CN115146948A - Electric vehicle charging pile health state assessment method based on subjective and objective comprehensive fuzzy evaluation method - Google Patents
Electric vehicle charging pile health state assessment method based on subjective and objective comprehensive fuzzy evaluation method Download PDFInfo
- Publication number
- CN115146948A CN115146948A CN202210752495.8A CN202210752495A CN115146948A CN 115146948 A CN115146948 A CN 115146948A CN 202210752495 A CN202210752495 A CN 202210752495A CN 115146948 A CN115146948 A CN 115146948A
- Authority
- CN
- China
- Prior art keywords
- index
- evaluation
- charging pile
- weight
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 99
- 238000000034 method Methods 0.000 title claims abstract description 59
- 230000036541 health Effects 0.000 title claims abstract description 33
- 230000008569 process Effects 0.000 claims abstract description 11
- 230000010354 integration Effects 0.000 claims abstract description 10
- 230000007246 mechanism Effects 0.000 claims abstract description 4
- 238000007619 statistical method Methods 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 56
- 239000013598 vector Substances 0.000 claims description 24
- 230000036039 immunity Effects 0.000 claims description 12
- 230000003862 health status Effects 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 238000010606 normalization Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000011161 development Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 claims description 4
- 230000009286 beneficial effect Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000005672 electromagnetic field Effects 0.000 claims description 3
- 230000005855 radiation Effects 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 230000001052 transient effect Effects 0.000 claims description 3
- 230000005484 gravity Effects 0.000 description 2
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 239000006227 byproduct Substances 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides an electric vehicle charging pile health state assessment method based on an subjective and objective comprehensive fuzzy evaluation method, the method comprises the following steps: converting a plurality of qualitative index values in the charging pile by adopting an integrated statistical analysis method to obtain score values; sequentially determining subjective weight values of the performance indexes of the charging piles according to the charging pile mechanism and an analytic hierarchy process; obtaining objective weight values of the corresponding performance indexes according to the grade values by an entropy weight method; determining a weight coefficient suitable for comprehensive evaluation according to the relative importance degree of the subjective weight value and the objective weight value to the index by an addition integration weighting method; and combining the weight coefficient of the comprehensive evaluation, and finally obtaining the health grade of the charging pile by using a fuzzy comprehensive evaluation method. The invention can reflect the overall health state of the health of the charging pile more comprehensively and more finely.
Description
Technical Field
The invention relates to the field of health state evaluation of electric automobile charging piles, in particular to an evaluation method for the health state of an electric automobile charging pile based on an objective and subjective comprehensive fuzzy evaluation method.
Background
Along with the rapid development of electric vehicles, the matching scale of the charging pile is gradually enlarged. However, the popularization rate of the charging piles in China is not high enough at present, part of the charging piles installed in the early stage are not standardized and intelligent enough, new national standards cannot be compatible, and the management, overhaul and health evaluation means of the existing charging piles are not suitable for the charging piles which are put into operation.
Disclosure of Invention
The invention aims to provide an electric vehicle charging pile health state assessment method which adopts a subjective analytic hierarchy process, an objective entropy weight method and a fuzzy comprehensive assessment method to assess five aspects of an electrical performance index E, an economic performance index M, an electromagnetic compatibility performance index C, a general performance index G and a safety performance index S, and finally obtains one assessment grade of six states of serious faults, slight faults, abnormity, attention, normality and goodness.
An electric vehicle charging pile health state assessment method based on an subjective and objective comprehensive fuzzy evaluation method comprises the following steps:
a: converting a plurality of qualitative index values in the charging pile by adopting an integrated statistical analysis method to obtain score values;
b: according to a charging pile mechanism, sequentially determining subjective weight values of performance indexes of the charging piles according to an analytic hierarchy process;
c: b, obtaining an objective weight value corresponding to the performance index according to the entropy weight method by using the score value obtained by the conversion in the step A;
d: determining a weight coefficient suitable for comprehensive evaluation according to the relative importance degree of the subjective weight value and the objective weight value to the index by an addition integration weighting method;
e: and finally obtaining the health grade of the charging pile by combining the weight coefficient of the comprehensive evaluation and applying a fuzzy comprehensive evaluation method.
Further, the health state evaluation performance indexes of the charging pile comprise a general performance index G, an economic performance index M, an electrical performance index E, a safety performance index S and an electromagnetic compatibility performance index C, wherein qualitative indexes contained in the general performance index G are as follows: appearance performance, IP protection level, noise intensity layer; the safety performance index S includes the following qualitative indexes: safety warning, output overvoltage protection, output overcurrent protection, input undervoltage protection, input overvoltage protection, power frequency withstand voltage, impact withstand voltage and grounding protection; the electromagnetic compatibility performance index C includes qualitative indexes: surge impact immunity, electrical fast transient burst immunity, radio frequency electromagnetic field radiation immunity, electrostatic discharge immunity.
