Self-sustaining heat management system for extremely cold energy storage equipment and regulation and control method

Abstract

The invention provides a self-sustaining thermal management system for extremely cold energy storage equipment and a regulation and control method, and relates to the technical field of thermal management, and the system comprises a multi-parameter sensing module which is used for deploying a high-precision sensor network and carrying out the real-time collection of key parameters; the waste heat recovery and energy management module is used for collecting waste heat generated in the equipment operation process and converting the waste heat into available heat energy through a heat exchange technology; the decision and control module is used for intelligently judging and issuing a regulation and control instruction according to real-time monitoring data and a preset strategy, coordinating all the modules to work cooperatively, realizing efficient energy management of the energy storage unit through accurate temperature prediction and multi-target optimization, and realizing energy management of the energy storage unit through dynamic regulation of the strategy. The response speed and the energy utilization efficiency of the system are improved, stable operation under different extremely cold working conditions is ensured, the service life of equipment is prolonged, and intelligent and efficient energy regulation and control are achieved.

Description

Technical Field

[0001] This invention relates to the field of thermal management technology, and more specifically, to a self-sustaining thermal management system and control method for extreme cold energy storage equipment. Background Technology

[0002] In scenarios such as polar scientific expeditions, high-latitude winter energy storage, and deep space exploration, energy storage devices (such as lithium batteries and flow batteries) need to operate in extreme low-temperature environments below -40°C to meet the demand for continuous power supply.

[0003] At low temperatures, problems such as electrolyte solidification, impaired lithium-ion transport, and increased internal resistance can occur. In severe cases, these problems can even lead to a sudden drop in capacity, thermal runaway, or equipment damage. Therefore, in existing technologies, thermal management systems are usually required to address these issues. Existing thermal management systems typically rely on external heat sources, such as the power grid or auxiliary heating devices, to maintain the temperature of the energy storage equipment, thereby enabling it to operate normally in low-temperature environments.

[0004] However, existing technologies that rely on external heat sources suffer from problems such as high energy consumption and low reliability. Especially in remote or extreme environments, it is difficult to achieve continuous power supply for the thermal management system by relying solely on external heat sources. Furthermore, in extremely cold environments, the maintenance and replenishment of external heat sources are extremely difficult, which can lead to problems such as electrolyte freezing, reduced electrode activity, and a surge in internal resistance resulting in capacity decay, power reduction, and failure, seriously threatening equipment safety. Summary of the Invention

[0005] The main objective of this invention is to provide a self-sustaining thermal management system and control method for extreme cold energy storage devices, so as to solve the problem that energy storage devices in extreme cold environments are difficult to maintain heat in the prior art.

[0006] To achieve the above objectives, according to one aspect of the present invention, a self-sustaining system for extreme cold energy storage equipment is provided, comprising:

[0007] The multi-parameter sensing module is used to deploy a high-precision sensor network and collect key parameters in real time, including the temperature of the energy storage unit, the ambient temperature, operating data, and waste heat power.

[0008] The collaborative heat storage module is used to store and release heat. The module includes a phase change material layer and an active heat exchange module. The phase change material layer is composed of a composite phase change material and filler. The composite phase change material is selected from materials that have good phase change temperature matching with the optimal operating range of energy storage.

[0009] The waste heat recovery and energy management module is used to collect waste heat generated during equipment operation and convert it into usable thermal energy through heat exchange technology. The module includes a waste heat recovery unit and an energy priority control unit. The waste heat recovery unit directionally transfers the waste heat generated during charging and discharging to the phase change material layer for storage through thermoelectric conversion or direct heat conduction.

[0010] The decision and control module is used to intelligently judge and issue control commands based on real-time monitoring data and preset strategies, and coordinate the collaborative work of various modules. This module includes a prediction unit, a multi-objective optimization unit, and an early warning unit. The prediction unit is used to predict future operating conditions and trends. The multi-objective optimization unit dynamically optimizes the thermal management strategy based on the prediction results and real-time data. The early warning unit monitors abnormal situations in real time, issues alarms in a timely manner, and initiates emergency measures.

