Lithium ion battery energy storage system electrical safety grading evaluation method based on fuzzy analytic hierarchy process

Abstract

The invention discloses a lithium ion battery energy storage system electrical safety grading evaluation method based on a fuzzy analytic hierarchy process, and the method comprises the steps: S1, constructing a hierarchical structure index system which comprises a target layer, a criterion layer and an index layer, the criterion layer comprises system file data examination, key component electrical safety evaluation and system overall electrical safety evaluation; s2, establishing a fuzzy complementary judgment matrix of the criterion layer relative to the target layer and a fuzzy complementary judgment matrix of the index layer relative to the criterion layer, converting the fuzzy complementary judgment matrix into a fuzzy consistent matrix, calculating a weight value of each layer, and finally obtaining a final weight value of each index of the index layer relative to the target layer; s3, examining and evaluating system file data; s4, evaluating the electrical safety of the key component; s5, evaluating the overall electrical safety of the system; s6, calculating a final score of the to-be-evaluated system; and S7, performing system electrical safety grading evaluation. The evaluation method is rapid in evaluation and easy to operate.

Description

Technical Field

[0001] This invention belongs to the field of electrical safety assessment of electrochemical energy storage systems, specifically referring to an electrical safety evaluation index system for lithium-ion battery energy storage systems based on fuzzy analytic hierarchy process (FAHP) and a method for grading based on the evaluation results. Background Technology

[0002] Electrical safety assessment is a crucial approach to ensuring the stable operation and sustainable development of electrochemical energy storage systems. It is a systemic issue encompassing key components such as individual battery cells, battery modules, battery clusters, power conversion systems (PCS), battery management systems (BMS), energy management systems (EMS), and thermal management systems. Current research on the safety assessment of electrochemical energy storage systems is relatively limited. Most standards and studies focus on the components that make up the system, such as GB / T36276, IEC 62619, and IEC 63056 for batteries, GB / T 34131 for BMS, and GB / T 34120 and IEC 62477-1 for PCS. Standards for energy storage systems include IEC 62933-5-2, UL 9540, and GB / T44026, primarily focusing on basic safety and performance market access standards.

[0003] Patent document CN120317673 A discloses a method and system for safety assessment of energy storage systems. The method involves constructing a safety risk assessment index system for the energy storage system to be assessed and calculating the weights of each index; obtaining the cloud feature values ​​of each index based on the calculated weights; calculating the comprehensive cloud feature values ​​of each index based on these cloud feature values; generating a comprehensive evaluation cloud map and a secondary index evaluation cloud map based on the obtained comprehensive cloud feature values; and calculating the membership degree of the comprehensive evaluation cloud to each safety assessment level based on the comprehensive evaluation cloud map.

[0004] The patent document with publication number CN118050646A discloses a battery energy storage online safety assessment and risk warning positioning strategy based on the FAHP method. It is also aimed at online detection during the operation of the built energy storage system. The assessment is mainly based on two components: battery cells and battery modules, without a systematic evaluation.

[0005] Patent document CN117196308A discloses a method and apparatus for safety risk assessment of energy storage systems. The method involves acquiring the battery safety risk of the energy storage system, scoring various safety indicators of the energy storage power station to obtain a safety indicator standard matrix; obtaining a weighted positive matrix composed of risk assessment indicators of the energy storage power station based on the safety indicator standard matrix and the indicator weight matrix; standardizing the weighted positive matrix to obtain a standardized matrix; obtaining the positive and negative ideal values ​​of all risk assessment indicators of the energy storage power station based on the standardized matrix; obtaining a quantitative score for the risk level of the energy storage power station based on the positive and negative ideal values ​​of the risk assessment indicators; and conducting a risk level assessment based on the quantitative score of the risk level of the energy storage power station.

[0006] The patent document with publication number CN120317673 A mainly focuses on the on-site evaluation of electrochemical energy storage power stations. This invention is applied to the electrical design of lithium-ion battery energy storage systems for safety evaluation and classification, and is used for enterprise R&D pilot evaluation, product quality acceptance evaluation, and third-party testing agency evaluation.

