-objective optimization control strategy generates control commands to dynamically adjust the thermal resistance state of the energy self-regulating interface layer. This invention achieves efficient heat conduction and uniform distribution through the design of a structured composite phase change material with gradient thermal conductivity. Furthermore, the phase change material state assessment algorithm and multi-objective optimization control strategy ensure temperature control accuracy while considering system energy consumption and equipment lifespan, resulting in optimal overall performance and improved thermal management efficiency.

[0015] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a thermal management method for micro / nano composite phase change materials used in energy storage devices according to an embodiment of the present invention; Figure 2 This is a structural diagram of a thermal management device for a micro-nano composite phase change material used in energy storage equipment according to an embodiment of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0019] The following describes, with reference to the accompanying drawings, a thermal management method and apparatus for micro / nano composite phase change materials for energy storage devices according to embodiments of the present invention.

[0020] Figure 1 This is a flowchart of a thermal management method for micro-nano composite phase change materials used in energy storage devices according to an embodiment of the present invention.

[0021] like Figure 1 As shown, the method includes: S1, Construct a phase change management structure layer, wherein the phase change management structure layer includes a structured composite phase change material with a gradient thermal conductivity, a distributed embedded sensor network, and an energy self-regulating interface layer set between the phase change material and the external heat sink.

[0022] In one embodiment of the present invention, the above-mentioned energy self-regulating interface layer adopts a smart thermal switch structure based on shape memory alloy, wherein the smart thermal switch structure includes a shape memory alloy spring drive system, a multi-layer composite thermal interface and an elastic support buffer mechanism.

[0023] In one embodiment of the present invention, the above-mentioned shape memory alloy spring drive system adopts a nickel-titanium based alloy material with a two-way shape memory effect. It can be made to generate reversible expansion and contraction deformation within the critical temperature range of the phase change material's heat storage saturation through a thermomechanical training process. Its phase change temperature point is designed according to the thermophysical parameters of the phase change material. The shape memory alloy spring drive system automatically triggers the deformation response when the phase change material is about to reach the heat saturation state.

[0024] Furthermore, in one embodiment of the present invention, the aforementioned multilayer composite thermally conductive interface is made of a high thermal conductivity copper-based composite material, the surface of the interface is treated with a micro-nano structure, and the two sides of the interface are tightly bonded to a phase change material and an external heat sink, respectively.

[0025] Furthermore, in one embodiment of the present invention, the above-mentioned elastic support buffer mechanism is made of a ceramic material with low thermal conductivity, which provides a stable reverse support force when the shape memory alloy spring is not activated, so that the thermal interfaces on both sides are kept in an appropriate separation state.

[0026] In one embodiment of the present invention, when the temperature of the phase change material is detected to reach or exceed the set critical temperature, the shape memory alloy spring undergoes an austenitic phase transformation upon heating, resulting in elongation deformation and overcoming the resistance of the elastic support mechanism, thereby pressing the thermally conductive interfaces on both sides together to form a low thermal resistance path.

[0027] Furthermore, in one embodiment of the present invention, the aforementioned sensor network may include an array of fiber optic temperature sensors, a miniature heat flux sensor, and a phase change state detection unit arranged in different functional layers of the phase change material.

[0028] Furthermore, in one embodiment of the present invention, a forced cooling program is automatically activated when an abnormally high temperature is detected, so that the thermal switch can operate safely under extreme conditions.

[0029] In one embodiment of the present invention, the reversible deformation response of the shape memory alloy ensures the reliability and durability of the energy storage device, avoiding mechanical failure. Furthermore, the multilayer composite thermally conductive interface treatment effectively reduces contact thermal resistance and enhances heat transfer efficiency.

[0030] S2, acquires the first temperature gradient distribution, the first heat flux density and the phase change state information of the phase change material in real time through the sensor network, and establishes a thermal state monitoring dataset based on the first temperature gradient distribution, the first heat flux density and the phase change state information.

[0031] In one embodiment of the present invention, the method for acquiring the first temperature gradient distribution, the first heat flux density, and the phase transition state information of the phase change material in real time through a sensor network may include the following steps: S21, by using wavelength division multiplexing technology through an optical fiber temperature sensor array, the temperature gradient in the thickness direction of the phase change material is continuously monitored by setting multiple measurement points on a single optical fiber, and the first temperature gradient distribution is obtained. S22, based on the Seebeck effect principle and using a thin-film thermopile structure, directly measures the first heat flux density through each functional layer using a micro heat flux sensor. S23, by combining active thermal excitation and passive temperature monitoring through a phase change state detection unit, the starting point and ending point of the phase change process are identified by analyzing the transient thermal response characteristics of the material, and the starting point and ending point of the phase change process are determined as the phase change state information of the phase change material.

