Immersed energy storage cooling liquid type selection method and device, terminal equipment and storage medium

Abstract

The invention discloses an immersed energy storage cooling liquid type selection method and device, terminal equipment and a storage medium, and relates to the field of energy storage heat management.The method comprises the steps that relative important weight scores of all performance index combinations in each comprehensive performance of cooling liquid to be evaluated by experts in a target scene are obtained, a weight judgment matrix is generated, and the weight judgment matrix is calculated; determining a consistency ratio; when the consistency ratio of all the comprehensive performance is smaller than a preset threshold value, determining a subjective weight value according to the weight judgment matrix; acquiring sample actual measurement data of each cooling liquid to be evaluated in the same experimental environment, and determining an objective weight value according to the sample actual measurement data; determining a comprehensive weight value according to the subjective weight value and the objective weight value; and determining an applicable result of the to-be-evaluated cooling liquid in the target scene according to the comprehensive weight value, and further determining the applicable cooling liquid in the target scene. By implementing the cooling liquid model selection method and device, more accurate cooling liquid model selection is achieved, and then the problem that energy storage scenes are neglected in existing cooling liquid model selection is solved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage thermal management, and in particular to a method, device, terminal equipment and storage medium for selecting an immersion energy storage coolant. Background Art

[0002] With the deepening low-carbon, green transformation of the power grid and the rapid development of renewable energy generation, energy storage power stations are becoming a crucial component of stable power system operation, serving as a key means of addressing the instability and intermittency of wind and photovoltaic power generation and enhancing the security and flexibility of energy system supply. Electrochemical energy storage power stations, primarily based on lithium-ion batteries, account for over 90% of my country's new energy storage capacity. However, lithium-ion batteries have high energy density and are highly sensitive to operating temperature environments. The performance degradation and safety issues caused by their high heat generation are becoming increasingly prominent. Efficient battery heat dissipation is a prerequisite for the safe and stable operation of lithium-ion battery energy storage power stations.

[0003] Currently, the main thermal management methods used in lithium-ion battery energy storage power stations are air cooling and indirect liquid cooling. Both suffer from insufficient heat dissipation capacity and the inability to suppress thermal runaway behavior in the batteries. Immersion cooling, by completely immersing the batteries in a dielectric liquid, achieves more efficient heat dissipation, reduces system weight and volume, and improves the stability and balance of battery temperature control, offering broad application prospects.

[0004] The existing coolant selection process for energy storage systems uses laboratory testing to obtain various performance parameters of the coolant. However, this process ignores the energy storage scenario. Different energy storage scenarios have different requirements for coolant indicators. For example, in scenarios with high battery heat generation, high thermal conductivity is required. In user-side energy storage scenarios near residential areas, ignition point and flash point become core considerations for safe selection. Summary of the Invention

[0005] The embodiments of the present invention provide a method, apparatus, terminal device, and storage medium for selecting an immersion energy storage coolant, which can achieve more accurate coolant selection and thereby solve the problem that existing coolant selection ignores the energy storage scenario.

[0006] An embodiment of the present invention provides a method for selecting an immersion energy storage coolant, comprising:

[0007] Obtain the experts' relative importance weight scores for all performance indicator combinations in each comprehensive performance of each coolant to be evaluated in the target scenario; the performance indicator combination is determined by any two performance indicators in the same comprehensive performance;

[0008] For each comprehensive performance of each coolant to be evaluated, a weight judgment matrix is ​​generated according to the relative importance weight scores of all performance index combinations in the comprehensive performance, and the consistency ratio is determined based on the weight judgment matrix;

[0009] When the consistency ratio of all comprehensive performance indicators is less than the preset threshold, the subjective weight value of each performance indicator in each comprehensive performance is determined according to the weight judgment matrix of each comprehensive performance indicator;

[0010] Obtain the measured data of each sample of each performance indicator of each coolant to be evaluated under the same experimental environment;

[0011] For each coolant to be evaluated, determine the objective weight value of each performance indicator based on the measured data of each sample of each performance indicator;

[0012] Determine the comprehensive weight value of each performance indicator based on the subjective weight value of each performance indicator and the objective weight value of each performance indicator;

[0013] Determine the applicability of the coolant to be evaluated in the target scenario based on the comprehensive weight of each performance indicator;

[0014] Based on the applicability results of each coolant to be evaluated in the target scenario, determine the coolant suitable for the target scenario.

[0015] Furthermore, before determining the applicability of the coolant to be evaluated in the target scenario based on the comprehensive weight value of each performance indicator, the following steps are also included:

[0016] Testing each performance indicator of each coolant to be evaluated under the target scenario to obtain actual measured data for each performance indicator of each coolant to be evaluated;

[0017] For each coolant to be evaluated, the membership degree of each performance indicator in each applicable level is calculated based on the measured data of each performance indicator and the preset membership function in each applicable level;

[0018] Based on the comprehensive weight of each performance indicator, determine the applicability of the coolant to be evaluated in the target scenario, including:

[0019] For each applicable level, the comprehensive weight value of each performance indicator is multiplied by the membership degree of the corresponding performance indicator in the applicable level and the sum is calculated to obtain the comprehensive evaluation value of the applicable level;

[0020] Among the comprehensive evaluation values ​​of all applicable levels, the applicable level corresponding to the maximum comprehensive evaluation value is selected as the applicability result of the coolant to be evaluated in the target scenario.

