Lithium battery assembly risk analysis method

Abstract

The invention discloses a lithium battery assembly risk analysis method. The method comprises the following steps: S1, evaluating the severity, occurrence frequency and detection degree of a failure mode in lithium battery assembly by using a double-layer hesitant fuzzy language term set; wherein the double-layer hesitant fuzzy language term set comprises a double-layer language term set and a hesitant fuzzy language term set, and the double-layer language term set and the hesitant fuzzy language term set are both composed of evaluation information of evaluation personnel; s2, calculating an evaluation personnel weight based on similarity measurement, and calculating influence factor weights of severity, occurrence frequency and detection degree based on a water injection principle; s3, aggregating evaluation information of evaluation personnel by adopting a combined compromise sorting method, and calculating a failure mode risk value; s4, determining optimal evaluation information through a multi-target aggregation technology; according to the method, the main layer expresses basic semantics and the auxiliary layer modifies the degree, so that the effects of accurately describing the risk degree and avoiding misjudgment of an extreme value are achieved. Meanwhile, the semantic constraint rule is used for eliminating logic conflicts and improving the expression efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of quality management, and in particular to a lithium battery assembly risk analysis method. Background Art

[0002] In recent years, with the surge in sales of new energy vehicles, demand for lithium batteries has also increased significantly. To meet market demand, lithium battery manufacturers have expanded production capacity and improved efficiency. Compared to other types of batteries, lithium battery manufacturing involves more automated processes and higher precision control. Failures in lithium battery processing can severely impact production quality and efficiency. Therefore, before installing a lithium battery production line, a quality analysis of the production line design is necessary. This helps identify potential failure modes and implement preventive measures for high-risk failure modes.

[0003] In the existing technology, the FMEA method is suitable for the analysis and evaluation of potential failure modes in the product manufacturing stage. The FMEA method analyzes the structure tree and function tree of the product to obtain the potential failure modes and failure consequences of the product, and then forms an evaluation team to evaluate the severity (S), frequency (O) and detectability (D) of the failure mode using integers from 1 to 10. Finally, according to Calculate the RPN value of the failure mode and then determine the risk level of the failure mode based on the value. Therefore, it can be seen that FMEA is simple to operate and easy to promote, so it is widely used in various industries, such as industry, medical care, logistics, etc. However, with the development of informatization, the application of FMEA in complex and uncertain environments has exposed some shortcomings: First, the use of integers as an evaluation language is unrealistic. In actual production, it is unrealistic for evaluators to accurately assess the consequences of failure modes, especially during the product design phase. Therefore, using integers to evaluate failure mode risks fails to capture the evaluators' hesitation during assessment.

[0004] Second, the weighting of evaluators is neglected. When conducting an FMEA, there are often multiple evaluators, often from various departments with varying roles, work experience, educational background, and other objective factors. This leads to different perceptions of failure modes and, consequently, different evaluation values. Therefore, assigning different weights to evaluators of varying experience ensures that the final evaluation results are realistic.

[0005] Third, evenly distribute the weights of influencing factors. Traditional FMEA doesn't consider the weights of influencing factors when calculating RPN. However, in practice, for the same RPN value, failure modes with higher severity are often prioritized for prevention. This means that in practice, severity is given a higher weight. Therefore, evenly distributing the weights of influencing factors can lead to inaccurate risk assessment results.

[0006] Fourth, the effectiveness of RPN in guiding risk prevention is low. In traditional FMEAs, RPN is the cumulative product of the influencing factor evaluation values. This can easily result in multiple failure modes receiving the same RPN value, meaning they share the same risk level. Consequently, preventive measures must be developed for all of these failure modes, defeating the purpose of conducting an FMEA. Summary of the Invention

[0007] The technical problems solved by the present invention are: limitations in evaluation semantics, unreasonable weight distribution, repeated risk ranking, cumulative RPN values ​​leading to the same scores for multiple failure modes, lack of verification methods, and lack of objective standards for comparing the advantages and disadvantages of different optimization methods.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: A lithium battery assembly risk analysis method comprises the following steps: Step S1, using a double-layer hesitant fuzzy language term set to evaluate the severity, frequency, and detectability of failure modes in lithium battery assembly; wherein the double-layer hesitant fuzzy language term set includes a double-layer language term set and a hesitant fuzzy language term set, both of which are composed of evaluation information of evaluators; Step S2: Calculate the weight of the evaluator based on the similarity measure, and calculate the weight of the influencing factors of severity, frequency of occurrence, and detection based on the water injection principle; Step S3, using a combined compromise ranking method to aggregate the evaluation information of the evaluators and calculate the failure mode risk value; Step S4: Determine the optimal evaluation information through multi-objective aggregation technology.