[0032] In one embodiment of the present invention, the aforementioned fiber optic temperature sensor array enables high-resolution temperature gradient monitoring, and the miniature heat flux sensor directly measures heat flux density, thus improving measurement accuracy. Furthermore, the phase transition state detection unit can accurately capture the phase transition process, enhancing the ability to sense changes in the material's state.

[0033] Furthermore, in one embodiment of the present invention, the method for establishing a thermal state monitoring dataset based on a first temperature gradient distribution, a first heat flux density, and phase transition state information may include the following steps: Step 1: Identify outlier data points in the first temperature gradient distribution and the first heat flux density through cross-validation, and fill them in using a data reconstruction algorithm to form a complete second temperature gradient distribution and second heat flux density. Step 2: Use wavelet transform algorithm to denoise and filter the second temperature gradient distribution and the second heat flux density to obtain the third temperature gradient distribution and the third heat flux density. Step 3: Perform time series analysis on the third temperature gradient distribution to extract thermal state characteristic parameters, including phase change plateau characteristics, temperature rise rate characteristics, and spatial temperature distribution characteristics. Step 4: Based on the third temperature gradient distribution, third heat flux density, phase transition state information, and thermal state characteristic parameters, establish a thermal state monitoring dataset.

[0034] In one embodiment of the present invention, the phase transition plateau characteristics can be obtained by calculating the change of the average temperature over time in the third temperature gradient distribution.

[0035] Furthermore, in one embodiment of the present invention, the average temperature rise rate of the entire process in the third temperature gradient distribution is calculated to obtain the temperature rise rate characteristic. Specifically, the temperature rise rate characteristic can be obtained by dividing the temperature change from the beginning to the end of the third temperature gradient distribution by the total time.

[0036] Furthermore, in one embodiment of the present invention, the temperature standard deviation of all spatial points at each time point is calculated to obtain the spatial temperature distribution characteristics.

[0037] In one embodiment of the present invention, after obtaining the thermal state characteristic parameters through the above steps, the third temperature gradient distribution, the third heat flux density, the phase change state information, and the thermal state characteristic parameters can be determined as a thermal state monitoring dataset.

[0038] S3, based on the thermal state monitoring dataset, performs state assessment using a phase change material state assessment algorithm to obtain the state assessment results.

[0039] In one embodiment of the present invention, after obtaining the thermal state monitoring dataset through the above steps, the state can be evaluated based on the thermal state monitoring dataset using a phase change material state evaluation algorithm to obtain the state evaluation result.

[0040] Specifically, in one embodiment of the present invention, the method for obtaining the state assessment result by performing state assessment using a phase change material state assessment algorithm based on a thermal state monitoring dataset may include the following steps: S31. Based on the third temperature gradient distribution and thermal conductivity parameters of each functional layer of the phase change material in the thermal state monitoring dataset, a heat flow distribution calculation model is constructed. S32, a regularized optimization algorithm is used to stabilize the heat flux distribution calculation model, obtain the heat flux density vector field at any time in the entire phase change material, and generate a heat flux distribution cloud map based on the heat flux density vector field; S33. Based on the temperature time series data in the distribution cloud map and the start and end points of the phase change process in the thermal state monitoring dataset, as well as the characteristics of the phase change platform, a calculation model for the phase change interface propagation rate is established. S34, in the phase change interface propulsion rate calculation model, the evolution trajectory of the phase change interface is tracked by the level set method, and the latent heat release effect in the phase change process is handled by the enthalpy method model. The propulsion rate of the phase change interface is inverted in real time by using the numerical solution method of the moving boundary problem. S35. Based on the heat flow distribution and the propulsion rate of the phase change interface in the distribution cloud map, a model for assessing the remaining thermal storage capacity is constructed. S36, Based on the remaining thermal storage capacity assessment model, the estimated value of the remaining thermal storage capacity is obtained; S37, the distribution cloud map of heat flow, the advance rate of the phase change interface, and the estimated value of the remaining thermal storage capacity are determined as the state assessment results.

[0041] In one embodiment of the present invention, the third temperature gradient distribution from the thermal state monitoring dataset and pre-stored thermal conductivity parameters precisely matched to each functional layer of the gradient-structured composite phase change material are invoked. A computational domain is established based on the material's geometry, and spatial discretization is performed using the finite element method. Specifically, the temperature gradient distribution data is mapped as known field variables to mesh nodes to construct a discrete temperature field for computation. .

