Multi-energy storage equipment cooling capacity cascade multiplexing liquid cooling system based on temperature rise prediction

Abstract

The multi-energy storage equipment cooling capacity cascade multiplexing liquid cooling system based on temperature rise prediction comprises a refrigerating unit, an energy storage equipment battery cluster, a sensor group, a calorific value prediction unit and a cooling capacity adjustment control unit, and the calorific value prediction unit can predict the calorific value of the energy storage equipment battery cluster according to operation parameters collected by the sensor group; the priority of the energy storage device battery cluster is determined, then the cooling capacity adjusting control unit controls the refrigerant to flow in the large circulation loop according to the priority of the energy storage device battery cluster, cascade reuse of the cooling capacity is achieved, waste of the cooling capacity is avoided, and meanwhile the cooling capacity adjusting control unit can further adjust the cooling capacity of the energy storage device battery cluster when a cooling capacity gap exists. A refrigerant is directly conveyed to a battery cluster of the energy storage equipment through the parallel branch, precise cold supplementing is achieved, and through the structure that main refrigeration is coupled with the parallel branch for cold supplementing, the energy efficiency can be maximized, the investment and operation energy consumption of refrigeration equipment can be reduced, the cooling uniformity of all the equipment is guaranteed, and the operation stability and the full-life-cycle economical efficiency of the energy storage system are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage equipment technology, and in particular to a liquid cooling system for multi-energy storage equipment with cascaded reuse of cooling capacity based on temperature rise prediction. Background Technology

[0002] Driven by the "dual carbon" goals, the new energy power generation industry, such as wind power and photovoltaics, has experienced explosive growth. As a key supporting technology for smoothing power output fluctuations and ensuring grid stability, electrochemical energy storage systems have seen their application scale and deployment density continue to climb. However, energy storage batteries inevitably generate a large amount of heat during charging and discharging, especially in scenarios with high-rate operation and dense deployment. If the heat is not dissipated in a timely and efficient manner, it will directly lead to uneven battery module temperature and local overheating, which in turn will cause battery capacity decay, shortened cycle life, and even serious safety accidents such as thermal runaway. This has become one of the core bottlenecks restricting the safe, efficient, and economical operation of large-scale energy storage power stations. Therefore, developing an efficient, reliable cooling system with fine-tuning capabilities is crucial for ensuring the operational stability and economy of energy storage systems.

[0003] Currently, most mainstream liquid cooling solutions in the industry adopt a one-unit-one-cluster approach, meaning each battery cluster is independently equipped with a set of refrigeration units and circulation pipelines. This approach has the following drawbacks in practical applications: First, the cooling capacity utilization rate is low, lacking a cascade reuse mechanism. Because each refrigeration unit operates independently and lacks a coordinated scheduling mechanism, refrigerant that has completed one cooling task but still contains a certain amount of cooling capacity is directly sent back to the refrigeration unit for re-cooling, failing to effectively release its cooling potential, resulting in energy waste and low system efficiency. Second, the control strategies are mostly based on real-time temperature feedback, lacking the ability to predict future heating trends. This passive adjustment method has a lag in response and is difficult to adapt to operating conditions with rapid power fluctuations. Third, the system configuration is redundant. In the one-unit-one-cluster mode, the number of refrigeration equipment, pipelines, and control units needs to increase synchronously with the number of battery clusters, resulting in high initial investment and subsequent operation and maintenance costs, significantly increasing the total life cycle cost of the energy storage power station. Summary of the Invention

[0004] In view of this, the present invention proposes a liquid cooling system for multi-energy storage device cooling capacity cascade reuse based on temperature rise prediction, which can realize efficient cascade reuse of cooling capacity and solve the problems of cooling capacity waste, response lag and high cost in the existing technology.