[0011] Furthermore, the composite phase change material is selected from materials that have good phase change temperature matching with the optimal operating range of energy storage. When the optimal operating range of energy storage is 25 degrees Celsius to 40 degrees Celsius, paraffin or hydrated salt composite phase change material is selected, and graphene is selected as the filler. The active heat exchange module adopts an integrated liquid cooling circuit or heat pipe, which is coupled with the phase change material layer.

[0012] According to another aspect of the present invention, a method for regulating a self-sustaining thermal management system for an extreme cold energy storage device is provided, the method comprising:

[0013] It receives key parameters collected from the multi-parameter sensing module, including the temperature of the energy storage unit itself, the ambient temperature, operating data, and waste heat power, and performs temperature response analysis on the energy storage device based on different temperature change rates.

[0014] A mathematical model of temperature change rate and performance degradation is established. The model is used to simulate performance changes under different operating conditions. An energy priority strategy is established based on the performance changes. The energy priority strategy includes the order of utilization of waste heat energy, phase change material layer energy and self-heating element energy.

[0015] By establishing a predictive model, the future temperature of energy storage devices is predicted, and execution signals are generated to the energy priority strategy based on the prediction results, thus balancing temperature regulation and energy management.

[0016] Multi-objective optimization is performed based on the prediction results of the prediction model. A comprehensive evaluation is conducted based on the prediction results and real-time data to dynamically adjust the energy priority strategy and feed the adjusted strategy back to the execution system.

[0017] Furthermore, temperature response analysis of energy storage devices based on different rates of temperature change includes:

[0018] Determine the performance degradation of the energy storage device at different temperature change rates, and determine the critical state of the energy storage device based on the degradation.

[0019] By combining critical state data, assess the risk of thermal runaway of energy storage devices under different operating conditions;

[0020] Repeat the above steps to perform cyclic testing on the energy storage device, collect and analyze multiple test data, and correct for performance degradation.

[0021] Furthermore, the performance degradation is equal to the capacity loss rate per unit temperature change, which refers to the percentage reduction in the capacity of the energy storage unit per unit time.

[0022] Furthermore, the critical state refers to the state in which the performance of the energy storage device degrades to a preset threshold at a specific rate of temperature change. The preset threshold is set and compared with the actual calculated performance degradation. If the actual degradation exceeds the preset threshold, the device is determined to have entered the critical state; otherwise, the device is considered to still be within the safe operating range.

[0023] Furthermore, the prediction model adopts the ARIMA model based on time series analysis, and outputs a temperature change prediction value. This temperature change prediction value can be positive or negative. When the temperature change prediction value is greater than 0, it indicates that the energy storage device will heat up. When the temperature change prediction value is less than 0, it indicates that the energy storage device will cool down. When the temperature change prediction value is equal to 0, it indicates that the temperature of the energy storage device is stable.

[0024] Furthermore, when the energy storage unit temperature is lower than the target temperature and the predicted temperature change is less than 0, it indicates that the temperature is continuously decreasing. In this case, waste heat recovery is activated first. When the waste heat power is less than the required heat, the phase change material layer and the self-heating element are activated in sequence. When the energy storage unit temperature is higher than the target temperature and the predicted temperature change is greater than 0, it indicates that the temperature is continuously rising. In this case, the power of the self-heating element is reduced first, and the excess heat is absorbed by the phase change material layer in conjunction with the heat dissipation device. When the energy storage unit temperature is within the target range and the predicted temperature change is approximately equal to 0, the current energy strategy is maintained.

[0025] Furthermore, the dynamic adjustment of the energy priority strategy includes: when the prediction results show that the temperature will rise rapidly and approach the critical value, prioritizing the reduction of the power of the self-heating element while activating the heat dissipation device; when the prediction results show that the temperature will drop rapidly and approach the critical value, prioritizing the activation of waste heat recovery while activating the phase change material layer and the self-heating element.

[0026] By applying the technical solution of this invention, efficient energy management of the energy storage unit is achieved through accurate temperature prediction and multi-objective optimization. Furthermore, by dynamically adjusting the strategy, the system's response speed and energy utilization efficiency are improved, ensuring stable operation under different extreme cold conditions, reducing energy loss, extending equipment life, and realizing intelligent and efficient energy regulation.

