Battery energy storage cabin reliability evaluation method and device, medium and product

Abstract

The invention discloses a battery energy storage cabin reliability evaluation method and device, a medium and a product, and relates to the technical field of battery energy storage cabin evaluation, and the method comprises the steps: enabling a reliability evaluation index system to comprise a basic dimension index system, a time dimension index system and a space dimension index system; establishing an extension cloud model based on the reliability evaluation index system, and calculating an association degree matrix according to the extension cloud model; calculating the index weight of each reliability index in the reliability evaluation index system by using a preset weighting method; and establishing an equal-value weight extension cloud model according to the association degree matrix and the index weight of each reliability index, and performing reliability comprehensive grade evaluation according to the equal-value weight extension cloud model to obtain a comprehensive evaluation result. An index system is constructed through three dimensions of foundation, time and space, so that the accuracy of the evaluation result is ensured, and the reliability evaluation efficiency is improved. And meanwhile, the method is more refined and has pertinence.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy storage compartment evaluation, and in particular to a battery energy storage compartment reliability evaluation method, device, medium and product. Background Art

[0002] Renewable energy has become an indispensable part of the power system, and the energy storage battery compartment, as a core component, its performance degradation and internal monomer differences will have a profound impact on the reliability of the system. The energy storage battery compartment plays a key role in the power system. It can not only balance the fluctuations between power supply and demand, but also improve the stability and reliability of the power system. However, with the use and operation of the energy storage battery compartment, its performance will gradually decay, and the differences between internal monomers may also lead to system instability and reduced reliability. Therefore, the reliability assessment of the battery compartment is particularly important. In order to take into account factors such as battery performance degradation rate, consistency between internal monomers, and system failure rate, in order to comprehensively evaluate the reliability level of the battery compartment, it is crucial to establish a comprehensive and sophisticated battery compartment reliability assessment index system. The reliability assessment of the battery compartment is not only related to the stability of the energy storage system. Summary of the invention

[0003] The purpose of the present invention is to provide a battery energy storage cabin reliability assessment method, device, medium and product, which can ensure the accuracy of the assessment results through a three-dimensional indicator system of foundation, time and space, and at the same time be more refined and targeted.

[0004] To achieve the above object, the present invention provides the following solution.

[0005] A method for evaluating the reliability of a battery energy storage compartment comprises the following steps.

[0006] A reliability evaluation index system for a battery energy storage system is established based on the reliability indicators of several battery energy storage power stations; the reliability evaluation index system includes a basic dimension index system, a time dimension index system and a space dimension index system.

[0007] A cloud extension model is established based on the reliability evaluation index system, and a correlation matrix is ​​calculated according to the cloud extension model; the cloud extension matrix corresponding to each classification level of the cloud extension model is formed; the correlation matrix is ​​formed by the correlation between each reliability index and its classification level; The indicator weight of each reliability indicator in the reliability evaluation indicator system is calculated using a preset weighting method.

[0008] An equal value weighted extension cloud model is established according to the correlation matrix and the indicator weights of each reliability indicator, and a comprehensive reliability grade evaluation is performed according to the equal value weighted extension cloud model to obtain a comprehensive evaluation result.

[0009] A computer device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned battery energy storage compartment reliability assessment method.

[0010] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned battery energy storage compartment reliability assessment method.

[0011] A computer program product includes a computer program, which, when executed by a processor, implements the steps of the above-mentioned battery energy storage compartment reliability assessment method.

[0012] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the present invention provides a method, device, medium and product for evaluating the reliability of a battery energy storage cabin, and establishes a reliability evaluation index system for a battery energy storage system based on the reliability indicators of several battery energy storage power stations; the reliability evaluation index system includes a basic dimension index system, a time dimension index system and a space dimension index system; an extension cloud model is established based on the reliability evaluation index system, and a correlation matrix is ​​calculated according to the extension cloud model; the extension cloud matrix corresponding to each classification level of the extension cloud model is composed; the correlation matrix is ​​composed of the correlation between each reliability index and its classification level; the index weight of each reliability index in the reliability evaluation index system is calculated using a preset weighting method; an equal value weight extension cloud model is established according to the correlation matrix and the index weight of each reliability index, and a comprehensive reliability level evaluation is performed according to the equal value weight extension cloud model to obtain a comprehensive evaluation result. The present invention ensures the accuracy of the evaluation results through the three-dimensional index system of foundation, time and space, and is more refined and targeted at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A schematic flow chart of a method for evaluating the reliability of a battery energy storage compartment provided in Example 1 of the present invention.

