Energy efficiency improvement method of immersion energy storage system based on liquid cooling technology

Abstract

The present invention discloses a method for improving the energy efficiency of an immersion energy storage system based on liquid cooling technology, which relates to the field of thermal management technology for energy storage systems. The method includes: real-time collection of cooling parameters and heat dissipation efficiency information of the immersion energy storage system, and adaptation verification thereof; if the verification result is incompatibility, reading the coolant property information and collecting the coolant circulation information, combining the two to perform a coolant quality degradation analysis to generate a quality degradation feature; and making a coolant cleaning or replacement decision based on the quality degradation feature. The present invention solves the technical problem that the performance of the coolant in existing immersion energy storage systems degrades due to long-term operation, affecting the heat exchange efficiency and system energy efficiency. It achieves the technical effect of improving the system's heat dissipation efficiency and overall energy efficiency by real-time monitoring of coolant quality changes and intelligently deciding to clean or replace the coolant.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management of energy storage systems, and in particular to a method for improving the energy efficiency of an immersion energy storage system based on liquid cooling technology. Background Art

[0002] With the rapid development of energy storage technology, immersion energy storage systems are widely used in large-scale energy storage as an efficient thermal management solution. This system uses liquid cooling technology to dissipate heat from the energy storage unit, thereby improving the system's energy efficiency and stability. However, during long-term operation, the coolant may accumulate impurities, sediments, bubbles, and other factors, resulting in a decrease in thermal conductivity and impaired fluidity, which in turn affects heat exchange efficiency and reduces the overall performance of the energy storage system. Currently, traditional cooling systems often rely on manual regular inspections or periodic replacement of the coolant, which is prone to delayed response and low efficiency. Summary of the Invention

[0003] This application provides an energy efficiency improvement method for an immersion energy storage system based on liquid cooling technology, which is used to solve the technical problem that the performance of the coolant in the existing immersion energy storage system decreases due to long-term operation, affecting the heat exchange efficiency and system energy efficiency.

[0004] The present application provides a method for improving the energy efficiency of an immersion energy storage system based on liquid cooling technology, the method comprising: collecting cooling parameters of a liquid cooling unit of the immersion energy storage system and heat dissipation efficiency information of the energy storage unit in real time; performing adaptation verification on the cooling parameters and the heat dissipation efficiency information under a preset mapping loss, and generating an adaptation verification result; if the adaptation verification result is mismatch, reading coolant property information of the immersion energy storage system and collecting coolant circulation information in the immersion energy storage system; performing a coolant quality degradation analysis based on the coolant circulation information and the coolant property information, and generating a quality degradation feature; and making a coolant cleaning or replacement decision based on the quality degradation feature.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] The present application provides a method for improving the energy efficiency of an immersion energy storage system based on liquid cooling technology, which relates to the technical field of thermal management of energy storage systems. The method collects cooling parameters and heat dissipation efficiency information of the immersion energy storage system in real time and performs adaptation verification on the information. If the verification result is incompatibility, the method reads coolant property information and collects coolant circulation information, analyzes the degradation of coolant quality, and cleans or replaces the coolant based on the quality degradation characteristics. This method solves the technical problem of the existing immersion energy storage system in which the performance of the coolant degrades due to long-term operation, affecting the heat exchange efficiency and system energy efficiency. The method achieves the technical effect of improving the heat dissipation efficiency and overall energy efficiency of the system by real-time monitoring of coolant quality changes and intelligent decision-making to clean or replace the coolant. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0008] Figure 1 A flow chart of a method for improving energy efficiency of an immersion energy storage system based on liquid cooling technology provided in an embodiment of the present application;

[0009] Figure 2 Schematic diagram of the process of analyzing the degradation of coolant quality in the method for improving the energy efficiency of an immersion energy storage system based on liquid cooling technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] This application provides an energy efficiency improvement method for an immersion energy storage system based on liquid cooling technology, which is used to solve the technical problem that the performance of the coolant in the existing immersion energy storage system decreases due to long-term operation, affecting the heat exchange efficiency and system energy efficiency.

[0011] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0013] Example 1, as Figure 1 As shown, the present application provides a method for improving energy efficiency of an immersion energy storage system based on liquid cooling technology, the method comprising:

[0014] P10: Real-time collection of cooling parameters and heat dissipation efficiency information of the liquid cooling unit of the immersion energy storage system. The immersion energy storage system includes an energy storage unit and a liquid cooling unit. The liquid cooling unit includes a coolant container, coolant, and a heat exchange device. The coolant is located in the coolant container and is pumped and heat exchanged by the heat exchange device. The energy storage unit is immersed in the coolant container.