[0027] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0028] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0029] Figure 1 A schematic diagram of a self-sustaining thermal management system module for an extreme cold energy storage device according to the present invention is shown.

[0030] Figure 2 A schematic flowchart of a control method for a self-sustaining thermal management system for an extreme cold energy storage device according to the present invention is shown. Detailed Implementation

[0031] 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.

[0032] 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.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0035] Example 1: As Figure 1 As shown, this application proposes a self-sustaining system for extreme cold energy storage equipment. This system constructs a closed-loop control mechanism encompassing sensing, decision-making, execution, and feedback, and achieves stable operation of the equipment in extreme low-temperature environments through self-sustaining thermal management. This ensures that energy storage efficiency is not affected, while simultaneously reducing energy consumption and extending equipment lifespan. The system includes:

[0036] The multi-parameter sensing module is used to deploy a high-precision sensor network and collect key parameters in real time, including the temperature of the energy storage unit, the ambient temperature, operating data, and waste heat power.

[0037] To achieve self-sustaining thermal management, it is essential to collect various key parameters. Therefore, a high-precision sensor network is employed to monitor key parameters such as the energy storage unit's body temperature, ambient temperature, operating data, and waste heat power in real time. The energy storage unit's body temperature includes temperature data from the tabs, the center of the cells, and key locations in the BMS. The ambient temperature includes the temperature of the equipment's outer surface and the surrounding air temperature, thus constructing a comprehensive temperature monitoring system. Operating data includes charging and discharging current, voltage, cumulative capacity, SOC, and SOH, serving as a basis for decision-making and ensuring precise system control. Waste heat power is calculated using formulas or indirectly through thermocouples, providing data support for optimizing thermal management strategies. This allows for accurate assessment of the equipment's operating status, dynamic adjustment of thermal management strategies, and ensures that the energy storage unit's temperature remains within its optimal operating range even in extreme low-temperature environments.

[0038] The collaborative heat storage module is used to store and release heat. The module includes a phase change material layer and an active heat exchange module. The phase change material layer is composed of a composite phase change material and fillers. The composite phase change material is selected to have good phase change temperature matching with the optimal energy storage operating range. For example, when the optimal energy storage operating range is 25 degrees Celsius to 40 degrees Celsius, paraffin or hydrated salt composite phase change material is selected, and graphene or carbon nanotubes are selected as fillers to improve thermal conductivity. The active heat exchange module adopts an integrated liquid cooling circuit or heat pipe, which is coupled with the phase change material layer to realize the switching between heat storage and heat release.

[0039] The waste heat recovery and energy management module collects waste heat generated during equipment operation and converts it into usable thermal energy through heat exchange technology. This module includes a waste heat recovery unit and an energy priority control unit. The waste heat recovery unit uses thermoelectric conversion or direct heat conduction to directionally transfer the waste heat generated during charging and discharging to the phase change material layer for storage. During charging and discharging, internal resistance heat and heat dissipated by the inverter are generated, which are effectively collected by the waste heat recovery unit. The energy priority control unit achieves optimal energy allocation through waste heat, the activation and deactivation of the phase change material layer, and self-heating elements. It prioritizes the use of waste heat to maintain temperature. When waste heat is insufficient, the phase change material layer is activated to release the stored heat, ensuring temperature stability. If further replenishment is needed, self-heating elements, such as PTC heating elements or miniature resistance wires, are activated to achieve precise temperature control.

[0040] The decision and control module is used to intelligently judge and issue control commands based on real-time monitoring data and preset strategies, and coordinate the collaborative work of various modules. This module includes a prediction unit, a multi-objective optimization unit, and an early warning unit. The prediction unit is used to predict future operating condition changes, the multi-objective optimization unit dynamically optimizes the thermal management strategy based on the prediction results and real-time data, and the early warning unit monitors abnormal situations in real time, issues alarms in a timely manner, and initiates emergency measures to ensure the safe and stable operation of the system.

[0041] Through the design and optimization of the above system, efficient thermal management of energy storage equipment in extreme environments has been achieved, improving the overall performance and reliability of the system, extending the service life of the equipment, reducing operation and maintenance costs, maximizing energy utilization, reducing dependence on external energy sources, and further enhancing the environmental adaptability of the energy storage system.