[0015] Figure 2 A schematic diagram of the specific implementation process of the battery energy storage compartment reliability assessment method provided in Example 1 of the present invention.

[0016] Figure 3A schematic diagram of the multi-state and multi-dimensional indicator system for the battery energy storage cabin and the equivalent value extension cloud reliability comprehensive evaluation process provided in Example 1 of the present invention.

[0017] Figure 4 This is a diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The purpose of the present invention is to provide a battery energy storage cabin reliability assessment method, device, medium and product, which aims to ensure the accuracy of the assessment results through a three-dimensional indicator system of foundation, time and space, while being more refined and targeted.

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1: Figure 1 , Figure 2 and Figure 3 As shown, this embodiment is used to provide a battery energy storage cabin reliability assessment method, including the following steps.

[0022] S1: A reliability evaluation index system for a battery energy storage system is established based on the reliability indicators of several battery energy storage power stations; the reliability evaluation index system includes a basic dimension index system, a time dimension index system and a space dimension index system.

[0023] S2: Establishing an extension cloud model based on the reliability evaluation index system, and calculating a correlation matrix according to the extension cloud model; the extension cloud matrix corresponding to each classification level of the extension cloud model is composed; the correlation matrix is ​​composed of the correlation between each reliability index and its classification level.

[0024] S3: Calculate the indicator weight of each reliability indicator in the reliability evaluation indicator system using a preset weighting method.

[0025] S4: establishing an equal value weighted extension cloud model according to the correlation matrix and the indicator weights of each reliability indicator, and performing a comprehensive reliability grade evaluation according to the equal value weighted extension cloud model to obtain a comprehensive evaluation result.

[0026] In step S1, the reliability index includes primary index and secondary index; the primary index includes basic dimension index, time dimension index and space dimension index; the secondary index includes the planned outage coefficient, unplanned outage coefficient, availability coefficient and utilization coefficient of the entire battery compartment, the relative number of failures of the battery cluster, the failure rate of the battery pack, the threshold reliability, the threshold expected capacity, the capacity margin, the threshold reliability decrease rate, the threshold expected capacity attenuation rate, the equivalent instantaneous failure rate, the reliability improvement contribution, the expected capacity improvement contribution, the reliability decrease influence, the expected capacity decrease influence, the reliability influence consistency coefficient and the expected capacity influence consistency coefficient.

[0027] This embodiment starts from the perspective of multi-state system reliability, integrates the existing reliability indicators of battery energy storage power stations, and proposes a reliability evaluation index system suitable for battery energy storage systems. Among them, the first-level indicators include three aspects: basic dimension indicators, time dimension indicators, and space dimension indicators; each first-level indicator contains several second-level indicators, a total of 18 indicators. The newly established reliability index system integrates the reliability indicators of traditional power stations, and further considers the overall impact of battery performance attenuation from a time perspective and single-cell changes from a spatial perspective.

[0028] The basic dimension indicators reflect the basic situation of energy storage based on direct statistical data such as failure time and number of failed batteries, including six secondary indicators: the planned outage coefficient, unplanned outage coefficient, availability coefficient, utilization coefficient of the entire battery compartment, the relative number of failures of the battery cluster, and the failure rate of the battery pack. The definitions and calculations are as follows.

[0029] (1) Planned outage coefficient: The planned outage coefficient is the ratio of the planned outage time of the energy storage due to inspection and maintenance to the total time of the statistical period.

[0030] (1).

[0031] in, is the planned outage coefficient of energy storage; The number of hours of planned outage; The number of hours in the statistical period.

[0032] (2) Unplanned outage coefficient: The unplanned outage coefficient is the ratio of the outage time caused by sudden failure of energy storage or insufficient capacity due to battery capacity decay to the total time of the statistical period.