[0021] Furthermore, after obtaining the measured data of each sample of each performance indicator of each coolant to be evaluated under the same experimental environment, the following steps are also included:

[0022] Divide all performance indicators into positive and negative indicators;

[0023] For each coolant to be evaluated, each sample measured data of the positive indicator is normalized by the range, and each sample measured data of the negative indicator is reverse normalized by the range, so as to obtain the normalized measured data of each sample for each performance indicator.

[0024] Furthermore, the consistency ratio is determined based on the weight judgment matrix, including:

[0025] According to the weight judgment matrix, determining the maximum characteristic root of the weight judgment matrix;

[0026] The consistency index is calculated based on the maximum eigenvalue of the weight judgment matrix and the number of performance indicators in the comprehensive performance;

[0027] Determine the random consistency index according to the number of performance indicators in the comprehensive performance and the preset random consistency index value table;

[0028] According to the consistency index and the random consistency index, the consistency ratio is calculated.

[0029] Furthermore, according to the weight judgment matrix of each comprehensive performance, the subjective weight value of each performance indicator in each comprehensive performance is determined, including:

[0030] According to the weight judgment matrix of each comprehensive performance, determine the relative weight coefficient of each performance indicator in each comprehensive performance;

[0031] The relative weight coefficient of each performance indicator in each comprehensive performance is multiplied by the corresponding preset comprehensive performance weight value to obtain the subjective weight value of each performance indicator in each comprehensive performance.

[0032] Furthermore, based on the measured data of each sample of each performance indicator, the objective weight value of each performance indicator is determined, including:

[0033] According to the measured data of each sample of each performance indicator, the proportion of the measured data of each sample of each performance indicator is calculated;

[0034] According to the proportion of each sample measured data of each performance indicator, the indicator entropy value of each performance indicator is calculated;

[0035] According to the index entropy value of each performance index, the information entropy redundancy of each performance index is calculated;

[0036] According to the information entropy redundancy of each performance indicator, the indicator weight of each performance indicator is calculated;

[0037] According to the indicator weight of each performance indicator and the measured data of each sample of each performance indicator, the objective weight value of each performance indicator is calculated.

[0038] Furthermore, based on the subjective weight value of each performance indicator and the objective weight value of each performance indicator, a comprehensive weight value of each performance indicator is determined, including:

[0039] According to the subjective weight value of each performance indicator and the objective weight value of each performance indicator, the comprehensive weight value of each performance indicator is calculated by the following formula;

[0040]

[0041] Among them, ω k Indicates the comprehensive weight value of the kth performance indicator, A k represents the subjective weight value of the kth performance indicator, B k represents the objective weight value of the kth performance indicator, N represents the total number of performance indicators, and k represents the index of the performance indicator.

[0042] Based on the above method embodiment, the present invention provides a corresponding device embodiment, including: a score acquisition module, a consistency ratio determination module, a subjective weight determination module, a sample measured data acquisition module, an objective weight determination module, a comprehensive weight determination module, an applicable result determination module, and a coolant selection module;

[0043] A score acquisition module is used for the score acquisition step: obtaining the relative importance weight scores of all performance indicator combinations in each comprehensive performance of each coolant to be evaluated under the target scenario by experts; the performance indicator combination is determined by any two performance indicators in the same comprehensive performance;

[0044] a consistency ratio determination module for generating a weight judgment matrix for each comprehensive performance of each coolant to be evaluated based on the relative importance weight scores of all performance indicator combinations in the comprehensive performance, and determining the consistency ratio based on the weight judgment matrix;

[0045] A subjective weight determination module is used to determine the subjective weight value of each performance indicator in each comprehensive performance according to the weight judgment matrix of each comprehensive performance when the consistency ratio of all comprehensive performance is less than a preset threshold;

[0046] The sample measured data acquisition module is used to obtain the measured data of each sample of each performance indicator of each coolant to be evaluated under the same experimental environment;

[0047] An objective weight determination module is used to determine the objective weight value of each performance indicator for each coolant to be evaluated based on the measured data of each sample of each performance indicator;

[0048] A comprehensive weight determination module is used to determine the comprehensive weight value of each performance indicator based on the subjective weight value of each performance indicator and the objective weight value of each performance indicator;

[0049] An applicable result determination module is used to determine the applicable result of the coolant to be evaluated in the target scenario based on the comprehensive weight value of each performance indicator;

[0050] The coolant selection module is used to determine the coolant suitable for the target scenario based on the applicability results of each coolant to be evaluated in the target scenario.

[0051] Based on the above-mentioned method embodiment, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps of the immersion energy storage coolant selection method as described in the present invention are implemented.

[0052] Based on the above-mentioned method embodiment, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which controls the device where the computer-readable storage medium is located to execute the steps of the immersion energy storage coolant selection method as described in the present invention when the computer program is running.