[0042] Furthermore, in one embodiment of the present invention, according to Fourier's law of heat conduction... , by gradient and thermal conductivity Calculate heat flux density To address the inherent ill-posedness of retrieving full-field heat flow from finite observation points, this invention employs the Tikhonov regularization optimization algorithm for stabilization, constructing the following objective function to obtain a physically reasonable stable solution. :

[0043] in, To observe the temperature vector, For the forward modeling operator of heat conduction, For regularization parameters, This is the Laplace operator used to guarantee the smoothness of the solution.

[0044] Furthermore, in one embodiment of the present invention, after solving through the above steps, the heat flux density vector field for the entire space and the entire time is output. It generates a heat flow distribution cloud map of its spatiotemporal evolution, thereby revealing the internal heat transfer situation and helping to optimize thermal design.

[0045] Furthermore, in one embodiment of the present invention, the heat flux density vector field output through the above steps... This is used as a key input, serving as the heat source term driving the phase transition process. Simultaneously, to accurately define the computational interval and phase transition characteristics, the phase transition initiation points from the dataset are used. and the end point of phase transition The phase transition temperature range is calibrated using the characteristics of the phase transition platform.

[0046] Level set method: Introducing level set function The location of the phase transition interface is represented by the level set function, which is defined as follows:

[0047] By solving the level set equations, the evolution of the phase transition interface can be traced:

[0048] in, .

[0049] Enthalpy model: Within the phase transition region, considering the release or absorption of latent heat, the enthalpy model is used to handle the phase transition process. Wherein, enthalpy... The definition is as follows:

[0050] in, This is the current temperature; This is a reference temperature; It is specific heat capacity; It is the latent heat of phase transition; It represents the degree of phase transition completion (0 indicates a completely solid state, and 1 indicates a completely liquid state).

[0051] The heat conduction equation of the enthalpy method model is:

[0052] in, It is a velocity field.

[0053] Furthermore, the moving boundary problem is solved by combining the level set method and the enthalpy method model. The moving boundary problem is solved numerically to invert the propagation rate of the phase transition interface in real time. During the solution process, auxiliary verification is performed based on the temperature rise rate characteristics to ensure that the inverted interface dynamics are consistent with the macroscopic temperature change. The spatiotemporal location of the phase transition interface and its normal propagation rate field are obtained, thereby accurately tracking the phase transition process and improving the ability to predict the phase transition behavior of materials.

[0054] Furthermore, in one embodiment of the present invention, the state-space model defines a vector containing key state variables of the remaining thermal storage capacity. A state-space model framework for the thermal behavior of phase change materials is established, including equations of state and observation equations that reflect state evolution. Equations of state:

[0055] Observation equation:

[0056] in, It is a state vector, including heat flux distribution and the location of the phase change interface; It is the state transition matrix; It is the control input matrix; It controls the input vector; It is an observation vector, derived from sensor data; It is the observation matrix; It is observation noise.

[0057] Distributed Kalman Filter Algorithm: This algorithm employs a distributed Kalman filter to fuse data from multiple sensor sources. It addresses measurement noise and system uncertainty through prediction and update steps. Prediction steps:

[0058] Update steps:

[0059] in, It is a priori state estimation; It is the prior estimation error covariance; It is the process noise covariance; It is the Kalman gain; It is a posterior state estimate; It is the covariance of the posterior estimation error; It is the observation noise covariance.

[0060] Furthermore, in one embodiment of the present invention, by combining the thermophysical properties of the phase change material (such as specific heat capacity, thermal conductivity, latent heat of phase change) and usage history (such as cumulative heat storage / release and number of phase change cycles), a dynamic optimal estimate of the remaining heat storage capacity is achieved, and a confidence interval assessment of the estimation result is given simultaneously.

[0061] In one embodiment of the present invention, after obtaining the heat flow distribution cloud map, the advancing rate of the phase change interface, and the estimated value of the remaining thermal storage capacity through the above steps, the heat flow distribution cloud map, the advancing rate of the phase change interface, and the estimated value of the remaining thermal storage capacity can be determined as the state assessment result.

[0062] S4, based on the state assessment results, generates control commands through a multi-objective optimization control strategy to dynamically adjust the thermal resistance state of the energy self-regulating interface layer.

[0063] In one embodiment of the present invention, after determining the state assessment result through the above steps, control commands can be generated based on the state assessment result through a multi-objective optimization control strategy to dynamically adjust the thermal resistance state of the energy self-regulating interface layer.