[0007] The patent document with publication number CN118050646A mainly focuses on online detection during the operation of the built electrochemical energy storage system. The evaluation is mainly based on two components: battery cells and battery modules, without a systematic evaluation.

[0008] The patent document with publication number CN117196308 A mainly focuses on the assessment of various safety risk factors and safety status during the operation of lithium-ion battery energy storage power stations. Summary of the Invention

[0009] The purpose of this invention is to provide an electrical safety classification and evaluation method for lithium-ion battery energy storage systems based on fuzzy hierarchical analysis, including constructing an electrical safety evaluation index system and classifying the system according to the evaluation results. This evaluation method can quickly and systematically evaluate the electrical design of lithium-ion battery energy storage systems and is easy to operate.

[0010] This objective of the present invention is achieved through the following technical solution: a method for electrical safety classification and evaluation of lithium-ion battery energy storage systems based on fuzzy hierarchical analysis, characterized in that the evaluation method includes the following steps:

[0011] S1: Based on the fuzzy hierarchical analysis method, a hierarchical index system for electrical safety evaluation of lithium-ion battery energy storage system is constructed, including target layer O, criterion layer C and index layer P. Among them, the target layer is the electrical safety evaluation of lithium-ion battery energy storage system, and the criterion layer includes system document review C1, key component electrical safety evaluation C2 and overall system electrical safety evaluation C3.

[0012] S2: Establish fuzzy complementary judgment matrices for the criterion layer relative to the target layer and for the indicator layer relative to the criterion layer. Then, convert the fuzzy complementary judgment matrices into fuzzy consistent matrices, calculate the weight values ​​of each layer, and finally obtain the final weight values ​​of each indicator in the indicator layer relative to the target layer.

[0013] S3: System Documentation Review C1 Evaluation: Obtain information on the lithium-ion battery energy storage system and components to be evaluated from the information source, and score each indicator at the indicator level; each indicator must meet at least the basic condition of being qualified, otherwise the next step will not be carried out.

[0014] S4: Evaluation of electrical safety of key components C2; obtain samples of key components of the energy storage system from the information source, conduct tests on each indicator of the corresponding indicator layer, and score according to the test results; each indicator must meet the basic condition of being qualified, otherwise the next step will not be carried out;

[0015] S5: Evaluation of the overall electrical safety of the system (C3); obtain energy storage system samples from the information source, conduct tests on each indicator corresponding to the indicator layer, and score based on the test results; each indicator must meet at least the qualification requirement as a basic condition, otherwise the next step will not be carried out;

[0016] S6: Calculate the final score of the system to be evaluated by using the final weight values ​​of each indicator in the indicator layer relative to the target layer obtained in step S2 and the scores of each indicator obtained in steps S3-S5.

[0017] S7: Based on the final score obtained in step S6, conduct an electrical safety classification evaluation of the lithium-ion battery energy storage system according to the classification evaluation rules.

[0018] This invention's evaluation method targets lithium-ion battery energy storage systems, such as AC energy storage cabinets and DC containerized energy storage cabinets, not energy storage power stations built using such systems. Based on fuzzy hierarchical analysis, it constructs an electrical safety evaluation index system for lithium-ion battery energy storage systems. It proposes a multi-dimensional method for evaluating and classifying the electrical safety of lithium-ion battery energy storage systems, combining system document review with testing, and considering key components to the entire system. This method is both economical (precise testing items to avoid unnecessary testing costs) and timely (system document review, accepting type test results of key components). Based on the evaluation index results and weighting coefficients of each dimension, this method can derive a total safety evaluation score for the lithium-ion battery energy storage system and then perform a graded evaluation.

[0019] In this invention, step S2 specifically includes the following calculation process:

[0020] S21: Establishing the fuzzy complementary judgment matrix: Invite a set number of experts in the field of electrochemical energy storage to compare the two elements using the 0.1-0.9 scaling method to obtain the fuzzy complementary judgment matrix. The 0.1-0.9 scaling method is shown in Table 1.