[0005] The technical solution of this invention is implemented as follows: A liquid cooling system for multi-energy storage device cooling capacity cascade reuse based on temperature rise prediction includes a chiller unit, at least two energy storage device battery clusters, a sensor group, a heat generation prediction unit, and a cooling capacity regulation and control unit. The chiller unit is connected in series with each energy storage device battery cluster through a main refrigerant pipeline network to form a large circulation loop. A main circulation pump is installed on the main refrigerant pipeline network. Each energy storage device battery cluster is connected to the chiller unit through a parallel branch, and a regulating valve is installed on the parallel branch. The sensor group is installed on the energy storage device battery clusters to collect operating parameters. The heat generation prediction unit is communicatively connected to the sensor group and is used to predict the heat generation of the energy storage device battery clusters based on the operating parameters. The cooling capacity regulation and control unit is communicatively connected to the main circulation pump, the regulating valve, and the heat generation prediction unit, and is used to control the flow path of the refrigerant in the large circulation loop, the speed of the main circulation pump, and the opening and closing degree of the regulating valve based on the heat generation.

[0006] Preferably, the sensor group includes a temperature sensor and a power sensor, and the operating parameters include the real-time temperature and real-time output power of the energy storage device's battery cluster.

[0007] Preferably, the heat generation prediction unit is configured to predict the heat generation and temperature rise curve of the battery cluster of the energy storage device in the future period based on the operating parameters collected by the sensor group, by combining the thermal balance micro equation with the thermal characteristic parameters.

[0008] Preferably, the execution steps of the calorific value prediction unit are as follows: The current parameters are calculated based on the output power of the energy storage device's battery cluster, and the preset internal resistance, thermal coefficient, and specific heat capacity of the energy storage device's battery cluster are retrieved as inherent parameters. Estimate the heat flow parameters carried away by the current cooling system, and calculate the heat generation power of the current energy storage device's battery cluster; By substituting the operating parameters, current parameters, inherent parameters, heat flow parameters, and heat generation power into the thermal balance differential equation, the temperature change rate of the battery cluster of the energy storage device can be calculated. Predict the temperature change and final temperature within a set time period based on the rate of temperature change, and plot the temperature rise curve. Based on the assumption of constant heating power, the heat generation within a set time period is estimated by integrating the heating power.

[0009] Preferably, the cooling capacity regulation and control unit is configured to sort the heating levels according to the heat generation of each energy storage device battery cluster, and control the refrigerant to flow preferentially through the energy storage device battery clusters with high heating levels in the large circulation loop.

[0010] Preferably, the specific steps for controlling the refrigerant to preferentially flow through the high-heat-generating energy storage device battery cluster in the large circulation loop are as follows: Based on the predicted heat generation power output by the heat generation prediction unit and the preset control cycle duration, the cooling capacity required by the energy storage device battery cluster in the next cycle is calculated: Q_need_i=P_pred_i*△t, where Q_need_i is the cooling capacity required by the i-th energy storage device battery cluster in the next cycle, P_pred_i is the predicted heat generation power of the i-th energy storage device battery, and △t is the control cycle duration. The refrigerant is sorted according to the cooling capacity required by all energy storage device battery clusters in the next cycle. The main circulation pump is controlled to make the refrigerant in the main refrigerant pipe flow first through the energy storage device battery cluster with the highest required cooling capacity, then through the energy storage device battery cluster with the second highest required cooling capacity, and so on, so as to achieve cascade reuse.

[0011] Preferably, the cooling capacity regulation control unit is further configured to control the opening and closing degree of the regulating valve when it is predicted that the temperature rise rate of a certain energy storage device battery cluster will exceed a preset threshold within a future set time period or that there is still a cooling capacity gap after cascade reuse, so that the refrigerant is transferred from the parallel branch to the energy storage device battery cluster.

[0012] Preferably, the step for determining the cold energy gap is as follows: Obtain the specific heat capacity of the refrigerant in the main refrigerant pipeline and the current flow rate of the main circulation pump. Calculate the refrigerant inlet temperature based on the simulated flow sequence of the energy storage device's battery clusters in the main refrigerant pipeline. The cooling capacity provided by the refrigerant in the main refrigerant network to the energy storage device battery cluster is calculated based on the target temperature of the energy storage device battery cluster: Q_avail_i=c*m_main*(T_in_i-T_target), where Q_avail_i is the cooling capacity provided by the refrigerant in the main refrigerant network to the i-th energy storage device battery cluster, c is the specific heat capacity, m_main is the current flow rate of the main circulation pump, T_in_i is the refrigerant inlet temperature of the i-th energy storage device battery cluster, which is calculated based on the predicted temperature rise of the upstream energy storage device battery cluster in the simulated flow sequence, and T_target is the target temperature; Compare Q_avail_i with Q_need_i. If Q_need_i > Q_avail_i, it is determined that there is a cooling capacity gap.