[0053] Compared with the prior art, the beneficial effects of the embodiment of this solution are:

[0054] The present invention obtains the expert's relative importance weight score for all performance indicator combinations in each comprehensive performance of each coolant to be evaluated under the target scenario. By introducing the expert's subjective score for the target scenario, the expert can fully consider the special requirements of different energy storage scenarios for coolant performance indicators based on their experience and professional knowledge. For each comprehensive performance of each coolant to be evaluated, a weight judgment matrix is ​​generated based on the relative importance weight score of all performance indicator combinations in the comprehensive performance, and a consistency ratio is determined based on the weight judgment matrix. The consistency ratio can verify the rationality of the weight judgment matrix. When the consistency ratio of all comprehensive performance is less than a preset threshold, it means that the expert score is relatively reasonable. Based on the weight judgment matrix of each comprehensive performance, the subjective weight value of each performance indicator in each comprehensive performance is determined, ensuring the rigor of the subjective weight determination process. Obtain the actual measured data for each sample of each performance indicator of each coolant to be evaluated under the same experimental environment. For each coolant to be evaluated, determine the objective weight value of each performance indicator based on the actual measured data of each sample. Based on the subjective weight value of each performance indicator and the objective weight value of each performance indicator, determine the comprehensive weight value of each performance indicator. This not only utilizes the expert's understanding and experience of the target scenario, but also combines the actual performance data of the coolant, taking into account both the scenario and the objective parameters of the coolant performance. Finally, based on the comprehensive weight value, determine the applicability results of the coolant to be evaluated in the target scenario. Based on the applicability results of each coolant to be evaluated in the target scenario, select the coolant suitable for the target scenario, solving the problem of existing coolant selection ignoring the energy storage scenario and achieving more accurate coolant selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 1 is a flow chart of a method for selecting a cooling fluid for immersion energy storage provided by one embodiment of the present invention;

[0056] Figure 2 This is a hierarchical analysis structure diagram for coolant applicability evaluation provided by one embodiment of the present invention;

[0057] Figure 3 This is another flow chart of a method for selecting a cooling liquid for immersion energy storage provided by one embodiment of the present invention;

[0058] Figure 4 It is a structural schematic diagram of an immersion energy storage coolant selection device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0061] like Figure 1 As shown, in order to solve the problem that the existing coolant selection ignores the energy storage scenario, an embodiment of the present invention provides a method for selecting an immersion energy storage coolant, which includes at least the following steps:

[0062] Step S1: Obtaining the relative importance weight scores of all performance indicator combinations in each comprehensive performance of each coolant to be evaluated under the target scenario by experts; the performance indicator combination is determined by any two performance indicators in the same comprehensive performance;

[0063] For step S1, Figure 2 As shown in the figure, a hierarchical architecture with the applicability results of coolant as the goal is to evaluate the applicability of immersion coolant. The comprehensive performance includes thermal performance indicators, safety performance indicators and stability performance indicators. Among them, the thermal performance indicators include dynamic viscosity, thermal conductivity, density and specific heat capacity. These four performance indicators determine the efficiency and effect of heat transfer of the coolant. For example, in the scenario of high-heat electronic equipment, efficient heat transfer is required to prevent the equipment from overheating; safety performance indicators include ignition point, flash point, electrical conductivity and interfacial tension. These four performance indicators involve risk control and reliable operation during use. For example, in the scenario of electrical components, high safety performance indicators are required to ensure equipment safety; stability performance indicators include dielectric loss, acid value and moisture. These three performance indicators affect the performance durability of the coolant in multiple environments and working conditions. For example, in complex working conditions with different temperatures and humidity, high stability is required to ensure the long-term and effective operation of the coolant.

[0064] Obtain expert scores for the relative importance weights of all performance indicator combinations within each comprehensive performance of each coolant being evaluated, based on the target scenario. The target scenario refers to the application scenario for coolant selection, and the performance indicator combination is determined by any two performance indicators within the same comprehensive performance. For example, within thermal performance indicators, dynamic viscosity and thermal conductivity can form a single indicator combination, and experts are required to rate their relative importance weights. This is because different target scenarios place varying emphasis on various performance indicator combinations, and expert scoring allows for a more realistic evaluation.

[0065] In this embodiment, the coolants to be evaluated include dimethyl silicone oil, water-ethylene glycol solution, natural ester, and synthetic ester.

[0066] Step S2: For each comprehensive performance of each coolant to be evaluated, generate a weight judgment matrix based on the relative importance weight scores of all performance indicator combinations in the comprehensive performance, and determine the consistency ratio based on the weight judgment matrix;

[0067] In a preferred embodiment, determining the consistency ratio according to the weight judgment matrix includes:

[0068] According to the weight judgment matrix, determining the maximum characteristic root of the weight judgment matrix;

[0069] The consistency index is calculated based on the maximum eigenvalue of the weight judgment matrix and the number of performance indicators in the comprehensive performance;

[0070] Determine the random consistency index according to the number of performance indicators in the comprehensive performance and the preset random consistency index value table;

[0071] According to the consistency index and the random consistency index, the consistency ratio is calculated.

[0072] In step S2, for each comprehensive performance of each coolant to be evaluated, a weight judgment matrix is ​​constructed based on the relative importance weight scores of all performance index combinations in the comprehensive performance:

[0073]

[0074] Among them, T m represents the weight judgment matrix of the mth comprehensive performance, m represents the index of the comprehensive performance, n m represents the number of performance indicators in the mth comprehensive performance, t ij Represents the weight judgment matrix T m The elements in , i∈n m , j∈n m , the relative importance weight score of the i-th performance indicator and the j-th performance indicator.