[0021] Table 1: Explanation of the 0.1-0.9 Scale Method

[0022]

[0023] Fuzzy complementary judgment matrix

[0024] In the formula:

[0025] R represents elements a1, a2, ..., a3 at the same level. n A fuzzy complementary judgment matrix for pairwise comparison of importance;

[0026] r ij Representing element a i and element a j Compared to the importance of the previous level of objectives, r ij +r ji =1;

[0027] S22: The fuzzy complementary judgment matrix is ​​transformed into a fuzzy consistent matrix using the row sum normalization method. The formula for calculating the row sum is as follows:

[0028]

[0029] Further calculations of the elements of the fuzzy uniform moments of the transformation are as follows: Where n is the matrix order;

[0030] The transformed fuzzy consistency matrix is: R'=(r′) ij ) n×n ;

[0031] Furthermore, by examining r′ ij =1-r′ ji The validity of the fuzzy consistency matrix is ​​verified by checking whether it holds true.

[0032] S23: Calculate the weights W of the fuzzy consistency matrix R′ using the row sum normalization method. i First, calculate the sum of the elements in each row of the fuzzy consistency matrix R′. Then, normalize the row sums to obtain the formula for calculating the weight of a single-layer index.

[0033] S24: Based on the method of steps S21-S23, first calculate the weight of the criterion layer relative to the target layer, then calculate the weight of the indicator layer relative to the criterion layer, and calculate the final weight value of each indicator in the indicator layer relative to the target layer using the following formula.

[0034] w i =w Ci ×w Pi ;

[0035] In the formula:

[0036] w i This refers to the weight value of a certain indicator relative to the target layer.

[0037] w Ci This represents the weight of the criterion layer relative to the target layer.

[0038] w Pi This is the weight value of the indicator relative to the criterion layer.

[0039] In this invention, in step S4, the key component samples include battery module, battery cluster, BMS and PCS samples.

[0040] In this invention, the grading evaluation rule in step S7 is as follows: the evaluation results are divided into three levels: qualified, good and excellent according to the score, wherein the score [60, 80) is the qualified level, the score [80, 90) is the good level, and the score [90, 100] is the excellent level.

[0041] This invention constructs an electrical safety evaluation index system for lithium-ion battery energy storage systems based on fuzzy hierarchical analysis. By scoring each index and combining the weight values ​​of each index, the overall evaluation score of the energy storage system is obtained, and the energy storage system is classified according to the classification evaluation rules.

[0042] Compared with existing technologies, the present invention has the following significant advantages: 1. It scientifically addresses the ambiguity and subjectivity in evaluation; 2. It ensures the logical consistency of judgment through mathematical transformation; 3. It systematically quantifies multi-level and multi-dimensional indicators; 4. It provides quantitative basis for graded evaluation based on electrical safety risks. Attached Figure Description

[0043] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0044] Figure 1 A schematic diagram of the hierarchical index system constructed for the electrical safety classification and evaluation method of this invention;

[0045] Figure 2 This is a flowchart of the electrical safety classification and evaluation method for lithium-ion battery energy storage systems according to the present invention. Detailed Implementation

[0046] A specific embodiment of the present invention takes a containerized DC lithium-ion battery energy storage system as an example to conduct an electrical safety classification evaluation. The evaluation process is as follows: Figure 2As shown.

[0047] First, based on the analysis of historical accidents involving lithium-ion battery energy storage systems both domestically and internationally, and research on electrical safety standards, an evaluation index system is established with the goal of evaluating the electrical safety of lithium-ion battery energy storage systems, such as... Figure 1 As shown.

[0048] Table 2 shows the descriptions of each indicator in the indicator layer. Each indicator layer is divided into Level I, Level II, and Level III indicators, with scores of 100, 80, and 60 respectively for meeting the corresponding indicator requirements.

[0049] Table 2: Description of each indicator in the indicator layer.