[0033] (2).

[0034] in, is the unplanned shutdown coefficient of the energy storage power station; The number of unplanned downtime hours.

[0035] (3) Availability coefficient: The availability coefficient is the ratio of the available time of the energy storage battery to the total statistical time.

[0036] (3).

[0037] in, is the availability factor of the energy storage power station; It is the number of hours available during the evaluation period.

[0038] (4) Utilization coefficient: The utilization coefficient is the ratio of the operating time of the energy storage battery compartment to the total statistical time.

[0039] (4).

[0040] in, is the energy storage utilization coefficient; is the number of hours of energy storage operation.

[0041] (5) Relative number of battery cluster failures: the ratio of the number of battery cluster failures during the evaluation period to the total number of battery clusters.

[0042] (5).

[0043] in, is the relative failure number of battery clusters; is the relative failure number of the battery cluster; is the number of battery clusters in the energy storage cabin.

[0044] (6) Battery pack failure rate: The ratio of the cumulative number of battery packs that fail due to faults or capacity attenuation during the evaluation period to the total number of battery packs.

[0045] (6).

[0046] in, is the battery pack failure rate; is the number of failed battery packs; is the total number of battery packs.

[0047] The first-level indicators of the time dimension are based on the performance decay of the battery, and are used to reflect the changes in the reliability probability and capacity of the battery energy storage system over time. The second-level indicators are all indicators with time-varying characteristics, including threshold reliability, threshold expected capacity, capacity margin, threshold reliability decline rate, threshold expected capacity decay rate, and equivalent instantaneous failure rate. The definitions and calculations are as follows.

[0048] (1) Threshold reliability: Threshold reliability is used to describe the ability of battery energy storage to meet performance requirements and is defined as the sum of the probabilities that the energy storage performance is greater than the required threshold state. Based on a given performance level threshold , assuming The corresponding state is Status , the reliability can be calculated using the following formula.

[0049] (7).

[0050] in, Status probability.

[0051] (2) Threshold expected capacity: The expected capacity is defined as the expected value of the capacity corresponding to each battery state that is greater than the state threshold.

[0052] (8).

[0053] in, is the initial capacity of the battery compartment; Status The performance level value can be taken as the average value of the corresponding SOH range.

[0054] (9).

[0055] in, Status The upper limit of the corresponding SOH range; Status The lower limit of the corresponding SOH range.

[0056] (3) Capacity margin: The difference between the current expected capacity and the capacity value corresponding to the SOH threshold.

[0057] (10).

[0058] (11).

[0059] in, Indicates the capacity margin, is the capacity threshold.

[0060] (4) Threshold reliability decline rate: The differential operation of reliability with respect to time, which is used to reflect the speed of decline of energy storage reliability.

[0061] (12).

[0062] (5) Threshold expected capacity decay rate: The differential operation of the energy storage capacity expectation with respect to time, reflecting the speed of capacity decay.

[0063] (13).

[0064] (6) Equivalent instantaneous failure rate: The state of the energy storage power station that is less than the threshold is uniformly equivalent to the fault state. According to the relationship between the failure rate and reliability, the equivalent failure rate can be calculated as shown in the following formula.

[0065] (14).

[0066] in, represents the first derivative of the reliability function.

[0067] The spatial dimension index takes into account the spatial structural characteristics of battery energy storage composed of a large number of battery cell modules, and reflects the influence and connection between battery cells and the whole. It includes 6 indicators, including reliability improvement contribution, expected capacity improvement contribution, reliability reduction influence, expected capacity reduction influence, reliability impact consistency coefficient, and expected capacity impact consistency coefficient. Among them, the reliability improvement contribution and expected capacity improvement contribution mainly consider that the overall performance of energy storage can be improved by replacing batteries with poor performance to extend the service life of the energy storage system. It can be used to analyze the degree of improvement in overall performance brought about by replacing different batteries to formulate the optimal replacement strategy. The reliability reduction influence and the expected capacity reduction influence analyze the degree of influence of different battery cell performance reductions on the overall performance in different time periods from the perspective of battery performance degradation, which can be used to explore the weak links in the energy storage system. The reliability impact consistency coefficient and the expected capacity impact consistency coefficient reflect the differences between different battery cells from the perspective of reliable probability and reliable capacity respectively. Its definition and calculation are as follows.