[0013] Preferably, the specific steps are as follows: The sensor array collects the operating parameters of each energy storage device's battery cluster in real time. The operating parameters are processed by the heat generation prediction unit to predict the future heat generation and cooling requirements of each energy storage device's battery cluster. By comparing the cooling demand of each energy storage device's battery clusters with the cooling capacity regulation and control unit, the flow sequence of the battery clusters in the large circulation loop is determined to achieve cascade reuse of cooling capacity. The cooling capacity regulation and control unit determines whether there is a cooling capacity gap in each energy storage device's battery cluster after cascade reuse. If so, it generates control commands in real time, dynamically adjusts the main circulation pump speed and the opening of the corresponding regulating valve, and opens the parallel branch for supplemental cooling.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention discloses a multi-energy storage device cooling capacity cascade reuse liquid cooling system based on temperature rise prediction, used for efficient cooling of multiple energy storage device battery clusters. Each energy storage device battery cluster is equipped with a sensor group to collect operating parameters such as temperature and power. A heat generation prediction unit can predict and prioritize the heat generation of the energy storage device battery clusters based on the operating parameters, and simultaneously determine the required cooling capacity. Then, the cooling capacity regulation and control unit delivers refrigerant to the energy storage device battery clusters in order of required cooling capacity through a preset large circulation loop, achieving cascade reuse of cooling capacity. If the cascade reused cooling capacity is insufficient to cool the energy storage device battery clusters, the cooling capacity regulation and control unit can open the parallel branch through the regulating valve, allowing the refrigerant to be directly delivered to the energy storage device battery clusters through the parallel branch, achieving precise supplemental cooling and cascade reuse of cooling capacity. This solves the problems of cooling capacity waste, response lag, and high cost existing in the prior art, ensuring the normal operation of the energy storage device battery clusters. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the structure of a multi-energy storage device cooling capacity cascade reuse liquid cooling system based on temperature rise prediction according to the present invention; In the diagram, 1 is the refrigeration unit; 2 is the main circulating pump; 3 is the main refrigerant piping network; 4 is the energy storage device battery cluster; 5 is the sensor group; 6 is the heat generation prediction unit; 7 is the cooling capacity regulation and control unit; 8 is the parallel branch; and 9 is the regulating valve. Detailed Implementation

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. In the following description, the expression "some embodiments" refers to a subset of all possible embodiments; however, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0018] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0019] It should be understood that the present invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Furthermore, the terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0020] It should also be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0021] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0022] See Figure 1 This invention provides a multi-energy storage device cooling capacity cascade reuse liquid cooling system based on temperature rise prediction, comprising a refrigeration unit 1, at least two energy storage device battery clusters 4, a sensor group 5, a heat generation prediction unit 6, and a cooling capacity regulation and control unit 7. The refrigeration unit 1 is connected in series with each energy storage device battery cluster 4 through a main refrigerant pipeline network 3, forming a large circulation loop. A main circulation pump 2 is installed on the main refrigerant pipeline network 3. Each energy storage device battery cluster 4 is connected to the refrigeration unit 1 through a parallel branch 8, and a regulating valve 9 is installed on the parallel branch 8. The sensor group 5 is installed on the energy storage device battery cluster 4 for collecting operating parameters. The heat generation prediction unit 6 is communicatively connected to the sensor group 5 and is used to predict the heat generation of the energy storage device battery cluster 4 based on the operating parameters. The cooling capacity regulation and control unit 7 is communicatively connected to the main circulation pump 2, the regulating valve 9, and the heat generation prediction unit 6, and is used to control the flow path of the refrigerant in the large circulation loop, the speed of the main circulation pump 2, and the opening and closing degree of the regulating valve 9 based on the heat generation.