[0075] In the weight judgment matrix, t ij Satisfy t ij >0, and t ij = 1 (i = j), forming a positive reciprocal matrix distribution. This means that when comparing the relative importance of two objects, the importance of object i relative to object j is the reciprocal of the importance of object j relative to object i. When comparing the objects themselves, their importance is equal, so their relative importance weight score is 1.

[0076] Then, the weight vector of each performance index in the weight judgment matrix is calculated by using the geometric mean method:

[0077]

[0078] The weight vector of each performance index in the weight judgment matrix is normalized to obtain the normalized weight vector of each performance index:

[0079]

[0080] wherein, represents the weight vector of the ith performance index in the weight judgment matrix of the mth comprehensive performance, and a mi represents the normalized weight vector of the ith performance index in the weight judgment matrix of the mth comprehensive performance.

[0081] The consistency test is carried out, and the maximum eigenvalue λ of the weight judgment matrix is calculated by the following formula according to the weight judgment matrix and the normalized weight vector of each performance index in the weight judgment matrix: max

[0082]

[0083] According to the maximum eigenvalue of the weight judgment matrix and the number of performance indexes in the comprehensive performance, the consistency index CI is calculated by the following formula, which preliminarily quantifies the inconsistency degree of the matrix:

[0084]

[0085] According to the number n of performance indexes in the comprehensive performance, m , the random consistency index RI is determined by consulting the preset random consistency index value table (Table 1), which is obtained by statistical analysis of a large number of randomly generated judgment matrices. Different index numbers correspond to different RI values, reflecting the average level of the possible inconsistency degree of the judgment matrix in the random case;

[0086] Table 1 Random consistency index value table

[0087] <![CDATA[n m ]]> 1 2 3 4 5 6 7 8 9 10 11 12 12 13 14 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59

[0088] According to the consistency index and the random consistency index, the consistency ratio is calculated by the following formula:

[0089]

[0090] wherein, λ max ​represents the maximum eigenvalue of the weight judgment matrix, CI represents the consistency index of the weight judgment matrix, RI represents the random consistency index of the weight judgment matrix, and CR represents the consistency ratio of the weight judgment matrix. The smaller the CR value, the closer the weight judgment matrix is ​​to a completely consistent state, and the better the consistency.

[0091] Step S3: When the consistency ratio of all comprehensive performance indicators is less than a preset threshold, the subjective weight value of each performance indicator in each comprehensive performance indicator is determined according to the weight judgment matrix of each comprehensive performance indicator;

[0092] In a preferred embodiment, the subjective weight value of each performance indicator in each comprehensive performance is determined according to the weight judgment matrix of each comprehensive performance, including:

[0093] According to the weight judgment matrix of each comprehensive performance, determine the relative weight coefficient of each performance indicator in each comprehensive performance;

[0094] The relative weight coefficient of each performance indicator in each comprehensive performance is multiplied by the corresponding preset comprehensive performance weight value to obtain the subjective weight value of each performance indicator in each comprehensive performance.

[0095] For step S3, the calculated consistency ratio can be used to judge whether the constructed weight judgment matrix is ​​reasonable, that is, whether the experts' scores on the relative importance of each performance indicator are logically consistent. If the consistency ratio of all comprehensive performance is less than the preset threshold, it means that the consistency of the weight judgment matrix is ​​acceptable. According to the weight judgment matrix of each comprehensive performance, the subjective weight value of each performance indicator in each comprehensive performance is determined. Specifically, according to the weight judgment matrix T of each comprehensive performance m , determine the relative weight coefficient a of each performance indicator in each comprehensive performance mi Then, the relative weight coefficient of each performance indicator in each comprehensive performance is multiplied by the corresponding preset comprehensive performance weight value to obtain the subjective weight value of each performance indicator in each comprehensive performance:

[0096] A mi =H m a mi

[0097] Among them, A mi represents the subjective weight value of the i-th performance indicator in the m-th comprehensive performance, H m Indicates the preset comprehensive performance weight value of the mth comprehensive performance, where m = 1, 2, 3. In this embodiment, the preset threshold is set to 0.1.

[0098] If the consistency ratio of comprehensive performance is greater than or equal to the preset threshold, it indicates that the consistency of the weight judgment matrix is ​​poor, indicating that there may be contradictions or irrationality in the scoring. For example, when evaluating the performance of coolant, the importance scores of two indicators compared with each other may be contrary to the logical relationship between them when compared with other indicators. In this case, the weight judgment matrix needs to be readjusted to let experts re-score the relative importance weights of all performance indicator combinations in each comprehensive performance of each coolant to be evaluated under the target scenario.

[0099] Step S4: obtaining measured data of each sample of each performance indicator of each coolant to be evaluated under the same experimental environment;

[0100] For step S4, in order to accurately and objectively evaluate each coolant to be evaluated, the actual measured data of each sample of each performance indicator of each coolant to be evaluated is obtained under the same experimental environment to ensure the comparability of the data under the same experimental environment. The actual measured data of each sample is obtained through actual experimental measurement, such as using specific instruments and equipment to measure the thermal conductivity, flash point, moisture content, etc. of the coolant.

[0101] It should be noted that the performance indicators in step S4 refer to all performance indicators under all comprehensive performances, namely, dynamic viscosity, thermal conductivity, density, specific heat capacity, ignition point, flash point, electrical conductivity, interfacial tension, dielectric loss, acid value and moisture, totaling N = 11 performance indicators, where k is the serial index in the sorting of all performance indicators, k = 1, 2, ..., N, and N represents the total number of performance indicators.