[0064] Specifically, in one embodiment of the present invention, the method for generating control commands based on state assessment results through a multi-objective optimization control strategy to dynamically adjust the thermal resistance state of the energy self-regulating interface layer may include the following steps: S41, construct a multi-objective optimization problem with the thermal resistance state of the energy self-regulating interface layer as the control variable, and the optimization objectives being the uniformity of heat flow distribution, the stability of the phase change interface propagation rate, and the availability of remaining thermal storage capacity, and including temperature constraints, energy consumption constraints, and equipment life constraints. S42, based on the state assessment results, the thermal uniformity control requirement level, energy consumption control requirement level and lifetime protection requirement level are obtained by quantification through fuzzy reasoning mechanism. S43, based on the working mode of energy storage equipment and various demand levels, adopts an adaptive weight allocation method to dynamically adjust the weight coefficients of each optimization objective in the multi-objective optimization problem; S44, based on the model predictive control framework, uses the current state evaluation results and dynamically adjusted weight coefficients to solve the multi-objective optimization problem through a rolling optimization algorithm, obtains the optimal control sequence for the thermal resistance state of the energy self-regulating interface layer in the future time domain, and generates the optimal control command for the current moment.

[0065] In one embodiment of the present invention, the control variable can be R(t) = [R1(t), R2(t), ..., Rn(t)], where R i(t) represents the thermal resistance of the i-th control unit at time t, n is the total number of control units, and the state variables may include: temperature field distribution T=T(x,y,z,t), where x, y, z are spatial coordinates, t is time, and T is temperature; heat flux density Q in each region. i (t), where Q i Represents the heat flux density of the i-th region; the location of the phase transition interface. ,in, Let be the volume of the liquid phase at time t. Cross-sectional area; remaining thermal storage capacity , For maximum thermal storage capacity, C extracted (t) represents the amount of heat extracted at time t.

[0066] In one embodiment of the present invention, the optimization objective corresponding to the uniformity of heat flow distribution is:

[0067] in, The average value of heat flux.

[0068] Furthermore, in one embodiment of the present invention, the optimization objective for the stability of the phase change interface propulsion rate is:

[0069] Furthermore, in one embodiment of the present invention, the optimization objective for the utilization rate of remaining thermal storage capacity is:

[0070] in, Let be the extraction efficiency coefficient at time t.

[0071] Furthermore, in one embodiment of the present invention, the aforementioned multi-objective optimization problem, which includes temperature constraints, energy consumption constraints, and equipment lifespan constraints, is as follows:

[0072] Constraints: ; ;

[0073] in, For overall energy consumption control, This represents the maximum energy consumption. Total fatigue damage, This represents the maximum fatigue damage. This is the minimum temperature. This represents the maximum temperature.

[0074] In one embodiment of the present invention, quantifying the thermal uniformity control requirement level, energy consumption control requirement level, and lifetime protection requirement level based on the state assessment results through a fuzzy inference mechanism may include: extracting input feature quantities from the state assessment results; establishing a fuzzy inference expert rule base; and employing a Mamdani-type fuzzy inference system for real-time acquisition of precise inputs. This yields the corresponding demand level.

[0075] In one embodiment of the present invention, the aforementioned input feature quantity may include: temperature non-uniformity index. Based on the heat flow distribution cloud map, calculate the temperature standard deviation of all monitoring points at the current moment; phase change rate fluctuation coefficient. : Calculate the coefficient of variation of the propulsion rate time series based on the phase transition interface within a certain time window; remaining capacity decay factor. Based on the estimated remaining thermal storage capacity and its changing trend, calculate its percentage decay rate relative to the initial capacity; and define linguistic variables and membership functions for each input feature: The linguistic variables are: {low, medium, high}, and their membership functions are defined using trigonometric functions on the universe of discourse [0, Max_T]. The linguistic variables are: {stable, general, unstable}, and their membership functions are defined using trapezoidal functions; The linguistic variables are: {sufficient, moderate, insufficient}, and their membership functions are defined using Gaussian functions.

[0076] Furthermore, in one embodiment of the present invention, all core rules covering typical cases can be set according to actual needs to establish a fuzzy reasoning expert rule base. For example, rule R1:IF For "high" AND The thermal uniformity control requirement level is "extremely high" for "unstable"; Rule R2:IF For "middle" AND The energy consumption control requirement level is set to "Medium" for "Sufficient"; Rule R3:IF The life protection requirement level is "high" for "insufficient".