[0015] Furthermore, step P10 in the embodiment of the present application further includes:

[0016] P11: Read the cooling parameters, including the coolant circulation flow rate and the temperature of the circulating coolant container, through a heat exchange device; P12: Monitor the surface temperature drop data of the energy storage unit in real time through multiple temperature sensors set on the surface of the energy storage unit, calculate the heat dissipation efficiency, and generate the heat dissipation efficiency information.

[0017] It should be understood that the real-time monitoring of the submerged energy storage system is mainly composed of an energy storage unit and a liquid cooling unit. Among them, the liquid cooling unit is a key component of the system, including a coolant container, coolant, and a heat exchange device. The coolant container is used to hold the coolant, and the coolant itself is the medium for heat exchange. It is pumped and heat exchanged through the heat exchange device, thereby removing the heat generated by the energy storage unit, maintaining the energy storage unit within a suitable operating temperature range, and ensuring its normal operation. The energy storage unit is immersed in the coolant container and in direct contact with the coolant to facilitate rapid heat transfer.

[0018] In order to achieve real-time monitoring of the immersed energy storage system, the cooling parameters are first read through the heat exchange device. These cooling parameters mainly include the coolant circulation flow rate and the temperature of the circulating coolant container. The coolant circulation flow rate is an important indicator to measure the speed of the coolant flow in the system. It directly affects the heat exchange efficiency between the coolant and the energy storage unit. In actual operation, the coolant circulation flow rate can be monitored in real time by installing a flow sensor at the outlet of the heat exchange device. The flow sensor can accurately measure the amount of coolant passing through per unit time, thereby obtaining the flow rate of the coolant. If the flow rate is too slow, the heat may not be carried away in time, causing the temperature of the energy storage unit to rise; conversely, if the flow rate is too fast, it may increase the energy consumption of the system, and it may not necessarily achieve the best cooling effect.

[0019] The temperature of the circulating coolant container reflects the state of the coolant before it enters the coolant container. This temperature value is of great significance for judging whether the coolant can effectively absorb the heat generated by the energy storage unit. In actual operation, a temperature sensor can be installed on the pipe where the coolant flows into the coolant container to monitor the temperature of the coolant in real time. By comparing the temperature difference between the coolant flowing into and out of the coolant container, the heat exchange effect of the coolant can be preliminarily judged. If the temperature difference is too small, it means that the heat exchange capacity of the coolant is insufficient, and the properties of the coolant or the operating parameters of the heat exchange device may need to be adjusted.

[0020] Next, multiple temperature sensors installed on the surface of the energy storage unit monitor the drop in surface temperature of the energy storage unit in real time. These temperature sensors are distributed at various locations within the energy storage unit, enabling comprehensive and accurate acquisition of surface temperature changes. In practice, the number and location of temperature sensors can be optimally arranged based on the shape and size of the energy storage unit to ensure comprehensive and accurate monitoring data. By collecting and analyzing this temperature data, the heat dissipation efficiency can be calculated, generating heat dissipation efficiency information. Heat dissipation efficiency refers to the ratio of the amount of heat dissipated by the energy storage unit per unit time to the total amount of heat generated. It reflects the ease with which the energy storage unit dissipates heat and the cooling effectiveness of the liquid cooling unit. The following formula can be used to calculate heat dissipation efficiency: Heat dissipation efficiency = (initial temperature of energy storage unit - current temperature of energy storage unit) / initial temperature of energy storage unit × 100%.

[0021] By collecting these cooling parameters and heat dissipation efficiency information in real time, the system can optimize liquid cooling technology in real time. If anomalies in coolant flow rate or heat dissipation efficiency are detected, the system can automatically adjust the coolant flow rate or temperature, or provide a basis for subsequent coolant quality analysis. This process ensures that the energy storage system is always in optimal operating condition, improves energy efficiency, extends equipment life, and avoids system failures caused by insufficient heat dissipation or coolant quality issues, effectively ensuring the stability and reliability of the immersion energy storage system.

[0022] P20: Performing an adaptation check on the cooling parameter and the heat dissipation efficiency information under a preset mapping loss to generate an adaptation check result.

