A safety state quantitative evaluation method of a battery energy storage system and a related device

Abstract

The present application belongs to the technical field of electrochemical energy storage, and aims at the problems of strong subjectivity and insufficient quantification in safety evaluation of existing battery energy storage systems, and discloses a safety state quantification evaluation method and related device for battery energy storage system. The method firstly constructs a hierarchical evaluation index system including a target layer, a criterion layer and an index layer; then calculates the combined weight of the index layer to the target layer by using the analytic hierarchy process and constructs a weight matrix; then determines the membership based on the actual detection data of the secondary index and constructs a fuzzy relation matrix; finally, the comprehensive evaluation result vector is obtained through matrix multiplication operation, and the quantification evaluation of the safety state is realized. The present application combines the analytic hierarchy process and the fuzzy comprehensive evaluation method, and the evaluation process is scientific and objective, and the result is reliable, which can provide effective quantification support for the design optimization, operation risk management and operation and maintenance decision of the electrochemical energy storage power station.

Description

Technical Field

[0001] This invention belongs to the field of electrochemical energy storage technology, and relates to the safety of electrochemical energy storage power stations, and in particular to a method and related device for quantitatively evaluating the safety status of a battery energy storage system. Background Technology

[0002] New energy storage is a core technological support and fundamental equipment for building new power systems. Among them, electrochemical energy storage, represented by lithium-ion batteries, has achieved rapid growth in installed capacity in recent years due to its comprehensive advantages in terms of technology and economy, industrial maturity, and market adaptability, accounting for over 98% of the total installed capacity of new energy storage. With the continuous expansion of battery energy storage capacity, the safety and reliability issues of energy storage power stations have become increasingly prominent. In actual operation, many energy storage power stations have exposed serious quality defects, and safety accidents such as battery thermal runaway and system short circuits occur frequently.

[0003] Ensuring the safety of energy storage power stations requires breakthroughs not only in core aspects such as the battery itself and safety protection technologies, but also in the refinement of operational risk management. Safety and reliability assessment is a core component of operational risk management for energy storage power stations, yet relevant research is still relatively scarce. The assessment results directly determine the effectiveness of subsequent risk control measures; only accurate safety and reliability assessments can effectively prevent and reduce the occurrence of safety accidents in energy storage power stations. Explaining this, from the perspective of the entire life cycle characteristics of energy storage batteries, during cyclic use, they are susceptible to low-temperature environments, high-rate charging and discharging, and overcharging / over-discharging conditions, leading to problems such as lithium deposition and loss of active materials. This results in battery aging and performance degradation, ultimately inducing safety accidents such as insulation damage, partial short circuits, and even thermal runaway. Therefore, scientifically assessing the safety status of battery energy storage systems provides core technical support for system safety early warning, fault diagnosis, and operation and maintenance optimization, and is a crucial prerequisite for ensuring the safe and reliable operation of energy storage power stations.

[0004] Currently, existing methods for evaluating the safety status of battery energy storage systems still suffer from incomplete systems and insufficient comprehensiveness and systematicity. Specifically, these limitations manifest in single-indicator evaluation or static weighted multi-indicator evaluation: First, most evaluation methods focus only on single-dimensional indicators such as battery thermal runaway and mechanical failure, ignoring the coupled influence of multi-level and multi-dimensional factors during the operation of the energy storage system. They fail to fully cover core dimensions such as the safety performance, health status, service life, and maintenance level of key equipment, resulting in one-sided and unreliable evaluation results. Second, existing traditional evaluation methods often simply list influencing factors, determine their weights one by one, and then conduct evaluations directly without understanding the hierarchical relationships between the factors, which can easily lead to evaluation bias. Third, the existing methods for setting indicator weights are highly subjective and lack scientific mathematical model support, making it difficult to ensure the objectivity and rationality of weight allocation. In summary, existing methods for evaluating the safety status of battery energy storage systems still have some problems. These problems result in evaluation results that cannot accurately reflect the safety status of the system, making it difficult to effectively guide risk control, safety early warning, and operation and maintenance optimization of energy storage power stations. There is an urgent need to develop new quantitative evaluation methods to solve the above problems and achieve a scientific and accurate evaluation of the safety risks of battery energy storage systems. Summary of the Invention

[0005] The purpose of this invention is to provide a method and related apparatus for quantitatively evaluating the safety status of battery energy storage systems, in order to solve one or more of the aforementioned technical problems. Specifically, the technical solution disclosed in this invention is a quantitative evaluation scheme for the safety status of battery energy storage systems based on fuzzy hierarchical analysis. This scheme integrates the Analytic Hierarchy Process (AHP) and the Fuzzy Comprehensive Evaluation (FCE) method, resulting in more objective and reliable evaluation results. It can provide effective quantitative support for the design improvement and operation and maintenance decisions of electrochemical energy storage power stations.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for quantitatively evaluating the safety status of a battery energy storage system, comprising the following steps: Based on the selected battery energy storage system, a hierarchical evaluation index system is constructed. The hierarchical evaluation index system includes: target layer, criterion layer and index layer. The target layer is the safety and reliability of the battery energy storage system. The criterion layer sets up multi-dimensional primary indicators. The index layer sets up multiple secondary indicators corresponding to each primary indicator. Based on the hierarchical evaluation index system, the combined weights of the index layer to the target layer are calculated using the analytic hierarchy process, and a weight matrix is ​​constructed based on the combined weights. Based on the fuzzy comprehensive evaluation method, the actual detection data of the secondary indicators are determined according to the numerical range of each safety level in the risk assessment set, and a fuzzy relation matrix is ​​constructed based on the determined membership degree. The weight matrix and the fuzzy relation matrix are multiplied to obtain a comprehensive evaluation result vector, which is used as the quantitative evaluation result of the safety status of the battery energy storage system.