[0009] As a preferred embodiment of the lithium battery assembly risk analysis method of the present invention, wherein: In step S1, the dual-layer language terminology set It includes the main language term set and the sub-language term set, and its expression is as follows: ; The main language terminology Used to describe basic semantics, including the following expressions: ; The secondary language terminology Used to modify semantic strength, including the following expressions: ; in, is the boundary parameter of the main language term set, The index value of the main language term in the main language term set. is the boundary parameter of the sub-layer language term set, It is the index value of the secondary language term in the primary language term set.

[0010] As a preferred embodiment of the lithium battery assembly risk analysis method of the present invention, wherein: In step S1, the expression of the hesitant fuzzy language term set is as follows: ; in, is a finite, ordered set of continuous language terms on S3, is a language terminology set, , is a non-empty set, yes An element in exist middle, It represents an element in the hesitant fuzzy language term set, and its expression is as follows: ; in, a finite and continuous set of linguistic terms; The hesitant fuzzy language term set is converted into membership degree through the conversion function.

[0011] As a preferred embodiment of the lithium battery assembly risk analysis method of the present invention, wherein: In step S2, the steps for calculating the evaluator weights include: Step S211, obtaining the membership of the evaluator in the failure mode influencing factors, calculating the non-membership, and generating an ideal solution; Step S212, calculating the Euclidean distance between the evaluator and the ideal solution; Step S213, calculating the similarity between the evaluator and the ideal solution; Step S214: Calculate the evaluator weight based on the similarity.

[0012] As a preferred embodiment of the lithium battery assembly risk analysis method of the present invention, wherein: In step S2, the calculation steps of the influencing factor weights include: Step S221, calculating the mean and variance of the evaluation matrix; Step S222, calculating the injection level; Step S223: Calculate the weight of the influencing factors based on the water level.

[0013] As a preferred embodiment of the lithium battery assembly risk analysis method of the present invention, wherein: The execution of step S3 includes the following steps: Step S311: Based on the evaluator weights, the evaluation information is aggregated by a simple intuitionistic fuzzy weighted geometric operator to obtain the membership and non-membership of the evaluator; Step S312, calculating the weighted sum and weighted product of the evaluation information to obtain the integration result of the evaluation information; Step S313, calculating the intermediate risk value using three scoring strategies; Step S314: Calculate the intermediate risk values ​​obtained by the three scoring strategies to obtain a final risk assessment value.

[0014] As a preferred embodiment of the lithium battery assembly risk analysis method of the present invention, wherein: In step S313, the formulas of the three scoring strategies are as follows: ; ; ; in, is the intermediate risk value of the first scoring strategy, is the intermediate risk value of the second scoring strategy, is the middle risk value of the third scoring strategy; is the weighted sum integration, is the weighted product; is the tendency factor.

[0015] As a preferred embodiment of the lithium battery assembly risk analysis method of the present invention, wherein: In step S314, the calculation formula of the final risk assessment value is as follows: ; in, is the final risk assessment value.

[0016] As a preferred embodiment of the lithium battery assembly risk analysis method of the present invention, wherein: The execution of step S4 includes the following steps: Step S41, summarizing the methods of ranking failure modes in the risk ranking, generating a corresponding set, and calculating the cumulative number of occurrences of the failure mode in the risk ranking; Step S42, based on the different evaluation information of the failure mode under the influencing factors by the evaluators, the evaluation value actually participating in the final risk calculation of the corresponding evaluation information is calculated; Step S43, setting an allocation matrix according to the weight of the risk ranking; Step S44: Setting a multi-objective mathematical model, the formula of its objective function is as follows: ; ; The constraints are as follows: ; in, represents the number of failure modes, ; Indicates the number of influencing factors, ; Indicates the number of evaluators, ; represents the risk ranking, ; To optimize the number of methods, ; Failure mode Ranking in risk order The cumulative number of occurrences, among which, ; The weight of the risk ranking is expressed as is the element in the allocation matrix; Failure Mode Ranked in risk ranking The method set above, Failure Mode Ranking in risk order The number of times it appears on Representation optimization method Medium influencing factors The weight of Representation optimization method Middle-level evaluators The weight of For evaluators Failure Mode Influencing factors Evaluation information below; ;and For evaluation information The scoring function of ; The advantages and disadvantages of the optimization methods are judged and the optimal evaluation information is determined by the difference between the calculation results of different failure mode optimization methods and the optimal solutions.