[0042] Example 2: Figure 2As shown, this application also proposes a control method for a self-sustaining thermal management system of an extreme cold energy storage device. This method operates within the self-sustaining thermal management system of the energy storage device in Embodiment 1. By monitoring the ambient temperature and equipment status in real time, it dynamically adjusts the operating modes of waste heat recovery, phase change material layer, and self-heating elements to ensure that the system can maintain the optimal operating temperature under extreme cold conditions. This effectively avoids performance degradation caused by low temperatures and improves the stability and reliability of the energy storage device in harsh environments. The method includes the following steps:

[0043] Step 01: Receive key parameters collected from the multi-parameter sensing module. These key parameters include the temperature of the energy storage unit itself, the ambient temperature, operating data, and waste heat power. Perform temperature response analysis on the energy storage device based on different temperature change rates.

[0044] To achieve self-sustaining thermal management, it is essential to collect various key parameters. Therefore, a high-precision sensor network is employed to monitor key parameters such as the energy storage unit's body temperature, ambient temperature, operating data, and waste heat power in real time. The energy storage unit's body temperature includes temperature data from the tabs, the center of the cells, and key locations in the BMS. The ambient temperature includes the temperature of the equipment's outer surface and the surrounding air temperature, thus constructing a comprehensive temperature monitoring system. Operating data includes charging and discharging current, voltage, cumulative capacity, SOC, and SOH, serving as a basis for decision-making and ensuring precise system control. Waste heat power is calculated using formulas or indirectly through thermocouples, providing data support for optimizing thermal management strategies. This allows for accurate assessment of the equipment's operating status, dynamic adjustment of thermal management strategies, and ensures that the energy storage unit's temperature remains within its optimal operating range even in extreme low-temperature environments.

[0045] After obtaining the key parameters, it is also necessary to perform temperature response analysis on the energy storage device based on different temperature change rates in order to identify potential thermal runaway risks. Specifically, the temperature response analysis of the energy storage device based on different temperature change rates includes the following steps:

[0046] Step 101: Determine the performance degradation of the energy storage device at different temperature change rates, and determine the critical state of the energy storage device based on the degradation.

[0047] During operation, the performance degradation of energy storage devices is closely related to the rate of temperature change. Higher rates of temperature change exacerbate performance degradation. Therefore, it is necessary to accurately assess the degree of degradation at different rates. The method for determining the amount of performance degradation is to calculate the capacity loss rate per unit temperature change. The capacity loss rate refers to the percentage reduction in the capacity of the energy storage unit per unit time. The specific calculation formula is: Capacity Loss Rate = (Initial Capacity - Current Capacity) / Initial Capacity × 100%. For example, if the capacity of the energy storage device drops from 100% to 94% for every 10 degrees Celsius decrease in temperature within ten minutes, the capacity loss rate is 6% * 10 minutes, which translates to 3.6% per hour. This process can be repeated to calculate the capacity loss rate at different rates of temperature change.

[0048] After determining the performance degradation amount, the critical state of the energy storage device can be determined based on this value. The critical state refers to the state when the performance degradation of the energy storage device reaches a preset threshold at a specific temperature change rate. Therefore, it is necessary to set a reasonable preset threshold and then compare it with the actual calculated performance degradation amount. If the actual degradation amount exceeds the preset threshold, the device is determined to have entered the critical state; otherwise, the device is considered to still be within the safe operating range.

[0049] Step 102: Based on critical state data, assess the risk of thermal runaway of energy storage devices under different operating conditions;

[0050] By combining critical state data for evaluation, the thermal runaway risk level of energy storage devices under different operating conditions can be determined. This allows for numerical simulation of energy storage devices to predict their thermal runaway probability under specific operating conditions. For example, the predicted thermal runaway probability is 12% under high temperature and high load conditions, while it drops to 2% under low temperature and low load conditions. This ensures that energy storage devices can be accurately evaluated under different operating conditions. The calculation of the thermal runaway probability can be performed using statistical models or machine learning algorithms, combined with historical data and real-time monitoring data, to conduct a multi-factor comprehensive analysis and obtain the thermal runaway probability value.

