Energy storage performance improving method and system based on solar cooling management

Abstract

The invention discloses an energy storage performance improvement method and system based on solar cooling management, and the method comprises the following steps: constructing an energy storage equipment performance evaluation index system, and selecting energy storage equipment; establishing a three-dimensional energy storage model based on the structure parameters and the material attributes of the energy storage equipment, and performing simulation analysis on the three-dimensional energy storage model to obtain temperature field and stress field distribution results under different cooling conditions; based on the temperature field and stress field distribution results under different cooling conditions, establishing a mapping relation between the energy storage equipment and cooling system parameters; optimizing the parameters of the cooling system based on the mapping relation between the energy storage equipment and the parameters of the cooling system; and the optimized cooling system parameters are applied to actual energy storage equipment, so that the energy storage performance is improved. Scientific model selection of energy storage equipment and optimal design of a heat management system are achieved, the performance and reliability of the energy storage system are improved, and technical support is provided for large-scale energy storage application.

Description

Technical Field

[0001] The present invention belongs to the technical field of solar cooling management, and in particular relates to a method and system for improving energy storage performance based on solar cooling management. Background Art

[0002] Among the methods for improving the energy storage performance of solar cooling management, selecting the right energy storage device is a key link. However, different types of energy storage devices differ in performance, cost, and applicable scenarios, which brings technical contradictions to the selection and optimization of energy storage devices. For example, lithium-ion batteries have high energy density and good cycle life, but are relatively expensive; sodium-sulfur batteries have relatively low costs, but poor energy density and cycle life; supercapacitors have fast charging and discharging speeds, but low energy density. In addition, the performance of energy storage devices is also affected by the efficiency of the cooling system. The design of the cooling system needs to consider factors such as the heat dissipation characteristics, operating temperature range, and environmental conditions of the energy storage device. Different cooling methods, such as air cooling, liquid cooling, or phase change cooling, also differ in heat dissipation effects and energy consumption. How to meet the temperature management needs of energy storage equipment while taking into account the energy efficiency and reliability of the cooling system is a complex technical issue.

[0003] In summary, the matching optimization of energy storage equipment and cooling system involves the trade-off of multiple parameters. Therefore, it is urgent to propose a method and system for improving energy storage performance based on solar cooling management, which comprehensively considers the electrochemical and thermal characteristics of energy storage equipment and the heat transfer mechanism and control strategy of the cooling system to achieve efficient operation and long life of the energy storage system. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a method and system for improving energy storage performance based on solar cooling management to solve the problems existing in the above prior art.

[0005] To achieve the above object, the present invention provides a method for improving energy storage performance based on solar cooling management, comprising the following steps:

[0006] Construct an energy storage equipment performance evaluation index system and select energy storage equipment;

[0007] Based on the structural parameters and material properties of the energy storage device, a three-dimensional energy storage model is established, and simulation analysis is performed on the three-dimensional energy storage model to obtain temperature field and stress field distribution results under different cooling conditions;

[0008] Based on the temperature field and stress field distribution results under the different cooling conditions, a mapping relationship between the energy storage device and the cooling system parameters is established;

[0009] Optimizing the cooling system parameters based on the mapping relationship between the energy storage device and the cooling system parameters;

[0010] Apply the optimized cooling system parameters to actual energy storage equipment to improve energy storage performance.

[0011] Optionally, a performance evaluation index system for energy storage equipment is constructed, and the process of selecting energy storage equipment includes:

[0012] Obtain the comprehensive scores of each energy storage device and rank them, and select the energy storage device with the highest comprehensive score as the preliminary selection plan; under the preliminary selection plan, obtain the key performance parameters of different types of energy storage devices, and build an energy storage device performance evaluation index system based on actual application needs. Use fuzzy comprehensive evaluation method to score and rank the performance of each energy storage device, and select the energy storage device with the highest score as the final selection plan.

[0013] Optionally, the process of obtaining the comprehensive score of each energy storage device includes:

[0014] Based on the multiple attributes of energy storage devices, a decision attribute hierarchy model for energy storage device selection is established, and the weight coefficient of each attribute is calculated using the hierarchical analysis method. For each candidate energy storage device, the score of each attribute is obtained, and combined with the corresponding attribute weight coefficient, the comprehensive score of the candidate energy storage device is obtained by weighted summation.

[0015] Optionally, the process of using multi-physics field simulation to simulate and analyze the three-dimensional energy storage model to obtain temperature field and stress field distribution results under different cooling conditions includes:

[0016] The three-dimensional energy storage model is simulated and analyzed by multi-physics field simulation, and different cooling conditions are set as boundary conditions and load conditions of the simulation. The three-dimensional energy storage model is solved by finite element analysis method to obtain the temperature field and stress field distribution results of the energy storage device under different cooling conditions.