[0068] (1) Reliability improvement contribution: defined as the ratio of the reliability improvement brought about by the improvement of battery cell / pack performance to the overall reliability change.

[0069] (15).

[0070] in, For the The importance of reliability changes of individual battery cells / packs; For the The SOH improvement of each battery cell / pack; Indicates Improvement of SOH of each battery cell / pack Then all battery cells SOH are collected; Indicates in the collection The reliability of the battery compartment under the conditions of the battery cell performance indicated, Indicates that the performance of all battery cells in the battery compartment has been improved Reliability difference between front and rear battery compartments.

[0071] (2) Contribution of expected capacity improvement: defined as the ratio of the expected capacity improvement brought about by the improvement of battery cell performance to the overall expected capacity change.

[0072] (16).

[0073] in, For the Importance of expected capacity change of each battery cell / pack; Indicates in the collection The expected capacity of the battery compartment under the indicated battery cell performance; For the The expected capacity of the battery compartment before each battery cell / battery pack is changed; Indicates that the performance of all battery cells in the battery compartment has been improved The difference in expected capacity between the front and rear battery compartments.

[0074] (3) Impact of reliability degradation: It is defined as the ratio of the reliability reduction caused by the reduction of SOH of a certain battery cell to the overall reliability reduction of the energy storage system, that is, the degree of change in the overall reliability caused by the performance change of a certain cell.

[0075] (17).

[0076] in, For the The contribution of each battery cell to the reliability change; For the SOH decay of each battery cell Then all battery cells SOH are collected; Because of the The reliability of the battery box decreases due to the SOH decay of each battery cell; To reduce the size of the battery compartment reliability.

[0077] (4) Impact of expected capacity decrease: It is defined as the ratio of the overall expected capacity decrease of energy storage caused by the decrease in SOH of a certain battery cell to the overall expected capacity decrease, that is, the degree of change in the overall expected capacity caused by the performance change of a certain cell.

[0078] (18).

[0079] in, No. The expected capacity change contribution of each battery cell; Because of the The decrease in the overall expected capacity of energy storage caused by the SOH decay of each battery cell; Desired capacity reduction for the battery compartment.

[0080] (5) Reliability impact consistency coefficient: It is defined as the standard deviation of the reliability degradation impact of different cells / packs, and is used to reflect the difference in the impact of different battery cell performance changes on the overall reliability.

[0081] (19).

[0082] in, is the number of battery cells; is the mean of the reliability change importance of all battery cells.

[0083] (6) Expected capacity impact consistency coefficient: defined as the standard deviation of the expected capacity reduction impact of different cells / packs, used to reflect the degree of difference in the impact of changes in battery cell performance on the overall expected capacity.

[0084] (20).

[0085] in, is the mean of the importance of expected capacity change of all battery cells.

[0086] In this embodiment, in step S2, a extension cloud model corresponding to each classification level is established based on the reliability evaluation index system, which specifically includes the following steps.

[0087] Constructing the initial matter-element model , where R represents the initial matter-element model, N represents the reliability index, and C represents the index value of the reliability index. Represents a characteristic quantity; using a Gaussian cloud model as the characteristic quantity in the initial matter-element model to establish an extension cloud model corresponding to each division level; the Gaussian cloud model is composed of the expectation of the reliability index, the entropy of the reliability index and the super entropy of the reliability index.

[0088] By introducing cloud theory into extenics, the physical element model The characteristic quantity in By using the cloud model instead, we can get the extension cloud model.

[0089] This embodiment adopts the Gaussian cloud model To replace the characteristic quantity in the matter-element model .in is the expectation, which represents the distribution center of each cloud droplet in the cloud model and is the point value that best reflects the attribute concept; Entropy is a measure of the uncertainty of an attribute concept, reflecting the range of values ​​that can be accepted by the concept; is the super entropy, which is a measure of entropy uncertainty and reflects the discreteness of cloud droplets. The specific calculation of each parameter is as follows.