[0077] Furthermore, in one embodiment of the present invention, a Mamdani-type fuzzy inference system can be used for real-time acquisition of precise input. This yields the corresponding demand level.

[0078] Specifically, in one embodiment of the present invention, the process involves: fuzzification: calculating the membership degree of each rule to each linguistic variable; rule evaluation: activating all rules whose preconditions are met, and determining the activation strength of the output conclusion of each rule based on the precondition membership degree (usually using the "smallest" or "product" operator); aggregation: aggregating the output fuzzy sets of all activated rules to form a total output fuzzy set; and defuzzification: calculating the clear numerical output of the aggregated output fuzzy set (representing the "demand level") using the centroid method. The numerical values ​​represent three quantified levels of demand, with higher values ​​indicating a more urgent need.

[0079] Furthermore, in one embodiment of the present invention, the aforementioned optimal control command is issued in the form of a PWM duty cycle vector sequence to adjust the Joule heating power of the shape memory alloy spring moment by moment, thereby precisely controlling the phase change driving force and displacement output of the SMA, enabling the thermal interface to complete the continuously adjustable action of pressing and releasing within a millisecond time scale, achieving a thermal resistance of 0.05-0.8 K·W. - ¹Infinite variation within the range; simultaneously, the instruction frame embeds an emergency disconnect flag and a working mode flag, which are used to trigger hardware-level safety interlocks during transient overheating or operating condition switching, ensuring that the energy storage device always operates on the optimal thermo-mechanical coupling trajectory, ultimately achieving optimal comprehensive performance in terms of temperature gradient suppression, energy consumption minimization, and lifespan extension.

[0080] In one embodiment of the present invention, the operating mode of the above-mentioned energy storage device can be either high-power operating mode or normal operating mode.

[0081] Furthermore, in one embodiment of the present invention, the aforementioned adaptive weight allocation can flexibly adapt to different operating conditions, improving the robustness and adaptability of the system; the model predictive control framework reduces control lag and enhances temperature stability and energy efficiency through forward optimization; rolling optimization ensures that control commands are updated in real time, responding to rapidly changing heat loads. Thus, through the above steps, while ensuring temperature control accuracy, system energy consumption can be significantly reduced, equipment lifespan extended, and intelligent and efficient thermal management achieved.

[0082] This invention discloses a thermal management method for energy storage devices using micro / nano composite phase change materials. The method constructs a phase change management structure layer, which includes a structured composite phase change material with gradient thermal conductivity, a distributed embedded sensor network, and an energy self-regulating interface layer positioned between the phase change material and an external heat sink. The sensor network acquires real-time information on a first temperature gradient distribution, a first heat flux density, and the phase change state of the phase change material. A thermal state monitoring dataset is then established based on this data. A phase change material state assessment algorithm is used to evaluate the state of the material, yielding the assessment results. Based on these results, a multi-objective optimization control strategy generates control commands to dynamically adjust the thermal resistance state of the energy self-regulating interface layer. This invention achieves efficient heat conduction and uniform distribution through the design of a structured composite phase change material with gradient thermal conductivity. Furthermore, the phase change material state assessment algorithm and multi-objective optimization control strategy ensure temperature control accuracy while balancing system energy consumption and equipment lifespan, resulting in optimal overall performance and improved thermal management efficiency.

[0083] To achieve the above embodiments, such as Figure 2 As shown, this embodiment also provides a thermal management device 10 for micro / nano composite phase change materials used in energy storage devices. This device includes: Module 201 is used to construct a phase change management structure layer, wherein the phase change management structure layer includes a structured composite phase change material with a gradient thermal conductivity, a distributed embedded sensor network, and an energy self-regulating interface layer disposed between the phase change material and an external heat sink. Module 202 is established to acquire the first temperature gradient distribution, the first heat flux density and the phase change state information of the phase change material in real time through the sensor network, and to establish a thermal state monitoring dataset based on the first temperature gradient distribution, the first heat flux density and the phase change state information. The state assessment module 203 is used to perform state assessment based on the thermal state monitoring dataset and through the phase change material state assessment algorithm to obtain the state assessment result. The control module 204 is used to generate control commands based on the state assessment results through a multi-objective optimization control strategy, so as to dynamically adjust the thermal resistance state of the energy self-regulating interface layer.