[0102] In a preferred embodiment, after obtaining the measured data of each sample of each performance indicator of each coolant to be evaluated under the same experimental environment, the method further includes:

[0103] Divide all performance indicators into positive and negative indicators;

[0104] For each coolant to be evaluated, each sample measured data of the positive indicator is normalized by the range, and each sample measured data of the negative indicator is reverse normalized by the range, so as to obtain the normalized measured data of each sample for each performance indicator.

[0105] In one embodiment of the present invention, in order to eliminate the problem of large numerical differences between different performance indicators due to different dimensions, N performance indicators are dedimensionalized. First, all performance indicators are divided into positive indicators and negative indicators. Positive indicators include thermal conductivity, ignition point, flash point and specific heat capacity. The larger the values ​​of these performance indicators, the better the performance; negative indicators include dynamic viscosity, density, electrical conductivity, interfacial tension, dielectric loss, acid value and moisture. The smaller the values ​​of these performance indicators, the better the performance. The range transformation method is used to process different data with different algorithms.

[0106] For positive indicators, the range normalization process is performed on each sample measured data of the positive indicator using the following formula:

[0107]

[0108] For negative indicators, the range reverse normalization processing is performed on each sample measured data of the negative indicator using the following formula:

[0109]

[0110] Where x′ kh represents the measured data of the hth sample after normalization of the kth performance index, x kh represents the measured sample data of the hth sample of the kth performance indicator, where h = 1, 2, …, H, and H represents the total number of samples.

[0111] Step S5: for each coolant to be evaluated, determine the objective weight value of each performance indicator based on the measured data of each sample of each performance indicator;

[0112] In a preferred embodiment, determining the objective weight value of each performance indicator based on each sample measured data of each performance indicator includes:

[0113] According to the measured data of each sample of each performance indicator, the proportion of the measured data of each sample of each performance indicator is calculated;

[0114] According to the proportion of each sample measured data of each performance indicator, the indicator entropy value of each performance indicator is calculated;

[0115] According to the index entropy value of each performance index, the information entropy redundancy of each performance index is calculated;

[0116] According to the information entropy redundancy of each performance indicator, the indicator weight of each performance indicator is calculated;

[0117] According to the indicator weight of each performance indicator and the measured data of each sample of each performance indicator, the objective weight value of each performance indicator is calculated.

[0118] In step S5, the sample measured data of the performance indicators of each coolant to be evaluated under the same experimental environment obtained in step S4 are processed. First, based on each sample measured data of each performance indicator, the proportion of each sample measured data of each performance indicator is calculated using the following formula to determine the relative share of each sample data in the total:

[0119]

[0120] According to the proportion of each sample measured data of each performance index, the index entropy value of each performance index is calculated by the following formula, which is used to measure the discrete degree of sample data distribution:

[0121]

[0122] According to the index entropy value of each performance index, the information entropy redundancy of each performance index is calculated by the following formula, which reflects the degree of effective information contained in the performance index:

[0123] d k =1-e k k=1,2,…,N

[0124] According to the information entropy redundancy of each performance index, the index weight of each performance index is calculated by the following formula, so as to normalize the information entropy redundancy of each performance index, which is used to determine the relative importance of the kth performance index in all performance indexes:

[0125]

[0126] According to the index weight of each performance index and each sample measured data of each performance index, the objective weight value of each performance index is calculated by the following formula:

[0127]

[0128] Wherein, p kh represents the proportion of the hth sample measured data of the kth performance index, e k represents the index entropy value of the kth performance index, d k represents the information entropy redundancy of the kth performance index, s k represents the index weight of the kth performance index, and β k represents the objective weight value of the kth performance index.

[0129] The index weight calculated by information entropy is based on the distribution of sample measured data of each performance index. Information entropy measures the discrete degree or uncertainty of data, which does not depend on subjective judgment. For example, if the data difference of a performance index is large, the information entropy is large, which means that the index is more important in distinguishing different cooling liquid performance. The weight calculation is based on the objective characteristics of data, rather than artificial subjective setting. Moreover, when calculating the objective weight value, the normalized sample measured data are combined, which can objectively reflect the numerical value of the real performance of the cooling liquid.

[0130] Step S6: According to the subjective weight value of each performance index and the objective weight value of each performance index, the comprehensive weight value of each performance index is determined;

[0131] In a preferred embodiment, the comprehensive weight value of each performance index is determined according to the subjective weight value of each performance index and the objective weight value of each performance index, comprising:

[0132] The comprehensive weight value of each performance index is calculated according to the subjective weight value of each performance index and the objective weight value of each performance index by the following formula:

[0133]

[0134] Wherein, ω k represents the comprehensive weight value of the kth performance index, A k represents the subjective weight value of the kth performance index, B k represents the objective weight value of the kth performance index, N represents the total number of performance indexes, and k represents the index of the performance index.

[0135] For step S6, the subjective weight value of each performance index is obtained by step S3, which is the weight measurement of experts for each performance index in the target scene, reflecting the importance of each index in the target scene. For example, in the deep sea equipment coolant evaluation, the expert gives a higher subjective weight to the antifreeze performance of the coolant according to the characteristics of the equipment operating environment. The objective weight value of each performance index is obtained by step S5, which is based on the objective characteristics of the measured data of each performance index sample. The subjective weight value and the objective weight value are multiplied to fuse the expert experience and the data objective characteristics, and then normalized to make the sum of the comprehensive weight values of all performance indexes equal to 1, and the weight value of each performance index after considering the subjective and objective factors is obtained.