[0017] Optionally, based on the temperature field and stress field distribution results under the different cooling conditions, the process of establishing a mapping relationship between the energy storage device and the cooling system parameters includes:

[0018] A machine learning algorithm is selected, and the temperature field and stress field distribution results under different cooling conditions are used as training data to train the selected machine learning algorithm to establish a mapping relationship between the energy storage device and the cooling system parameters; wherein the machine learning algorithm includes a support vector machine and a neural network.

[0019] Optionally, based on the mapping relationship between the energy storage device and the cooling system parameters, the process of optimizing the cooling system parameters includes:

[0020] When the obtained temperature and stress of the energy storage device do not meet the preset simulation requirements, the cooling system parameters are adjusted based on the mapping relationship between the energy storage device and the cooling system parameters, and the simulation analysis is performed again until the obtained simulation results meet the preset simulation requirements, thereby obtaining optimized cooling system parameters.

[0021] Optionally, the process of applying the optimized cooling system parameters to an actual energy storage device further includes:

[0022] Based on the electrochemical and thermal characteristics of the energy storage device, an electrothermal coupling model of the energy storage device is established. The multi-physics field simulation method is used to obtain the electric field distribution, temperature field distribution and stress field distribution of the energy storage device during the charging and discharging process. By analyzing the distribution results, the structural layout and material selection of the energy storage device are optimized.

[0023] The present invention also provides an energy storage performance improvement system based on solar cooling management, and an energy storage performance improvement method based on solar cooling management, comprising: an energy storage device selection module, an energy storage simulation analysis module, a cooling system optimization module, and an energy storage performance improvement module;

[0024] The energy storage device selection module is used to construct an energy storage device performance evaluation index system and select energy storage devices;

[0025] The energy storage simulation analysis module is used to establish a three-dimensional energy storage model based on the structural parameters and material properties of the energy storage device, perform simulation analysis on the three-dimensional energy storage model, and obtain temperature field and stress field distribution results under different cooling conditions;

[0026] The cooling system optimization module is used to establish a mapping relationship between the energy storage device and the cooling system parameters based on the temperature field and stress field distribution results under the different cooling conditions; and is used to optimize the cooling system parameters based on the mapping relationship between the energy storage device and the cooling system parameters;

[0027] The energy storage performance improvement module is used to apply the optimized cooling system parameters to the actual energy storage device to achieve energy storage performance improvement.

[0028] The present invention also provides an electronic device, comprising: a memory and a processor; the memory is used to store a program; the processor is used to execute the program to implement each step of the energy storage performance improvement method based on solar cooling management.

[0029] The present invention also provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, each step of the method for improving energy storage performance based on solar cooling management is implemented.

[0030] Compared with the prior art, the present invention has the following advantages and technical effects:

[0031] The present invention first evaluates and screens the performance of different types of energy storage devices to select the optimal energy storage device. Then, based on the structural parameters and material properties of the energy storage device, a three-dimensional energy storage model is established, and the three-dimensional energy storage model is simulated and analyzed to obtain the temperature field and stress field distribution results under different cooling conditions; based on the temperature field and stress field distribution results under the different cooling conditions, a mapping relationship between the energy storage device and the cooling system parameters is established; based on the mapping relationship between the energy storage device and the cooling system parameters, the cooling system parameters are optimized; the optimized cooling system parameters are applied to the actual energy storage device to achieve improved energy storage performance. The present invention realizes the scientific selection of energy storage equipment and the optimal design of the thermal management system, improves the performance and reliability of the energy storage system, and provides technical support for large-scale energy storage applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 A schematic diagram of a process for selecting an energy storage device according to an embodiment of the present invention;

[0034] Figure 2 Schematic diagram of a comprehensive score acquisition process for an energy storage device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0036] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] Embodiment 1

[0038] like Figure 1-Figure 2 As shown, this embodiment provides a method for improving energy storage performance based on solar cooling management, comprising the following steps:

[0039] Construct an energy storage equipment performance evaluation index system and select energy storage equipment;

[0040] Based on the structural parameters and material properties of the energy storage device, a three-dimensional energy storage model is established, and simulation analysis is performed on the three-dimensional energy storage model to obtain temperature field and stress field distribution results under different cooling conditions;

[0041] Based on the temperature field and stress field distribution results under the different cooling conditions, a mapping relationship between the energy storage device and the cooling system parameters is established;

[0042] Optimizing the cooling system parameters based on the mapping relationship between the energy storage device and the cooling system parameters;

[0043] Apply the optimized cooling system parameters to actual energy storage equipment to improve energy storage performance.