[0017] As a preferred embodiment of the lithium battery assembly risk analysis method of the present invention, wherein: In step S44, the calculation formula of the difference is as follows: ; in, Representation optimization method Failure Mode The sorting, Failure modes solved for multi-objective models Optimal sorting, Representation optimization method Failure Mode Influencing factors Evaluation information, Failure modes solved for multi-objective models Influencing factors Best evaluation information, for The maximum value of for The maximum value of .

[0018] Beneficial effects of the present invention: First, the lithium battery assembly risk analysis method provided by this invention accurately captures expert hesitation through the dual-layer language structure of DHHFLTS (a primary layer expressing basic semantics, and a secondary layer expressing the degree of modification). This allows for a more accurate description of risk levels and avoids misjudgment of extreme values. Furthermore, semantic constraint rules can eliminate logical conflicts and improve expression efficiency.

[0019] Second, the lithium battery assembly risk analysis method provided by this invention completely resolves the issue of duplicate RPN values ​​by integrating three strategies. First, it uses weighted sums and weighted products to enhance global and local features, respectively. Then, it uses three scoring strategies for cross-validation to adapt the propensity factors to different decision preferences. Finally, a hybrid geometric and arithmetic calculation ensures unique ranking. This method effectively distinguishes different failure modes and significantly reduces the rate of numerical conflicts. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of a lithium battery assembly risk analysis method provided by the present invention; Figure 2 A schematic diagram of the structure of a double-layer language terminology set in a lithium battery assembly risk analysis method provided by the present invention; Figure 3 A distribution diagram of the sub-layer language terminology set in a lithium battery assembly risk analysis method provided by the present invention; Figure 4 A schematic diagram of a risk analysis optimization process in a lithium battery assembly risk analysis method provided by the present invention; Figure 5 This is a schematic structural diagram of the square lithium battery provided by the present invention; Figure 6 A processing flow chart of the square lithium battery provided by the present invention; Figure 7This is a flow chart of the assembly workshop process of the square lithium battery provided by the present invention; Figure 8 A schematic diagram of the ultrasonic welding station function tree provided by the present invention; Figure 9 A schematic diagram of the ultrasonic welding station failure network provided by the present invention; Figure 10 Schematic diagram of the laser welding station and battery gluing machine failure network provided by the present invention; Figure 11 A schematic diagram of experimental data of the Pareto frontier solved by the multi-objective mathematical model provided by the present invention; Figure 12 This is a schematic diagram of experimental data for ranking failure modes under different tendency factors provided by the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0022] Since FMEA uses integers as the evaluation language, it is not realistic. In the actual production process, it is unrealistic for evaluators to accurately evaluate the consequences of failure modes, especially in the product design stage. Therefore, using integers to evaluate the risk of failure modes cannot reflect the hesitation of evaluators during the evaluation. Reference Figure 1 To solve the above problems, the present invention provides a lithium battery assembly risk analysis method, comprising the following steps: Step S1, using a double-layer hesitant fuzzy language term set to evaluate the severity, frequency, and detectability of failure modes in lithium battery assembly; wherein the double-layer hesitant fuzzy language term set includes a double-layer language term set and a hesitant fuzzy language term set, both of which are composed of evaluation information of evaluators; Step S2: Calculate the weight of the evaluator based on the similarity measure, and calculate the weight of the influencing factors of severity, frequency of occurrence, and detection based on the water injection principle; Step S3, using a combined compromise ranking method to aggregate the evaluation information of the evaluators and calculate the failure mode risk value; Step S4: Determine the optimal evaluation information through multi-objective aggregation technology.

[0023] In step S1, the dual-layer language terminology set includes a primary-layer language terminology set and a secondary-layer language terminology set, and its expression is as follows: ; The main language terminology set is used to describe basic semantics, including the following expressions: ; The sub-layer language terminology set is used to modify semantic intensity, including the following expressions: ; in, is the boundary parameter of the main language term set, is the index value (subscript) of the main language term in the main language term set, is the boundary parameter of the sub-layer language term set, It is the index value (subscript) of the secondary language term in the primary language term set.