[0023] This invention discloses a multi-energy storage device cooling capacity cascade reuse liquid cooling system based on temperature rise prediction, used for efficient heat dissipation of energy storage batteries. Multiple energy storage device battery clusters 4 are connected via liquid cooling pipelines. The first and last energy storage device battery clusters 4 are connected to a cooling unit 1. Simultaneously, the cooling unit 1 is connected in series with each energy storage device battery cluster 4 through a main refrigerant network 3, forming a large circulation loop. The refrigerant generated by the cooling unit 1 can be transported in this large circulation loop, cooling each energy storage device battery cluster 4 as it is delivered, preventing excessive heat buildup and reduced lifespan. Furthermore, a parallel branch 8 is provided between the cooling unit 1 and each energy storage device battery cluster 4, with a regulating valve 9 installed on the parallel branch 8. The refrigerant generated by the cooling unit 1 can be directly and individually delivered to each energy storage device battery cluster 4 through the parallel branch 8, achieving precise supplemental cooling.

[0024] Each energy storage device battery cluster 4 is equipped with a corresponding sensor group 5, which can collect operating parameters during operation. These operating parameters are then transmitted to the heat generation prediction unit 6. The heat generation prediction unit 6 can predict the heat generation of the energy storage device battery cluster 4 based on the operating parameters. Based on the heat generation, the required cooling capacity for the energy storage device battery cluster 4 can be determined, and the energy storage device battery clusters 4 can be prioritized accordingly. Then, the flow order of the refrigerant in the large circulation loop can be controlled according to the priority ranking, so that the refrigerant can flow first to the energy storage device battery clusters 4 with higher cooling capacity, and then flow to the energy storage device battery clusters 4 with the next higher cooling capacity, and so on. The main refrigerant pipeline network 3 realizes the series coupling of multiple energy storage device battery clusters 4 and the refrigeration unit 1. Based on the real-time heat load, the refrigerant distribution path is dynamically optimized, so that the cooling capacity is prioritized to meet the high heat load equipment, and the surplus cooling capacity is supplied to the low heat load equipment in a cascade manner. This breaks through the traditional architecture, significantly improves the refrigerant utilization rate and system energy efficiency, reduces refrigeration energy consumption, and avoids the waste of cooling capacity.

[0025] Furthermore, after predicting the heat generation of the energy storage device battery cluster 4 by the heat generation prediction unit 6, the cooling capacity required for cooling the energy storage device battery cluster 4 can be obtained. Then, the cooling capacity regulation and control unit 7 determines whether the cooling capacity delivered to the energy storage device battery cluster 4 can still meet the requirements during the cascade reuse process. If the requirements are not met, the cooling capacity regulation and control unit 7 can directly deliver the cooling capacity to the energy storage device battery cluster 4 through the parallel branch 8 by controlling the speed of the main circulation pump 2 and the opening of the regulating valve 9, so as to achieve precise supplementary cooling. Through the integrated architecture of main refrigeration system coupled with parallel branch 8 for supplementary cooling, redundant configuration of refrigeration equipment is reduced, and initial investment and operation and maintenance costs are reduced. Combined with intelligent control strategy, cooling capacity is supplied on demand, and the economic benefits throughout the entire life cycle are significant.

[0026] Preferably, the sensor group 5 includes a temperature sensor and a power sensor, and the operating parameters include the real-time temperature and real-time output power of the energy storage device battery cluster 4.

[0027] The temperature sensor, such as NB1-ICSS-14U-12, can collect the real-time temperature of the energy storage device battery cluster 4, while the power sensor can collect the real-time output power of the energy storage device battery cluster 4. The real-time temperature and real-time output power are packaged as operating parameters and sent to the heat generation prediction unit 6 for heat generation prediction.