[0090] (twenty one).

[0091] (twenty two).

[0092] (twenty three).

[0093] in, is the maximum index value; is the minimum indicator value; is a constant, which can be adjusted according to the uncertainty of the corresponding indicator and the actual situation. In this embodiment, it can be taken as 0.02.

[0094] Will After replacing with the Gaussian cloud model, the extension cloud matrix corresponding to the division level is shown in the following formula.

[0095] (twenty four).

[0096] in, For the Extension cloud matrix corresponding to the division levels; Representative Reliability index, is the number of reliability indicators; For the The index value of a reliability index is the value of each first-level index or second-level index. For example, when the index is the expected capacity change contribution, its index value is Specific value; For the The cloud cover value of the reliability index is For the The expectation of a reliability index, For the The entropy of the reliability index is For the The super entropy of a reliability index, n is the number of classification levels.

[0097] The following is the calculation process of cloud correlation: First, the various indicators of the energy storage battery box to be evaluated are As a cloud droplet, it then generates a , standard deviation is Normally distributed random numbers , and finally calculate the index value using formula (25) The correlation with the normal cloud .

[0098] (25).

[0099] And get the final correlation matrix .

[0100] (26).

[0101] in, is the number of classification levels, For the The reliability index and The degree of relevance.

[0102] This embodiment divides the reliability performance of the battery energy storage system into five levels: "good, decay, defect, risk, and failure".

[0103] In step S3, the indicator weight of each reliability indicator in the reliability evaluation indicator system is calculated using a preset weighting method, which specifically includes the following steps.

[0104] The subjective weighting method is used to calculate the subjective weight vector of each reliability indicator in the reliability evaluation indicator system; the objective weighting method is used to calculate the objective weight vector of each reliability indicator in the reliability evaluation indicator system; and the combined weighting method based on game theory is used to calculate the indicator weight of each reliability indicator according to the subjective weight vector and the objective weight vector.

[0105] The subjective weighting method is the network analysis method, and the objective weighting method is the entropy weight method.

[0106] Specifically, considering the different emphases of different weighting methods, this embodiment selects a subjective weighting method and an objective weighting method to calculate the weights. The network analytic hierarchy process is used to obtain the subjective weight vector, and the entropy weight method is used to obtain the objective weight vector. Then, a combined weighting method based on game theory is used to solve the comprehensive weight.

[0107] The calculation method of the combined weighting method based on game theory is shown in formula (27).

[0108] (27).

[0109] The constraints are shown in formula (28).

[0110] (28).

[0111] in, For the iThe weight coefficient of a vector, which is a subjective weight vector or an objective weight vector; is the subjective weight vector; is the objective weight vector.

[0112] Considering that battery energy storage can be widely used in peak load regulation, frequency regulation and other services due to its flexible characteristics. In different service scenarios, the degree of energy storage life loss and the resulting reduction in reliability vary greatly. In order to be able to compare and measure the comprehensive evaluation value of energy storage participating in different services, this embodiment introduces the service value of energy storage participating in different services as an adjustment factor when calculating the indicator weights, and obtains equal value weights. The calculated equal value weights (i.e., the combined weight vector ) as the indicator weight of the reliability indicator.

[0113] (29).

[0114] (30).

[0115] in, is the standardized weight coefficient; is the combined weight vector; Count down the value of energy storage participation services.

[0116] In step S4, a comprehensive reliability level assessment is performed according to the equal value weight extension cloud model to obtain a comprehensive evaluation result, which specifically includes the following steps.

[0117] A comprehensive reliability level assessment is performed according to the equal value weight extension cloud model to obtain the evaluation value corresponding to each division level; the division level corresponding to the evaluation value with the largest median value among all the evaluation values ​​corresponding to the division levels is taken as the comprehensive evaluation result.

[0118] The comprehensive evaluation value is calculated based on the indicator weights.

[0119] (31).

[0120] in, For the The indicator weight of each reliability indicator.

[0121] Calculation of comprehensive evaluation level: by obtaining the equivalent value weight vector Cloud association vector , calculate the comprehensive grade judgment vector And the comprehensive evaluation score. The specific calculation is shown in formula (32) and formula (33).