[0007] A further improvement to the technical solution of the present invention lies in that, The criteria layer includes multi-dimensional primary indicators such as: basic condition of energy storage batteries, system operation status, safety management, and working environment. In the aforementioned indicator layer, the secondary indicators for the basic condition dimension of energy storage batteries include: energy storage battery appearance, energy storage battery capacity, cycle performance, safety performance, and electrical connection status; the secondary indicators for the system operation status dimension include: battery consistency, battery temperature difference, battery health status, and battery state of charge; the secondary indicators for the safety management dimension include: battery management system, fire safety system, gas monitoring system, and safety linkage system; and the secondary indicators for the working environment dimension include: ventilation status, temperature regulation, and dust control. Each secondary indicator has corresponding preset core evaluation points.

[0008] A further improvement to the technical solution of this invention lies in the step of calculating the combined weights of the indicator layer to the target layer using the analytic hierarchy process (AHP) based on the hierarchical evaluation index system, which includes: Based on the first-level indicators of the criterion layer and the second-level indicators of the indicator layer, the importance of each pair of indicators of the same level within each level is compared to construct the judgment matrix of the corresponding level. Based on the judgment matrices of each level, the maximum eigenvalue is solved, the normalized weight vector is calculated, and the consistency test is performed in sequence to obtain the weights of each first-level indicator of the criterion layer to the target layer and the weights of each second-level indicator of the indicator layer to its respective first-level indicator. The combined weights of the indicator layer on the target layer are obtained by multiplying the weights of each primary indicator in the criterion layer on the target layer with the weights of each secondary indicator in the indicator layer on its respective primary indicator.

[0009] A further improvement to the technical solution of the present invention lies in that, Based on the first-level indicators of the criterion layer and the second-level indicators of the indicator layer, the importance of each pair of indicators of the same level within each level is compared. When constructing the judgment matrix of the corresponding level, the 1-9 scale method is used to obtain the judgment matrix. The maximum eigenvalue can be obtained by solving the characteristic equation of the judgment matrix or by using the power method for iteration. During the consistency check, the corresponding normalized weight vector is used as the effective weight for the combined weight calculation only if the consistency check passes.

[0010] A further improvement to the technical solution of this invention lies in that the step of calculating the normalized weight vector includes: Based on the judgment matrix, the geometric mean of each row of elements is calculated to obtain the geometric mean vector of each row of elements in the judgment matrix; the geometric mean vector is then normalized to obtain the normalized weight vector. Among them, the calculation of the first i During the geometric mean of the row elements, the first row of the judgment matrix is... i Row elements, calculate the product and then open the root. n The power of 1 yields the 1st power. i Geometric mean of row elements n To determine the order of a matrix, during the normalization process, all elements of the geometric mean vector are summed, and then each element is divided by the sum to obtain the normalized weight vector.

[0011] A further improvement to the technical solution of this invention lies in the consistency verification step, The consistency check passed. in, , CR represents the consistency ratio. As a consistency indicator, n To determine the order of a matrix, It is the largest eigenvalue.

[0012] A further improvement of the technical solution of the present invention is that, in the step of performing matrix multiplication on the weight matrix and the fuzzy relation matrix to obtain a comprehensive evaluation result vector and using it as the quantitative evaluation result of the safety status of the battery energy storage system, after obtaining the comprehensive evaluation result vector, the safety and reliability level of the battery energy storage system is determined by matching a preset risk assessment set based on the maximum membership degree in the comprehensive evaluation result vector.

[0013] In a second aspect, the present invention provides a quantitative evaluation system for the safety status of a battery energy storage system, comprising: The indicator system construction module is used to construct a hierarchical evaluation indicator system based on the selected battery energy storage system. The hierarchical evaluation indicator system includes: a target layer, a criterion layer, and an indicator layer. The target layer is the safety and reliability of the battery energy storage system. The criterion layer sets up multi-dimensional primary indicators. The indicator layer sets up multiple secondary indicators corresponding to each primary indicator. The weight matrix acquisition module is used to calculate the combined weights of the indicator layer to the target layer based on the hierarchical evaluation index system using the analytic hierarchy process, and to construct a weight matrix based on the combined weights. The fuzzy relation matrix acquisition module is used to determine the membership degree of the actual detection data of the secondary indicators according to the numerical range of each safety level in the risk assessment set based on the fuzzy comprehensive evaluation method, and to construct the fuzzy relation matrix based on the determined membership degree. The evaluation result acquisition module is used to perform matrix multiplication operation between the weight matrix and the fuzzy relation matrix to obtain a comprehensive evaluation result vector, which is used as the quantitative evaluation result of the safety status of the battery energy storage system.

[0014] In a third aspect, the present invention provides an electronic device, including 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 a method for quantitatively evaluating the safety status of a battery energy storage system as described in any one of the first aspects of the present invention.

[0015] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for quantitatively evaluating the safety status of a battery energy storage system as described in any one of the first aspects of the present invention.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a quantitative evaluation method for the safety status of battery energy storage systems. Through deep integration of AHP (Advanced Performance Hierarchy Processing) and FCE (Fuel Component Evaluation), it constructs a comprehensive, objective, accurate, and practical method for evaluating the safety status of battery energy storage systems. This fundamentally solves the technical problems of existing traditional evaluation methods, such as incomplete systems, subjective weighting, one-sided results, and difficulty in application. This invention upgrades the evaluation of the safety status of battery energy storage systems from subjective qualitative description to scientific quantitative judgment, and from single-dimensional analysis to multi-dimensional hierarchical evaluation. It can form a closed-loop guidance from evaluation to operation and maintenance, providing scientific and quantitative technical basis for the design improvement, operational risk management, and operation and maintenance decisions of electrochemical energy storage power stations. This effectively prevents and reduces the occurrence of safety accidents in energy storage power stations, improves the safety and reliability of energy storage power station operation, and plays a vital supporting role in the healthy development of the electrochemical energy storage industry under the new power system. The evaluation steps of this invention are clear and the process is standardized. The required data can be obtained through existing testing equipment and operation monitoring systems without the need for additional complex testing devices. It is applicable to different types of energy storage battery systems, such as large-scale energy storage power stations, distributed energy storage systems, and residential energy storage systems. It has strong engineering application value and a wide range of applications.