[0023] The preset mapping loss represents the mismatch between cooling parameters and heat dissipation efficiency in the actual operating environment, and includes coolant flow resistance loss and coolant flow environment loss. The coolant flow resistance loss is acquired by collecting coolant flow characteristics and flow surface characteristics, and then inputting them into a pre-built resistance loss database based on cooling properties for matching. The resistance loss database includes flow resistance losses corresponding to different flow characteristic samples and flow surface characteristic samples constructed based on historical data. The coolant flow environment loss is determined by collecting real-time ambient temperature and performing temperature loss analysis based on the thermal conductivity of the insulating material between the environment and the coolant.

[0024] Specifically, the cooling parameters and heat dissipation efficiency information are adaptively checked under preset mapping losses to ensure that the cooling effect of the immersion energy storage system meets the design requirements. Specifically, the preset mapping loss refers to the fact that in the actual operating environment, the coolant flow and heat dissipation efficiency in the cooling system may be affected by various factors, resulting in adaptation deviations between the cooling parameters and heat dissipation efficiency. Therefore, the system needs to compensate for these deviations through adaptive verification to optimize the system's cooling effect.

[0025] Coolant flow resistance loss refers to the reduction in coolant flow efficiency caused by friction between the flow characteristics and the flow surface, as well as other obstructions, during the coolant flow process, thereby affecting its heat exchange capacity. In this step, the system collects coolant flow characteristics (such as flow velocity, flow rate, etc.) and flow surface characteristics (such as surface roughness, shape, etc.), inputs these data into a pre-built resistance loss database based on cooling properties for matching, and thus obtains the resistance loss value. This resistance loss database is constructed based on historical data and records the resistance loss corresponding to different flow characteristic samples and flow surface characteristic samples. By matching the data in the database, the system can accurately calculate the coolant flow resistance loss under the current operating state and make corresponding compensation.

[0026] Coolant flow environmental loss refers to the loss of coolant flow due to changes in environmental factors (such as ambient temperature and humidity). In actual applications, the coolant temperature may be affected by the ambient temperature, thereby affecting its heat exchange capacity. To this end, temperature loss analysis can be performed by collecting real-time ambient temperature data and combining it with the thermal conductivity of the insulating material between the coolant and the surrounding environment. This analysis aims to evaluate the heat loss experienced by the coolant during flow and determine the value of the flow environmental loss. By considering these environmental factors, the coolant flow efficiency and heat dissipation effect can be more precisely adjusted during the adaptation and calibration.

[0027] Finally, after collecting cooling parameters (such as the coolant circulation flow rate, the temperature of the circulating coolant entering the coolant container, etc.) and heat dissipation efficiency information (such as the surface temperature drop data of the energy storage unit and the heat dissipation efficiency calculation results), adaptation verification is performed in combination with the preset mapping loss. The specific steps are as follows: First, the collected cooling parameters and heat dissipation efficiency information are standardized to facilitate unified comparison and analysis. Then, the coolant flow resistance loss and coolant flow environment loss are calculated according to the above method. Finally, the standardized cooling parameters and heat dissipation efficiency information are compared with the preset mapping relationship, and the calculated preset mapping loss is taken into account to determine whether the current system is in the optimal adaptation state. If the adaptation deviation exceeds the preset threshold, the system is considered unsuitable; otherwise, the system is considered adapted.

[0028] Based on the above adaptation and verification process, an adaptation and verification result is generated. The adaptation and verification result will directly guide subsequent operations, such as whether the coolant needs to be cleaned or replaced, to ensure the efficient operation of the immersion energy storage system. Through this detailed adaptation and verification process, the embodiment of the present application can accurately evaluate the adaptation between cooling parameters and heat dissipation efficiency, promptly identify and resolve problems in system operation, and improve the energy efficiency and reliability of the system.

[0029] Furthermore, step P20 in this embodiment of the present application further includes:

[0030] P21: Based on the coolant property information and the immersion energy storage system structure information, twin modeling is performed to construct a twin immersion liquid cooling model; P22: Liquid cooling simulation is performed on the cooling parameters using the twin immersion liquid cooling model to generate simulated heat dissipation efficiency data; P23: The simulated heat dissipation efficiency data is compared with the heat dissipation efficiency information to calculate the heat dissipation efficiency difference; P24: After adapting and correcting the heat dissipation efficiency difference according to the preset mapping loss, it is determined whether it is greater than the preset efficiency deviation index, the adaptation verification is completed, and the adaptation verification result is generated.

[0031] Optionally, the adaptation verification process can be further refined, and the accuracy and reliability of the adaptation verification can be improved by introducing digital twin technology and liquid cooling simulation.