[0028] Preferably, the heat generation prediction unit 6 is configured to predict the heat generation and temperature rise curve of the energy storage device battery cluster 4 in the future period based on the operating parameters collected by the sensor group 5 and by combining the thermal balance micro equation with the thermal characteristic parameters.

[0029] The calorific value prediction unit 6 can be a standalone processor or integrated into the system main control. Its built-in algorithm calculates the calorific value and temperature rise curve based on the collected operating parameters. The specific execution steps are as follows: The current parameters are calculated based on the output power of the energy storage device battery cluster 4, and the preset internal resistance, thermal coefficient and specific heat capacity of the energy storage device battery cluster 4 are retrieved as inherent parameters. Estimate the heat flow parameters carried away by the current cooling, and calculate the heat generation power of the current energy storage device battery cluster 4; By substituting the operating parameters, current parameters, inherent parameters, heat flow parameters, and heat generation power into the thermal balance differential equation, the temperature change rate of the energy storage device battery cluster 4 is calculated. Predict the temperature change and final temperature within a set time period based on the rate of temperature change, and plot the temperature rise curve. Based on the assumption of constant heating power, the heat generation within a set time period is estimated by integrating the heating power.

[0030] First, the current real-time temperature T_now of the energy storage device's battery cluster 4 is obtained and kept on standby. Then, the current parameters are calculated based on the output power. Simultaneously, the preset internal resistance, thermal coefficient, and battery specific heat capacity are used as inherent parameters. After estimating the heat flow parameters and heating power, these parameters are substituted into the thermal balance differential equation, i.e., 10000*dT / dt, where 10000 is the battery specific heat capacity. Then, dT / dt can be solved to obtain the temperature change rate, in °C / s, which is the temperature change per second. For example, when the temperature change rate is -0.2 °C / s, it means that the energy storage device's battery cluster 4 will change at a rate of approximately 0.2 °C per second. Based on the decreasing rate of temperature change, the temperature change within the next 60 seconds can be predicted. The temperature rise curve can be plotted based on the current real-time temperature T_now, the rate of temperature change dT / dt, and the time period. Meanwhile, the total heat generation power P_heat_now of the energy storage device battery cluster 4 consists of Joule heat and reaction heat, etc. The predicted total heat generation within the next 60 seconds is estimated by integrating P_heat_now over time. Assuming the heat generation power is constant, it is 900J. The heat generation prediction unit 6 performs the above calculations on each energy storage device battery cluster 4 and outputs the predicted temperature rise curve and the predicted total heat generation for each battery cluster.

[0031] Preferably, the cooling capacity regulation and control unit 7 is configured to sort the heat generation levels of each energy storage device battery cluster 4 according to their heat output, and control the refrigerant to preferentially flow through the energy storage device battery cluster 4 with higher heat generation levels in the large circulation loop. The specific steps are as follows: Based on the predicted heat generation power output by the heat generation prediction unit 6 and the preset control cycle duration, the cooling capacity required by the energy storage device battery cluster 4 in the next cycle is calculated: Q_need_i=P_pred_i*△t, where Q_need_i is the cooling capacity required by the i-th energy storage device battery cluster 4 in the next cycle, P_pred_i is the predicted heat generation power of the i-th energy storage device battery, and △t is the control cycle duration; According to the cooling capacity required by all energy storage device battery clusters 4 in the next cycle, the main circulation pump 2 is controlled to make the refrigerant in the main refrigerant pipe flow first through the energy storage device battery cluster 4 with the highest required cooling capacity, then through the energy storage device battery cluster 4 with the second highest required cooling capacity, and so on, so as to achieve cascade reuse.

[0032] The cooling capacity regulation and control unit 7 can be a PLC or an industrial control computer, which communicates with the heat generation prediction unit 6, the main circulation pump 2, and the regulating valve 9. It can receive the predicted heat generation of all energy storage device battery clusters 4, determine the heat generation level ranking, and control the refrigerant to flow in the large circulation loop according to priority. The cooling capacity Q_need_i required by the energy storage device battery cluster 4 in the next cycle can be calculated based on the predicted heat generation power output by the heat generation prediction unit 6 and the control cycle duration. The required cooling capacity Q_need_i is sorted to obtain a level table. The energy storage device battery cluster 4 with the highest required cooling capacity Q_need_i will be cooled by the refrigerant first. The refrigerant will still have residual cooling after cooling and can continue to be transported to the next level of energy storage device battery cluster 4 for further cooling. This process can be repeated to achieve the cascade reuse of cooling capacity.