[0044] It is feasible to build an energy storage equipment performance evaluation index system. The process of selecting energy storage equipment includes:

[0045] A multi-attribute decision model for energy storage equipment selection was constructed, and the weight of each attribute was determined using the analytic hierarchy process. The comprehensive score of each energy storage device was calculated by weighted summation, and the energy storage device with the highest score was obtained as the preliminary solution.

[0046] Specifically, according to the multiple attributes of energy storage equipment, a decision attribute hierarchy model for energy storage equipment selection is established, and the weight coefficient of each attribute is calculated by the hierarchical analysis method. For each candidate energy storage device, its score on each attribute is obtained, and combined with the attribute weight coefficient, the comprehensive score of the energy storage device is calculated by weighted summation. The comprehensive scores of each energy storage device are sorted to obtain the energy storage device with the highest comprehensive score. The energy storage device with the highest comprehensive score is used as the preliminary selection scheme, and the technical parameters of the scheme are further demonstrated and optimized according to the characteristics of the actual application scenario. The support vector machine algorithm is used to train the evaluation model for energy storage equipment selection with the attribute parameters of the energy storage device as input and the comprehensive score as output. The trained support vector machine model is used to evaluate the attribute parameters of the new energy storage device and predict its comprehensive score, providing a reference for the selection of energy storage equipment. The results of energy storage equipment selection are analyzed, and the degree of influence of each attribute on the selection result is summarized, providing a basis for improving the multi-attribute decision model and continuously optimizing the selection scheme of energy storage equipment.

[0047] As an implementable method, in order to select the most suitable equipment for a specific application scenario from a large number of energy storage devices, a multi-attribute decision analysis method is used, and a hierarchical analysis method is combined with a support vector machine algorithm to establish a complete energy storage device selection model. The first step is to build a decision attribute hierarchy model for energy storage device selection. This model is like a tree diagram, with the top layer being the goal, that is, selecting the best energy storage device. Then, the next layer is the key attributes that affect the selection, which can be regarded as several major aspects that need to be considered when selecting energy storage devices. For example, when considering energy storage devices, the capacity, power, number of charge and discharge cycles, and efficiency of the equipment are all key factors that must be considered. For example, taking the selection of power system energy storage as the target application, a hierarchical model is established, the top layer is "selecting the best energy storage device", and then the second layer can be the three main aspects of "technical performance", "economic efficiency" and "safety". Next, "technical performance" can be further decomposed into "energy storage capacity", "power", "response time", "cycle life", "conversion efficiency", etc.; "economic efficiency" can be subdivided into "initial investment cost", "operation and maintenance cost", "unit capacity cost", "energy cost", etc.; "safety" can consider "chemical stability", "thermal stability", "mechanical stability", etc. "Environmental adaptability" can be decomposed into "temperature range", "humidity range", "footprint", etc. "Energy storage capacity" is the maximum energy that an energy storage device can store. In power system applications, large-capacity devices ranging from several megawatt-hours to hundreds of megawatt-hours may be required. For example, a device with a capacity of 100 megawatt-hours means that it can theoretically discharge at a power of 100 megawatts for 1 hour. "Power" refers to the charge and discharge rate of the energy storage device. Assuming that the rated power of an energy storage device is 50 megawatts, it means that the device can charge or discharge at a maximum power of 50 megawatts under ideal conditions. "Response time" refers to the time difference between receiving a dispatch instruction and actually executing charging and discharging. For applications that need to participate in grid frequency regulation, a millisecond-level response speed is crucial. For example, the device can start charging or discharging within 50 milliseconds of receiving the command. "Cycle life" refers to the number of charge and discharge cycles that the device can complete before its performance significantly degrades. Long-life energy storage equipment means longer service life and lower replacement frequency, less investment and maintenance costs. For example, the cycle life of some batteries can reach more than 5,000 times. "Initial investment cost" refers to the upfront investment required to purchase and install energy storage equipment. "Operation and maintenance costs" are related to the expenses of daily operation, maintenance and overhaul of the equipment. In a typical scenario, the initial cost may account for a large part of the total investment. For example, the equipment procurement and installation costs per megawatt-hour can be as high as several million yuan, depending on the battery technology, supplier and complexity of the installation project. "Operation and maintenance costs" are relatively stable, and the annual maintenance cost per megawatt-hour may be tens of thousands of yuan.