[0032] After collecting cooling parameters and heat dissipation efficiency information, twin modeling is first performed based on the coolant properties and the immersion energy storage system structure information to construct a twin immersion liquid cooling model. Coolant properties include physical properties such as thermal conductivity, specific heat capacity, and viscosity, while immersion energy storage system structure information covers the size, shape, and layout of the energy storage unit, as well as the piping design of the liquid cooling unit and the parameters of the heat exchange device. With this detailed information, using technologies such as computer-aided design (CAD) and computational fluid dynamics (CFD), a digital twin model that is highly consistent with the actual immersion energy storage system is constructed. This model can accurately simulate the flow of coolant in the system and the heat exchange process with the energy storage unit, providing a foundation for subsequent liquid cooling simulation.

[0033] Next, the constructed twin immersion liquid cooling model is used to simulate the collected cooling parameters and generate simulated heat dissipation efficiency data. During the liquid cooling simulation, cooling parameters (such as coolant flow rate and temperature) are input into the twin model. By simulating the coolant flow and heat exchange process in the system, the heat dissipation efficiency of the energy storage unit is calculated under the current cooling parameters. The simulated heat dissipation efficiency data reflects the ideal heat dissipation effect that the system should achieve, providing a reference for evaluating actual heat dissipation efficiency.

[0034] The simulated heat dissipation efficiency data is then compared with the actual heat dissipation efficiency data collected to calculate the heat dissipation difference. This difference is an important indicator for measuring the difference between the actual system heat dissipation performance and the ideal state. By calculating the heat dissipation difference, we can intuitively understand the heat dissipation issues existing in the system under the current operating state. If the heat dissipation difference is too large, it indicates that the actual heat dissipation performance is far from the ideal state, and there may be problems such as poor coolant flow and low heat exchange efficiency.

[0035] Finally, the heat dissipation efficiency difference is adapted and corrected according to the preset mapping loss. The preset mapping loss takes into account factors that cause adaptation deviations between cooling parameters and heat dissipation efficiency in the actual operating environment, such as coolant flow resistance loss and coolant flow environment loss. By combining the heat dissipation efficiency difference with the preset mapping loss, the heat dissipation efficiency difference is adapted and corrected, which can more accurately reflect the heat dissipation performance of the system in the actual operating environment. The corrected heat dissipation efficiency difference is compared with the preset efficiency deviation index. If it is greater than the preset efficiency deviation index, the system is considered unsuitable and requires corresponding adjustments and optimization; otherwise, the system is considered suitable. Through this series of steps, the adaptation verification is completed and the adaptation verification result is generated.

[0036] Through these steps, the cooling effect can be accurately simulated and adjusted to ensure that the coolant flow and heat dissipation efficiency of the energy storage system are always maintained at an optimal level, thereby improving the overall efficiency and operational reliability of the energy storage system.

[0037] P30: If the adaptation check result is unmatched, read the coolant property information of the immersion energy storage system and collect the coolant circulation information in the immersion energy storage system.

[0038] Specifically, if the adaptation verification results indicate that there is a mismatch between the current cooling parameters and the heat dissipation efficiency of the immersion energy storage system, that is, the system fails to achieve the ideal heat dissipation effect, further measures will be taken to explore the cause of the mismatch and provide a basis for subsequent optimization decisions.

[0039] Specifically, the coolant property information of the immersion energy storage system is first read. Coolant property information is key to evaluating coolant performance. It covers a variety of physical and chemical properties of the coolant, such as thermal conductivity, specific heat capacity, viscosity, chemical stability, and corrosiveness. Thermal conductivity reflects the coolant's ability to conduct heat. Higher thermal conductivity means that the coolant can more effectively transfer heat from the energy storage unit. Specific heat capacity indicates the coolant's ability to absorb or release heat per unit mass. A larger specific heat capacity helps the coolant maintain a smaller temperature change after absorbing heat, thereby maintaining a relatively stable cooling effect. Viscosity affects the coolant's flow characteristics. Appropriate viscosity can ensure smooth coolant flow in the system, avoiding problems such as increased flow resistance due to excessively high viscosity or leakage due to too low viscosity. Chemical stability and corrosiveness are related to the coolant's performance during long-term operation and potential damage to system components. A stable coolant can maintain its performance under different operating conditions and will not corrode components such as pipes and heat exchangers.