Further, in step A, it is assumed that for a certain index, the k-th expert gives a value of interval score [ u [ ] 1 (k) ,u 2 (k) ]Where k =1,2,3, …, n; n represents the total number of experts involved in the scoring, the collection-valued statistical sequence is:
[u 1 (1) ,u 2 (1) ],[u 1 (2) ,u 2 (2) ],…,[u 1 (n) ,u 2 (n) ] (1)
the n intervals are called as random sets, and the quantity representing the probability significance can be known as a falling shadow according to the knowledge of statistics;
is described by a functional relation as:
wherein:
u min 、u max the minimum value and the maximum value possible for the index are as follows:
u max ＝max{u 2 (k) | k＝1,2,…,n } (5)
u min ＝min{u 1 (k) | k＝1,2,…,n } (6)
according to formula (7) and formula (8):
obtaining a score value calculation formula of the qualitative index:
further, in the step B, subjective weight values of the performance indexes of the charging piles are sequentially determined according to an analytic hierarchy process, and the method specifically comprises the following steps:
b1, constructing a hierarchical structure model beneficial to evaluation and development: firstly, an evaluation index system is constructed aiming at an evaluation target, and then various indexes in the index system are generally classified according to the layering mode of a target layer, a criterion layer and an index layer;
b2, establishing a judgment matrix;
b3, calculating the judgment matrix to obtain the maximum eigenvalue and the corresponding eigenvector:
1) And (3) normalizing each column of the judgment matrix:
2) After each column of the matrix is judged to be normalized, summing the matrix according to rows:
3) Normalizing the obtained vector:
the characteristic vector of the judgment matrix obtained after normalization processing is as follows:
W＝[W 1 ，W 2 ，W 3 ，…，W n ] T (14)
wherein W is divided into relative weight vectors found in the hierarchical single ordering;
4) Calculating the maximum eigenvalue root of the judgment matrix:
wherein (AW) i Represents the ith component of AW;
b4, consistency test:
1) Firstly, calculating a consistency index CI:
when CI =0, the judgment matrix is completely consistent, and if the value of CI is larger, the consistency of the judgment matrix is poorer;
2) Calculating a consistency ratio CR:
RI is an average random consistency index, the consistency of the judgment matrix is considered to be satisfactory only when CR is less than 0.1, otherwise, the value in the original judgment matrix is readjusted, and the step B3 is repeated until the requirement of consistency check is met finally;
b5, total hierarchical ordering: assuming that the highest target layer is O, the criterion layer is P, and the index layer is Q, wherein P comprises m indexes, and P is P 1 ～P m And the weight ordering feature vector of the criterion layer relative to the target layer is:
p＝(a 1 ,a 2 ,…,a m ) (18)
wherein Q comprises n indexes, and the index layer is opposite to a certain index P in the criterion layer j The weight ranking feature vector of (a) is:
q＝(b 1j ,b 2j ,…,b nj )；(j＝1,2,…,m) (19)
b6, the weight value of the ith index in the index layer Q in the total hierarchical sorting relative to the target layer O is as follows:
further, step C, the objective weight value corresponding to the performance index is obtained from the score value obtained by conversion in step a according to an entropy weight method, and specifically includes:
c1, forming a decision matrix: assume that the set of objects participating in the evaluation is:
M＝(M 1 ，M 2 ，…，M n ) (21) the set of indices is:
D＝(D 1 ，D 2 ，…，D m ) (22)
then the object M is evaluated i For index D j Is denoted as x ij (i =1,2, …, n; j =1,2, …, m) forming a decision matrix of:
c2, calculating the characteristic specific gravity of the ith evaluation object under the jth index: assuming n evaluation objects, the observed values of m evaluation indexes are x ij (i =1,2, …, n; j =1,2, …, m) and satisfies:
then the characteristic proportion of the ith evaluation object under the jth index is as follows:
c3, calculating the entropy value of the j index as follows:
where k > 0 is a constant, typically k =1/ln (n); e.g. of the type j If the observed value difference of the jth index is larger, the entropy value is smaller, otherwise, the entropy value is larger;
c4, calculating the difference coefficient of the j index as follows:
g j ＝1-e j (j＝1,2，…，m) (27)
if the observed value of the jth index is more different, the difference coefficient g is larger j The larger the difference is, the more important the j index is;
c5, calculating the weight coefficient of the j index as follows:
further, the integration weighting method in step D assumes that for the j index, the weight coefficient determined by the subjective weighting method is a j B is the weight coefficient determined by the objective weighting method j If the j-th index is the following, the comprehensive weight of the j-th index is:
further, step E specifically includes:
e1: construction factor set U = { U = 1 ，u 2 ，…，u n }，u n Representing indexes which have influence on the evaluation result in the evaluation index system, wherein n represents the number of the indexes;
e2: construction of a judgment set V = { V = { V = 1 ，v 2 ，…，v m }，v m Representing the evaluation result of the mth grade belonging to the evaluation set for a certain index, wherein m represents the number of the evaluation grades;
e3: carrying out single factor evaluation, and constructing a single factor evaluation matrix:
wherein r is not less than 0 ij 1 ≦ 1 (i =1,2, …, n; j =1,2, …, m) for the subject of evaluation from factor u i From an angle of view, for v j The degree of membership of the grade is determined by combining trapezoidal distribution and triangular distribution, and a specific distribution diagram of the membership function is shown in fig. 4, wherein the expression of the membership function is as follows:
the above equation is a membership function with a comment grade of k, wherein k = {1,2,3,4,5,6} represents six grades of serious fault, slight fault, abnormal, attention, normal and good respectively;
from f, a fuzzy relation matrix R = (R) ij ) n×m Also known as R, a single factor evaluation matrix, generally has the following form:
e4: determining fuzzy weight of the evaluation index:
determining the index u according to the membership function i For v j Degree of membership w of i Determining the weight value of each index;
e5: the fuzzy weighted value is synthesized into a final comprehensive evaluation vector by selecting a proper operator and a judgment matrix
Wherein
Is a fuzzy operator symbol.
Further, the method also comprises the following steps: dividing the charging pile health state evaluation into 6 grades, constructing a comment set V = { serious fault V1, slight fault V2, abnormal V3, attention V4, normal V5 and good V6}, converting the fuzzy comprehensive evaluation result obtained in the step E into a percentile numerical value, and taking the middle value of each grade numerical segment in the charging pile health state table as a score vector M = (M is a vector of the score M) 1 ,m 2 ,m 3 ,m 4 ,m 5 ,m 6 ) The percentage system value obtained by conversion is:
Q＝B·M (41)。
the invention starts from five dimensions of the charging pile for the first time, includes electrical performance, economic performance, electromagnetic compatibility, general performance and safety performance, specifically includes 25 charging pile performance indexes such as output current error, current stabilization precision, charging efficiency, power factor, ripple factor and the like, and establishes a multi-dimensional health state evaluation system which includes the key electrical indexes of the charging pile and also includes general indexes, so that the overall health state of the health of the charging pile can be reflected more comprehensively and finely; subjective weights of various performances of the charging pile are determined by an analytic hierarchy process; in addition, an entropy weight method is used for determining objective weights of various performance indexes, an addition integration weighting method is used for fusing the subjective and objective weights for the first time, the integration characteristics of subjective and objective information are included, the equal importance of subjective and objective weights to index comprehensive weights is highlighted, and the health grade of the charging pile is divided into 6 grades for the first time: serious faults, slight faults, abnormity, attention, normality and goodness, can determine the final health grade of the charging pile more scientifically and finely, and convert the finally determined health grade into a percentile grade, thereby providing powerful theoretical support for comprehensive health management and maintenance of the charging pile.
Drawings
FIG. 1 is a flow chart of the analytic hierarchy process for calculating weights of the present invention;
FIG. 2 is a flow chart of an embodiment of the method for evaluating the health status of an electric vehicle charging pile based on an subjective and objective comprehensive fuzzy evaluation method;
FIG. 3 is a graph of the distribution of the score values of the indicators according to the present invention;
FIG. 4 is a membership function of the present invention;
FIG. 5 is a diagram illustrating a health status evaluation performance index of a charging pile according to the present invention;
fig. 6 is a health status grade scoring criteria for a charging pile according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 2, an embodiment of the invention provides an electric vehicle charging pile health state assessment method based on an subjective and objective comprehensive fuzzy evaluation method, which includes the following steps;
a: converting a plurality of qualitative index values in the charging pile by adopting an integrated statistical analysis method to obtain score values; as shown in fig. 5, the health state evaluation performance indexes of the charging pile in the embodiment of the present invention include a general performance index G, an economic performance index M, an electrical performance index E, a safety performance index S, and an electromagnetic compatibility performance index C. Wherein, the qualitative indexes contained in the general performance index G are as follows: appearance performance, IP protection level, noise intensity layer; the safety performance index S includes the following qualitative indexes: safety warning, output overvoltage protection, output overcurrent protection, input undervoltage protection, input overvoltage protection, power frequency withstand voltage, impact withstand voltage and grounding protection; the electromagnetic compatibility performance index C includes qualitative indexes: surge impact immunity, electric fast transient pulse group immunity, radio frequency electromagnetic field radiation immunity, and electrostatic discharge immunity.