[0048] Next, the weights of these attributes are determined using the analytic hierarchy process. The weight reflects the relative importance of each attribute. For example, in the application scenario of emergency backup power supply, "power" and "response time" may be more important than "energy storage capacity"; while in the scenario of renewable energy consumption, "energy storage capacity" may be more important than "power" and "response time". The determination of weight coefficients is generally based on application requirements and is obtained through discussion and analysis by experts and engineering and technical personnel. Now assume that the weight coefficients have been obtained through the analytic hierarchy process, and three candidate energy storage devices, lithium-ion batteries, sodium-sulfur batteries, and flow batteries, have been obtained. For example, assuming that the weights have been determined as 0.25 for capacity, 0.2 for power, 0.3 for cycle life, 0.15 for response time, and 0.1 for operation and maintenance cost, in order to calculate the comprehensive scores of these devices, it is necessary to first obtain their specific scores for each attribute. Assuming that each attribute is scored in percentage, the key information of the three batteries is first collected for each attribute, and then, based on the key performance and characteristics of each device, the experts give a score. In terms of capacity, the scores of the three energy storage devices are 85 for lithium-ion batteries, 78 for sodium-sulfur batteries, and 90 for flow batteries; in terms of power, the scores are 92 for lithium-ion batteries, 78 for sodium-sulfur batteries, and 80 for flow batteries; in terms of cycle life, the scores are 88 for lithium-ion batteries, 75 for sodium-sulfur batteries, and 80 for flow batteries; in terms of response time, the scores are 95 for lithium-ion batteries, 88 for sodium-sulfur batteries, and 86 for flow batteries; in terms of operation and maintenance costs, the scores are 75 for lithium-ion batteries, 78 for sodium-sulfur batteries, and 88 for flow batteries. The comprehensive score of each energy storage device is calculated by weighted summation, that is, the score of each attribute is multiplied by the corresponding weight, and then all the products are added together. Assume that the above method is used to obtain: the comprehensive score of lithium-ion batteries is 87.25 points, the comprehensive score of sodium-sulfur batteries is 78.9 points, and the comprehensive score of flow batteries is 85.8 points. In this way, lithium-ion batteries have the highest comprehensive score. Now we can draw a preliminary conclusion: lithium-ion batteries are the best choice under the currently set attributes and weights.

[0049] However, further verification is needed based on actual scenarios. For example, if the application scenario is a solar power station that requires long-term energy storage, although the lithium-ion battery has the highest comprehensive score, its "cycle life" is a shortcoming, and in the long run, the cost-effectiveness is not as good as the long-life liquid flow battery. Liquid flow batteries have unique advantages in large-scale and long-term energy storage, with a cycle life of up to tens of thousands of times, safe and reliable. Then it is necessary to reconsider the weight of the attribute "cycle life", or add other batteries that are more suitable for long-term energy storage to the candidate solutions. The application of the support vector machine algorithm is to establish a more intelligent evaluation model. This algorithm is particularly good at handling classification and regression problems, and can be regarded as an intelligent scoring machine. Historical data on the selection of energy storage equipment for different projects in different periods can be collected. For example, there are 10 alternative energy storage equipment for the selection of energy storage equipment under each project, and each alternative energy storage equipment has a comprehensive score obtained by using the hierarchical analysis method based on the project application requirements. According to the collected data, each device has parameter values ​​for attributes such as "energy storage capacity", "power", "response time", "cycle life", "initial investment cost", "operation and maintenance cost", "chemical stability", "thermal stability", "temperature range", and "footprint". Then, the support vector machine is trained with the attribute parameters of the energy storage equipment such as "energy storage capacity", "power", "response time", "cycle life", "initial investment cost", "operation and maintenance cost", "chemical stability", "thermal stability", "temperature range", and "footprint" as input, and the comprehensive score of each candidate energy storage device as output. The principle of the support vector machine is to find an optimal hyperplane that can separate samples of different categories as well as possible. For each candidate device, these attribute parameters are a set of values ​​to be input into the support vector machine model. An algorithm is used to establish an association between these attribute parameters and the known comprehensive score, so that the algorithm can learn how to evaluate these attribute parameters and predict a comprehensive score. The support vector machine model is trained with a large amount of historical data. The large amount here means that the historical projects collected are much larger than the candidate energy storage devices under each project. For example, for the parameter values ​​and comprehensive score data of 10,000 candidate energy storage devices collected from 1,000 historical projects, 8,000 energy storage devices are randomly selected for training. Once the support vector machine is trained, it can predict the comprehensive score of new energy storage devices, even if some of the attribute combinations of these devices have never been seen before. For example, a new energy storage device has performance parameters that have never been seen before. This case is missing in the training model stage. At this time, the model can fully apply the previously trained decision-making thinking, use the association between the mapping of attributes and results, and intelligently give a comprehensive score prediction. Finally, analyze the evaluation results to see which attributes have the greatest impact on the final choice, so as to continuously optimize the decision model.