[0017] The technical solution of this invention is a battery energy storage system safety status evaluation scheme based on fuzzy hierarchical analysis. Its core is to achieve a quantitative assessment of the safety status of battery energy storage systems through a process of "indicator system construction → hierarchical analysis weight determination → comprehensive score calculation → safety and reliability evaluation." Specifically, this invention constructs a hierarchical and comprehensive evaluation indicator system, solving the problems of single-dimensional evaluation or incomplete indicator systems in existing evaluation methods. This invention clarifies the hierarchical relationship between the target layer, criterion layer, and indicator layer through AHP, covering four core dimensions—basic condition of the energy storage battery, system operation status, safety management, and working environment—and 15 specific indicators. This comprehensively covers the key factors affecting the safety of energy storage battery systems, avoiding the one-sidedness of evaluation results and improving the systematicness and comprehensiveness of the evaluation.

[0018] This invention provides a scientific weight allocation method for evaluation indicators at various levels by combining AHP with pairwise comparisons and consistency tests. Compared with the subjective weight setting method in traditional evaluation methods, this significantly improves the objectivity and rationality of weight allocation, laying the foundation for accurate evaluation. This invention achieves the scientific quantification of evaluation weights, reducing the influence of subjective factors on the evaluation results.

[0019] This invention effectively addresses the fuzziness and uncertainty of indicators in the safety evaluation of energy storage battery systems by combining FCE (Fuzzy Quantitative Evaluation). Through membership determination and fuzzy comprehensive calculation, it integrates qualitative evaluation with quantitative analysis, accurately reflecting the safety status level of the system and providing clear decision-making basis for operation and maintenance rectification and risk prevention. This invention achieves fuzzy quantitative evaluation of safety status, improving the accuracy and practicality of evaluation results. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for quantitatively evaluating the safety status of a battery energy storage system, as described in Embodiment 1 of the present invention.

[0022] Figure 2 This is a schematic diagram of the modular structure of a battery energy storage system safety status quantitative evaluation system in Embodiment 9 of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention; obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0024] Based on the technical solutions disclosed in the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0025] Example 1 Please see Figure 1 The present invention provides a method for quantitatively evaluating the safety status of a battery energy storage system, the specific process of which is as follows: Step 1: Based on the selected battery energy storage system, construct a hierarchical evaluation index system; wherein, the hierarchical evaluation index system includes: target layer, criterion layer and index layer, the target layer is the safety and reliability of the battery energy storage system, the criterion layer is set with multi-dimensional primary indicators, and the index layer is set with multiple secondary indicators corresponding to each primary indicator; further explained, the multi-dimensional primary indicators are selected based on the selected battery energy storage system; Step 2: Based on the hierarchical evaluation index system, the combined weights of the index layer to the target layer are calculated using the Analytic Hierarchy Process (AHP), and a weight matrix is ​​constructed based on the combined weights. In a specific exemplary technical solution, the step of calculating the combined weights of the index layer to the target layer using AHP includes: based on the first-level indicators of the criterion layer and the second-level indicators of the index layer, comparing the importance of each pair of indicators within the same level within each level to construct a judgment matrix for the corresponding level; according to the judgment matrix of each level, sequentially completing the maximum eigenvalue solution, normalized weight vector calculation, and consistency check to obtain the weights of each first-level indicator of the criterion layer to the target layer and the weights of each second-level indicator of the index layer to its corresponding first-level indicator; by multiplying the weights of each first-level indicator of the criterion layer to the target layer and the weights of each second-level indicator of the index layer to its corresponding first-level indicator level by level, the combined weights of the index layer to the target layer are obtained, providing a scientific and objective weight basis for subsequent fuzzy comprehensive evaluation. Step 3: Based on the fuzzy comprehensive evaluation method (FCE), the actual detection data of each secondary indicator are used to determine the membership degree according to the numerical range of each safety level in the risk assessment set, and a fuzzy relation matrix is ​​constructed based on the determined membership degree. Step 4: Perform matrix multiplication on the weight matrix and the fuzzy relation matrix to obtain the comprehensive evaluation result vector, which serves as the quantitative evaluation result of the safety status of the battery energy storage system. In the example illustrative technical solution, the calculation expression for the comprehensive evaluation result vector G is: G = W × R, where W is the weight matrix and R is the fuzzy relation matrix. Further illustratively, the above operation enables the coupling analysis of weights and the actual state of indicators, ultimately outputting the quantitative evaluation result.

[0026] In a specific exemplary technical solution, in step 4, the safety and reliability level of the battery energy storage system can be determined by matching the preset risk assessment set based on the maximum membership degree in the comprehensive evaluation result vector according to the fuzzy comprehensive calculation quantification results; subsequently, targeted operation and maintenance guidance suggestions can be output based on the safety and reliability level of the battery energy storage system to complete the entire closed-loop process of safety status evaluation and operation and maintenance guidance.