[0040] While reading the coolant property information, the coolant circulation information in the immersed energy storage system also needs to be collected. The coolant circulation information includes the flow rate, flow rate, circulation path and circulation pressure of the coolant. The flow rate and flow rate are important parameters for measuring the flow state of the coolant in the system. They directly determine the heat exchange efficiency between the coolant and the energy storage unit. The appropriate flow rate and flow rate can ensure that the coolant will not affect the heat dissipation performance of the system due to too fast or too slow flow while taking away the heat; the circulation path describes the flow trajectory of the coolant in the system. A reasonable circulation path design can ensure that the coolant is in full contact with the energy storage unit to achieve uniform heat dissipation; the circulation pressure reflects the pressure exerted on the coolant during the circulation process. It affects the flow dynamics of the coolant and the sealing of the system. Excessive circulation pressure may cause damage to system components, while too low circulation pressure may cause poor coolant flow.

[0041] By reading coolant properties and collecting coolant circulation information, we can fully understand the current coolant performance and its circulation status in the system. This information will provide important data support for subsequent coolant quality degradation analysis, helping to determine the specific causes of system mismatch, such as coolant performance degradation and circulation system failure. Targeted optimization and adjustment measures can then be taken to restore the system's good heat dissipation performance and improve the energy efficiency and reliability of the immersion energy storage system.

[0042] P40: Perform coolant quality degradation analysis based on the coolant circulation information and the coolant property information to generate quality degradation characteristics.

[0043] Further, such as Figure 2 As shown, step P40 in this embodiment of the application also includes:

[0044] P41: The coolant circulation information includes multiple circulation parameters of the coolant, including circulation flow rate, flow rate and temperature; P42: Combining the multiple circulation parameters and the coolant property information, a quality degradation distribution predictor is constructed to perform coolant quality analysis and generate the quality degradation characteristics, specifically including the distribution of the number of elements that affect the coolant quality.

[0045] It should be understood that during long-term operation, the coolant may become contaminated, or the coolant's thermal conductivity may decrease due to factors such as chemical reactions and temperature changes. This quality degradation prevents the coolant from effectively conducting heat, resulting in the energy storage unit being unable to dissipate heat in a timely manner, thereby reducing system efficiency. Sediments, impurities, or bubbles may accumulate in the coolant, all of which can lead to poor flow, increase system energy consumption, and further reduce overall efficiency. To prevent the deterioration of coolant quality from affecting the system's heat dissipation effect, the system needs to monitor the quality of the coolant in real time, promptly identify potential problems, and take measures to address them.

[0046] In order to further explore the reasons for the deterioration of coolant quality, a comprehensive analysis will be conducted by combining coolant circulation information and coolant property information to identify factors that may cause the deterioration of coolant quality during long-term operation.

[0047] Specifically, the coolant's status is first understood by collecting coolant circulation information. This circulation information includes parameters such as the coolant's flow rate, flow rate, and temperature. The coolant's flow rate and flow rate reflect its flow state. If the flow rate is too slow or the flow rate is insufficient, the coolant may not fully cover the energy storage unit, affecting the heat dissipation effect; and too high a flow rate may also lead to incomplete heat exchange. The coolant's temperature directly affects its thermal conductivity. If the temperature is too high or too low, it may lead to reduced heat exchange efficiency. Therefore, monitoring these parameters can help the system understand the coolant's flow conditions and provide a basis for subsequent quality analysis.

[0048] On this basis, a more detailed analysis is performed in combination with the coolant's property information. Coolant properties include its chemical composition, viscosity, thermal conductivity, etc. These factors will change over time, thus affecting the coolant's heat transfer capacity. The system constructs a quality degradation distribution predictor by combining the coolant's multiple cycle parameters and property information. This predictor uses machine learning or statistical analysis methods to evaluate the changing trend of coolant quality based on historical data and real-time data, and generate quality degradation characteristics, including the distribution of the number of elements in the coolant that affect the quality. These elements may be sediments, impurities, bubbles, etc., which accumulate as the coolant flows, affecting the coolant's fluidity and heat exchange effect. By constructing this predictor, the system can predict and analyze the coolant quality, determine when quality degradation may occur, and generate relevant features to provide a decision basis for subsequent coolant cleaning or replacement.