[0050] Furthermore, the key performance parameters of different types of energy storage devices are obtained, and the energy storage device performance evaluation index system is constructed in combination with actual application requirements. The fuzzy comprehensive evaluation method is used to score and rank the performance of each energy storage device, and the energy storage device with the best performance is selected as the final selected solution.

[0051] By acquiring the key performance parameters of different energy storage devices, a complete energy storage device performance evaluation index system is constructed. According to the constructed performance evaluation index system, a fuzzy comprehensive evaluation method is used to quantitatively score the performance of each energy storage device. According to the performance score results of each energy storage device obtained by the fuzzy comprehensive evaluation method, the energy storage devices are ranked by performance to form a ranking list. From the energy storage device performance ranking list, the energy storage device with the highest comprehensive performance score is selected as a candidate alternative. The actual application requirements and scenario conditions are obtained to evaluate the applicability of the alternative high-performance energy storage device solutions. By evaluating the applicability of the alternative energy storage device solutions and combining them with actual needs, the energy storage device with the best performance and the most suitable performance is finally determined. The final selected energy storage device solution with the best performance and the most suitable performance is output as a result to guide the actual selection and application of energy storage equipment.

[0052] As an implementable method, by obtaining the key performance parameters of different energy storage devices, a complete energy storage device performance evaluation index system is constructed. For example, for three energy storage devices, lithium-ion batteries, lead-acid batteries and supercapacitors, key performance parameters such as energy density, power density, cycle life, cost, safety and environmental impact can be selected. Energy density reflects the energy that can be stored per unit mass or volume of energy storage equipment, power density reflects the energy that can be released per unit time, cycle life refers to the number of times the energy storage device can be charged and discharged, cost includes initial investment and operation and maintenance costs, safety involves the stability of the equipment under extreme conditions, and environmental impact considers the environmental load during its production and disposal. For the constructed performance evaluation index system, a fuzzy comprehensive evaluation method is used to quantitatively score the performance of each energy storage device. The fuzzy comprehensive evaluation method converts qualitative evaluation into quantitative scoring by establishing an evaluation matrix and a weight vector. For example, experts are invited to score lithium-ion batteries on indicators such as energy density and power density, and a scoring standard of 1-10 points is used to obtain the scoring vectors of each indicator. Assume that the energy density score is 8 points, the power density score is 7 points, the cycle life score is 9 points, the cost score is 6 points, the safety score is 8 points, and the environmental impact score is 5 points. According to the performance score results of each energy storage device obtained by the fuzzy comprehensive evaluation method, the energy storage devices are ranked by performance to form a ranking list. For example, the comprehensive score is calculated by weighted summation. Assuming that the weights of each indicator are 0.3 for energy density, 0.2 for power density, 0.2 for cycle life, 0.1 for cost, 0.1 for safety, and 0.1 for environmental impact, the comprehensive score of lithium-ion batteries is 0.3×8+0.2×7+0.2×9+0.1×6+0.1×8+0.1×5=7.5 points. Similarly, the comprehensive scores of lead-acid batteries and supercapacitors are calculated. Assuming that they are 6.8 points and 7.2 points respectively, the ranking list is lithium-ion batteries, supercapacitors, and lead-acid batteries. From the energy storage device performance ranking list, select the energy storage device with the highest comprehensive performance score as a candidate alternative. For example, lithium-ion batteries were selected as an alternative due to their high comprehensive scores. Next, the actual application requirements and scenario conditions were obtained to evaluate the applicability of the alternative high-performance energy storage equipment solutions. Through the systematic evaluation and selection process, the risks brought by blind selection were avoided, the adaptability and economy of energy storage equipment in actual applications were improved, and a scientific basis was provided for the promotion and application of energy storage technology.

[0053] It is feasible to establish a three-dimensional energy storage model based on the structural parameters and material properties of the energy storage device, perform simulation analysis on the three-dimensional energy storage model, and obtain the temperature field and stress field distribution results under different cooling conditions; based on the temperature field and stress field distribution results under different cooling conditions, establish a mapping relationship between the energy storage device and the cooling system parameters; based on the mapping relationship between the energy storage device and the cooling system parameters, the process of optimizing the cooling system parameters includes:

[0054] According to the structural parameters and material properties of the energy storage device, a three-dimensional energy storage model is established, and different cooling conditions are set as the boundary conditions and load conditions of the simulation. The finite element analysis software is used to solve and calculate the three-dimensional energy storage model, and the temperature field and stress field distribution results of the energy storage device under different cooling conditions are obtained. By analyzing the simulation results, the influence of the current cooling system design parameters on the temperature and stress of the energy storage device is evaluated to determine whether the design requirements and performance indicators are met. If the simulation results do not meet the requirements, the design parameters of the cooling system, such as the cooling medium flow rate, cooling channel layout, etc., are adjusted, and the finite element analysis is performed again until the optimal cooling system design scheme is obtained. The simulation results under different cooling conditions are used as training data, and appropriate machine learning algorithms, such as support vector machines and neural networks, are selected to establish a mapping relationship model between the performance of the energy storage device and the cooling system parameters. The established machine learning model is used to predict the performance of the energy storage device under different cooling system parameter combinations, providing a reference for the design optimization of the cooling system. According to the prediction results of the machine learning model, the optimal cooling system design parameters are determined and applied to the actual design of the energy storage device cooling system to improve the thermal management performance and reliability of the energy storage device.