[0084] In this embodiment of the disclosure, the above-mentioned energy self-regulating interface layer adopts a smart thermal switch structure based on shape memory alloy, wherein the smart thermal switch structure includes a shape memory alloy spring drive system, a multi-layer composite thermal interface and an elastic support buffer mechanism. The shape memory alloy spring drive system uses a nickel-titanium based alloy material with a two-way shape memory effect; The multi-layer composite thermal interface is made of a high thermal conductivity copper-based composite material. The surface of the interface is treated with micro-nano structure. The two sides of the interface are tightly bonded to the phase change material and the external heat sink, respectively. The elastic support and buffer mechanism is made of ceramic material with low thermal conductivity, which provides a stable reverse support force when the shape memory alloy spring is not activated.

[0085] In one embodiment of the present invention, the sensor network includes an array of fiber optic temperature sensors, a miniature heat flux sensor, and a phase change state detection unit arranged in different functional layers of the phase change material; the establishment module 202 is specifically used for: By using wavelength division multiplexing technology with an optical fiber temperature sensor array, and setting multiple measurement points on a single optical fiber to continuously monitor the temperature gradient in the thickness direction of the phase change material, the first temperature gradient distribution is obtained. Based on the Seebeck effect principle, a micro heat flux sensor is used to directly measure the first heat flux density through each functional layer using a thin-film thermopile structure. By combining active thermal excitation and passive temperature monitoring with a phase change state detection unit, the starting and ending points of the phase change process are identified by analyzing the transient thermal response characteristics of the material, and the starting and ending points of the phase change process are determined as the phase change state information of the phase change material.

[0086] In one embodiment of the present invention, the above-mentioned establishing module 202 is further configured to: Cross-validation is used to identify outlier data points in the first temperature gradient distribution and the first heat flux density, and a data reconstruction algorithm is used to fill in the gaps to form a complete second temperature gradient distribution and second heat flux density. The wavelet transform algorithm is used to denoise and filter the second temperature gradient distribution and the second heat flux density to obtain the third temperature gradient distribution and the third heat flux density. Time series analysis was performed on the third temperature gradient distribution to extract thermal state characteristic parameters, including phase change plateau characteristics, temperature rise rate characteristics, and spatial temperature distribution characteristics. A thermal state monitoring dataset is established based on the third temperature gradient distribution, the third heat flux density, phase transition state information, and thermal state characteristic parameters.

[0087] In one embodiment of the present invention, the state evaluation module 203 is specifically used for: Based on the third temperature gradient distribution and thermal conductivity parameters of each functional layer of the phase change material in the thermal state monitoring dataset, a heat flow distribution calculation model is constructed. A regularized optimization algorithm is used to stabilize the heat flux distribution calculation model, thereby obtaining the heat flux density vector field at any time in the entire phase change material, and generating a heat flux distribution cloud map based on the heat flux density vector field. Based on the temperature time series data in the distribution cloud map and the start and end points of the phase change process in the thermal state monitoring dataset, as well as the characteristics of the phase change platform, a calculation model for the phase change interface propagation rate is established. In the phase transition interface propulsion rate calculation model, the evolution trajectory of the phase transition interface is tracked by the level set method, and the latent heat release effect in the phase transition process is handled by the enthalpy method model. The propulsion rate of the phase transition interface is inverted in real time by using the numerical solution method of the moving boundary problem. Based on the heat flow distribution and the propulsion rate of the phase change interface in the distribution cloud map, a model for assessing the remaining thermal storage capacity is constructed. Based on the remaining thermal storage capacity assessment model, an estimated value of the remaining thermal storage capacity is obtained; The distribution cloud map of heat flux, the advance rate of the phase change interface, and the estimated value of the remaining thermal storage capacity are determined as the state assessment results.

[0088] In one embodiment of the present invention, the control module 204 is specifically used for: A multi-objective optimization problem is constructed, with the thermal resistance state of the energy self-regulating interface layer as the control variable, and the optimization objectives being the uniformity of heat flow distribution, the stability of the phase change interface propagation rate, and the availability of remaining thermal storage capacity, and including temperature constraints, energy consumption constraints, and equipment life constraints. Based on the condition assessment results, the thermal uniformity control requirement level, energy consumption control requirement level and lifetime protection requirement level are obtained by quantification through fuzzy reasoning mechanism. Based on the working mode and demand levels of energy storage devices, an adaptive weight allocation method is adopted to dynamically adjust the weight coefficients of each optimization objective in the multi-objective optimization problem. Based on the model predictive control framework, using the current state evaluation results and dynamically adjusted weight coefficients, a rolling optimization algorithm is used to solve a multi-objective optimization problem, obtain the optimal control sequence for the thermal resistance state of the energy self-regulating interface layer in the future time domain, and generate the optimal control command for the current moment.