[0049] For example, if the level of a certain impurity in the coolant gradually increases, the predictor can identify the distribution trend of this impurity and use it as a characteristic of quality degradation. In this way, the specific circumstances of the coolant quality degradation can be fully understood, including which elements or factors have affected the coolant quality and the extent of these effects. These characteristics not only help the system assess the current state of the coolant, but also predict possible quality problems in the future and take measures in advance to adjust or replace the coolant. Through this real-time monitoring and prediction, it is possible to ensure that the energy storage system maintains efficient heat dissipation during long-term operation, avoid energy efficiency losses caused by deteriorating coolant quality, and ensure stable system operation.

[0050] Furthermore, step P42 of the embodiment of the present application further includes:

[0051] P42-1: Construct quality degradation elements, including the number of sediments, impurities and bubbles; P42-2: Using the coolant property information as a constraint, collect cycle parameter samples and quality degradation element distribution samples; P42-3: Use cycle parameter samples and quality degradation element distribution samples as training data, perform regression training, construct a quality degradation distribution predictor, analyze the multiple cycle parameters, and generate the quality degradation characteristics.

[0052] Optionally, the process of building the quality degradation distribution predictor can be further refined to more accurately analyze the causes and characteristics of coolant quality degradation.

[0053] First, identify the primary factors that can degrade coolant quality, including deposits, impurities, and the number of bubbles. These elements are key factors affecting coolant quality and performance. Deposits and impurities can result from chemical reactions during long-term coolant use, external contamination, or wear and tear of system components, while bubbles can be caused by pressure changes or air intrusion during coolant circulation. The presence of these elements can hinder coolant flow, reduce heat transfer efficiency, and ultimately affect the heat dissipation performance of the entire energy storage system.

[0054] Subsequently, using coolant property information as a constraint, cycle parameter samples and quality degradation element distribution samples are collected. Coolant property information provides basic physical and chemical properties of the coolant, such as thermal conductivity, specific heat capacity, and viscosity, which play an important role in constraining coolant quality degradation analysis. Cycle parameter samples include parameters such as flow rate, flow rate, and temperature over multiple cycles, which reflect the actual operating state of the coolant in the system. Quality degradation element distribution samples record the amount and distribution of deposits, impurities, and bubbles. These sample data form the basis for building a quality degradation distribution predictor.

[0055] Finally, the collected cycle parameter samples and quality degradation element distribution samples are used as training data for regression training to construct a quality degradation distribution predictor. Regression training is a statistical analysis method that describes the relationship between input variables (cycle parameters) and output variables (quality degradation element distribution) by building a mathematical model. During this process, the model is trained using the training data, enabling it to learn the patterns and laws governing coolant quality degradation. After training, the quality degradation distribution predictor can analyze new cycle parameters over multiple cycles to predict the distribution of deposits, impurities, and bubbles in the coolant, thereby generating quality degradation signatures. These quality degradation signatures provide a detailed description of the coolant quality degradation, providing a scientific basis for subsequent decisions regarding coolant cleaning or replacement. For example, if the predictor detects a gradual increase in the amount of deposits in the coolant, concentrated in specific circulation paths, targeted cleaning of these areas can be initiated. If an excessive number of bubbles is detected, it may be necessary to investigate the coolant circulation system for air intrusion and implement appropriate remedial measures.

[0056] Through the above steps, this application can build an accurate quality degradation prediction model based on real-time and historical data, dynamically monitoring and providing early warnings for coolant quality. Through regression training and data modeling, it can continuously optimize coolant management and ensure that the energy storage system is always in optimal working condition.

[0057] P50: Decision on cleaning or replacing the coolant is made based on the quality degradation characteristics.

[0058] P51: Receive a decision mechanism for cleaning or replacing coolant, wherein the decision mechanism includes a cleaning decision threshold and a replacement decision threshold based on a quality degradation indicator; P52: Input the quality degradation feature into the decision mechanism for decision triggering, thereby generating a target decision.

[0059] It should be understood that whether to clean or replace the coolant is determined based on the quality degradation characteristics obtained through the aforementioned analysis to ensure efficient operation of the cooling system.

[0060] First, a decision mechanism regarding cleaning or replacing the coolant is received. This decision mechanism operates based on two primary thresholds: the cleaning decision threshold and the replacement decision threshold. These two thresholds are preset by the system based on the changing characteristics of the coolant quality and system operating experience. The cleaning decision threshold means that when the coolant quality deteriorates to a certain level, the coolant's performance can be restored by cleaning out elements such as impurities, sediment, or bubbles. The replacement decision threshold, on the other hand, requires complete replacement of the coolant when the coolant quality deteriorates to a more severe level to ensure long-term cooling performance. These thresholds are set based on the degree of coolant quality degradation, allowing for flexible adaptation to different operating environments and conditions.