[0055] Specifically, establishing a three-dimensional energy storage model based on the structural parameters and material properties of the energy storage equipment is the basis of thermal management design. Taking a certain type of lithium-ion battery module as an example, its structural parameters include battery cell size, arrangement, module shell thickness, etc., and material properties include thermal conductivity and specific heat capacity of battery cells and shells. By inputting these parameters into finite element analysis software, such as ANSYS, a three-dimensional model of the battery module can be constructed. Setting different cooling conditions as boundary conditions and load conditions for simulation is to simulate various working conditions in actual operation. For example, the inlet temperature of the cooling medium can be set to 25°C, and the flow rate can be set to 0.1m 3 / h、0.15m 3 / h and 0.2m 3 / h, the cooling channel layout includes parallel layout and staggered layout. Changes in these conditions will affect the heat dissipation effect of the battery module, thereby affecting its temperature field and stress field distribution. Finite element analysis software is used to solve and calculate the model to obtain the temperature field and stress field distribution results of the energy storage device under different cooling conditions. Assuming that the simulation results show that at a flow rate of 0.1m3 / h, the maximum temperature inside the battery module is 45°C, and the maximum stress in the stress concentration area is 50MPa; 3 / h, the maximum temperature drops to 40°C and the maximum stress drops to 45MPa. These results intuitively show the impact of cooling conditions on the thermal management and structural stability of the battery module. By analyzing the simulation results, the impact of the current cooling system design parameters on the temperature and stress of the energy storage device is evaluated to determine whether the design requirements and performance indicators are met. For example, if the design requires that the maximum temperature of the battery module does not exceed 40°C and the maximum stress does not exceed 50MPa, the flow rate is 0.1m 3 / h solution does not meet the requirements, and the flow rate is 0.2m 3 / h solution meets the requirements. If the simulation results do not meet the requirements, it is necessary to adjust the design parameters of the cooling system, such as increasing the cooling medium flow rate, optimizing the cooling channel layout, etc., and re-perform finite element analysis until the optimal cooling system design is obtained. Assume that after multiple iterations, the final flow rate is determined to be 0.18m 3 / h, and the staggered cooling channel solution is the optimal design. At this time, the maximum temperature of the battery module is 38°C and the maximum stress is 42MPa, which meets the design requirements. The simulation results under different cooling conditions are used as training data, and appropriate machine learning algorithms, such as support vector machines, neural networks, etc., are selected to establish a mapping relationship model between the performance of energy storage equipment and the cooling system parameters. For example, 100 sets of simulation data under different flow rates and channel arrangements are collected, including inlet temperature, flow rate, channel arrangement method, and the corresponding maximum temperature and maximum stress, and input into the neural network model for training. Through training, the model can learn the complex nonlinear relationship between cooling system parameters and energy storage equipment performance. Using the established machine learning model, the performance of energy storage equipment under different combinations of cooling system parameters is predicted, providing a reference for the design optimization of the cooling system. For example, a new set of cooling parameters (such as flow rate 0.17m 3 / h, parallel arrangement), the model can predict the maximum temperature and maximum stress of the battery module, helping designers to quickly evaluate the feasibility of the solution. Based on the prediction results of the machine learning model, the optimal cooling system design parameters are determined and applied to the actual energy storage equipment cooling system design to improve the thermal management performance and reliability of the energy storage equipment. Assuming that the model prediction results show that the flow rate is 0.18m 3 / h, staggered arrangement scheme performs best under various working conditions, and this scheme is applied to actual production. Through actual operation verification, the temperature distribution of the battery module is uniform, the maximum temperature is controlled within 38°C, the stress distribution is reasonable, and there is no structural deformation, which significantly improves the stability and service life of the equipment. The advantage of this method is that through the combination of finite element analysis and machine learning, the performance of the cooling system can be accurately predicted and optimized in the design stage, avoiding the inefficiency and high cost of the traditional trial and error method. At the same time, the established machine learning model can also be used for real-time monitoring and dynamic adjustment of cooling system parameters to ensure that the energy storage equipment always operates in the best working state, further improving the reliability and safety of the system. Through the above analysis and examples, it can be seen that the thermal management design of energy storage equipment is a complex process of multi-parameter and multi-factor collaborative optimization. Through scientific methods and tools, the performance and reliability of energy storage equipment can be effectively improved, ensuring its stable operation under various environmental conditions, extending its service life and improving safety. This method not only provides an important reference for the design of energy storage systems, but also provides strong support for maintenance and optimization in actual operation.