[0122] (32).

[0123] (33).

[0124] when When established, the comprehensive evaluation level is .

[0125] Since in calculating quantitative indicators Correlation with the normal cloud In the process of random number generation, random numbers are randomly generated. Therefore, multiple calculations are performed to calculate the score of each level evaluation result, and then the expected value and standard deviation of the evaluation result score are calculated, and the credibility factor is introduced to evaluate the credibility of the calculation result.

[0126] (34).

[0127] (35).

[0128] (36).

[0129] (37).

[0130] in, is the score value of each evaluation result; is the first Level element value; For level The score values ​​of the five levels can be 1, 2, 3, 4, and 5 respectively; is the expected value of the evaluation result score at each level; To calculate the number of times; is the standard deviation of the scores for each evaluation result, It is the credibility factor, and the larger its value is, the lower the credibility is.

[0131] This embodiment is a comprehensive evaluation method based on a multi-dimensional indicator system of a battery energy storage cabin and equal value weights. First, from the perspective of multi-state system reliability, the existing reliability indicators of battery energy storage power stations are integrated, and based on the three dimensions of basic dimension indicators, time dimension indicators and space dimension indicators, a reliability evaluation indicator system suitable for battery energy storage systems is proposed; secondly, by introducing cloud theory into extenics, a Gaussian cloud model is used to replace the feature quantity of the matter-element model, an extenic cloud model is constructed and cloud correlation calculation is performed; considering the different emphases of different weighting methods, the network hierarchy analysis method and the entropy weight method are respectively used to obtain the subjective weight vector and the objective weight vector, and the combined weighting method based on game theory is used to solve the equal value indicator weight; finally, based on the equal value weighted extenic cloud, the energy storage participation in different service values ​​is introduced, and an equal value weighted extenic cloud model is established to perform a comprehensive grade evaluation calculation on the reliability of the energy storage battery cabin.

[0132] This embodiment has the following beneficial effects.

[0133] (1) This embodiment takes into account the capacity decay process of the battery energy storage system, and proposes a multi-dimensional reliability index system for the energy storage battery compartment from the three dimensions of foundation, time, and space by integrating traditional indicators, providing a comprehensive measurement basis for system reliability evaluation. Compared with the existing single or partial reliability indicators of energy storage systems, this invention forms a complete and comprehensive index system, improves the indicators for evaluating the energy storage battery compartment, ensures the accuracy of the evaluation results, and is more refined and targeted.

[0134] (2) This embodiment introduces cloud theory and extenics, constructs an extenic cloud model and performs cloud correlation calculation to more comprehensively consider the correlation between indicators, providing a more accurate method for comprehensive evaluation. Compared with traditional models, this method focuses more on the response to the differences of a large number of internal battery cells, and by supplementing dynamic and spatial dimension indicators to build a more sophisticated and comprehensive indicator system, it is more sensitive to the impact of battery attenuation and connection structure.

[0135] (3) This embodiment combines game theory to solve the weights of equal value indicators, adopts different weighting methods to comprehensively consider subjective and objective factors, and finally conducts a comprehensive level evaluation based on the equal value weight extension cloud model, providing a scientific and effective evaluation framework for the reliability of energy storage battery compartments. Compared with the traditional comprehensive evaluation method, this method proposes to take into account the value weights of multiple services, remove the differences in energy storage participating in multiple service scenarios, and better reflect the essential reliability performance of the battery compartment.

[0136] This embodiment establishes a sophisticated and comprehensive battery compartment reliability assessment index system and combines it with advanced monitoring and diagnostic technologies. By continuously optimizing the reliability assessment of the battery compartment, it can better cope with the challenges in the power system and promote the development and application of clean energy technologies.

[0137] Embodiment 2: This embodiment provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps of a battery energy storage compartment reliability assessment method in Embodiment 1.

[0138] Embodiment 3: This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of a battery energy storage compartment reliability assessment method in Embodiment 1 are implemented.

[0139] Embodiment 4: This embodiment provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of a battery energy storage compartment reliability assessment method in Embodiment 1.