[0033] Preferably, the cooling capacity regulation control unit 7 is further configured to control the opening and closing degree of the regulating valve 9 when it is predicted that the temperature rise rate of a certain energy storage device battery cluster 4 will exceed a preset threshold within a set time period in the future, or when there is still a cooling capacity gap after cascade reuse, so that the refrigerant is transferred from the parallel branch 8 to the energy storage device battery cluster 4. The steps for determining the cooling capacity gap are as follows: Obtain the specific heat capacity of the refrigerant in the main refrigerant network 3 and the current flow rate of the main circulation pump 2. Calculate the refrigerant inlet temperature based on the simulated flow sequence of the energy storage device battery cluster 4 in the main refrigerant network 3. The cooling capacity provided by the refrigerant in the main refrigerant network 3 to the energy storage device battery cluster 4 is calculated based on the target temperature of the energy storage device battery cluster 4: Q_avail_i=c*m_main*(T_in_i-T_target), where Q_avail_i is the cooling capacity provided by the refrigerant in the main refrigerant network 3 to the i-th energy storage device battery cluster 4, c is the specific heat capacity, m_main is the current flow rate of the main circulation pump 2, T_in_i is the refrigerant inlet temperature of the i-th energy storage device battery cluster 4, which is calculated based on the predicted temperature rise of the upstream energy storage device battery cluster 4 in the simulated flow sequence, and T_target is the target temperature; Compare Q_avail_i with Q_need_i. If Q_need_i > Q_avail_i, it is determined that there is a cooling capacity gap.

[0034] In addition to the cascaded reuse of cooling capacity through the large circulation loop, the cooling capacity regulation and control unit 7 also monitors the cooling of the energy storage device battery cluster 4 in real time. It is also configured to activate separate supplementary cooling when the future cooling of the energy storage device battery cluster 4 fails to meet requirements. There are two scenarios where the cooling of the energy storage device battery cluster 4 will not meet requirements: one is that the temperature rise rate will exceed a preset threshold within a set time period, thus requiring separate supplementary cooling in advance; the other is that when the refrigerant flows through the large circulation loop, the cooling capacity is insufficient when it reaches the next energy storage device battery cluster 4, i.e., there is a cooling capacity gap, which also requires separate supplementary cooling for the energy storage device battery cluster 4. The determination of the cooling capacity gap is combined with the energy storage device battery cluster... 4. The specific heat capacity of the refrigerant and the current flow rate of the main circulation pump 2 are extracted from the main refrigerant pipeline 3. The inlet temperature of the refrigerant when it flows to the target energy storage device battery cluster 4 is calculated according to the simulated flow sequence of the refrigerant. Then, the formula for the amount of cooling that the refrigerant can provide to the energy storage device battery cluster 4 is used to calculate Q_avail_i. Q_avail_i is compared with Q_need_i. If Q_need_i > Q_avail_i, it is determined that there is a cooling capacity shortage. At this time, the cooling capacity adjustment and control unit 7 can control the opening of the parallel branch 8 through the main circulation pump 2 and the regulating valve 9 to achieve precise cooling capacity replenishment.

[0035] Preferably, the specific execution steps of the entire liquid cooling system are as follows: The sensor group 5 collects the operating parameters of each energy storage device battery cluster 4 in real time, including real-time temperature and real-time output power. The operating parameters are processed by the heat generation prediction unit 6 to predict the future heat generation and cooling requirements of each energy storage device battery cluster 4. The cooling capacity regulation and control unit 7 compares the cooling capacity requirements of each energy storage device battery cluster 4 and determines its flow sequence in the large circulation loop to achieve cascade reuse of cooling capacity. The cooling capacity regulation and control unit 7 determines whether there is a cooling capacity gap in each energy storage device battery cluster 4 after cascade reuse. If so, it generates control commands in real time, dynamically adjusts the speed of the main circulation pump 2 and the opening of the corresponding regulating valve 9, and opens the parallel branch 8 for supplemental cooling.