[0056] As an additional implementation method, an electric-thermal coupling model of the energy storage device is established based on the electrochemical and thermal characteristics of the energy storage device. The multi-physics field simulation method is used to analyze the electric field distribution, temperature field distribution and stress field distribution of the energy storage device during the charging and discharging process, and the performance and reliability of the energy storage device are evaluated.

[0057] Specifically, according to the electrochemical and thermal characteristic parameters of the energy storage device, a multi-physical field simulation model of the energy storage device is established, and the mutual coupling relationship of the electric field, temperature field and stress field is comprehensively considered in the model. The finite element simulation method is used to simulate and calculate the charging and discharging process of the energy storage device to obtain the three-dimensional current density distribution, temperature distribution and stress-strain distribution inside the energy storage device. By analyzing the electric field strength, temperature change trend and stress-strain level of each part of the energy storage device during the charging and discharging process, it is judged whether the energy storage device has potential safety hazards and reliability risks. According to the simulation analysis results, the structural layout and material selection of the energy storage device are optimized and improved to improve the charging and discharging performance and service life of the energy storage device while ensuring safety and reliability. The machine learning algorithm is used to monitor and predict the state parameters of the energy storage device in real time, and the degradation law of the equipment performance is mastered through big data analysis, and the operation strategy is dynamically adjusted. If the key parameters of the energy storage device exceed the safety threshold range, the early warning mechanism is activated, and emergency measures are taken in time to avoid accidents and ensure the safe and stable operation of the energy storage device. Comprehensively analyze the performance of energy storage equipment under different working conditions, establish a health management plan for the entire life cycle of the equipment, maximize the use efficiency of the energy storage equipment, and reduce operation and maintenance costs.

[0058] As an additional implementation method, an energy storage system test platform is built based on the optimized design of energy storage equipment and cooling system. The feasibility and effectiveness of the optimization scheme are verified through experimental testing and data analysis.

[0059] Specifically, according to the characteristics of energy storage equipment and cooling system, an optimization scheme is designed, and a reasonable value range is determined for the key parameters of the optimization scheme. A multi-objective optimization algorithm is used to comprehensively consider the performance indicators of energy storage equipment and cooling system to obtain the optimal design parameter combination. According to the design parameters of the optimization scheme, an energy storage system test platform is built, including energy storage equipment, cooling system, data acquisition and monitoring system, etc. A detailed experimental test plan is formulated, test cases under different working conditions are designed, and the operating data of the energy storage system is obtained. The experimental data is preprocessed to remove outliers and noise data and extract key characteristic parameters. A machine learning algorithm is used to establish an energy storage system performance prediction model and analyze the impact of the optimization scheme on system performance. By comparing the system performance data before and after optimization, the feasibility and effectiveness of the optimization scheme are evaluated, and the optimization method is further improved.

[0060] This embodiment also provides an energy storage performance improvement system based on solar cooling management, and an energy storage performance improvement method based on solar cooling management, including: an energy storage device selection module, an energy storage simulation analysis module, a cooling system optimization module, and an energy storage performance improvement module;

[0061] The energy storage device selection module is used to construct an energy storage device performance evaluation index system and select energy storage devices;

[0062] The energy storage simulation analysis module is used to establish a three-dimensional energy storage model based on the structural parameters and material properties of the energy storage device, perform simulation analysis on the three-dimensional energy storage model, and obtain temperature field and stress field distribution results under different cooling conditions;

[0063] The cooling system optimization module is used to establish a mapping relationship between the energy storage device and the cooling system parameters based on the temperature field and stress field distribution results under the different cooling conditions; and is used to optimize the cooling system parameters based on the mapping relationship between the energy storage device and the cooling system parameters;

[0064] The energy storage performance improvement module is used to apply the optimized cooling system parameters to the actual energy storage device to achieve energy storage performance improvement.

[0065] Embodiment 2

[0066] This embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store a program; the processor is used to execute the program to implement each step of the energy storage performance improvement method based on solar cooling management.

[0067] Embodiment 3

[0068] This embodiment also provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step of the method for improving energy storage performance based on solar cooling management is implemented.