[0089] According to an embodiment of the present invention, a thermal management device for energy storage devices using micro / nano composite phase change materials comprises a phase change management structure layer, wherein the phase change management structure layer includes a structured composite phase change material with gradient thermal conductivity, a distributed embedded sensor network, and an energy self-regulating interface layer disposed between the phase change material and an external heat sink. The sensor network acquires real-time information on a first temperature gradient distribution, a first heat flux density, and the phase change state of the phase change material, and establishes a thermal state monitoring dataset based on the first temperature gradient distribution, the first heat flux density, and the phase change state information. Based on the thermal state monitoring dataset, a phase change material state evaluation algorithm is used to perform a state evaluation, obtaining a state evaluation result. Based on the state evaluation result, a multi-objective optimization control strategy is used to generate control commands to dynamically adjust the thermal resistance state of the energy self-regulating interface layer. This invention achieves efficient heat conduction and uniform distribution through the design of a structured composite phase change material with gradient thermal conductivity. Furthermore, the phase change material state evaluation algorithm and multi-objective optimization control strategy ensure temperature control accuracy while considering system energy consumption and equipment lifespan, achieving optimal overall performance and improving thermal management efficiency.

[0090] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0091] Furthermore, the terms "first" and "second" are used for descriptive purposes 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 as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A thermal management method for micro / nano composite phase change materials used in energy storage devices, characterized in that, The method includes: A phase change management structure layer is constructed, wherein the phase change management structure layer includes a structured composite phase change material with a gradient thermal conductivity, a distributed embedded sensor network, and an energy self-regulating interface layer disposed between the phase change material and an external heat sink; The sensor network is used to acquire the first temperature gradient distribution, the first heat flux density, and the phase change state information of the phase change material in real time, and a thermal state monitoring dataset is established based on the first temperature gradient distribution, the first heat flux density, and the phase change state information. Based on the aforementioned thermal state monitoring dataset, a state assessment is performed using a phase change material state assessment algorithm to obtain the state assessment results. Based on the state assessment results, control commands are generated through a multi-objective optimization control strategy to dynamically adjust the thermal resistance state of the energy self-regulating interface layer.

2. The method according to claim 1, characterized in that, The energy self-regulating interface layer adopts an intelligent thermal switch structure based on shape memory alloy, wherein the intelligent thermal switch structure includes a shape memory alloy spring drive system, a multi-layer composite thermal interface and an elastic support buffer mechanism. The shape memory alloy spring drive system uses a nickel-titanium based alloy material with a two-way shape memory effect; The multilayer composite thermally conductive interface is made of a high thermal conductivity copper-based composite material. The surface of the interface is treated with a micro-nano structure. The two sides of the interface are tightly bonded to the phase change material and the external heat sink, respectively. The elastic support and buffer mechanism is made of a ceramic material with low thermal conductivity and provides a stable reverse support force when the shape memory alloy spring is not activated.

3. The method according to claim 1, characterized in that, The sensor network includes an array of fiber optic temperature sensors, a miniature heat flux sensor, and a phase change state detection unit arranged in different functional layers of the phase change material; the real-time acquisition of the first temperature gradient distribution, the first heat flux density, and the phase change state information of the phase change material through the sensor network includes: The fiber optic temperature sensor array employs wavelength division multiplexing technology to continuously monitor the temperature gradient along the thickness direction of the phase change material by setting multiple measurement points on a single fiber, thereby obtaining the first temperature gradient distribution. Based on the Seebeck effect, the micro heat flux sensor directly measures the first heat flux density through each functional layer using a thin-film thermopile structure. By combining active thermal excitation and passive temperature monitoring, the phase change state detection unit identifies the start and end points of the phase change process by analyzing the transient thermal response characteristics of the material, and determines the start and end points of the phase change process as the phase change state information of the phase change material.

4. The method according to claim 3, characterized in that, The establishment of a thermal state monitoring dataset based on the first temperature gradient distribution, the first heat flux density, and the phase transition state information includes: Abnormal data points in the first temperature gradient distribution and the first heat flux density are identified by cross-validation and filled in using a data reconstruction algorithm to form a complete second temperature gradient distribution and second heat flux density. The second temperature gradient distribution and the second heat flux density are denoised and filtered using a wavelet transform algorithm to obtain the third temperature gradient distribution and the third heat flux density. Time series analysis was performed on the third temperature gradient distribution to extract thermal state characteristic parameters, including phase change plateau characteristics, temperature rise rate characteristics, and spatial temperature distribution characteristics. A thermal state monitoring dataset is established based on the third temperature gradient distribution, the third heat flux density, the phase transition state information, and the thermal state characteristic parameters.