[0061] Next, the previously generated quality degradation characteristics (such as deposits, impurities, and bubble counts) are input into the decision-making mechanism. Based on these inputs, the decision-making mechanism analyzes and judges, triggering appropriate decisions. For example, if the coolant's quality degradation characteristics reach the cleaning decision threshold, the system triggers a cleaning operation; if the quality degradation exceeds the replacement decision threshold, the system triggers a coolant replacement operation. This decision-making process ensures that the system can respond to changes in coolant quality in real time during operation and promptly implement necessary maintenance measures.

[0062] Through the above-mentioned cleaning or replacement decisions, the heat exchange efficiency of the coolant can be effectively maintained, energy efficiency loss caused by coolant quality problems can be avoided, and the service life of the energy storage system can be extended.

[0063] Furthermore, step P50 of the embodiment of the present application further includes:

[0064] P51a: Real-time monitoring of the liquid level line of the coolant container; P52a: Determine whether the liquid level line meets the preset liquid level line. If not, make a coolant replenishment decision based on the liquid level line difference distance.

[0065] In a possible embodiment of the present application, the liquid level line of the coolant container can be monitored in real time to ensure that the coolant is always kept within a safe range to support effective heat exchange and heat dissipation of the energy storage unit.

[0066] First, while making decisions about cleaning or replacing the coolant, monitor the coolant container's liquid level in real time. The liquid level refers to the actual height of the coolant in the container, a parameter that indicates whether the energy storage unit is safely submerged. A liquid level sensor installed on the coolant container can obtain real-time liquid level data, allowing accurate understanding of the coolant level. In the coolant system, changes in the liquid level may be caused by coolant evaporation, leakage, or other factors. These changes in the liquid level directly affect the degree of coolant submersion in the energy storage unit, thereby affecting the heat dissipation effect. If the liquid level is too low, the energy storage unit may not be fully immersed in the coolant, resulting in insufficient heat dissipation, which in turn reduces the cooling effect and system energy efficiency. Therefore, real-time monitoring of the liquid level can promptly detect abnormal liquid levels and make appropriate adjustments.

[0067] Next, determine whether the liquid level meets the preset level, that is, whether the energy storage unit can be kept safely submerged at all times. If the liquid level is lower than the preset level, it means that the coolant fails to adequately cover the energy storage unit, which may result in reduced heat dissipation. At this point, a decision to replenish the coolant can be made based on the liquid level difference, that is, the difference between the current liquid level and the preset level. The replenishment decision may include activating the coolant replenishing device to add the required amount of coolant to the coolant container to ensure that the coolant level returns to the preset standard, thereby ensuring the safe submersion of the energy storage unit and the normal operation of the system.

[0068] By monitoring and determining the coolant level in real time, replenishment measures can be taken promptly when the coolant level changes, preventing low coolant levels from negatively impacting cooling efficiency and system operation. This decision-making mechanism, which comprehensively considers coolant quality and level, can more comprehensively ensure the efficient operation and safety of the submerged energy storage system, improving system reliability and stability.

[0069] In summary, the embodiments of the present application have at least the following technical effects:

[0070] This application continuously monitors the coolant quality by collecting the coolant's circulation parameters and heat dissipation efficiency information in real time, and dynamically adjusts the system operating parameters according to the adaptation verification to ensure that the cooling system is always in the optimal efficiency state; by combining the characteristics of coolant quality degradation, the system can automatically make decisions to clean or replace the coolant, reduce manual intervention, and improve the intelligence level of the system; by timely cleaning or replacing the coolant, the system's heat dissipation efficiency can be maintained, the energy efficiency of the energy storage system can be improved, the equipment service life can be extended, and the occurrence of failures can be reduced.

[0071] The technical effect is achieved by real-time monitoring of coolant quality changes and intelligent decision-making to clean or replace the coolant, thereby improving the system's heat dissipation efficiency and overall energy efficiency.