[0024] For example, this application order hour, , ; At this point, the schematic diagram of the two-layer language terminology set is as follows Figure 2 As shown, Figure 2 middle express and Two language terminology sets.

[0025] At the same time, in order to avoid semantic logic problems, when the main language terminology set is , the sub-layer language terminology set is then taken In order to avoid duplication of semantic expressions, this application makes the following provisions for the use of the double-layer language terminology set, the distribution diagram of which is shown as follows: Figure 3 shown.

[0026] 1. and hour, ; 2. and hour, ; 3. hour, ; 4. hour, .

[0027] Furthermore, in step S1, the hesitant fuzzy language term set The expression is as follows: ; in, is a finite, ordered set of continuous language terms on S, is a language terminology set, , is a non-empty set, yes An element in exist middle, It represents an element in the hesitant fuzzy language term set, and its expression is as follows: ; in, A finite and continuous set of linguistic terms; for example, express Affiliated to The probability of and between.

[0028] The hesitant fuzzy language term set is converted into membership through the conversion function and used to participate in the calculation of the final evaluation result. is a language terminology set, For the hesitant and vague elements, Indicates the number of language term sets. If the membership and in If they express the same semantics, they can be converted to each other through conversion functions. is expressed as "very high", while , can also be expressed as "very high", so they can be converted into each other.

[0029] Specifically, the expression of the conversion function is as follows: ; ; At the same time, the transformation function is extended to the entire language term set With hesitant blurred elements When , its expression is as follows: ; ; in, for The boundary parameters within For hesitant and vague language terms in The index value (subscript) in ; is the converted membership value, which is a real number in the interval [0,1].

[0030] Finally, based on the bilingual term set and the hesitant fuzzy term set, the DHHFLTS concept is formed, and its expression is as follows: ; , where the meaning of each symbol in the expression is the same as that of the symbols in the above expression.

[0031] Furthermore, a conversion function is used to convert DHHFLTS into membership degree, which is used to participate in the judgment of the final failure mode risk level.

[0032] For example, suppose is a hesitant fuzzy element, then in DHHFLTS, the conversion function expression is as follows: ;

[0033] Further extend the transformation function to the entire hesitant fuzzy language element With hesitant blurred elements , the conversion function expression is as follows: ; ; The meanings of the symbols in the expressions are the same as those in the above expressions; When DHHFLTS participates in the calculation, it is necessary to follow the calculation rules. and are two DHHFLTS, and , then the operation rule expression is as follows: ; ; ; ; in, and The meaning is as shown above. is a constant, , and They are and The hesitant and vague elements.

[0034] Furthermore, For evaluators Failure Mode Influencing factors The evaluation information under this condition can be divided into two parts according to the calculation process of SM-WFT: one is to determine the weight of the evaluator, and the other is to calculate the weight of the influencing factors.

[0035] Specifically, in step S2, the first part of the calculation steps for the evaluator weights includes: Step S211, obtaining the membership of the evaluator in the failure mode influencing factors, calculating the non-membership, and generating an ideal solution; It is assumed that the evaluator In failure mode Influencing factors The membership degree in is The specific calculation can be done by the above formula Calculated, the non-membership degree is Then, the evaluation matrix can be written as .

[0036] The ideal solution is calculated as follows: ; in, represents the number of failure modes, ; Indicates the number of influencing factors, ; Indicates the number of evaluators, .

[0037] Step S212: Calculate the Euclidean distance between the evaluator and the ideal solution Specifically, the Euclidean distance between the evaluator and the ideal solution is calculated as follows: ; Step S213: Calculate the similarity between the evaluator and the ideal solution Specifically, the formula for calculating the similarity between the evaluator and the ideal solution is as follows: ; in, represents the complement of the ideal solution, .

[0038] Step S214: Calculate the weight of the evaluator based on the similarity Specifically, the calculation formula for the evaluator weight is as follows: ; The meanings of the symbols in the expression are the same as those in the above expression.

[0039] Specifically, the water injection principle refers to the optimization of communication power allocation. If the channel conditions are good, then a high communication power is allocated; otherwise, a low communication power is allocated. At this time, when calculating the weights of influencing factors in FMEA in this application, if the evaluators have a high degree of consensus on the influencing factors of the same failure mode, then a high weight is allocated; otherwise, a low weight is allocated. Furthermore, in step S2, the second part of the calculation steps for the weights of influencing factors includes: Step S221, calculate the evaluation matrix The mean and variance ; Specifically, the calculation formula is as follows: ; ; in, represents the score function, Failure Mode Influencing factors The evaluation information mean matrix of the evaluators is as follows: Influencing factors The following is the situation where the evaluators reach a consensus.