[0051] The calculation method for the probability of thermal runaway risk is as follows: P thermal runaway = ρ(w0 + w1 * η (r) + w2 * r + w3 * T), where P thermal runaway is between 0 and 1, ρ () is the activation function, which is mainly used to map the linear combination to the range of 0 to 1, w0, w1, w2, and w3 are weight coefficients obtained by machine learning, η (r) is the capacity loss rate corresponding to the temperature change rate r, and T is the current ambient temperature. The probability of thermal runaway under different operating conditions can be predicted by this formula.

[0052] Step 103: Repeat the above steps to perform cyclic testing on the energy storage device, collect and analyze multiple test data, and correct for performance degradation.

[0053] By repeating the above steps, a complete dataset covering the performance degradation of energy storage devices under different temperature change rates will be obtained. This dataset can not only reflect the performance stability of energy storage devices under long-term temperature changes, but also provide optimization references for the design and manufacturing process of energy storage devices. Considering the temperature response during the manufacturing process of energy storage devices is beneficial to improving the durability and reliability of energy storage devices and ensuring their efficiency in variable environments.

[0054] Step 02: Establish a mathematical model of temperature change rate and performance degradation, simulate performance changes under different operating conditions through the model, and establish an energy priority strategy based on performance changes. The energy priority strategy includes the hierarchical utilization order of waste heat energy, phase change material layer energy and self-heating element energy.

[0055] After obtaining a complete dataset of performance degradation of energy storage devices at different temperature change rates, a mathematical model can be established based on this data. This mathematical model is based on the relationship between the temperature change rate and the performance degradation. For example, in this embodiment, a multinomial regression model can be used for fitting. The multinomial regression model is a common nonlinear regression method that can well describe the complex relationship between the temperature change rate and performance degradation, thereby simulating the performance changes under different operating conditions, especially simulating the performance degradation trend of energy storage devices in extremely cold scenarios, which facilitates the subsequent formulation of energy priority strategies.

[0056] The energy priority strategy includes the sequential utilization of waste heat energy, phase change material layer energy, and self-heating element energy. When the energy storage device is in a low-temperature environment, waste heat energy is used first for preheating, then the phase change material layer is activated to release latent heat, and finally the self-heating element is activated to supplement heat, ensuring that the device can maintain normal operation at low temperatures and extend its service life.

[0057] Step 03: By establishing a predictive model, the future temperature of the energy storage device is predicted, and an execution signal is generated to the energy priority strategy based on the prediction results to balance temperature regulation and energy management.

[0058] Since the aforementioned process only simulates and optimizes the performance degradation and temperature response of energy storage devices, and does not involve dynamic control in actual operation, it is also necessary to establish a prediction model to predict the future temperature changes of energy storage devices and generate temperature change prediction values. The temperature change prediction values ​​are used to determine whether the energy storage devices will be in a state of heating up or cooling down, and then corresponding execution signals are generated to guide the dynamic adjustment of energy priority strategies.

[0059] The prediction model employs the ARIMA model based on time series analysis. This statistical model effectively captures temperature change trends. Trained using historical temperature data, it accurately predicts future temperature fluctuations, ensuring real-time adjustment of the energy priority strategy. The model outputs a predicted temperature change value, which can be positive or negative. A predicted value greater than 0 indicates the energy storage device will heat up; a predicted value less than 0 indicates the energy storage device will cool down; and a predicted value equal to 0 indicates the energy storage device temperature is stable. Therefore, the energy priority strategy can be adjusted by comparing the energy storage unit temperature with the target temperature and combining this with the predicted temperature change value. The target temperature is determined by the energy storage type and current operating conditions. Specifically:

[0060] When the temperature of the energy storage unit is lower than the target temperature and the predicted temperature change is less than 0, it indicates that the temperature continues to drop. At this time, waste heat recovery is activated first. When the waste heat power is less than the required heat, the phase change material layer and the self-heating element are activated in sequence. The self-heating element is a low-power element with a power of less than or equal to 5% of the rated power to prevent performance loss caused by overheating.