[0027] This invention discloses a method for quantitatively evaluating the safety status of battery energy storage systems. It is based on a combination of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation, achieving a fuzzy quantitative evaluation that combines qualitative and quantitative methods by constructing a standardized hierarchical evaluation system and scientifically quantifying index weights. This method specifically addresses the technical problems still existing in current battery energy storage system safety status evaluation methods, such as "incomplete system, subjective weight setting, single evaluation dimensions, and lack of accuracy and practicality in results." The technical solution of this invention, through standardized processes, easily accessible data, and scenario adaptability, solves the problem of the difficulty in engineering application of traditional methods, ultimately achieving a comprehensive, objective, and accurate evaluation of the safety status of energy storage systems, providing a scientific quantitative basis for energy storage power station operation and maintenance decisions. The technical solution of this invention, through a standardized process of "system construction, weight calculation, fuzzy evaluation, and level determination," forms a progressive problem-solving logic, fundamentally overcoming the shortcomings of existing technologies.

[0028] Example 2 In this embodiment of the invention, based on the technical solution disclosed in Embodiment 1, the specific indicator configuration of the hierarchical evaluation index system is as follows: The multi-dimensional primary indicators include: basic condition dimension of the energy storage battery, system operation status dimension, safety management dimension, and working environment dimension; the secondary indicators of the basic condition dimension of the energy storage battery include: energy storage battery appearance, energy storage battery capacity, cycle performance, safety performance, and electrical connection status; the secondary indicators of the system operation status dimension include: battery consistency, battery temperature difference, battery health status, and battery state of charge; the secondary indicators of the safety management dimension include: battery management system, fire safety system, gas monitoring system, and safety linkage system; the secondary indicators of the working environment dimension include: ventilation status, temperature regulation, and dust control; furthermore, each secondary indicator can be preset with corresponding core evaluation points to better provide quantitative and qualitative standards for determining membership.

[0029] The technical solution of this invention constructs a three-level hierarchical evaluation index system comprising "target layer, criterion layer, and indicator layer," breaking down the safety evaluation of energy storage systems into "overall safety and reliability target → four primary indicators: basic condition of energy storage battery / system operation status / safety management / working environment → 15 detectable and identifiable secondary indicators." This not only clarifies the hierarchical relationship between various influencing factors but also comprehensively covers the core dimensions affecting the safety of energy storage systems, such as the battery itself, system operation, management and protection, and the external environment. It completely changes the problem of traditional methods that only focus on single dimensions such as thermal runaway and mechanical failure, or simply list indicators without hierarchy, making the evaluation system more systematic and comprehensive.

[0030] In this embodiment of the invention, the specific description of constructing a hierarchical evaluation index system is as follows.

[0031] The Analytic Hierarchy Process (AHP) is a systematic and hierarchical multi-criteria decision-making method. It compares and judges the importance of each pair of indicators and establishes a judgment matrix. Furthermore, by calculating the maximum eigenvalue and corresponding eigenvector of the judgment matrix, it obtains the weights of the importance of different options, which can provide a basis for selecting the best option.

[0032] In the technical solution of this invention, the hierarchical evaluation index system adopts a hierarchical structure, dividing the evaluation objectives of the battery energy storage system into three levels: the target layer, the criterion layer, and the index layer. Among them, the target layer focuses on the safety and reliability of the battery energy storage system; the criterion layer establishes a risk evaluation index system for the battery energy storage system from four aspects, including: A. Basic condition of the energy storage battery, B. System operation status, C. Safety management, and D. Working environment; each criterion layer in the index layer contains more than three secondary indicators; for example, Table 1 shows the index system, weights, and core evaluation points, and some key indicators can be found in Table 1.

[0033] Table 1. Indicator System, Weights, and Core Evaluation Points

[0034] In this embodiment of the invention, for the safety evaluation of energy storage battery systems, a hierarchical indicator system covering four dimensions is created, namely "basic condition of energy storage battery - system operation status - safety management - working environment". The system clarifies the core evaluation standards of 15 specific indicators, which solves the problems of incompleteness and incomplete coverage of existing technical indicator systems. The composition and evaluation points of this indicator system are the core protection content.

[0035] Example 3 In this embodiment of the invention, based on the technical solutions disclosed in any of the above embodiments, the process of calculating the combined weights of the indicator layer to the target layer using the analytic hierarchy process (AHP) based on the hierarchical evaluation index system, and constructing a weight matrix based on the combined weights, is as follows: Based on the hierarchical relationship of the hierarchical evaluation index system, the importance of all index elements at the same level is compared pairwise, and the judgment matrix is ​​obtained by using the 1-9 scale method, which provides a basic matrix for subsequent weight calculation. The maximum eigenvalue is found based on the judgment matrix; the solution method is to solve the characteristic equation of the judgment matrix or to use the power method for iteration to obtain the maximum eigenvalue. A consistency check is performed on the largest eigenvalue, with CR < 0.1 as the passing standard. Only when the consistency check passes will the corresponding normalized weight vector be used as an effective weight for subsequent combined weight calculation, ensuring the rationality and reliability of the weight allocation. The derivation process for calculating the combined weights is as follows: The normalized weights of the indicator layer to its corresponding criterion layer are denoted as... W Q The normalized weights of the criterion layer to the target layer are denoted as... W P Through formula W R = W Q × W P The final combined weights of the indicator layer to the overall goal are obtained; wherein, the sum of the weights of all criterion layers to the goal layer is 1, and the sum of the weights of all secondary indicators of the indicator layer under each criterion layer is 1, ensuring the normalization characteristics of the combined weights and providing standardized weight data for the construction of the weight matrix.

[0036] In the technical solution of this invention embodiment, the specific process for determining indicator weights based on AHP is as follows: (1) Constructing a judgment matrix; among them, several experts with more than 10 years of experience in the design, manufacturing and operation of battery energy storage systems were invited to compare the importance of all elements in this layer according to the hierarchical structure of AHP, and the 1~9 scale method was used (1-equally important, 3-slightly important, 5-significantly important, 7-strongly important, 9-extremely important; 2, 4, 6, 8 represent the median values ​​of the above adjacent judgments) to obtain the judgment matrix, which is expressed as follows: ; in, express The judgment matrix of order, the number of matrix elements is n 2 indivual; The first in the matrix i Line 1 jColumn element, representing the first i The first indicator is relative to the first j The matrix satisfies the importance of each indicator. The matrix satisfies reflexivity The matrix satisfies reciprocity. .