[0040] Step S222, further according to the water injection principle and the mean value obtained and variance , calculate the injection level, the calculation formula is as follows: ; in, ; The remaining symbols have the same meaning as above.

[0041] Step S223: Calculate the weight of the influencing factors based on the water level. The calculation formula is as follows: ; in, , the rest of the symbols have the same meaning as above.

[0042] Furthermore, the execution of step S3 includes the following steps: In step S311, based on the evaluator weights, the evaluation information is aggregated using the Simple Intuitionistic Fuzzy Weighted Geometric (SIFWG) operator to obtain the evaluator's membership and non-membership. The advantage of using the SIFWG operator is that it can ensure the consistency of the evaluator's evaluation information. The specific expression is as follows: ; in, For evaluators The weight of Calculated. They represent the membership and non-membership of the matrix after SIFWG operator aggregation respectively; since this application can consider two integration methods, weighted sum and weighted product, and then accurately evaluate the final results through three scoring strategies on this basis, the possibility of the same failure mode score occurring can be reduced.

[0043] Therefore, in step S312, the weighted sum and weighted product of the evaluation information are calculated to obtain the integrated result of the evaluation information; the weighted sum expression and the weighted product expression are as follows: ; ; in, Represents the aggregated matrix The score function of .

[0044] Step S313, calculate the intermediate risk value through three scoring strategies; specifically, after the evaluation information integration result is obtained, Calculate the three scoring strategies, the expressions are as follows: ; ; ; in, Represents the tendency factor, which represents the When , it means that the evaluators tend to use weighted and integrated methods; and when When , it means that the evaluator tends to use the weighted cumulative approach. .

[0045] Step S314: Calculate the intermediate risk value obtained by the three scoring strategies. The calculation expression is as follows: ; Then, the final risk assessment value is obtained , The larger the failure mode The higher the risk, the more timely the company should take preventive measures against potential high-risk failure modes.

[0046] Since there is currently a lack of objective and efficient methods for comparing the pros and cons of FMEA optimization methods, this application uses a new multi-objective aggregation technology solution to compare the pros and cons of different FMEA optimization methods.

[0047] First, according to the Borda rule, the calculation results of different FMEA optimization methods are similar to voting on the ranking of failure modes in risk ranking, and the best ranking should be assigned to the failure mode with the most votes.

[0048] Then, according to the Copeland rule, the Euclidean distance between the evaluation information of different FMEA optimization methods and the optimal evaluation information is calculated in sequence. The optimal evaluation information is determined with the minimum distance as the goal.

[0049] Finally, based on the aggregation technology, a high weight is given to the ranking with high risk, and a multi-objective mathematical model is constructed with the minimum risk ranking error and the minimum Euclidean distance to achieve the purpose of Borda rule and Copeland rule.

[0050] Therefore, the execution of step S4 includes the following steps: Step S41 , summarizing the methods of ranking failure modes in the risk ranking, generating a corresponding set, and calculating the cumulative number of occurrences of the failure mode in the risk ranking.

[0051] Among them, the failure mode Ranked in risk ranking The statistical set of methods on . That is, the failure mode Ranking in risk order The number of times it appears on .and Failure Mode Ranking in risk order The cumulative number of occurrences, The expression is as follows: ; in, , , .

[0052] Step S42, based on the different evaluation information of the failure mode under the influencing factors by the evaluators, calculate the evaluation value of the corresponding evaluation information actually participating in the final risk calculation; specifically, assume For the evaluators in this application Failure Mode Influencing factors The numerical calculation formula of the evaluation information actually involved in the final risk calculation under different FMEA optimization methods is as follows: ; in, Representation optimization method Medium influencing factors The weight of Representation optimization method Middle-level evaluators The weight of For evaluation information The score function Step S43, according to the risk ranking weight, set the allocation matrix; specifically, according to the risk ranking Weight , , set up the allocation matrix; among them, when Indicates failure mode Assigned to rank , other cases . Step S44: Setting a multi-objective mathematical model, the formula of its objective function is as follows: ; ; The constraints are as follows: ; in, represents the number of failure modes, ; Indicates the number of influencing factors, ; Indicates the number of evaluators, ; represents the risk ranking, ; To optimize the number of methods, ; Indicates failure mode Influencing factors The best evaluation information below.