[0072] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0074] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for improving energy efficiency of an immersion energy storage system based on liquid cooling technology, characterized in that: include: Real-time collection of cooling parameters of the liquid cooling unit of the immersion energy storage system and heat dissipation efficiency information of the energy storage unit; Performing an adaptation check on the cooling parameter and the heat dissipation efficiency information under a preset mapping loss to generate an adaptation check result; If the adaptation check result is unmatched, reading the coolant property information of the immersion energy storage system and collecting the coolant circulation information in the immersion energy storage system; performing a coolant quality degradation analysis based on the coolant circulation information and the coolant property information to generate a quality degradation feature; Making a decision on cleaning or replacing the coolant based on the quality degradation characteristics; The cooling parameters of the liquid cooling unit of the immersion energy storage system and the heat dissipation efficiency information of the energy storage unit are collected in real time, including: Reading the cooling parameters, including the cooling liquid circulation flow rate and the temperature of the circulating cooling liquid container, through the heat exchange device; By using multiple temperature sensors arranged on the surface of the energy storage unit, the surface temperature drop data of the energy storage unit is monitored in real time, the heat dissipation efficiency is calculated, and the heat dissipation efficiency information is generated; Performing an adaptation check on the cooling parameter and the heat dissipation efficiency information under a preset mapping loss to generate an adaptation check result, including: Based on the coolant property information and the immersion energy storage system structure information, twin modeling is performed to construct a twin immersion liquid cooling model; Performing liquid cooling simulation on the cooling parameters using the twin immersion liquid cooling model to generate simulated heat dissipation efficiency data; Comparing the simulated heat dissipation efficiency data with the heat dissipation efficiency information to calculate a heat dissipation efficiency difference; Adaptively correcting the heat dissipation efficiency difference according to the preset mapping loss, determining whether it is greater than a preset efficiency deviation index, completing the adaptation verification, and generating the adaptation verification result; The preset mapping loss is the adaptation deviation between the cooling parameters and the heat dissipation efficiency caused by the actual operating environment, including the coolant flow resistance loss and the coolant flow environment loss; The coolant flow resistance loss is obtained by collecting coolant flow characteristics and flow surface characteristics, inputting them into a resistance loss database pre-built based on cooling properties for matching. The resistance loss database includes flow resistance losses corresponding to different flow characteristic samples and flow surface characteristic samples built based on historical data; The coolant flow environment loss is determined by collecting the real-time ambient temperature and performing temperature loss analysis based on the thermal conductivity of the isolation material between the environment and the coolant.

2. The method for improving energy efficiency of an immersion energy storage system based on liquid cooling technology according to claim 1, wherein: The immersion energy storage system includes an energy storage unit and a liquid cooling unit, wherein the liquid cooling unit includes a coolant container, coolant and a heat exchange device; The coolant is located in the coolant container and is pumped and heat exchanged through the heat exchange device; the energy storage unit is immersed in the coolant container.

3. The method for improving energy efficiency of an immersion energy storage system based on liquid cooling technology according to claim 1, wherein: The coolant quality degradation analysis is performed in combination with the coolant circulation information and the coolant property information to generate a quality degradation feature, including: The coolant circulation information includes multiple circulation parameters of the coolant, including circulation flow rate, flow rate and temperature; Combining the multi-cycle parameters and the coolant property information, a quality degradation distribution predictor is constructed to perform coolant quality analysis and generate the quality degradation characteristics, specifically including the quantity distribution of elements that affect the coolant quality.

4. The method for improving energy efficiency of an immersion energy storage system based on liquid cooling technology according to claim 3, wherein: Combining the multi-cycle parameters and the coolant property information, constructing a quality degradation distribution predictor, performing coolant quality analysis, and generating the quality degradation characteristics, including: Build quality degradation elements, including sediment, impurities, and the number of bubbles; Taking the coolant property information as a constraint, collecting cycle parameter samples and quality degradation element distribution samples; Regression training is performed using cycle parameter samples and quality degradation element distribution samples as training data to construct a quality degradation distribution predictor, and the multiple cycle parameters are analyzed to generate the quality degradation features.

5. The method for improving energy efficiency of an immersion energy storage system based on liquid cooling technology according to claim 4, characterized in that: Decisions on cleaning or replacing the coolant are made based on the quality degradation characteristics, including: receiving a decision mechanism regarding cleaning or replacing the coolant, the decision mechanism comprising a cleaning decision threshold and a replacement decision threshold based on a quality degradation indicator; The quality degradation feature is input into the decision mechanism to trigger a decision and generate a target decision.

6. The method for improving energy efficiency of an immersion energy storage system based on liquid cooling technology according to claim 1, wherein: Decisions on cleaning or replacing coolant also include: Monitor the liquid level of the coolant container in real time; determine whether the liquid level meets the preset liquid level. If not, make a coolant replenishment decision based on the liquid level difference distance.

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