[0050]

[0051]

[0052]

[0053]

[0054] Furthermore, the voltage range / P31 and temperature range / P32 mentioned in Table 2 are calculated based on the cell voltage and temperature data collected by the BMS system, using the following formulas:

[0055] Voltage range U r =U max -U min ,

[0056] In the formula:

[0057] U max This represents the maximum voltage of a single battery cell in the system, measured in volts (V).

[0058] U min This represents the maximum voltage of a single battery cell in the system, measured in volts (V).

[0059] Temperature range T r =T max -T min ,

[0060] In the formula:

[0061] T max This refers to the system's maximum battery temperature, expressed in degrees Celsius (°C).

[0062] T min This is the minimum battery temperature of the system, expressed in degrees Celsius (°C).

[0063] The second step is to confirm the weight of each indicator in the indicator system.

[0064] A set number (five in this example) of experts in the field of electrochemical energy storage were invited to compare the indicators of each criterion layer and index layer using the 0.1-0.9 scaling method based on the basic data of the provided energy storage system and the experts' professional experience, so as to obtain the corresponding fuzzy complementary judgment matrix.

[0065] The fuzzy complementary judgment matrix of the criterion layer relative to the target layer:

[0066]

[0067] Fuzzy complementary judgment matrices of C1, C2, and C3 index layers relative to the criterion layer:

[0068]

[0069]

[0070] The fuzzy complementary judgment matrix is ​​transformed into a fuzzy consistent matrix using the row sum normalization method:

[0071]

[0072] All checks satisfy r′ ij =1-r′ ji .

[0073] The weights w of the fuzzy consistency matrix R′ are calculated using the row sum normalization method. i First, calculate the sum of the elements in each row of the fuzzy consistency matrix R'. Then, normalize the row sums to obtain the formula for calculating the weight of a single-layer index.

[0074] The weights of the indicators at each level were calculated as follows:

[0075] w O,i = [0.2778 0.3778 0.3444];

[0076] w C1,i = [0.2875 0.2625 0.2125 0.2375];

[0077] w C2,i = [0.1935 0.1637 0.1874 0.1736 0.1511 0.1307];

[0078] w C3,i = [0.2969 0.2719 0.2219 0.2093];

[0079] The final weight values ​​of each indicator in the indicator layer relative to the target layer are calculated using the following formula, as shown in Table 3.

[0080] W i =W Ci ×W Pi ;

[0081] In the formula:

[0082] W i This refers to the weight value of a certain indicator relative to the target layer.

[0083] W Ci This represents the weight of the criterion layer relative to the target layer.

[0084] W Pi This is the weight value of the indicator relative to the criterion layer;

[0085] Table 3: Final weight values ​​of each indicator relative to the target layer.

[0086] Indicator Code weight value Indicator Code weight value P11 0.0799 P24 0.0656 P12 0.0729 P25 0.0571 P13 0.0590 P26 0.0494 P14 0.0660 P31 0.1023 P21 0.0731 P32 0.0936 P22 0.0618 P33 0.0764 P23 0.0708 P34 0.0721

[0087] The third step is the evaluation of criterion layer C.

[0088] Information on the lithium-ion battery energy storage system and components to be evaluated was obtained from the information source, and scores were assigned to each indicator level. The results are shown in Table 4.

[0089] Table 4: Evaluation scores of each indicator on the indicator side.

[0090]

[0091]

[0092] Furthermore, based on the weights and scores of each indicator in the indicator layer, the final score of this lithium-ion battery system was calculated to be 89.64.

[0093] The fourth step is to evaluate the lithium-ion battery system as good if the score is in the range of [80, 90) according to the grading rules.