[0037] (2) Maximum eigenvalue Calculation; whereby the judgment matrix is ​​solved using the eigenvalue method. Maximum eigenvalue For example, the calculation steps include: solving the characteristic equation Or obtained by exponential iteration Suitable for precise calculations; among them, the characteristic equation is first constructed, that is: for The judgment matrix of order Constructing a determinant And as the characteristic equation, I for n An identity matrix of order 1. To determine the matrix The eigenvalues; solve the characteristic equation, i.e., the judgment matrix. All eigenvalues ​​are roots of the nth-degree polynomial equation; the largest eigenvalue is selected from all eigenvalues, thus obtaining the largest eigenvalue. .

[0038] (3) Weight vector calculation; whereby, first calculate the geometric mean of each row of elements to obtain the geometric mean vector, and then normalize it to obtain the normalized weight vector; where, In calculating the geometric mean of each row of elements, the product of the elements in the i-th row of the judgment matrix A is multiplied and then the nth root is obtained. The final geometric mean vector is represented as follows: T ; ; In the formula, To determine the geometric mean vector of the elements in each row of a matrix; For the first i The geometric mean of the row elements; During the normalization process, Summing all elements and then dividing each element by the sum, the normalized weight vector is represented as follows: T ; ; In the formula, For the first i The weights of each indicator; interpretively, For the firsti The number of rows; The sum of all components of the eigenvector; in the AHP calculation logic, the row number of the matrix is ​​determined. i With indicator number i One-to-one correspondence.

[0039] A further preferred technical solution may also include: (4) consistency verification; wherein, To pass, the consistency check formula is: , As a consistency indicator, n To determine the order of a matrix, The largest eigenvalue of the matrix (and in step (2)) To maintain consistency, RI is the average random consistency index (values ​​can be found in Table 2). In this invention, the consistency ratio CR of each judgment matrix is ​​calculated to be less than 0.1, and the largest eigenvalue... The weighting results meet the requirements of rationality and are reliable.

[0040] Table 2. RI Values

[0041] (5) Determine the combined weights; whereby the weights calculated by the indicator layer for the criterion layer are... Named W Q The weights calculated by the criterion layer for the target layer. Named W P ; W Q and W P Multiplying these yields the final weights of the indicator layer relative to the overall objective, named [weight name missing]. W R , represented as: W R = W Q × W P ; W Q The weights of the indicator layer relative to the criterion layer. W P The weight of each metric in the criterion layer; the sum of the weights of all criterion layers is 1, for example, W PA +W PB +W PC +...=1; the sum of the weights of all indicators under each criterion layer is 1, that is... =1, =1, ..., where, Choose any index from criterion layer A to determine the basic condition of the energy storage battery.i The weights relative to criterion layer A; For any indicator in the B criterion layer of system operation status i The weights are relative to the criteria layer B; and so on.

[0042] In the technical solution of this invention, a full-process scientific weight calculation logic is designed based on the Analytic Hierarchy Process (AHP). First, a judgment matrix is ​​constructed by pairwise comparisons by senior experts using a 1-9 scale. Then, the maximum eigenvalue is solved, the normalized weight vector is calculated, and consistency checks verify the rationality of the weights. Finally, the combined weight is obtained by multiplying "the weight of the first-level indicator on the target layer × the weight of the second-level indicator on its corresponding first-level indicator" level by level. This process, based on a mathematical model, avoids the bias of subjective assignment in traditional methods, ensures the logical rationality of the weights through consistency checks, and allows the weights of indicators at different levels and dimensions to be accurately aggregated to the overall target, achieving scientific quantification of weights.

[0043] Example 4 Fuzzy comprehensive evaluation (FCE) is an evaluation method based on the principles of fuzzy mathematical system analysis and centered on fuzzy reasoning. This method combines qualitative and quantitative analysis, effectively solving the challenge of quantitative assessment in complex problems. Given the complex and significant interactions among various safety indicators of battery energy storage systems, which are difficult to quantify directly, FCE, through fuzzy theory, can transform these difficult-to-quantify indicators into operable quantitative indicators. This enables an objective assessment of the safety risks of battery energy storage systems, thereby improving the accuracy of safety management in energy storage power station operations.

[0044] In this embodiment of the invention, the step of determining the evaluation level based on the fuzzy comprehensive evaluation method in the index quantification and scoring process specifically includes: (1) Establish risk assessment set: In the safety risk assessment of battery energy storage system, the assessment level is divided into 5 levels according to the severity of safety risk: {very good, good, average, poor, very poor}, and the corresponding values ​​are (90~100, 70~90, 50~70, 30~50, 10 or 0~30), denoted as V={V1, V2, V3, V4, V5}, which provides a unified standard for membership determination and safety level determination.

[0045] (2) Using the AHP method, determine the degree of influence of each risk on the overall index, and then calculate the weight value of each risk to form a weight matrix W.

[0046] (3) Assess the risk level of each risk and use the fuzzy matrix R to represent the membership degree of each risk.

[0047] (4) Using the fuzzy comprehensive evaluation method, combining the weight matrix W and the fuzzy matrix R, the safety risk level of the battery energy storage system is calculated by the formula G=W×R.

[0048] In the technical solution of this invention, fuzzy comprehensive evaluation method is integrated. The actual detection data of secondary indicators are combined with the five-level risk assessment set to determine membership degrees, constructing a fuzzy relation matrix. Then, matrix multiplication is used to achieve coupled analysis of "combined weights (indicator importance) and membership degrees (actual indicator status)". This improved method transforms safety status indicators that are difficult to quantify directly into calculable quantitative values, upgrading the evaluation results from traditional "qualitative descriptions" to "quantified membership vectors." This solves the fuzziness and uncertainty problems of safety evaluation in complex systems, making the evaluation results more accurate.