5. The method according to claim 4, characterized in that, The state assessment is performed based on the thermal state monitoring dataset using a phase change material state assessment algorithm to obtain the state assessment results, including: Based on the third temperature gradient distribution in the thermal state monitoring dataset and the thermal conductivity parameters of each functional layer of the phase change material, a heat flow distribution calculation model is constructed. The heat flux distribution calculation model is stabilized by a regularization optimization algorithm to obtain the heat flux density vector field at any time in the entire phase change material, and a heat flux distribution cloud map is generated based on the heat flux density vector field. Based on the temperature time series data in the distribution cloud map and the start and end points of the phase change process in the thermal state monitoring dataset, as well as the characteristics of the phase change platform, a calculation model for the phase change interface propagation rate is established. In the phase change interface propulsion rate calculation model, the evolution trajectory of the phase change interface is tracked by the level set method, and the latent heat release effect in the phase change process is handled by the enthalpy method model. The propulsion rate of the phase change interface is inverted in real time by using the numerical solution method of the moving boundary problem. Based on the heat flow distribution in the distribution cloud map and the propagation rate of the phase change interface, a model for assessing the remaining thermal storage capacity is constructed. Based on the remaining thermal storage capacity assessment model, an estimated value of the remaining thermal storage capacity is obtained; The heat flow distribution cloud map, the advancement rate of the phase change interface, and the estimated value of the remaining thermal storage capacity are determined as the state assessment results.

6. The method according to claim 5, characterized in that, The step of generating control commands based on the state assessment results through a multi-objective optimization control strategy to dynamically adjust the thermal resistance state of the energy self-regulating interface layer includes: A multi-objective optimization problem is constructed, with the thermal resistance state of the energy self-regulating interface layer as the control variable, and the optimization objectives being the uniformity of heat flow distribution, the stability of the phase change interface propagation rate, and the availability of remaining thermal storage capacity, and including temperature constraints, energy consumption constraints, and equipment life constraints. Based on the state assessment results, the thermal uniformity control requirement level, energy consumption control requirement level, and lifetime protection requirement level are obtained by quantification through fuzzy reasoning mechanism. Based on the working mode of the energy storage device and the various demand levels, an adaptive weight allocation method is used to dynamically adjust the weight coefficients of each optimization objective in the multi-objective optimization problem. Based on the model predictive control framework, using the current state evaluation results and the dynamically adjusted weight coefficients, the multi-objective optimization problem is solved through a rolling optimization algorithm to obtain the optimal control sequence for the thermal resistance state of the energy self-regulating interface layer in the future time domain, and to generate the optimal control command for the current moment.

7. A thermal management device for micro / nano composite phase change materials used in energy storage equipment, characterized in that, The device includes: A construction module is used to construct a phase change management structure layer, wherein the phase change management structure layer includes a structured composite phase change material with a gradient thermal conductivity, a distributed embedded sensor network, and an energy self-regulating interface layer disposed between the phase change material and an external heat sink. A module is established to acquire, in real time, the first temperature gradient distribution, the first heat flux density, and the phase change state information of the phase change material through the sensor network, and to establish a thermal state monitoring dataset based on the first temperature gradient distribution, the first heat flux density, and the phase change state information. The state assessment module is used to perform state assessment based on the thermal state monitoring dataset using a phase change material state assessment algorithm to obtain the state assessment result. The control module is used to generate control commands based on the state assessment results through a multi-objective optimization control strategy, so as to dynamically adjust the thermal resistance state of the energy self-regulating interface layer.

8. The apparatus according to claim 7, characterized in that, The energy self-regulating interface layer adopts an intelligent thermal switch structure based on shape memory alloy, wherein the intelligent thermal switch structure includes a shape memory alloy spring drive system, a multi-layer composite thermal interface and an elastic support buffer mechanism. The shape memory alloy spring drive system uses a nickel-titanium based alloy material with a two-way shape memory effect; The multilayer composite thermally conductive interface is made of a high thermal conductivity copper-based composite material. The surface of the interface is treated with a micro-nano structure. The two sides of the interface are tightly bonded to the phase change material and the external heat sink, respectively. The elastic support and buffer mechanism is made of a ceramic material with low thermal conductivity and provides a stable reverse support force when the shape memory alloy spring is not activated.

9. An electronic device, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

10. A computer storage medium, wherein, The computer storage medium stores computer-executable instructions; when executed by a processor, the computer-executable instructions can implement the method as described in any one of claims 1-6.

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