[0053] The advantages and disadvantages of the optimization methods are judged and the optimal evaluation information is determined by the difference between the calculation results of different failure mode optimization methods and the optimal solutions.

[0054] Specifically, in step S44, the calculation formula of the difference is as follows: ; in, Representation optimization method Failure Mode The sorting, Failure modes solved for multi-objective models Optimal sorting, Representation optimization method Failure Mode Influencing factors Evaluation information, Failure modes solved for multi-objective models Influencing factors Best evaluation information, for The maximum value of for The maximum value of .

[0055] Here, the present invention gives an example, a battery manufacturing company plans to produce a square lithium battery: the company uses the method proposed in this application to conduct a risk analysis of the potential failure modes of the square lithium battery assembly workshop. According to the requirements of the FMEA five-step method, the company formed an FMEA team, which consists of three evaluators, namely 、 、 .

[0056] First, the structure of the square lithium battery assembly production line is analyzed. Figure 5 As shown, the processing flow is as follows Figure 6 As shown, the present invention uses a process flow chart to visualize the assembly workshop, such as Figure 7 Then, taking the ultrasonic welding station as an example, the functional analysis of the station is visualized through the function tree, as shown in Figure 8 Secondly, the failure network is used to analyze the ultrasonic welding station, as shown in Figure 2. Figure 9 In addition to the ultrasonic welding station, the laser welding station and battery glue machine are also important processing equipment in the square lithium battery assembly workshop. Figure 10 shown.

[0057] exist Figure 9 and Figure 10 In the formula, FE represents failure effect, FM represents failure mode, and FC represents failure cause. Figure 9 and Figure 10 The failure network of the square lithium battery assembly production line is summarized, as shown in Table 1. Figure 4 The FMEA risk analysis flow chart was improved, and three evaluators used DHHFLTS to evaluate the failure modes in Table 1, as shown in Table 2.

[0058]

[0059]

[0060]

[0061]

[0062] In this application, , , .

[0063] Calculate the membership and non-membership of the evaluation information of the evaluator according to Table 2. In failure mode Influencing factors For example, , , Similarly, the membership and non-membership of the remaining failure mode evaluation information are calculated, as shown in Table 3 and Table 4.

[0064] To solve the evaluator weight, first, according to Table 3, Table 4 and formula , calculate the ideal solution, e.g., failure mode Influencing factors The ideal solution is , and the ideal solutions of the other failure modes are calculated in the same way, as shown in Table 5.

[0065] Then, according to Table 3, Table 4, Table 5 and Formula Calculate the Euclidean distance between the evaluator's evaluation information and the ideal solution.

[0066] For example, evaluators In failure mode Influencing factors The upper Euclidean distance is , the evaluator The Euclidean distance to the complement of the ideal solution is Similarly, the Euclidean distances of the remaining evaluators are calculated, as shown in Tables 6 and 7.

[0067] Finally, according to Table 6 and Table 7, the weight of the evaluator is calculated as 、 、 .

[0068]

[0069]

[0070]

[0071] To solve the weight of influencing factors, first, according to Table 3, Table 4 and the score function calculation formula , determine the evaluation information score value.

[0072] For example, evaluators In failure mode Influencing factors superior , Similarly, calculate the evaluation information scores of the remaining evaluators, as shown in Table 8.

[0073] Then, according to and The calculation expression calculates the mean and variance of the evaluation information after aggregation 、 、 、 、 and .

[0074] Secondly, according to Formula to calculate the water level Finally, according to Formula to calculate the weight of influencing factors 、 、 .

[0075]

[0076] The present invention uses a compromise method to calculate the failure mode risk value. First, according to Table 3, Table 4, and the evaluation personnel weight 、 、 as well as The formula is used to obtain the evaluation information after weighted aggregation of the SIFWG operator.

[0077] For example, failure mode Influencing factors The weighted data is ,Similarly, the results of weighted aggregation of the SIFWG operators of the ,rest of the failure modes are calculated, as shown in Table 9.

[0078] Then, according to the weight of the influencing factors 、 、 , perform weighted sum and weighted product calculations, for example, failure mode The weighted sum of , then the failure mode The weighted product of , similarly calculate the weighted sum and weighted product of the remaining failure modes, as shown in Table 10.