[0136] It should be noted that the subjective weight value of the kth performance index is equal to the subjective weight value of the ith performance index in the mth comprehensive performance, i.e. k . m×i .

[0137] Step S7: determining the application result of the to-be-evaluated coolant in the target scene according to the comprehensive weight value of each performance index;

[0138] For step S7, a performance index is selected as a target performance index for the target scene, and the comprehensive weight value of the target performance index is compared with the first preset application range. If the comprehensive weight value of the target performance index is within the first preset application range, it is considered that the to-be-evaluated coolant is applicable to the target scene, otherwise, it is considered that the to-be-evaluated coolant is not applicable to the target scene. Preferably, a comprehensive performance can also be selected as a target comprehensive performance for the target scene, and the application result of the to-be-evaluated coolant in the target scene is determined according to the comprehensive weight value of the target comprehensive performance and the second preset application range.

[0139] In a preferred embodiment, before determining the application result of the to-be-evaluated coolant in the target scene according to the comprehensive weight value of each performance index, the method further comprises:

[0140] Testing each performance index of each to-be-evaluated coolant in the target scene to obtain measured data of each performance index of each to-be-evaluated coolant;

[0141] For each to-be-evaluated coolant, calculating the membership degree of each performance index in each application level according to the measured data of each performance index and the preset membership function in each application level;

[0142] Determining the application result of the to-be-evaluated coolant in the target scene according to the comprehensive weight value of each performance index, comprising:

[0143] For each application level, multiplying the comprehensive weight value of each performance index by the membership degree of the corresponding performance index in the application level and summing to obtain a comprehensive evaluation value of the application level;

[0144] In all comprehensive evaluation values of the application levels, selecting the application level corresponding to the maximum comprehensive evaluation value as the application result of the to-be-evaluated coolant in the target scene.

[0145] In an embodiment of the present application, in order to comprehensively evaluate the applicability of the coolant in the target scene, the mathematical concept of fuzzy comprehensive evaluation is introduced, a fuzzy comprehensive evaluation factor set K={dynamic viscosity, thermal conductivity, density, specific heat capacity, ignition point, flash point, electrical conductivity, interfacial tension, dielectric loss, acid value, moisture content} is constructed, and an application level set L={completely applicable, basically applicable, generally applicable, not applicable, extremely not applicable} is constructed.

[0146] Testing each performance index of each to-be-evaluated coolant in the target scene to obtain measured data X={x1, x2,…,x k} of each performance index of each to-be-evaluated coolant.

[0147] For each to-be-evaluated coolant, substituting the measured data X={x1, x2,…,x k} of each performance index into the preset membership function in each application level, the membership degree r kl of the kth performance index in the lth application level can be calculated.

[0148]

[0149]

[0150] wherein, r klrepresents the membership of the kth performance indicator in the lth applicable level, k = 1, 2, ..., N, l = 1, 2, ..., 5, a1, a2, a3, a4 and a5 represent the boundary coefficients of different applicable level ranges.

[0151] According to the membership degree of each performance indicator at each applicable level, the corresponding evaluation matrix R is generated:

[0152]

[0153] According to the comprehensive weight set ω obtained in step S6, ω=(ω1, ω2,…, ω k ,…,ω N ) and the evaluation matrix R, the fuzzy comprehensive evaluation matrix Z is calculated:

[0154] Z=ω*R

[0155] The operation between the comprehensive weight set ω and the evaluation matrix R, specifically, for each applicable level, the comprehensive weight value of each performance indicator is multiplied by the membership degree of the corresponding performance indicator in the applicable level and then summed to obtain the comprehensive evaluation value of the applicable level.

[0156] Finally, from the comprehensive evaluation values ​​of each applicability level, the applicability level with the largest comprehensive evaluation value is taken as the applicability result of the coolant to be evaluated in the target scenario.

[0157] This invention uses membership to describe the relationship between performance indicators and applicability levels, rather than pursuing a precise binary judgment of "belongs" or "does not belong." For example, even if a coolant performance indicator doesn't fully meet the standards for a specific applicability level, the membership level can be used to reflect its tendency to fall within that level, better accommodating the ambiguity of performance in real applications.

[0158] Step S8: Determine a coolant suitable for the target scenario based on the applicability results of each coolant to be evaluated in the target scenario.

[0159] For step S8, the applicability results of each coolant to be evaluated in the target scenario are obtained according to step S7. First, coolants with applicability results of generally applicable, basically applicable, and completely applicable are selected to determine the coolant applicable to the target scenario. Second, the coolants preliminarily screened out are selected in the preferred order of completely applicable > basically applicable > generally applicable. If multiple coolants have the same applicability results, the selection can be made based on the coolant cost. Finally, the most preferred coolant in the target scenario is determined.