B: and sequentially determining subjective weight values of the performance indexes of the charging piles according to the charging pile mechanism and an analytic hierarchy process.
C: b, obtaining an objective weight value corresponding to the performance index according to the entropy weight method by using the score value obtained by the conversion in the step A;
d: determining a weight coefficient suitable for comprehensive evaluation according to the relative importance degree of the subjective weight value and the objective weight value to the index by an addition integration weighting method;
e: and combining the weight coefficient of the comprehensive evaluation, and finally obtaining the health grade of the charging pile by using a fuzzy comprehensive evaluation method.
In step A, it is assumed that for a certain index, the section score value given by the kth expert is [ u [ ] 1 (k) ,u 2 (k) ]Where k =1,2,3, …, n; n represents the total number of experts involved in the scoring, the collection-valued statistical sequence is:
[u 1 (1) ,u 2 (1) ],[u 1 (2) ,u 2 (2) ],…,[u 1 (n) ,u 2 (n) ] (1)
the n intervals are called as a random set, the quantity representing the probability significance can be known as a falling shadow according to the statistical knowledge, and the n intervals can form the distribution as shown in fig. 3 after being superposed.
The functional relationship can be described as:
wherein:
u min 、u max the minimum value and the maximum value possible for the index are as follows:
u max ＝max{u 2 (k) | k＝1,2,…,n } (5)
u min ＝min{u 1 (k) | k＝1,2,…,n } (6) can prove that:
therefore, a score value calculation formula of the qualitative index can be obtained:
and step B, sequentially determining the subjective weight values of the performance indexes of the charging piles by using an analytic hierarchy process, wherein the step B is as follows as shown in figure 1:
b1, constructing a hierarchical structure model beneficial to evaluation development: firstly, a clear evaluation index system is constructed aiming at an evaluation target, and then various indexes in the index system are generally classified according to the layering mode of a target layer, a criterion layer and an index layer.
B2, establishing a judgment matrix: the general properties G are described in further detail by way of example. The general performance indexes comprise three indexes of appearance performance G1, IP protection grade G2 and noise intensity layer G3, and the established judgment matrix is shown in table 1:
TABLE 1 decision matrix for general Performance G
| G | G1 | G2 | G3 | |
| G1 | ||||
| 1 | 1/2 | 1/3 | 0.1634 | |
| G2 | 2 | 1 | 1/2 | 0.2970 |
| G3 | 3 | 2 | 1 | 0.5396 |
Namely, the judgment matrix (1) G of the general performance index is as follows:
b3, calculating the judgment matrix to obtain the maximum eigenvalue and the corresponding eigenvector:
1) And (3) normalizing each column of the judgment matrix:
2) After each column of the matrix is judged to be normalized, summing the matrix according to rows:
3) Normalizing the obtained vector:
the characteristic vector of the judgment matrix obtained after normalization processing is as follows:
W＝[W 1 ，W 2 ，W 3 ，…，W n ] T (14)
where W is divided into relative weight vectors that are sought in the hierarchical single ordering.
4) Calculating the maximum eigenvalue root of the judgment matrix:
wherein (AW) i Representing the ith component of AW.
B4, consistency test:
1) Firstly, calculating a consistency index CI:
when CI =0, the judgment matrix has complete consistency, but as the order of the matrix is larger, the matrix is difficult to ensure complete consistency; more, ensuring that CI is close to 0, and only needing to reach satisfactory consistency, if the value of CI is larger, the consistency of the judgment matrix is worse.
2) Calculating a consistency ratio CR:
RI is an average random consistency index, and can be found by table look-up 2:
TABLE 2 average random consistency index
And only when CR is less than 0.1, the consistency of the judgment matrix can be considered to be satisfactory, otherwise, the value in the original judgment matrix needs to be readjusted, and the step B3 is repeated until the requirement of consistency check is met finally.