[0061] When the temperature of the energy storage unit is higher than the target temperature and the predicted temperature change is greater than 0, it indicates that the temperature continues to rise. At this time, the power of the self-heating element is reduced first, and the heat dissipation device is activated if necessary to prevent performance degradation caused by overheating. After the temperature drops, the heat dissipation is turned off again, and the excess heat is absorbed by the phase change material layer to achieve buffering.

[0062] When the temperature of the energy storage unit is within the target range and the predicted temperature change is approximately 0, the current energy strategy is maintained, with only monitoring and fine-tuning performed to ensure stable system operation and avoid energy loss caused by frequent adjustments.

[0063] Step 04: Perform multi-objective optimization based on the prediction results of the prediction model, conduct a comprehensive evaluation based on the prediction results and real-time data, dynamically adjust the energy priority strategy, and feed the adjusted strategy back to the execution system.

[0064] In addition to outputting prediction results, the predictive model can also perform multi-objective optimization based on these results. By comprehensively considering factors such as temperature stability, energy utilization efficiency, and equipment lifespan, it optimizes the operating parameters of the energy storage unit to ensure optimal energy management under different operating conditions. Furthermore, it performs a comprehensive evaluation based on prediction results and real-time data, thereby precisely adjusting the energy priority strategy. For example, when the prediction shows that the temperature will rise rapidly and approach a critical value, it prioritizes reducing the power of the self-heating element while activating the heat dissipation device to prevent overheating and ensure equipment safety, rather than the step-by-step adjustments mentioned earlier. This improves response speed and reduces energy waste. Alternatively, when the prediction shows that the temperature will drop rapidly and approach a critical value, it prioritizes waste heat recovery while activating the phase change material layer and self-heating element to prevent overcooling and ensure stable equipment operation. This avoids the delays and energy losses caused by step-by-step adjustments, achieving rapid response and efficient energy management. Adversarial training is employed, comparing the model with the step-by-step adjustment strategy in each selection, prioritizing the optimal strategy. Through continuous iterative optimization, the system's response speed and energy utilization efficiency are improved, ensuring optimal energy management under various operating conditions.

[0065] As can be seen from the above description, the above embodiments of the present invention achieve the following technical effects: through accurate temperature prediction and multi-objective optimization, efficient energy management of the energy storage unit is realized, and through dynamic adjustment strategies, the system's response speed and energy utilization efficiency are improved, ensuring stable operation under different extreme cold conditions, reducing energy loss, extending equipment life, and realizing intelligent and efficient energy regulation.

[0066] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0067] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0068] In the description of this invention, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is generally based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this invention; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0069] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A self-sustaining system for extreme cold energy storage equipment, characterized in that, include: The multi-parameter sensing module is used to deploy a high-precision sensor network and collect key parameters in real time, including the temperature of the energy storage unit, the ambient temperature, operating data, and waste heat power. The collaborative heat storage module is used to store and release heat. The module includes a phase change material layer and an active heat exchange module. The phase change material layer is composed of a composite phase change material and filler. The composite phase change material is selected from materials that have good phase change temperature matching with the optimal operating range of energy storage. The waste heat recovery and energy management module is used to collect waste heat generated during equipment operation and convert it into usable thermal energy through heat exchange technology. The module includes a waste heat recovery unit and an energy priority control unit. The waste heat recovery unit directionally transfers the waste heat generated during charging and discharging to the phase change material layer for storage through thermoelectric conversion or direct heat conduction. The decision and control module is used to intelligently judge and issue control commands based on real-time monitoring data and preset strategies, and coordinate the collaborative work of various modules. This module includes a prediction unit, a multi-objective optimization unit, and an early warning unit. The prediction unit is used to predict future operating conditions and trends. The multi-objective optimization unit dynamically optimizes the thermal management strategy based on the prediction results and real-time data. The early warning unit monitors abnormal situations in real time, issues alarms in a timely manner, and initiates emergency measures.

2. The self-sustaining system for extreme cold energy storage equipment according to claim 1, characterized in that, The composite phase change material is selected from materials that have good phase change temperature matching with the optimal operating range of energy storage. When the optimal operating range of energy storage is 25 degrees Celsius to 40 degrees Celsius, paraffin or hydrated salt composite phase change material is selected, and graphene is selected as filler. The active heat exchange module adopts an integrated liquid cooling circuit or heat pipe, which is coupled with the phase change material layer.