[0036] Each energy storage device battery cluster 4 collects operating parameters through a separate sensor group 5. Then, the heat generation prediction unit 6 predicts the heat generation of all energy storage device battery clusters 4. Based on the heat generation, the required cooling capacity can be determined. Then, the cooling priority of each energy storage device battery cluster 4 can be determined according to the heat generation. The higher the required cooling capacity, the higher the priority. The cooling capacity regulation unit can control the refrigerant to flow through the large circulation loop, according to the priority, and prioritize the cooling of the energy storage device battery clusters 4 with higher heat generation. Then, the residual cooling can be used to cool the energy storage device battery clusters 4 with lower priority, avoiding the waste of cooling capacity. At the same time, the cooling capacity regulation control unit 7 can also determine whether the cooling capacity delivered to the energy storage device battery clusters 4 meets the cooling requirements. If the cooling capacity delivered to the energy storage device battery clusters 4 cannot meet the requirements, the parallel branch 8 can be opened through the main circulation pump 2 and the regulating valve 9, so that the refrigerant of the refrigeration unit 1 can be directly delivered to the energy storage device battery clusters 4 through the parallel branch 8 to achieve precise direct supplemental cooling.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A liquid cooling system for cascaded reuse of cooling capacity of multiple energy storage devices based on temperature rise prediction, characterized in that, The system includes a refrigeration unit, at least two energy storage battery clusters, a sensor group, a heat generation prediction unit, and a cooling capacity regulation and control unit. The refrigeration unit is connected in series with each energy storage battery cluster via a main refrigerant pipeline network, forming a large circulation loop. A main circulation pump is installed on the main refrigerant pipeline network. Each energy storage battery cluster is connected to the refrigeration unit via a parallel branch, and a regulating valve is installed on the parallel branch. The sensor group is installed on the energy storage battery clusters to collect operating parameters. The heat generation prediction unit is communicatively connected to the sensor group and is used to predict the heat generation of the energy storage battery clusters based on the operating parameters. The cooling capacity regulation and control unit is communicatively connected to the main circulation pump, the regulating valve, and the heat generation prediction unit, and is used to control the flow path of the refrigerant in the large circulation loop, the speed of the main circulation pump, and the opening and closing degree of the regulating valve based on the heat generation.

2. The liquid cooling system for multi-energy storage devices based on temperature rise prediction and cascade reuse of cooling capacity according to claim 1, characterized in that, The sensor group includes a temperature sensor and a power sensor, and the operating parameters include the real-time temperature and real-time output power of the energy storage device's battery cluster.

3. The liquid cooling system for multi-energy storage devices with cascaded reuse of cooling capacity based on temperature rise prediction according to claim 2, characterized in that, The heat generation prediction unit is configured to predict the heat generation and temperature rise curve of the energy storage device's battery cluster in the future period based on the operating parameters collected by the sensor group, using thermal balance micro-equations combined with thermal characteristic parameters.

4. The liquid cooling system for multi-energy storage devices with cascaded reuse of cooling capacity based on temperature rise prediction according to claim 3, characterized in that, The execution steps of the heat generation prediction unit are as follows: The current parameters are calculated based on the output power of the energy storage device's battery cluster, and the preset internal resistance, thermal coefficient, and specific heat capacity of the energy storage device's battery cluster are retrieved as inherent parameters. Estimate the heat flow parameters carried away by the current cooling system, and calculate the heat generation power of the current energy storage device's battery cluster; By substituting the operating parameters, current parameters, inherent parameters, heat flow parameters, and heat generation power into the thermal balance differential equation, the temperature change rate of the battery cluster of the energy storage device can be calculated. Predict the temperature change and final temperature within a set time period based on the rate of temperature change, and plot the temperature rise curve. Based on the assumption of constant heating power, the heat generation within a set time period is estimated by integrating the heating power.