[0049] This invention, for the first time, deeply integrates the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) for the safety evaluation of battery energy storage systems, forming a complete technical path of "scientific quantification of weights → fuzzy membership determination → comprehensive calculation and rating." Particularly noteworthy is the entire process of AHP weight calculation, from "judgment matrix construction to eigenvalue solving to consistency verification to combined weight derivation," and the adaptation logic between FCE and this weight system. Quantitative thresholds based on engineering practice are set for each evaluation indicator (such as battery consistency deviation, temperature difference, capacity retention rate, etc.), achieving a direct mapping between "measured data and scores," overcoming the bottleneck of traditional fuzzy evaluation's "primarily qualitative, insufficient quantification." This invention innovatively designs a dynamic scenario adaptation mechanism, which can adjust the weights of the criterion layer and indicator layer for different application scenarios such as low temperature, residential, and distributed systems (e.g., increasing the weight of temperature regulation indicators in low temperature scenarios), avoiding a "one-size-fits-all" evaluation. This dynamic weight adjustment method is a crucial protection point. This invention standardizes the entire process of "indicator system construction → expert weight determination → data collection → fuzzy matrix construction → comprehensive calculation → grade determination → operation and maintenance guidance", and clarifies the execution standards for each step (such as expert qualification requirements, consistency test thresholds, evaluation set grading standards, etc.). The uniqueness and operability of this standardized process are the protected content.

[0050] Example 5 In this embodiment of the invention, a safety status evaluation of a 100MWh energy storage power station in Henan Province is taken as an example; wherein, The application scenario is as follows: a 100MWh energy storage power station, using lithium iron phosphate batteries, with an operating life of 2 years, located outdoors, undertaking the tasks of photovoltaic energy storage and grid peak shaving; The process for safety status assessment using the method of this invention is as follows: 1) Determining AHP weights: Five experts in the energy storage field were invited to construct a judgment matrix. The calculated weights for the criterion layer are ω=[0.35,0.30,0.20,0.15]. The weights for the indicator layer are... A =[0.20,0.50,0.30]、ω B =[0.25,0.20,0.35,0.20] etc. (all satisfy CR<0.1); 2) FCE Evaluation: The evaluation team consisted of 3 technical experts and 2 testing personnel. Combining power station operation data (battery consistency deviation ≤5%, battery temperature difference ≤3℃), test reports (energy storage battery capacity retention rate 92%, no loose electrical connections), and on-site inspection (good ventilation, dust content meets standards), the membership degree of each indicator was determined, and a fuzzy relation matrix was constructed. After fuzzy comprehensive calculation, the target layer evaluation result vector B=[0.45,0.40,0.10,0.05], with the maximum membership degree corresponding to V1 (excellent). 3) Evaluation conclusion: The energy storage power station is in excellent safety condition with extremely low safety risk and can continue to operate normally. It is recommended to carry out operation and maintenance according to the regular cycle.

[0051] Example 6 In this embodiment of the invention, the safety status evaluation of an electric vehicle charging energy storage system (5MWh) is taken as an example; wherein, The application scenario is as follows: A 5MWh electric vehicle charging energy storage system in Beijing is installed near the company's internal parking lot. It uses lithium iron phosphate batteries, has a service life of 3 years, and is mainly used for charging electric vehicles. The process for safety status assessment using the method of this invention is as follows: 1) Determining AHP weights: Four experts were invited to construct a judgment matrix. The weights for the criterion layer were ω=[0.32,0.33,0.22,0.13], and the weights for the indicator layer were ω... C =[0.30,0.25,0.20,0.25] etc. (satisfying CR<0.1); 2) FCE Evaluation: Combining system operation data (battery health status SOH=85%, state of charge (SOC) fluctuation range 20%-80%), test results (capacity retention rate of individual battery modules 88%, battery consistency deviation 6%), and on-site investigation (temperature control system is operating normally, but fire extinguishers in the fire safety system are nearing their expiration date), the membership degree is determined and a fuzzy relation matrix is ​​constructed; after calculation, the target layer evaluation result vector B=[0.15,0.55,0.25,0.05], and the maximum membership degree corresponds to V2 (good); 3) Evaluation conclusion: The electric vehicle charging and energy storage system is in good safety condition with low safety risk. The core indicators meet the requirements. Fire extinguishers that are close to their expiration date need to be replaced. After that, it should be operated and maintained according to routine procedures.

[0052] Example 7 In this embodiment of the invention, a safety status evaluation of a residential energy storage system (10kWh) is taken as an example; wherein, Application scenario: A 10kWh household energy storage system is installed on the balcony of a residential building. It uses lithium iron phosphate batteries and has a service life of 1 year. It is used for household photovoltaic power generation storage and daily power supply. The process for safety status assessment using the method of this invention is as follows: 1) Determining AHP weights: Three experts were invited to construct a judgment matrix. The weights for the criterion layer were ω=[0.38,0.25,0.17,0.20], and the weights for the indicator layer were ω... D =[0.35,0.40,0.25] etc. (satisfying CR<0.1); 2) FCE Evaluation: Combining system operation data (battery temperature difference ≤2℃, SOH=95%), test results (energy storage battery appearance undamaged, electrical connection firm) and on-site investigation (balcony ventilation conditions average, dust content low), the membership degree was determined and a fuzzy relation matrix was constructed; after calculation, the target layer evaluation result vector B=[0.20,0.45,0.30,0.05], and the maximum membership degree corresponds to V2 (good); 3) Evaluation conclusion: The safety status of this household energy storage system is good, but the ventilation conditions on the balcony need to be improved (such as by adding a ventilation fan) to enhance the safety of the working environment.