3. A method for regulating a self-sustaining thermal management system for an extreme cold energy storage device, wherein the method operates on the self-sustaining system for an extreme cold energy storage device as described in any one of claims 1 to 2, characterized in that, The method includes: It receives key parameters collected from the multi-parameter sensing module, including the temperature of the energy storage unit itself, the ambient temperature, operating data, and waste heat power, and performs temperature response analysis on the energy storage device based on different temperature change rates. A mathematical model of temperature change rate and performance degradation is established. The model is used to simulate performance changes under different operating conditions. An energy priority strategy is established based on the performance changes. The energy priority strategy includes the order of utilization of waste heat energy, phase change material layer energy and self-heating element energy. By establishing a predictive model, the future temperature of energy storage devices is predicted, and execution signals are generated to the energy priority strategy based on the prediction results, thus balancing temperature regulation and energy management. Multi-objective optimization is performed based on the prediction results of the prediction model. A comprehensive evaluation is conducted based on the prediction results and real-time data to dynamically adjust the energy priority strategy and feed the adjusted strategy back to the execution system.

4. The control method for a self-sustaining system of an extreme cold energy storage device according to claim 3, characterized in that, Temperature response analysis of energy storage devices based on different rates of temperature change includes: Determine the performance degradation of the energy storage device at different temperature change rates, and determine the critical state of the energy storage device based on the degradation. By combining critical state data, assess the risk of thermal runaway of energy storage devices under different operating conditions; Repeat the above steps to perform cyclic testing on the energy storage device, collect and analyze multiple test data, and correct for performance degradation.

5. The control method for a self-sustaining system of an extreme cold energy storage device according to claim 4, characterized in that, The performance degradation is equal to the capacity loss rate per unit temperature change, which refers to the percentage reduction in the capacity of the energy storage unit per unit time.

6. The control method for a self-sustaining system of an extreme cold energy storage device according to claim 4, characterized in that, The critical state refers to the state in which the performance of an energy storage device degrades to a preset threshold at a specific rate of temperature change. The preset threshold is set and compared with the actual calculated performance degradation. If the actual degradation exceeds the preset threshold, the device is determined to have entered the critical state; otherwise, the device is considered to still be within the safe operating range.

7. The control method for a self-sustaining system of an extreme cold energy storage device according to claim 3, characterized in that, The prediction model uses the ARIMA model based on time series analysis to output a temperature change prediction value. This temperature change prediction value can be positive or negative. When the temperature change prediction value is greater than 0, it indicates that the energy storage device will heat up. When the temperature change prediction value is less than 0, it indicates that the energy storage device will cool down. When the temperature change prediction value is equal to 0, it indicates that the temperature of the energy storage device is stable.

8. The control method for a self-sustaining system of an extreme cold energy storage device according to claim 7, characterized in that, When the energy storage unit temperature is lower than the target temperature and the predicted temperature change is less than 0, it indicates that the temperature is continuously decreasing. In this case, waste heat recovery is activated first. When the waste heat power is less than the required heat, the phase change material layer and the self-heating element are activated in sequence. When the energy storage unit temperature is higher than the target temperature and the predicted temperature change is greater than 0, it indicates that the temperature is continuously rising. In this case, the power of the self-heating element is reduced first, and the excess heat is absorbed by the phase change material layer in conjunction with the heat dissipation device. When the energy storage unit temperature is within the target range and the predicted temperature change is approximately equal to 0, the current energy strategy is maintained.

9. The control method for a self-sustaining system of an extreme cold energy storage device according to claim 7, characterized in that, Dynamically adjusting the energy priority strategy includes: when the prediction results show that the temperature will rise rapidly and approach the critical value, prioritizing the reduction of the power of the self-heating element and activating the heat dissipation device at the same time; when the prediction results show that the temperature will drop rapidly and approach the critical value, prioritizing the activation of waste heat recovery and activating the phase change material layer and the self-heating element at the same time.

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