[0079] Secondly, the results of failure mode under three scoring strategies are calculated, as shown in Table 10, where in formula (30), the propensity factor .

[0080] Finally, calculate the failure mode risk assessment results , for example, failure mode Evaluation results Similarly, the evaluation results of other failure modes are calculated, as shown in Table 10. According to the risk assessment results , the failure mode risk ranking is .

[0081]

[0082]

[0083] In order to prove the superiority of the FMEA optimization method of the present invention, the present invention compares and analyzes the calculation results of the new method with those of the other three methods. The new method is recorded as The other three methods are denoted as 、 and .in FMEA is optimized through Regret Theory and Weighted Aggregates SumProduct Assessment (WASPAS); FMEA was not optimized; FMEA was optimized using Spherical Fuzzy Step-Wise Weight Assessment Ratio Analysis (SF-SWARA).

[0084] In the comparative analysis, in order to ensure the objectivity of the analysis process, the four methods all use the evaluation data of the present invention, which is Table 8. After calculation, the weight information of the four methods is shown in Table 11. In Table 11, Regardless of the evaluator's weight, No weight information is considered.

[0085] But in the calculation, The evaluator weight is set to 1. All weight information is set to 1. By formula Calculate the data values ​​actually involved in the calculation for each method, as shown in Table 12. Then, use each method to calculate the evaluation results and ranking of the failure mode, as shown in Table 13. Based on Table 13, the set of methods that rank the failure mode in different risk rankings is statistically analyzed, as shown in Table 14. Secondly, based on Table 14, calculate the cumulative number of times the failure mode appears in the risk ranking, as shown in Table 15. Finally, set the multi-objective mathematical model as follows:

[0086] The present invention uses the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA), the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Grey Wolf Optimizer (MOGWO) to solve the multi-objective mathematical model. The results are as follows: Figure 11 As shown. Figure 11 It can be seen from the Pareto front that compared with the other two methods, the range of NSWOA solution results is larger and the convergence speed is faster. Therefore, the present invention uses the Pareto front solved by NSWOA to select the optimal solution. The optimal solution of NSWOA is the solution in the middle of the Pareto front, because FMEA prioritizes the formulation of failure mode prevention measures based on the risk ranking results. Therefore, the present invention selects The middle solution is the optimal solution, that is Figure 11 The solutions are marked with five-pointed stars, and the optimal solutions are shown in Table 16.

[0087] According to Table 12, Table 13, and Table 16, calculate the difference ,in , , , .

[0088] From the difference, it can be seen that compared with other methods, the new method proposed in this invention The difference from the optimal solution is minimal. The weight factor is not considered, so the difference is the largest. Only the weight of influencing factors is considered, so the difference is and This proves that in FMEA analysis, weight information has a great influence on the final evaluation results. Therefore, it is very necessary for the present invention to calculate the weight information. The weight information is calculated based on the dispersion of evaluation information, which does not conform to the FMEA application scenario, so its difference is greater than Through comparative analysis, the new method has better applicability and objectivity.

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] Finally, the present invention is to calculate When the propensity factor is introduced ,when When , the failure mode risk ranking result is as follows Figure 12 As shown. Figure 12 It can be seen that only the last two positions in the risk ranking 、 changes. hour, Difference , still far below , and Therefore, through sensitivity analysis, it is proved that the optimized FMEA proposed in the present invention has strong robustness.

[0096] Those skilled in the art will appreciate that embodiments of the present invention may provide methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A lithium battery assembly risk analysis method, characterized in that: The following steps are involved: Step S1, using a double-layer hesitant fuzzy language term set to evaluate the severity, frequency, and detectability of failure modes in lithium battery assembly; wherein the double-layer hesitant fuzzy language term set includes a double-layer language term set and a hesitant fuzzy language term set, both of which are composed of evaluation information of evaluators; Step S2: Calculate the weight of the evaluator based on the similarity measure, and calculate the weight of the influencing factors of severity, frequency of occurrence, and detection based on the water injection principle; Step S3, using a combined compromise ranking method to aggregate the evaluation information of the evaluators and calculate the failure mode risk value; Step S4: Determine the optimal evaluation information through multi-objective aggregation technology.

2. The lithium battery assembly risk analysis method according to claim 1, wherein: In step S1, the dual-layer language terminology set It includes the main language term set and the sub-language term set, and its expression is as follows: ； The main language terminology Used to describe basic semantics, including the following expressions: ； The secondary language terminology Used to modify semantic strength, including the following expressions: ； in, is the boundary parameter of the main language term set, The index value of the main language term in the main language term set. is the boundary parameter of the sub-layer language term set, It is the index value of the secondary language term in the primary language term set.

3. The lithium battery assembly risk analysis method according to claim 2, wherein: In step S1, the expression of the hesitant fuzzy language term set is as follows: ； in, is a finite, ordered set of continuous language terms on S3, is a language terminology set, , is a non-empty set, yes An element in exist middle, It represents an element in the hesitant fuzzy language term set, and its expression is as follows: ； in, a finite and continuous set of linguistic terms; The hesitant fuzzy language term set is converted into membership degree through the conversion function.

4. The lithium battery assembly risk analysis method according to claim 3, wherein: In step S2, the steps for calculating the evaluator weights include: Step S211, obtaining the membership of the evaluator in the failure mode influencing factors, calculating the non-membership, and generating an ideal solution; Step S212, calculating the Euclidean distance between the evaluator and the ideal solution; Step S213, calculating the similarity between the evaluator and the ideal solution; Step S214: Calculate the evaluator weight based on the similarity.

5. The lithium battery assembly risk analysis method according to claim 4, wherein: In step S2, the calculation steps of the influencing factor weights include: Step S221, calculating the mean and variance of the evaluation matrix; Step S222, calculating the injection level; Step S223: Calculate the weight of the influencing factors based on the water level.

6. The lithium battery assembly risk analysis method according to claim 5, characterized in that: The execution of step S3 includes the following steps: Step S311: Based on the evaluator weights, the evaluation information is aggregated by a simple intuitionistic fuzzy weighted geometric operator to obtain the membership and non-membership of the evaluator; Step S312, calculating the weighted sum and weighted product of the evaluation information to obtain the integration result of the evaluation information; Step S313, calculating the intermediate risk value using three scoring strategies; Step S314: Calculate the intermediate risk values ​​obtained by the three scoring strategies to obtain a final risk assessment value.

7. The lithium battery assembly risk analysis method according to claim 6, wherein: In step S313, the formulas of the three scoring strategies are as follows: ； ； ； in, is the intermediate risk value of the first scoring strategy, is the intermediate risk value of the second scoring strategy, is the middle risk value of the third scoring strategy; is the weighted sum integration, is the weighted product; is the tendency factor.

8. The lithium battery assembly risk analysis method according to claim 6, wherein: In step S314, the calculation formula of the final risk assessment value is as follows: ； in, is the final risk assessment value.

9. The lithium battery assembly risk analysis method according to claim 7, wherein: The execution of step S4 includes the following steps: Step S41, summarizing the methods of ranking failure modes in the risk ranking, generating a corresponding set, and calculating the cumulative number of occurrences of the failure mode in the risk ranking; Step S42, based on the different evaluation information of the failure mode under the influencing factors by the evaluators, the evaluation value actually participating in the final risk calculation of the corresponding evaluation information is calculated; Step S43, setting an allocation matrix according to the weight of the risk ranking; Step S44: Setting a multi-objective mathematical model, the formula of its objective function is as follows: ； ； The constraints are as follows: ； in, represents the number of failure modes, ; Indicates the number of influencing factors, ; Indicates the number of evaluators, ; represents the risk ranking, ; To optimize the number of methods, ; Failure mode Ranking in risk order The cumulative number of occurrences, among which, ; The weight of the risk ranking is expressed as is the element in the allocation matrix; Failure Mode Ranked in risk ranking The method set above, Failure Mode Ranking in risk order The number of times it appears on Representation optimization method Medium influencing factors The weight of Representation optimization method Middle-level evaluators The weight of For evaluators Failure Mode Influencing factors Evaluation information below; ;and For evaluation information The scoring function of ; The advantages and disadvantages of the optimization methods are judged and the optimal evaluation information is determined by the difference between the calculation results of different failure mode optimization methods and the optimal solutions.

10. The lithium battery assembly risk analysis method according to claim 9, wherein: In step S44, the calculation formula of the difference is as follows: ； in, Representation optimization method Failure Mode The sorting, Failure modes solved for multi-objective models Optimal sorting, Representation optimization method Failure Mode Influencing factors Evaluation information, Failure modes solved for multi-objective models Influencing factors Best evaluation information, for The maximum value of for The maximum value of .

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