[0160] like Figure 3Another flowchart of the immersion type energy storage coolant selection method is shown, and the present application is aimed at different application scenarios, and a complex coolant performance evaluation system is decomposed into multiple sub-problems with a hierarchical structure by an analytic hierarchy process (AHP), a weight judgment matrix is constructed by scoring the relative importance of each performance indicator by experts, and consistency checking is performed to ensure the scientificity and reliability of the weight determination process, thereby obtaining the subjective weight of each performance indicator. At the same time, the entropy weight measurement method is used to analyze the sample measured data of each performance indicator, and the objective weight of each indicator is calculated from the objective characteristics of the data, so as to fully tap the objective information contained in the data.

[0161] In addition, the present application also adopts a fuzzy comprehensive evaluation method, constructs an evaluation matrix based on the membership degree of each performance indicator at different application levels, combines the comprehensive weight set obtained by the analytic hierarchy process and the entropy weight measurement, and operates the evaluation matrix to generate a fuzzy comprehensive evaluation matrix, and then obtains the comprehensive evaluation value of each application level. According to the comprehensive evaluation value, the most advantageous coolant is selected from each immersion type coolant to be evaluated, and a precise, efficient and reliable cooling solution is provided for different application scenarios.

[0162] As shown in the above method embodiment, the corresponding device embodiment is provided; Figure 4

[0163] An embodiment of the present application provides an immersion type energy storage coolant selection device, which comprises a score acquisition module, a consistency ratio determination module, a subjective weight determination module, a sample measured data acquisition module, an objective weight determination module, a comprehensive weight determination module, an application result determination module and a coolant selection module.

[0164] The score acquisition module is used for the score acquisition step: acquiring the relative importance weight score of each expert for each combination of all performance indicators in each comprehensive performance of each coolant to be evaluated under a target scene; the combination of performance indicators is determined by any two performance indicators in the same comprehensive performance;

[0165] The consistency ratio determination module is used for generating a weight judgment matrix according to the relative importance weight score of each combination of all performance indicators in each comprehensive performance of each coolant to be evaluated, and determining a consistency ratio according to the weight judgment matrix;

[0166] The subjective weight determination module is used for determining the subjective weight value of each performance indicator in each comprehensive performance according to the weight judgment matrix of each comprehensive performance when the consistency ratio of all comprehensive performances is less than a preset threshold;

[0167] The sample measured data acquisition module is used for acquiring each sample measured data of each performance indicator of each coolant to be evaluated under the same experimental environment.​

[0168] an objective weight determining module configured to determine an objective weight value of each performance index according to each sample measured data of each performance index for each to-be-evaluated coolant;

[0169] a comprehensive weight determining module configured to determine a comprehensive weight value of each performance index according to the subjective weight value of each performance index and the objective weight value of each performance index;

[0170] a result determining module configured to determine a result of each to-be-evaluated coolant in the target scene according to the comprehensive weight value of each performance index;

[0171] a coolant selection module configured to determine a coolant suitable for the target scene according to the result of each to-be-evaluated coolant in the target scene.

[0172] It can be understood that the above-mentioned device item embodiments are corresponding to the method item embodiments of the present application, and can realize the immersion type energy storage coolant selection method provided by any one of the method item embodiments of the present application.

[0173] It should be noted that the device embodiments described above are only schematic, and part or all of the modules can be selected to achieve the purpose of the embodiment of the present application according to actual needs. In addition, in the device embodiment provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creating labor.

[0174] On the basis of the above-mentioned embodiment of the immersion type energy storage coolant selection method, another embodiment of the present application provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the immersion type energy storage coolant selection method of any one embodiment of the present application is realized.

[0175] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the terminal device.

[0176] The terminal device can be a desktop computer, a notebook computer, a palm computer and a cloud server, etc. The terminal device can include, but is not limited to, a processor and a memory.

[0177] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0178] Based on the above method embodiment, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the immersion energy storage coolant selection method described in any one of the above method embodiments of the present invention.

[0179] In particular, if the module / unit integrated into the immersion energy storage coolant selection device / terminal equipment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. In particular, the computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0180] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for selecting an immersion energy storage coolant, characterized in that: include: Obtain the experts' relative importance weight scores for all performance indicator combinations in each comprehensive performance of each coolant to be evaluated under the target scenario; The performance index combination is determined by any two performance indicators in the same comprehensive performance; For each comprehensive performance of each coolant to be evaluated, a weight judgment matrix is ​​generated according to the relative importance weight scores of all performance index combinations in the comprehensive performance, and the consistency ratio is determined based on the weight judgment matrix; When the consistency ratio of all comprehensive performance indicators is less than the preset threshold, the subjective weight value of each performance indicator in each comprehensive performance is determined according to the weight judgment matrix of each comprehensive performance indicator; Obtain the measured data of each sample of each performance indicator of each coolant to be evaluated under the same experimental environment; For each coolant to be evaluated, determine the objective weight value of each performance indicator based on the measured data of each sample of each performance indicator; Determine the comprehensive weight value of each performance indicator based on the subjective weight value of each performance indicator and the objective weight value of each performance indicator; Determine the applicability of the coolant to be evaluated in the target scenario based on the comprehensive weight of each performance indicator; Based on the applicability results of each coolant to be evaluated in the target scenario, determine the coolant suitable for the target scenario.