B5、And (3) overall hierarchical ordering: the total hierarchical ranking is to calculate the weight value of the index of the last layer to the target layer of the highest layer. Assuming that the highest target layer is O, the criterion layer is P, and the index layer is Q, wherein P comprises m indexes, and P is P 1 ～P m And the weight ordering feature vector of the criterion layer relative to the target layer is:
p＝(a 1 ,a 2 ,…,a m ) (18)
wherein Q comprises n indexes, and the index layer is opposite to a certain index P in the criterion layer j The weight ranking feature vector of (a) is:
q＝(b 1j ,b 2j ,…,b nj )；(j＝1,2,…,m) (19)
b6, the weight value of the ith index in the index layer Q in the total hierarchical sorting relative to the target layer O is as follows:
the specific implementation process of the step C is as follows:
c1, forming a decision matrix: assume that the set of objects participating in the evaluation is:
M＝(M 1 ，M 2 ，…，M n ) (21)
the index set is as follows:
D＝(D 1 ，D 2 ，…，D m ) (22)
then the object M is evaluated i For index D j Is denoted as x ij (i =1,2, …, n; j =1,2, …, m) forming a decision matrix of:
c2, calculating the characteristic specific gravity of the ith evaluation object under the jth index: assuming n evaluation objects, the observed values of m evaluation indexes are x ij (i＝1,2,…, n; j =1,2, …, m), and satisfies:
then the characteristic proportion of the ith evaluation object under the jth index is as follows:
c3, calculating the entropy value of the j index as follows:
where k > 0 is a constant, typically k =1/ln (n); e.g. of a cylinder j If the observed value difference of the j index is larger than 0, the entropy value is smaller, and otherwise, the entropy value is larger.
C4, calculating the difference coefficient of the j index as follows:
g j ＝1-e j (j＝1,2，…，m) (27)
if the observed value of the j index is more different, the difference coefficient g is larger j The larger the difference, the more important the j-th index is.
C5, calculating a weight coefficient of the j index as follows:
in the step D, the addition integration weighting method assumes that for the jth index, the weight coefficient determined by the subjective weighting method is a j B is the weight coefficient determined by the objective weighting method j Then, the comprehensive weight of the j-th index is:
taking general performance comprehensive weight as an example: subjective weighting W for general performance G (1) And objective weight W G (2) The general performance index comprehensive weight shown in table 3 can be obtained by synthesizing according to the additive integration weighting method:
TABLE 3 general Performance index Integrated weights
| Index (I) | Appearance Properties | IP protection class | Noise intensity layer |
| Subjective weighting | 0.1634 | 0.2970 | 0.5396 |
| Objective weight | 0.3333 | 0.3333 | 0.3333 |
| Composite weight | 0.2484 | 0.3151 | 0.4365 |
That is, the comprehensive weight vector of the general performance index is:
W G ＝(0.2484,0.3151,0.4365) (30)
step E, as shown in fig. 2, comprises the following steps:
e1: construction factor set U = { U = 1 ，u 2 ，…，u n }，u n And the indexes which have influence on the evaluation result in the evaluation index system are represented, and n represents the number of the indexes.
E2: constructing a judgment set V = { V = { V 1 ，v 2 ，…，v m }，v m And representing the evaluation result of the mth grade belonging to the evaluation set for a certain index, wherein m represents the number of the evaluation grades.
E3: carrying out single factor evaluation, and constructing a single factor evaluation matrix:
wherein r is not less than 0 ij 1 ≦ 1 (i =1,2, …, n; j =1,2, …, m) for the subject of evaluation i From an angle of view, for v j Degree of membership of the grade. The method of combining trapezoid distribution and triangle distribution is adopted to determine the specific distribution diagram of the membership function as shown in fig. 4, and the expression of the membership function is as follows:
the above equation is a membership function with a comment level of k, where k = {1,2,3,4,5,6} represents six levels of serious fault, light fault, abnormal, attentive, normal, and good, respectively.
From f, a fuzzy relation matrix R = (R) ij ) n×m Also known as R, a single factor evaluation matrix, generally has the following form:
e4: determining fuzzy weight of the evaluation index:
determining the index u according to the membership function i For v j Degree of membership w of i Namely, the weight value of each index is determined, and normalization processing is needed before the next work is carried out.
E5: the fuzzy weight value can be synthesized into a final comprehensive evaluation vector by selecting a proper operator and a judgment matrix
Wherein
For fuzzy operator symbols, the basic operator symbols have Zadeh operators, ring and product operators, weightingAverage operators, bounded operators, product-by-product operators, bounded and product-by operators, einstein operators, hamacher operators, yager operators, and the like. The commonly used operator model is shown in table 4:
TABLE 4 comparison of fuzzy comprehensive evaluation operators
As shown in fig. 6, the embodiment of the present invention divides the evaluation of the health status of the charging pile into 6 levels, and constructs a comment set V = { serious fault V1, slight fault V2, abnormal V3, attention V4, normal V5, and good V6}. Converting the fuzzy comprehensive evaluation result obtained in the step E into a percentile numerical value, and taking the middle value of each grade number section in the charging pile health state table chart 6 as a score vector M = (M =) 1 ,m 2 ,m 3 ,m 4 ,m 5 ,m 6 ) The percentage system value obtained by conversion is:
Q＝B·M (41)
the above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. The method for evaluating the health state of the charging pile of the electric automobile based on the subjective and objective comprehensive fuzzy evaluation method is characterized by comprising the following steps of: the method comprises the following steps:
a: converting a plurality of qualitative index values in the charging pile by adopting an integrated statistical analysis method to obtain score values;
b: according to a charging pile mechanism, sequentially determining subjective weight values of performance indexes of the charging piles according to an analytic hierarchy process;
c: b, obtaining an objective weight value corresponding to the performance index according to the entropy weight method by using the score value obtained by the conversion in the step A;
d: determining a weight coefficient suitable for comprehensive evaluation according to the relative importance degree of the subjective weight value and the objective weight value to the index by an addition integration weighting method;
e: and combining the weight coefficient of the comprehensive evaluation, and finally obtaining the health grade of the charging pile by using a fuzzy comprehensive evaluation method.