[0053] Example 8 In this embodiment of the invention, a safety status evaluation of a low-temperature environment energy storage system (-20℃ operating condition, 10MWh) is taken as an example; wherein, The application scenario is as follows: a 10MWh energy storage system is installed in a high-latitude cold region, with an operating environment temperature as low as -20℃. It uses low-temperature modified lithium iron phosphate batteries, has an operating life of 1.5 years, and is used for wind power energy storage. The process for safety status assessment using the method of this invention is as follows: 1) Determining AHP weights: Five experts were invited to construct a judgment matrix. Considering the impact of low-temperature environment, the weights of the criterion layer were adjusted to ω=[0.30,0.28,0.22,0.20]. In the index layer weights, ω... D =[0.25,0.50,0.25] (temperature regulation weight increase) etc. (satisfying CR<0.1); 2) FCE Evaluation: Combining system operation data (battery temperature difference ≤4℃, battery consistency deviation 7%), test results (energy storage battery capacity retention rate 85%, safety linkage system response time ≤0.5s) and on-site investigation (temperature regulation system operating load 70%, ventilation condition average, dust content meets standards), the membership degree is determined and a fuzzy relation matrix is ​​constructed; after calculation, the target layer evaluation result vector B=[0.05,0.20,0.60,0.15], and the maximum membership degree corresponds to V3 (qualified); 3) Evaluation conclusion: The safety status of the low-temperature environment energy storage system is qualified, but there are certain safety risks. It is necessary to optimize the temperature regulation system (such as adding a heating device), improve ventilation conditions, reduce battery consistency deviation, and conduct a new evaluation after rectification.

[0054] In summary, the core technical logic of this invention is as follows: Using the Analytic Hierarchy Process (AHP) combined with Fuzzy Comprehensive Evaluation (FCE) as its core, and through a standardized process of "indicator system construction → AHP weight determination → quantitative scoring → comprehensive score calculation → safety and reliability evaluation," it achieves a quantitative assessment of the safety status of energy storage battery systems, overcoming the limitations and subjectivity of traditional evaluation methods. The hierarchical indicator system constructs a three-tiered architecture: "target layer (safety and reliability) - criterion layer - indicator layer." The criterion layer covers four core dimensions: basic energy storage battery condition, system operation status, safety management, and working environment. The indicator layer includes 15 specific indicators (such as battery appearance, consistency, fire safety system, temperature regulation, etc.), comprehensively covering key factors affecting system safety. In the weight determination mechanism, experts with over 10 years of experience are invited to construct a judgment matrix using a 1-9 scale. This is combined with the eigenvalue method to find the maximum eigenvalue, the square root method to calculate the weight vector, and a consistency check (CR < 0.1) to verify rationality. Finally, the combined weights are obtained by multiplying the criterion layer weights by the indicator layer weights, ensuring the objectivity and reliability of the weight allocation. A fuzzy quantitative evaluation method was adopted, establishing a 5-level evaluation set (Excellent / Good / Average / Poor / Very Poor, corresponding to score ranges of 90~100 / 70~90, etc.). By constructing a fuzzy relation matrix and calculating membership degrees, combined with the formula G=W×R, a comprehensive evaluation combining qualitative and quantitative methods was achieved, accurately reflecting the system's safety level. In summary, the evaluation steps of the technical solution of this invention are standardized, and the required data can be obtained through existing testing equipment and operation monitoring systems without the need for additional complex devices. It is applicable to various scenarios such as large-scale energy storage power stations, distributed energy storage systems, residential energy storage systems, and low-temperature environment energy storage systems. It is highly operable, has wide applicability, and high engineering value.

[0055] Example 9 The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0056] Please see Figure 2 In this embodiment of the invention, a safety status quantitative evaluation system for a battery energy storage system is provided, comprising: The indicator system construction module is used to construct a hierarchical evaluation indicator system based on the selected battery energy storage system. The hierarchical evaluation indicator system includes: a target layer, a criterion layer, and an indicator layer. The target layer is the safety and reliability of the battery energy storage system. The criterion layer sets up multi-dimensional primary indicators. The indicator layer sets up multiple secondary indicators corresponding to each primary indicator. The weight matrix acquisition module is used to calculate the combined weights of the indicator layer to the target layer based on the hierarchical evaluation index system using the analytic hierarchy process, and to construct a weight matrix based on the combined weights. The fuzzy relation matrix acquisition module is used to determine the membership degree of the actual detection data of the secondary indicators according to the numerical range of each safety level in the risk assessment set based on the fuzzy comprehensive evaluation method, and to construct the fuzzy relation matrix based on the determined membership degree. The evaluation result acquisition module is used to perform matrix multiplication operation between the weight matrix and the fuzzy relation matrix to obtain a comprehensive evaluation result vector, which is used as the quantitative evaluation result of the safety status of the battery energy storage system.

[0057] This invention constructs a hierarchical safety risk assessment system for battery energy storage systems, determines the safety and reliability levels of these systems, avoids the influence of subjective factors, and makes the assessment results more objective and reliable, providing scientific guidance for the safe operation of subsequent energy storage power stations. This invention is applicable to the safety status evaluation of lithium-ion, sodium-ion, and lead-acid (carbon) battery energy storage systems, and can provide quantitative basis for design improvements and operation and maintenance decisions for electrochemical energy storage power stations.

[0058] Example 10 In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer-readable storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a computer-readable storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used to execute the operation of a method for quantitatively evaluating the safety status of a battery energy storage system.

[0059] Example 11 In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for quantitatively evaluating the safety status of the battery energy storage system in the above embodiments.

[0060] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0061] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for quantitatively evaluating the safety status of a battery energy storage system, characterized in that, Includes the following steps: Based on the selected battery energy storage system, a hierarchical evaluation index system is constructed. The hierarchical evaluation index system includes: target layer, criterion layer and index layer. The target layer is the safety and reliability of the battery energy storage system. The criterion layer sets up multi-dimensional primary indicators. The index layer sets up multiple secondary indicators corresponding to each primary indicator. Based on the hierarchical evaluation index system, the combined weights of the index layer to the target layer are calculated using the analytic hierarchy process, and a weight matrix is ​​constructed based on the combined weights. Based on the fuzzy comprehensive evaluation method, the actual detection data of the secondary indicators are determined according to the numerical range of each safety level in the risk assessment set, and a fuzzy relation matrix is ​​constructed based on the determined membership degree. The weight matrix and the fuzzy relation matrix are multiplied to obtain a comprehensive evaluation result vector, which is used as the quantitative evaluation result of the safety status of the battery energy storage system.

2. The method for quantitatively evaluating the safety status of a battery energy storage system according to claim 1, characterized in that, The criteria layer includes multi-dimensional primary indicators such as: basic condition of energy storage batteries, system operation status, safety management, and working environment. In the aforementioned indicator layer, the secondary indicators for the basic condition dimension of energy storage batteries include: energy storage battery appearance, energy storage battery capacity, cycle performance, safety performance, and electrical connection status; the secondary indicators for the system operation status dimension include: battery consistency, battery temperature difference, battery health status, and battery state of charge; the secondary indicators for the safety management dimension include: battery management system, fire safety system, gas monitoring system, and safety linkage system; and the secondary indicators for the working environment dimension include: ventilation status, temperature regulation, and dust control. Each secondary indicator has corresponding preset core evaluation points.

3. The method for quantitatively evaluating the safety status of a battery energy storage system according to claim 1, characterized in that, Based on the hierarchical evaluation index system, the steps for calculating the combined weights of the index layer to the target layer using the analytic hierarchy process include: Based on the first-level indicators of the criterion layer and the second-level indicators of the indicator layer, the importance of each pair of indicators of the same level within each level is compared to construct the judgment matrix of the corresponding level. Based on the judgment matrices of each level, the maximum eigenvalue is solved, the normalized weight vector is calculated, and the consistency test is performed in sequence to obtain the weights of each first-level indicator of the criterion layer to the target layer and the weights of each second-level indicator of the indicator layer to its respective first-level indicator. The combined weights of the indicator layer on the target layer are obtained by multiplying the weights of each primary indicator in the criterion layer on the target layer with the weights of each secondary indicator in the indicator layer on its respective primary indicator.

4. The method for quantitatively evaluating the safety status of a battery energy storage system according to claim 3, characterized in that, Based on the first-level indicators of the criterion layer and the second-level indicators of the indicator layer, the importance of each pair of indicators of the same level within each level is compared. When constructing the judgment matrix of the corresponding level, the 1-9 scale method is used to obtain the judgment matrix. The maximum eigenvalue can be obtained by solving the characteristic equation of the judgment matrix or by using the power method for iteration. During the consistency check, the corresponding normalized weight vector is used as the effective weight for the combined weight calculation only if the consistency check passes.

5. The method for quantitatively evaluating the safety status of a battery energy storage system according to claim 3, characterized in that, The steps for calculating the normalized weight vector include: Based on the judgment matrix, the geometric mean of each row of elements is calculated to obtain the geometric mean vector of each row of elements in the judgment matrix; the geometric mean vector is then normalized to obtain the normalized weight vector. Among them, the calculation of the first i During the geometric mean of the row elements, the first row of the judgment matrix is... i Row elements, calculate the product and then open the root. n The power of 1 yields the 1st power. i Geometric mean of row elements n To determine the order of a matrix, during the normalization process, all elements of the geometric mean vector are summed, and then each element is divided by the sum to obtain the normalized weight vector.

6. The method for quantitatively evaluating the safety status of a battery energy storage system according to claim 3, characterized in that, In the steps of consistency verification, The consistency check passed. in, , CR represents the consistency ratio. As a consistency indicator, n To determine the order of a matrix, It is the largest eigenvalue.

7. The method for quantitatively evaluating the safety status of a battery energy storage system according to claim 1, characterized in that, In the step of performing matrix multiplication on the weight matrix and the fuzzy relation matrix to obtain a comprehensive evaluation result vector and using it as the quantitative evaluation result of the safety status of the battery energy storage system, after obtaining the comprehensive evaluation result vector, the safety and reliability level of the battery energy storage system is determined by matching a preset risk assessment set based on the maximum membership degree in the comprehensive evaluation result vector.

8. A quantitative evaluation system for the safety status of a battery energy storage system, characterized in that, include: The indicator system construction module is used to construct a hierarchical evaluation indicator system based on the selected battery energy storage system. The hierarchical evaluation indicator system includes: a target layer, a criterion layer, and an indicator layer. The target layer is the safety and reliability of the battery energy storage system. The criterion layer sets up multi-dimensional primary indicators. The indicator layer sets up multiple secondary indicators corresponding to each primary indicator. The weight matrix acquisition module is used to calculate the combined weights of the indicator layer to the target layer based on the hierarchical evaluation index system using the analytic hierarchy process, and to construct a weight matrix based on the combined weights. The fuzzy relation matrix acquisition module is used to determine the membership degree of the actual detection data of the secondary indicators according to the numerical range of each safety level in the risk assessment set based on the fuzzy comprehensive evaluation method, and to construct the fuzzy relation matrix based on the determined membership degree. The evaluation result acquisition module is used to perform matrix multiplication operation between the weight matrix and the fuzzy relation matrix to obtain a comprehensive evaluation result vector, which is used as the quantitative evaluation result of the safety status of the battery energy storage system.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for quantitative evaluation of the safety status of the battery energy storage system as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for quantitatively evaluating the safety status of the battery energy storage system as described in any one of claims 1 to 7.

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