Claims

1. A method for electrical safety classification and evaluation of lithium-ion battery energy storage systems based on fuzzy hierarchical analysis, characterized in that, The evaluation method includes the following steps: S1: Based on the fuzzy hierarchical analysis method, a hierarchical index system for electrical safety evaluation of lithium-ion battery energy storage system is constructed, including target layer, criterion layer and index layer. Among them, the target layer is the electrical safety evaluation of lithium-ion battery energy storage system, and the criterion layer includes system document review, key component electrical safety evaluation and overall system electrical safety evaluation. S2: Establish fuzzy complementary judgment matrices for the criterion layer relative to the target layer and for the indicator layer relative to the criterion layer. Then, convert the fuzzy complementary judgment matrices into fuzzy consistent matrices, calculate the weight values ​​of each layer, and finally obtain the final weight values ​​of each indicator in the indicator layer relative to the target layer. S3: Evaluation of system document review: Obtain information on the lithium-ion battery energy storage system and components to be evaluated from the information source, and score each indicator level; each indicator must meet the basic condition of being qualified, otherwise the next step will not be carried out. S4: Evaluation of electrical safety of key components: Obtain samples of key components of the energy storage system from the information source, conduct tests on each indicator of the corresponding indicator layer, and score according to the test results; each indicator must meet the basic condition of being qualified, otherwise the next step will not be carried out. S5: Evaluation of overall electrical safety of the system: Obtain energy storage system samples from the information source, conduct tests on each indicator at the corresponding indicator layer, and score based on the test results; each indicator must meet at least the qualified indicator as a basic condition, otherwise the next step will not be carried out; S6: Calculate the final score of the system to be evaluated by using the final weight values ​​of each indicator in the indicator layer relative to the target layer obtained in step S2 and the scores of each indicator obtained in steps S3-S5. S7: Based on the final score obtained in step S6, conduct an electrical safety classification evaluation of the lithium-ion battery energy storage system according to the classification evaluation rules.

2. The method for electrical safety classification and evaluation of lithium-ion battery energy storage systems based on fuzzy hierarchical analysis as described in claim 1, characterized in that, Step S2 specifically includes the following calculation process: S21: Establishing a fuzzy complementary judgment matrix: Invite a set number of experts in the field of electrochemical energy storage to compare the two elements using the 0.1-0.9 scaling method to obtain the fuzzy complementary judgment matrix. Fuzzy complementary judgment matrix In the formula: R represents elements a1, a2, ..., a3 at the same level. n A fuzzy complementary judgment matrix for pairwise comparison of importance; r ij Representing element a i and element a j Compared to the importance of the previous level of objectives, r ij +r ji =1; S22: The fuzzy complementary judgment matrix is ​​transformed into a fuzzy consistent matrix using the row sum normalization method. The formula for calculating the row sum is as follows: Further calculations of the elements of the fuzzy uniform moments of the transformation are as follows: Where n is the matrix order; The transformed fuzzy consistency matrix is: R'=(r′) ij ) n×n ; Furthermore, by examining r′ ij =1-r′ ji The validity of the fuzzy consistency matrix is ​​verified by checking whether it holds true. S23: Calculate the weights W of the fuzzy consistency matrix R′ using the row sum normalization method. i First, calculate the sum of the elements in each row of the fuzzy consistency matrix R′. Then, normalize the row sums to obtain the formula for calculating the weight of a single-layer index. S24: Based on the method of steps S21-S23, first calculate the weight of the criterion layer relative to the target layer, then calculate the weight of the indicator layer relative to the criterion layer, and calculate the final weight value of each indicator in the indicator layer relative to the target layer using the following formula. In i =in Ci ×w Pi ; In the formula: w i This refers to the weight value of a certain indicator relative to the target layer. w Ci This represents the weight of the criterion layer relative to the target layer. w Pi This represents the weight value of the indicator relative to the criterion layer.

3. The method for electrical safety classification and evaluation of lithium-ion battery energy storage systems based on fuzzy hierarchical analysis as described in claim 1, characterized in that: In step S4, the key component samples include battery module, battery cluster, BMS and PCS samples.

4. The method for electrical safety classification and evaluation of lithium-ion battery energy storage systems based on fuzzy hierarchical analysis as described in claim 1, characterized in that, The grading evaluation rule in step S7 is as follows: the evaluation results are divided into three levels: qualified, good and excellent according to the score. Among them, the score [60, 80) is the qualified level, the score [80, 90) is the good level, and the score [90, 100] is the excellent level.

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