5. A liquid cooling system for multi-energy storage device cooling capacity cascade reuse based on temperature rise prediction according to claim 1, characterized in that, The cooling capacity regulation and control unit is configured to sort the heat generation levels of each energy storage device battery cluster according to the heat generation of each energy storage device battery cluster, and control the refrigerant to flow preferentially through the energy storage device battery clusters with high heat generation levels in the large circulation loop.

6. A liquid cooling system for multi-energy storage equipment with cascaded reuse of cooling capacity based on temperature rise prediction according to claim 5, characterized in that, The specific steps for controlling the refrigerant to preferentially flow through the high-heat-generating battery clusters of the energy storage device in the large circulation loop are as follows: Based on the predicted heat generation power output by the heat generation prediction unit and the preset control cycle duration, the cooling capacity required by the energy storage device battery cluster in the next cycle is calculated: Q_need_i=P_pred_i*△t, where Q_need_i is the cooling capacity required by the i-th energy storage device battery cluster in the next cycle, P_pred_i is the predicted heat generation power of the i-th energy storage device battery, and △t is the control cycle duration. The refrigerant is sorted according to the cooling capacity required by all energy storage device battery clusters in the next cycle. The main circulation pump is controlled to make the refrigerant in the main refrigerant pipe flow first through the energy storage device battery cluster with the highest required cooling capacity, then through the energy storage device battery cluster with the second highest required cooling capacity, and so on, so as to achieve cascade reuse.

7. A liquid cooling system for multi-energy storage device cooling capacity cascade reuse based on temperature rise prediction according to claim 6, characterized in that, The cooling capacity regulation and control unit is also configured to control the opening and closing degree of the regulating valve when it is predicted that the temperature rise rate of a certain energy storage device battery cluster will exceed a preset threshold within a set time period in the future or when there is still a cooling capacity gap after cascade reuse, so that the refrigerant is transferred from the parallel branch to the energy storage device battery cluster.

8. A liquid cooling system for multi-energy storage equipment with cascaded reuse of cooling capacity based on temperature rise prediction according to claim 7, characterized in that, The steps for determining the cold air gap are as follows: Obtain the specific heat capacity of the refrigerant in the main refrigerant pipeline and the current flow rate of the main circulation pump. Calculate the refrigerant inlet temperature based on the simulated flow sequence of the energy storage device's battery clusters in the main refrigerant pipeline. The cooling capacity provided by the refrigerant in the main refrigerant network to the energy storage device battery cluster is calculated based on the target temperature of the energy storage device battery cluster: Q_avail_i=c*m_main*(T_in_i-T_target), where Q_avail_i is the cooling capacity provided by the refrigerant in the main refrigerant network to the i-th energy storage device battery cluster, c is the specific heat capacity, m_main is the current flow rate of the main circulation pump, T_in_i is the refrigerant inlet temperature of the i-th energy storage device battery cluster, which is calculated based on the predicted temperature rise of the upstream energy storage device battery cluster in the simulated flow sequence, and T_target is the target temperature; Compare Q_avail_i with Q_need_i. If Q_need_i > Q_avail_i, it is determined that there is a cooling capacity gap.

9. A liquid cooling system for multi-energy storage device cooling capacity cascade reuse based on temperature rise prediction according to any one of claims 1-8, characterized in that, The specific steps are as follows: The sensor array collects the operating parameters of each energy storage device's battery cluster in real time. The operating parameters are processed by the heat generation prediction unit to predict the future heat generation and cooling requirements of each energy storage device's battery cluster. By comparing the cooling demand of each energy storage device's battery clusters with the cooling capacity regulation and control unit, the flow sequence of the battery clusters in the large circulation loop is determined to achieve cascade reuse of cooling capacity. The cooling capacity regulation and control unit determines whether there is a cooling capacity gap in each energy storage device's battery cluster after cascade reuse. If so, it generates control commands in real time, dynamically adjusts the main circulation pump speed and the opening of the corresponding regulating valve, and opens the parallel branch for supplemental cooling.

Images