[0069] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for improving energy storage performance based on solar cooling management, characterized in that: The following steps are involved: Construct an energy storage equipment performance evaluation index system and select energy storage equipment; Based on the structural parameters and material properties of the energy storage device, a three-dimensional energy storage model is established, and simulation analysis is performed on the three-dimensional energy storage model to obtain temperature field and stress field distribution results under different cooling conditions; Based on the temperature field and stress field distribution results under the different cooling conditions, a mapping relationship between the energy storage device and the cooling system parameters is established; Optimizing the cooling system parameters based on the mapping relationship between the energy storage device and the cooling system parameters; Apply the optimized cooling system parameters to actual energy storage equipment to improve energy storage performance.

2. The method according to claim 1, characterized in that Constructing the energy storage equipment performance evaluation index system, the process of selecting energy storage equipment includes: Obtain the comprehensive scores of each energy storage device and rank them, and select the energy storage device with the highest comprehensive score as the preliminary selection plan; under the preliminary selection plan, obtain the key performance parameters of different types of energy storage devices, and build an energy storage device performance evaluation index system based on actual application needs. Use fuzzy comprehensive evaluation method to score and rank the performance of each energy storage device, and select the energy storage device with the highest score as the final selection plan.

3. The method according to claim 2, characterized in that The process of obtaining a comprehensive score for each energy storage device includes: Based on the multiple attributes of energy storage devices, a decision attribute hierarchy model for energy storage device selection is established, and the weight coefficient of each attribute is calculated using the hierarchical analysis method. For each candidate energy storage device, the score of each attribute is obtained, and combined with the corresponding attribute weight coefficient, the comprehensive score of the candidate energy storage device is obtained by weighted summation.

4. The method according to claim 1, characterized in that: The process of simulating and analyzing the three-dimensional energy storage model using multi-physics field simulation to obtain the temperature field and stress field distribution results under different cooling conditions includes: The three-dimensional energy storage model is simulated and analyzed by multi-physics field simulation, and different cooling conditions are set as boundary conditions and load conditions of the simulation. The three-dimensional energy storage model is solved by finite element analysis method to obtain the temperature field and stress field distribution results of the energy storage device under different cooling conditions.

5. The method according to claim 4, characterized in that Based on the temperature field and stress field distribution results under different cooling conditions, the process of establishing a mapping relationship between the energy storage device and the cooling system parameters includes: A machine learning algorithm is selected, and the temperature field and stress field distribution results under different cooling conditions are used as training data to train the selected machine learning algorithm to establish a mapping relationship between the energy storage device and the cooling system parameters; wherein the machine learning algorithm includes a support vector machine and a neural network.

6. The method according to claim 1, characterized in that Based on the mapping relationship between the energy storage device and the cooling system parameters, the process of optimizing the cooling system parameters includes: When the obtained temperature and stress of the energy storage device do not meet the preset simulation requirements, the cooling system parameters are adjusted based on the mapping relationship between the energy storage device and the cooling system parameters, and the simulation analysis is performed again until the obtained simulation results meet the preset simulation requirements, thereby obtaining optimized cooling system parameters.

7. The method according to claim 1, characterized in that The process of applying the optimized cooling system parameters to the actual energy storage device also includes: Based on the electrochemical and thermal characteristics of the energy storage device, an electrothermal coupling model of the energy storage device is established. The multi-physics field simulation method is used to obtain the electric field distribution, temperature field distribution and stress field distribution of the energy storage device during the charging and discharging process. By analyzing the distribution results, the structural layout and material selection of the energy storage device are optimized.

8. A system for improving energy storage performance based on solar cooling management, characterized in that: The method for improving energy storage performance based on solar cooling management according to any one of claims 1 to 7 comprises: an energy storage device selection module, an energy storage simulation analysis module, a cooling system optimization module and an energy storage performance improvement module; The energy storage device selection module is used to construct an energy storage device performance evaluation index system and select energy storage devices; The energy storage simulation analysis module is used to establish a three-dimensional energy storage model based on the structural parameters and material properties of the energy storage device, perform simulation analysis on the three-dimensional energy storage model, and obtain temperature field and stress field distribution results under different cooling conditions; The cooling system optimization module is used to establish a mapping relationship between the energy storage device and the cooling system parameters based on the temperature field and stress field distribution results under the different cooling conditions; and is used to optimize the cooling system parameters based on the mapping relationship between the energy storage device and the cooling system parameters; The energy storage performance improvement module is used to apply the optimized cooling system parameters to the actual energy storage device to achieve energy storage performance improvement.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the method for improving energy storage performance based on solar cooling management as described in any one of claims 1 to 7.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the method for improving energy storage performance based on solar cooling management as described in any one of claims 1 to 7 is implemented.

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