2. The method for evaluating the health status of the charging pile of the electric vehicle based on the subjective and objective comprehensive fuzzy evaluation method as claimed in claim 1, wherein: the health state evaluation performance indexes of the charging pile comprise a general performance index G, an economic performance index M, an electrical performance index E, a safety performance index S and an electromagnetic compatibility performance index C, wherein qualitative indexes contained in the general performance index G are as follows: appearance performance, IP protection level, noise intensity layer; the safety performance index S includes the following qualitative indexes: safety warning, output overvoltage protection, output overcurrent protection, input undervoltage protection, input overvoltage protection, power frequency withstand voltage, impact withstand voltage and grounding protection; the electromagnetic compatibility performance index C includes qualitative indexes: surge impact immunity, electrical fast transient burst immunity, radio frequency electromagnetic field radiation immunity, electrostatic discharge immunity.
3. The method for evaluating the health status of the charging pile of the electric vehicle based on the subjective and objective comprehensive fuzzy evaluation method as claimed in claim 1, wherein:
in step A, it is assumed that for a certain index, the section score value given by the kth expert is [ u [ ] 1 (k) ,u 2 (k) ]Where k =1,2,3, …, n; n represents the total number of experts participating in the scoring, the collection-valued statistical sequence is:
[u 1 (1) ,u 2 (1) ],[u 1 (2) ,u 2 (2) ],…,[u 1 (n) ,u 2 (n) ] (1)
the n intervals are called as random sets, and the quantity representing the probability significance can be known as a falling shadow according to the knowledge of statistics;
is described by a functional relation as:
wherein:
u min 、u max the minimum value and the maximum value possible for the index are as follows:
u max ＝max{u 2 (k) | k＝1,2,…,n } (5)
u min ＝min{u 1 (k) | k＝1,2,…,n } (6)
according to formula (7) and formula (8):
obtaining a score value calculation formula of the qualitative index:
4. the method for evaluating the health status of the charging pile of the electric vehicle based on the subjective and objective comprehensive fuzzy evaluation method as claimed in claim 3, wherein: and B, sequentially determining subjective weight values of the performance indexes of the charging pile according to an analytic hierarchy process, wherein the method specifically comprises the following steps:
b1, constructing a hierarchical structure model beneficial to evaluation and development: firstly, an evaluation index system is constructed aiming at an evaluation target, and then various indexes in the index system are generally classified according to the layering mode of a target layer, a criterion layer and an index layer;
b2 establishing a judgment matrix;
b3, calculating the judgment matrix to obtain the maximum eigenvalue and the corresponding eigenvector:
1) And (3) carrying out normalization processing on each column of the judgment matrix:
2) After each column of the matrix is judged to be normalized, summing the matrix according to rows:
3) Normalizing the obtained vector:
the characteristic vector of the judgment matrix obtained after normalization processing is as follows:
W＝[W 1 ，W 2 ，W 3 ，…，W n ] T (14)
wherein W is divided into relative weight vectors found in the hierarchical single ordering;
4) Calculating the maximum eigenvalue root of the judgment matrix:
wherein (AW) i Represents the ith component of AW;
b4, consistency test:
1) Firstly, calculating a consistency index CI:
when CI =0, the judgment matrix is completely consistent, and if the value of CI is larger, the consistency of the judgment matrix is poorer;
2) Calculating a consistency ratio CR:
RI is an average random consistency index, and the consistency of the judgment matrix is considered to be satisfactory only when CR is less than 0.1, otherwise, the value in the original judgment matrix needs to be readjusted, and the step B3 is repeated until the requirement of consistency check is met finally;
b5, total hierarchical ordering: assuming that the highest target layer is O, the criterion layer is P, and the index layer is Q, wherein P comprises m indexes, and P is P 1 ～P m And the weight ordering feature vector of the criterion layer relative to the target layer is:
p＝(a 1 ,a 2 ,…,a m ) (18)
wherein Q comprises n indexes, and the index layer is opposite to a certain index P in the criterion layer j The weight ranking feature vector of (a) is:
q＝(b 1j ,b 2j ,…,b nj )；(j＝1,2,…,m) (19)
b6, the weight value of the ith index in the index layer Q in the total hierarchical sorting relative to the target layer O is as follows:
5. the method for evaluating the health status of the charging pile of the electric vehicle based on the subjective and objective comprehensive fuzzy evaluation method as claimed in claim 4, wherein: step C, obtaining objective weight values corresponding to the performance indexes according to the grade values obtained by conversion in the step A by an entropy weight method, and specifically comprising the following steps:
c1, forming a decision matrix: assume that the set of objects participating in the evaluation is:
M＝(M 1 ，M 2 ，…，M n ) (21)
the index set is as follows:
D＝(D 1 ，D 2 ，…，D m ) (22)
then the object M is evaluated i For index D j Is denoted as x ij (i =1,2, …, n; j =1,2, …, m) forming a decision matrix of:
c2, calculating the characteristic proportion of the ith evaluation object under the jth index: assuming n evaluation objects, the observed values of m evaluation indexes are x ij (i =1,2, …, n; j =1,2, …, m) and satisfies:
then the characteristic proportion of the ith evaluation object under the jth index is as follows:
c3, calculating the entropy value of the j index as follows:
where k > 0 is a constant, typically k =1/ln (n); e.g. of a cylinder j The observed value difference of the jth index is larger than 0, the entropy value is smaller, and otherwise, the entropy value is larger;
c4, calculating the difference coefficient of the j index as follows:
g j ＝1-e j (j＝1,2，…，m) (27)
if the observed value of the j index is more different, the difference coefficient g is larger j The larger the difference is, the more important the jth index is;
c5, calculating the weight coefficient of the j index as follows:
6. the method for evaluating the health status of the charging pile of the electric vehicle based on the subjective and objective comprehensive fuzzy evaluation method as claimed in claim 5, wherein: in the step D, the addition integration weighting method assumes that for the j index, the weight coefficient determined by the subjective weighting method is a j Determination of objective weighting has a weight coefficient of b j Then, the comprehensive weight of the j-th index is:
7. the method for assessing the health state of the charging pile of the electric vehicle based on the subjective and objective comprehensive fuzzy evaluation method as claimed in claim 6, wherein: the step E specifically comprises the following steps:
e1: construction factor set U = { U = 1 ，u 2 ，…，u n }，u n Representing indexes which have influence on the evaluation result in the evaluation index system, wherein n represents the number of the indexes;
e2: construction of a judgment set V = { V = { V = 1 ，v 2 ，…，v m }，v m Representing the evaluation result of the mth grade belonging to the evaluation set for a certain index, wherein m represents the number of the evaluation grades;
e3: carrying out single factor evaluation, and constructing a single factor evaluation matrix:
wherein r is more than or equal to 0 ij 1 ≦ 1 (i =1,2, …, n; j =1,2, …, m) for the subject of evaluation from factor u i From an angle of view, for v j The degree of membership of the grade is determined by combining trapezoidal distribution and triangular distribution, and a specific distribution diagram of the membership function is shown in fig. 4, wherein the expression of the membership function is as follows:
the above equation is a membership function with a comment grade of k, wherein k = {1,2,3,4,5,6} represents six grades of serious fault, slight fault, abnormal, attention, normal and good respectively;
from f, a fuzzy relation matrix R = (R) ij ) n×m Also known as R, is a single-factor evaluation matrix, generally in the form:
e4: determining fuzzy weight of the evaluation index:
determining the index u according to the membership function i For v j Degree of membership w i Determining the weight value of each index;
e5: the fuzzy weight value is synthesized into a final comprehensive evaluation vector by selecting a proper operator and a judgment matrix
Wherein
Is a fuzzy operator symbol.