[0140] Embodiment 5: This embodiment provides a computer device, which may be a database, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a battery energy storage cabin reliability assessment method in Example 1 is implemented.

[0141] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0142] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0143] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0144] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for evaluating the reliability of a battery energy storage compartment, characterized in that: include: A reliability evaluation index system for a battery energy storage system is established based on the reliability indicators of several battery energy storage power stations; the reliability evaluation index system includes a basic dimension index system, a time dimension index system and a space dimension index system; A cloud extension model is established based on the reliability evaluation index system, and a correlation matrix is ​​calculated according to the cloud extension model; the cloud extension matrix corresponding to each classification level of the cloud extension model is formed; the correlation matrix is ​​formed by the correlation between each reliability index and its classification level; Calculating the index weight of each reliability index in the reliability evaluation index system by using a preset weighting method; An equal value weighted extension cloud model is established according to the correlation matrix and the indicator weights of each reliability indicator, and a comprehensive reliability grade evaluation is performed according to the equal value weighted extension cloud model to obtain a comprehensive evaluation result.

2. A battery energy storage compartment reliability assessment method according to claim 1, characterized in that: The reliability index includes primary index and secondary index; the primary index includes basic dimension index, time dimension index and space dimension index; the secondary index includes the planned outage coefficient, unplanned outage coefficient, availability coefficient and utilization coefficient of the entire battery compartment, the relative failure times of the battery cluster, the battery pack failure rate of the battery pack, the threshold reliability, the threshold expected capacity, the capacity margin, the threshold reliability decrease rate, the threshold expected capacity attenuation rate, the equivalent instantaneous failure rate, the reliability improvement contribution, the expected capacity improvement contribution, the reliability decrease influence, the expected capacity decrease influence, the reliability influence consistency coefficient and the expected capacity influence consistency coefficient.

3. A battery energy storage compartment reliability assessment method according to claim 1, characterized in that: Based on the reliability evaluation index system, a corresponding extension cloud model for each classification level is established, specifically including: Constructing the initial matter-element model , where R represents the initial matter-element model, N represents the reliability index, and C represents the index value of the reliability index. Indicates the characteristic quantity; The Gaussian cloud model is used as the characteristic quantity in the initial matter-element model to establish the extension cloud model corresponding to each division level; the Gaussian cloud model is composed of the expectation of the reliability index, the entropy of the reliability index and the super entropy of the reliability index.

4. A battery energy storage compartment reliability assessment method according to claim 3, characterized in that: The extension cloud matrix corresponding to the classification level is as follows: ； in, For the Extension cloud matrix corresponding to the division levels; Representative Reliability index, is the number of reliability indicators; For the The index value of a reliability index; For the The cloud cover value of the reliability index is For the The expectation of a reliability index, For the The entropy of the reliability index is For the The super entropy of a reliability index, n is the number of classification levels.

5. A battery energy storage compartment reliability assessment method according to claim 1, characterized in that: The indicator weight of each reliability indicator in the reliability evaluation indicator system is calculated using a preset weighting method, specifically including: Calculating the subjective weight vector of each reliability index in the reliability evaluation index system by using a subjective weighting method; Calculating the objective weight vector of each reliability index in the reliability evaluation index system by using an objective weighting method; The index weight of each reliability index is calculated according to the subjective weight vector and the objective weight vector by using a combined weighting method based on game theory.

6. A battery energy storage compartment reliability assessment method according to claim 5, characterized in that: The subjective weighting method is the network analysis method, and the objective weighting method is the entropy weight method.

7. A battery energy storage compartment reliability assessment method according to claim 1, characterized in that: According to the equal value weight extension cloud model, a comprehensive reliability level assessment is performed to obtain a comprehensive evaluation result, which specifically includes: Perform a comprehensive reliability level assessment based on the equal value weight extension cloud model to obtain the evaluation value corresponding to each classification level; The classification level corresponding to the evaluation value with the largest median value among all the evaluation values ​​corresponding to the classification levels is taken as the comprehensive evaluation result.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a battery energy storage compartment reliability assessment method as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a battery energy storage compartment reliability assessment method described in any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of a battery energy storage compartment reliability assessment method described in any one of claims 1 to 7 are implemented.

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