2. The method for selecting a cooling liquid for immersion energy storage according to claim 1, wherein: Before determining the applicability of the coolant to be evaluated in the target scenario based on the comprehensive weight value of each performance indicator, the following steps may also be performed: Testing each performance indicator of each coolant to be evaluated under the target scenario to obtain actual measured data for each performance indicator of each coolant to be evaluated; For each coolant to be evaluated, the membership degree of each performance indicator in each applicable level is calculated based on the measured data of each performance indicator and the preset membership function in each applicable level; The determination of the applicability of the coolant to be evaluated in the target scenario based on the comprehensive weight value of each performance indicator includes: For each applicable level, the comprehensive weight value of each performance indicator is multiplied by the membership degree of the corresponding performance indicator in the applicable level and the sum is calculated to obtain the comprehensive evaluation value of the applicable level; Among the comprehensive evaluation values ​​of all applicable levels, the applicable level corresponding to the maximum comprehensive evaluation value is selected as the applicability result of the coolant to be evaluated in the target scenario.

3. The method for selecting a cooling liquid for immersion energy storage according to claim 1, wherein: After obtaining the measured data of each sample of each performance indicator of each coolant to be evaluated under the same experimental environment, it also includes: Divide all performance indicators into positive and negative indicators; For each coolant to be evaluated, each sample measured data of the positive indicator is normalized by the range, and each sample measured data of the negative indicator is reverse normalized by the range, so as to obtain the normalized measured data of each sample for each performance indicator.

4. The method for selecting a cooling fluid for immersion energy storage according to claim 1, wherein: Determining a consistency ratio according to the weight judgment matrix includes: According to the weight judgment matrix, determining the maximum characteristic root of the weight judgment matrix; The consistency index is calculated based on the maximum eigenvalue of the weight judgment matrix and the number of performance indicators in the comprehensive performance; Determine the random consistency index according to the number of performance indicators in the comprehensive performance and the preset random consistency index value table; According to the consistency index and the random consistency index, the consistency ratio is calculated.

5. The method for selecting a cooling fluid for immersion energy storage according to claim 1, wherein: According to the weight judgment matrix of each comprehensive performance, determine the subjective weight value of each performance indicator in each comprehensive performance, including: According to the weight judgment matrix of each comprehensive performance, determine the relative weight coefficient of each performance indicator in each comprehensive performance; The relative weight coefficient of each performance indicator in each comprehensive performance is multiplied by the corresponding preset comprehensive performance weight value to obtain the subjective weight value of each performance indicator in each comprehensive performance.

6. The method for selecting a cooling fluid for immersion energy storage according to claim 1, wherein: Determine the objective weight value of each performance indicator based on the measured data of each sample of each performance indicator, including: According to the measured data of each sample of each performance indicator, the proportion of the measured data of each sample of each performance indicator is calculated; According to the proportion of each sample measured data of each performance indicator, the indicator entropy value of each performance indicator is calculated; According to the index entropy value of each performance index, the information entropy redundancy of each performance index is calculated; According to the information entropy redundancy of each performance indicator, the indicator weight of each performance indicator is calculated; According to the indicator weight of each performance indicator and the measured data of each sample of each performance indicator, the objective weight value of each performance indicator is calculated.

7. The method for selecting a cooling fluid for immersion energy storage according to claim 1, wherein: According to the subjective weight value of each performance indicator and the objective weight value of each performance indicator, the comprehensive weight value of each performance indicator is determined, including: According to the subjective weight value of each performance indicator and the objective weight value of each performance indicator, the comprehensive weight value of each performance indicator is calculated by the following formula; Among them, ω k Indicates the comprehensive weight value of the kth performance indicator, A k represents the subjective weight value of the kth performance indicator, B k represents the objective weight value of the kth performance indicator, N represents the total number of performance indicators, and k represents the index of the performance indicator.

8. An immersion energy storage coolant selection device, characterized in that: include: Score acquisition module, consistency ratio determination module, subjective weight determination module, sample measured data acquisition module, objective weight determination module, comprehensive weight determination module, applicable result determination module, and coolant selection module; The scoring acquisition module is used in the scoring acquisition step: obtaining the relative importance weight scores of all performance indicator combinations in each comprehensive performance of each coolant to be evaluated in the target scenario by experts; the performance indicator combination is determined by any two performance indicators in the same comprehensive performance; The consistency ratio determination module is used to generate a weight judgment matrix for each comprehensive performance of each coolant to be evaluated according to the relative importance weight scores of all performance indicator combinations in the comprehensive performance, and determine the consistency ratio according to the weight judgment matrix; The subjective weight determination module is used to determine the subjective weight value of each performance indicator in each comprehensive performance according to the weight judgment matrix of each comprehensive performance when the consistency ratio of all comprehensive performance is less than a preset threshold; The sample measured data acquisition module is used to obtain the measured data of each sample of each performance indicator of each coolant to be evaluated under the same experimental environment; The objective weight determination module is used to determine the objective weight value of each performance indicator for each coolant to be evaluated based on the measured data of each sample of each performance indicator; The comprehensive weight determination module is used to determine the comprehensive weight value of each performance indicator based on the subjective weight value of each performance indicator and the objective weight value of each performance indicator; The applicable result determination module is used to determine the applicable result of the coolant to be evaluated in the target scenario based on the comprehensive weight value of each performance indicator; The coolant selection module is used to determine a coolant suitable for the target scenario based on the applicability results of each coolant to be evaluated in the target scenario.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for selecting a coolant for immersion energy storage according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for selecting a coolant for immersion energy storage according to any one of claims 1 to 7.

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