8. The subjective and objective comprehensive fuzzy evaluation method-based electric vehicle charging pile health state assessment method according to claim 7, characterized in that: further comprising: dividing the health state evaluation of the charging pile into 6 grades, constructing a comment set V = { serious fault V1, slight fault V2, abnormal V3, attention V4, normal V5 and good V6}, converting the fuzzy comprehensive evaluation result obtained in the step E into a percentile numerical value, and taking the middle value of each grade number section in the health state table of the charging pile as a score vector M = (M is a vector of M) 1 ,m 2 ,m 3 ,m 4 ,m 5 ,m 6 ) The percentage system value obtained by conversion is:
Q＝B·M (41)。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210752495.8A CN115146948A (en) | 2022-06-28 | 2022-06-28 | Electric vehicle charging pile health state assessment method based on subjective and objective comprehensive fuzzy evaluation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210752495.8A CN115146948A (en) | 2022-06-28 | 2022-06-28 | Electric vehicle charging pile health state assessment method based on subjective and objective comprehensive fuzzy evaluation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115146948A true CN115146948A (en) | 2022-10-04 |
Family
ID=83411115
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210752495.8A Pending CN115146948A (en) | 2022-06-28 | 2022-06-28 | Electric vehicle charging pile health state assessment method based on subjective and objective comprehensive fuzzy evaluation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115146948A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115782657A (en) * | 2022-11-17 | 2023-03-14 | 国网江苏省电力有限公司泰州供电分公司 | Charging pile performance evaluation method and device |
| CN115936309A (en) * | 2022-11-29 | 2023-04-07 | 青岛理工大学 | Building health monitoring method and system based on multi-feature factor integrated evaluation |
| CN116029611A (en) * | 2023-02-14 | 2023-04-28 | 西华大学 | Comprehensive performance test and evaluation method for fuel cell hybrid power system |
| CN116050950A (en) * | 2023-04-03 | 2023-05-02 | 西南交通大学 | Method, device, equipment and medium for evaluating railway junction passenger train schedule |
| CN116976694A (en) * | 2023-07-19 | 2023-10-31 | 珠江水利委员会珠江水利科学研究院 | Comprehensive evaluation method for beach ecosystem |
-
2022
- 2022-06-28 CN CN202210752495.8A patent/CN115146948A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115782657A (en) * | 2022-11-17 | 2023-03-14 | 国网江苏省电力有限公司泰州供电分公司 | Charging pile performance evaluation method and device |
| CN115936309A (en) * | 2022-11-29 | 2023-04-07 | 青岛理工大学 | Building health monitoring method and system based on multi-feature factor integrated evaluation |
| CN115936309B (en) * | 2022-11-29 | 2024-02-13 | 青岛理工大学 | Building health monitoring method and system based on multi-feature factor integrated evaluation |
| CN116029611A (en) * | 2023-02-14 | 2023-04-28 | 西华大学 | Comprehensive performance test and evaluation method for fuel cell hybrid power system |
| CN116050950A (en) * | 2023-04-03 | 2023-05-02 | 西南交通大学 | Method, device, equipment and medium for evaluating railway junction passenger train schedule |
| CN116050950B (en) * | 2023-04-03 | 2023-06-23 | 西南交通大学 | Method, device, equipment and medium for evaluating railway junction passenger train schedule |
| CN116976694A (en) * | 2023-07-19 | 2023-10-31 | 珠江水利委员会珠江水利科学研究院 | Comprehensive evaluation method for beach ecosystem |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115146948A (en) | Electric vehicle charging pile health state assessment method based on subjective and objective comprehensive fuzzy evaluation method | |
| CN106850254B (en) | Method for identifying key nodes in power communication network | |
| CN110991786B (en) | 10kV static load model parameter identification method based on similar daily load curve | |
| CN104318482A (en) | Comprehensive assessment system and method of smart distribution network | |
| CN110705873A (en) | Novel power distribution network operation state portrait analysis method | |
| CN111898839B (en) | Importance degree classification method and device for power users | |
| CN110738232A (en) | grid voltage out-of-limit cause diagnosis method based on data mining technology | |
| CN111612296B (en) | Method for quantitatively configuring online monitoring device of power equipment of converter station | |
| CN112308425A (en) | Method for constructing distribution transformer health evaluation index system | |
| CN113313145A (en) | Expressway traffic incident detection method based on mixed kernel correlation vector machine | |
| CN111461475A (en) | Method for evaluating performance state of electric vehicle charging equipment | |
| CN112381422A (en) | Method and device for determining performance of photovoltaic power station | |
| CN113327047B (en) | Power marketing service channel decision method and system based on fuzzy comprehensive model | |
| CN111798152A (en) | Intelligent store management method and device | |
| CN115526393A (en) | Construction cost prediction method based on transformer project key influence factor screening | |
| CN115271305A (en) | Game theory weight-based risk early warning method and device for electric vehicle charging equipment | |
| CN114741876A (en) | Intelligent inspection method for tower crane | |
| CN114236332A (en) | Power cable insulation state judgment method and system | |
| CN111008779A (en) | Transformer health state assessment method based on multi-factor assessment model | |
| CN115345392A (en) | Abnormal situation studying and judging and key index screening method for power distribution network | |
| CN113537759A (en) | User experience measurement model based on weight self-adaptation | |
| CN111126790A (en) | Power distribution network risk assessment method based on multi-level fuzzy comprehensive decision | |
| CN112633759B (en) | State evaluation method for primary substation main equipment | |
| CN115684835B (en) | Power distribution network fault diagnosis method, device, equipment and storage medium | |
| CN111080131B (en) | Rural power grid transformation method based on cascade combination scoring |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |