CN113869696A - Method, device, equipment and medium for evaluating energy efficiency of electric vehicle charging station - Google Patents
Method, device, equipment and medium for evaluating energy efficiency of electric vehicle charging station Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a medium for evaluating energy efficiency of an electric vehicle charging station. Wherein the method comprises the following steps: determining evaluation index data of the electric vehicle charging station, and establishing an evaluation index system according to the evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer; determining a first fuzzy relation matrix of evaluation index data of a target layer according to an evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector; and determining a target layer evaluation vector according to the first fuzzy relation matrix, and determining a grading grade of the electric vehicle charging station according to the target layer evaluation vector. Through executing the scheme, the efficient and rapid energy efficiency evaluation tool for the electric vehicle charging station can be realized, the high energy efficiency of the charging station is favorably ensured, and the energy conservation and emission reduction of charging facilities are promoted.
Description
Technical Field
The embodiment of the invention relates to the technical field of automobiles, in particular to a method, a device, equipment and a medium for evaluating energy efficiency of an electric automobile charging station.
Background
In order to popularize electric automobiles, the nation will increase the strength to build electric automobile infrastructure, effectively support the development of electric automobile charging and discharging technology, and further expand the market scale of domestic electric automobile charging and discharging equipment. The popularization of the electric automobile industry aims to develop energy conservation and emission reduction, so that the energy efficiency of related supporting facilities needs to be evaluated, and the realization of the energy conservation and emission reduction functions is ensured. The charging facility is one of the matching electric automobile industry main bodies, and the energy efficiency in the construction, operation and management determines the contribution of the electric automobile charging facility to energy conservation and emission reduction.
In the prior art, the energy efficiency of the electric automobile charging pile is evaluated from the aspect of hardware facilities, the electric automobile charging pile is divided into a direct current charging pile and an alternating current charging pile according to different types of the charging piles, metering points are arranged at input and output ends of a distribution transformer, a power line and the charging pile, electric energy parameters such as voltage and current are collected, and the energy efficiency loss conditions of an alternating current transmission line and the direct current transmission line are analyzed and calculated. In the charging pile, loss components of a main power electronic device diode and an IGBT under different power conditions and a general calculation method are analyzed, energy efficiency loss calculation formulas of all components of the charging pile are obtained, the energy efficiency condition of the charging pile is further calculated, and harmonic wave influence and electric energy quality after the electric automobile charging pile is connected to a power grid are analyzed. The defects that analysis programs are complex, calculation amount is large, different models of charging pile internal power electronic devices are different, and hardware metering points are required to be arranged to collect electric energy data exist.
Disclosure of Invention
The embodiment of the invention provides an energy efficiency assessment method, device, equipment and medium for an electric vehicle charging station, which can efficiently and quickly provide a tool for energy efficiency assessment of the electric vehicle charging station, can ensure that the charging station to be built keeps high energy efficiency after being built and operated, is favorable for ensuring high energy efficiency of the charging station for the built charging station, and promotes energy conservation and emission reduction of charging facilities.
In a first aspect, an embodiment of the present invention provides an energy efficiency evaluation method for an electric vehicle charging station, where the method includes: determining evaluation index data of the electric vehicle charging station, and establishing an evaluation index system according to the evaluation index data; the evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer;
determining a first fuzzy relation matrix of evaluation index data of a target layer according to the evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector;
and determining a target layer evaluation vector according to the first fuzzy relation matrix, and determining a grading level of the electric vehicle charging station according to the target layer evaluation vector.
In a second aspect, an embodiment of the present invention further provides an energy efficiency evaluation apparatus for an electric vehicle charging station, where the apparatus includes:
the evaluation index system establishing module is used for determining evaluation index data of the electric vehicle charging station and establishing an evaluation index system according to the evaluation index data; the evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer;
the fuzzy relation matrix determining module is used for determining a first fuzzy relation matrix of the evaluation index data of the target layer according to the evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector;
and the grading level determination module is used for determining a target layer evaluation vector according to the first fuzzy relation matrix and determining the grading level of the electric vehicle charging station according to the target layer evaluation vector.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for evaluating energy efficiency of an electric vehicle charging station according to any one of the embodiments of the present invention.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the energy efficiency assessment method for an electric vehicle charging station according to any one of the embodiments of the present invention.
According to the technical scheme provided by the embodiment of the invention, evaluation index data of the electric vehicle charging station are determined, and an evaluation index system is established according to the evaluation index data; the evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer; determining a first fuzzy relation matrix of evaluation index data of a target layer according to an evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector; and determining a target layer evaluation vector according to the first fuzzy relation matrix, and determining a grading grade of the electric vehicle charging station according to the target layer evaluation vector. By executing the technical scheme provided by the embodiment of the invention, a tool can be efficiently and quickly provided for energy efficiency evaluation of the electric vehicle charging station, the high energy efficiency of the charging station is favorably ensured, and the energy conservation and emission reduction of charging facilities are promoted.
Drawings
Fig. 1a is a flowchart of an energy efficiency evaluation method for an electric vehicle charging station according to an embodiment of the present invention;
FIG. 1b is a schematic diagram of an evaluation index system for energy efficiency evaluation of an electric vehicle charging station according to an embodiment of the invention;
FIG. 1c is a schematic diagram of the scaling principle of the analytic hierarchy process provided in the embodiment of the present invention;
fig. 1d is a schematic diagram illustrating a corresponding relationship between a value of a uniform average random consistency index and a matrix order according to an embodiment of the present invention;
FIG. 2 is a flowchart of another energy efficiency assessment method for an electric vehicle charging station according to an embodiment of the present invention;
fig. 3 is a flowchart of another energy efficiency evaluation method for an electric vehicle charging station according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an energy efficiency evaluation device of an electric vehicle charging station according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Fig. 1a is a flowchart of an electric vehicle charging station energy efficiency evaluation method according to an embodiment of the present invention, where the method may be performed by an electric vehicle charging station energy efficiency evaluation apparatus, which may be implemented by software and/or hardware, and the apparatus may be configured in an electronic device for evaluating electric vehicle charging station energy efficiency. The method is applied to a scene of energy efficiency evaluation of the electric vehicle charging station. As shown in fig. 1a, the technical solution provided by the embodiment of the present invention specifically includes:
and S110, determining evaluation index data of the electric vehicle charging station, and establishing an evaluation index system according to the evaluation index data.
The evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; and the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer.
Specifically, the energy efficiency evaluation index system of the electric vehicle charging station can be constructed based on a fuzzy comprehensive evaluation method by utilizing knowledge and experience of experts. Through the investigation of experts and repeated demonstration, an energy efficiency evaluation index system of the electric vehicle charging station is established from two aspects of management and technology. As shown in fig. 1b, the system includes evaluation index data of one target layer, i.e., performance evaluation, and evaluation index data of three criterion layers, i.e., ten evaluation index data of the power supply system, the charging device, the monitoring system, and the index layer. The evaluation index data of the target layer is related to the evaluation index data of at least one criterion layer, and the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer. And (4) carrying out overall design on an energy efficiency evaluation index system of the electric vehicle charging station according to the objectivity, guidance, scientificity and feasibility principles.
In this embodiment, optionally, the evaluation index data of the criterion layer includes at least one of a power supply system, a charging device, and a monitoring system; the evaluation index data of the index layer comprises at least one of a distribution transformer, a power distribution cabinet, a metering device, a harmonic treatment device, a rectifying device, a direct current charging device, a security monitoring system, a charging and discharging monitoring system and an intelligent charging and discharging monitoring device.
The power supply system may be a generic term of power equipment and distribution lines that provide power for operation of the charging station. The charging equipment can be special equipment for supplying electric energy to the power storage battery of the electric automobile, and comprises an alternating current charging pile, an off-board charger and the like. The monitoring system can be a system for collecting information such as power supply condition, charging equipment running state, environment monitoring and alarming of the electric vehicle charging station, and realizing monitoring, control and management of equipment in the station by applying computer and network communication technology. The power supply system is further divided into a distribution transformer, a power distribution cabinet, a metering device and a harmonic suppression device; the charging equipment is further divided into a rectifying device, a direct current charging device and a charging device; the monitoring system is further divided into a security monitoring system, a charging and discharging monitoring system and an intelligent charging and discharging monitoring device.
Therefore, the evaluation index data of the electric vehicle charging station and the established energy efficiency evaluation index system of the electric vehicle charging station are established into corresponding relations of different levels, and a basis can be provided for the execution of the subsequent steps.
And S120, determining a first fuzzy relation matrix of the evaluation index data of the target layer according to the evaluation index system.
Wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector.
Specifically, the first fuzzy relation matrix of the evaluation index data of the target layer can be determined based on a fuzzy comprehensive evaluation method. The first fuzzy relation matrix may be determined by criterion-level evaluation vectors of three evaluation index data of a criterion level. And the criterion layer evaluation vector can be determined by a weight vector of the evaluation index data of the criterion layer and a fuzzy relation matrix of the evaluation index data of the criterion layer.
The weight vector of the evaluation index data of the criterion layer can be determined according to the judgment matrix of the evaluation index data of the criterion layer determined by the analytic hierarchy process. The fuzzy relation matrix of the evaluation index data of the criterion layer can be determined by adopting an expert scoring method according to the scoring of the evaluation index data of the index layer under the three evaluation index data of the criterion layer by a group of experts according to the grade of the comment.
In another possible embodiment, optionally, the determining process of the criterion layer evaluation vector includes: determining a second fuzzy relation matrix of each evaluation index data of the criterion layer according to the evaluation index system based on a fuzzy comprehensive evaluation method; the second fuzzy relation matrix is obtained by constructing evaluation index data of at least one index layer related to the evaluation index data of the criterion layer; determining a weight vector of each evaluation index data of the criterion layer based on an analytic hierarchy process; and determining a criterion layer evaluation vector according to the second fuzzy relation matrix and the weight vector of the evaluation index data of the criterion layer.
Specifically, the method can establish factor domains influencing evaluation index data according to expert experience, and determine comment level domains and standard membership degree sets. Wherein, the comment grade domain V ═ (V)1,v2,…,vn) The set of standard membership, J ═ J (J)1,J2,…,Jm) And the corresponding relation between the standard membership and the grade is as follows: j ═ 5 (excellent), 4 (good), 3 (medium), 2 (good), 1 (bad) }. According to the scheme, charging station data can be inspected and consulted on the spot according to experts in related fields, evaluation index data of an index layer under evaluation index data of a criterion layer are scored according to comment grades, and a fuzzy relation matrix B of the evaluation index data of the criterion layer is establishedl＝(rij)m×nI.e. the second fuzzy relation matrix. Wherein, BlAnd the fuzzy relation matrix represents the ith evaluation index data of the criterion layer.
For example, taking a B1 power supply system as an example, the subordinate index layer has 4 evaluation index data, and the fuzzy relation matrix B is constructed corresponding to 5 comment levels1Comprises the following steps:
The scheme can also establish a judgment matrix of each evaluation index data of the criterion layer based on the scale principle of the analytic hierarchy process, and determine the weight vector of each evaluation index data of the criterion layer according to the analytic hierarchy process; and determining a criterion layer evaluation vector according to the second fuzzy relation matrixes and the weight vector of the evaluation index data of the criterion layer.
For example, taking the B1 power supply system as an example, the criterion layer evaluation vector of the B1 power supply system is E1Is shown to be
Wherein A is1Weight vector representing the B1 power supply system, B1An ambiguity relationship matrix representing the B1 power supply system.
Therefore, a second fuzzy relation matrix of each evaluation index data of the criterion layer is determined according to the evaluation index system based on a fuzzy comprehensive evaluation method; determining a weight vector of each evaluation index data of the criterion layer based on an analytic hierarchy process; and determining a criterion layer evaluation vector according to the second fuzzy relation matrixes and the weight vector of the evaluation index data of the criterion layer. Reliable data support can be provided for determining the target layer evaluation vector and finally determining the energy efficiency evaluation grade of the electric vehicle charging station.
In this embodiment, optionally, the determining process of the weight vector of the evaluation index data of the criterion layer includes: determining a second judgment matrix of the evaluation index data of the criterion layer based on an analytic hierarchy process; the second judgment matrix is obtained by constructing evaluation index data of at least one index layer related to the evaluation index data of the criterion layer; and carrying out normalization processing on the second judgment matrix to obtain a weight vector of the evaluation index data of the criterion layer.
Specifically, as shown in fig. 1c, the scaling rule may be to compare the importance degree between two evaluation index data of the same hierarchy. The method can respectively determine the judgment matrix of the evaluation index data of the target layer and the judgment matrix of the evaluation index data of the criterion layer based on the scale principle of the analytic hierarchy process. The judgment matrix of the evaluation index data of the target layer is shown in table 1, for example:
TABLE 1
| B | B1 | | B3 |
| B1 | |||
| 1 | 1/3 | 3 | |
| |
3 | 1 | 5 |
| |
1/3 | 1/5 | 1 |
B1, B2, B3 respectively denote three evaluation index data power supply systems, charging devices, and monitoring systems of the criterion layer. B represents evaluation index data of the target layer.
The judgment matrix corresponding to the B1 power supply system of the criterion layer is shown in table 2:
TABLE 2
| B1 | C1 | C2 | | C4 |
| C1 | ||||
| 1 | 5 | 7 | 5 | |
| |
1/5 | 1 | 3 | 1 |
| |
1/7 | 1/3 | 1 | 1/3 |
| |
1/5 | 1 | 3 | 1 |
Wherein, C1, C2, C3 and C4 respectively represent evaluation index data distribution transformer, switch board, metering device and harmonic suppression device of four index layers of the B1 power supply system.
The process of determining the weight vector of the B1 power supply system through the judgment matrix corresponding to the B1 power supply system of the criterion layer is as follows:
(1) using formulas
The results obtained by normalization processing of the judgment matrix are shown in table 3:
TABLE 3
(2) Adding the normalized matrixes in the table 3 according to rows to obtain a matrix with 1 row and 4 columns;
(3) normalizing the matrix in the previous step to obtain a weight vector with 1 row and 4 columns: a. the1＝[0.6374 0.1514 0.0624 0.1514]。
After the weight vector is obtained, whether the weight vector is reasonable or not can be verified and judged by the following steps:
(1) and calculating the maximum characteristic root of the judgment matrix.
(2) And (5) carrying out consistency check.
The calculation result of the judgment matrix obtained by calculation is CI of 0.0245, and CR of 0.0272 < 0.1, which indicates that the consistency is acceptable, and the relationship between the weights of the obtained weight vectors is reasonable. If CR is greater than 0.1, the judgment matrix of the evaluation index data needs to be readjusted according to the scaling principle. And calculating characteristic roots of the judgment matrixes of the evaluation index data of all the target layers and the judgment matrixes of the evaluation index data of the criterion layers, and then carrying out consistency check.
Thereby, a second judgment matrix of the evaluation index data of the criterion layer is determined by the analytic hierarchy process; and carrying out normalization processing on the second judgment matrix to obtain a weight vector of the evaluation index data of the criterion layer. Reliable data support can be provided for determining the criterion layer evaluation vector and the target layer evaluation vector and finally determining the energy efficiency evaluation grade of the electric vehicle charging station.
And S130, determining a target layer evaluation vector according to the first fuzzy relation matrix, and determining a grading grade of the electric vehicle charging station according to the target layer evaluation vector.
According to the scheme, the target layer evaluation vector can be determined according to the fuzzy relation matrix of the evaluation index data of the target layer and the weight vector of the evaluation index data of the target layer. Wherein, the weight vector of the evaluation index data of the target layer is determined according to the judgment matrix of the evaluation index data of the target layer. After determining the target layer evaluation vector, the present scheme may determine the target layer evaluation vector according to the target layer evaluation vector and the standard degree of membership J ═ 54321]Based on the formula
And determining the grading grade of the electric vehicle charging station. Wherein e isiRepresenting elements in a target-level evaluation vector E, E being a three-criteria-level evaluation vector E1、E2And E3Evaluation of the constructed target layerAnd the fuzzy relation matrix R of the index data is obtained by vector multiplication of the weight vector of the evaluation index data of the target layer.
According to the technical scheme provided by the embodiment of the invention, evaluation index data of the electric vehicle charging station are determined, and an evaluation index system is established according to the evaluation index data; the evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer; determining a first fuzzy relation matrix of evaluation index data of a target layer according to an evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector; and determining a target layer evaluation vector according to the first fuzzy relation matrix, and determining a grading grade of the electric vehicle charging station according to the target layer evaluation vector. By executing the technical scheme provided by the embodiment of the invention, a tool can be efficiently and quickly provided for energy efficiency evaluation of the electric vehicle charging station, the high energy efficiency of the charging station is favorably ensured, and the energy conservation and emission reduction of charging facilities are promoted.
Fig. 2 is a flowchart of an energy efficiency evaluation method for an electric vehicle charging station according to an embodiment of the present invention, and the embodiment is optimized based on the foregoing embodiment. As shown in fig. 2, the method for evaluating energy efficiency of an electric vehicle charging station according to an embodiment of the present invention may include:
and S210, determining evaluation index data of the electric vehicle charging station, and establishing an evaluation index system according to the evaluation index data.
And S220, determining a first fuzzy relation matrix of the evaluation index data of the target layer according to the evaluation index system.
And S230, determining the weight vector of the evaluation index data of the target layer.
In one possible embodiment, optionally, determining a weight vector of the evaluation index data of the target layer includes: determining a first judgment matrix of evaluation index data of the target layer based on an analytic hierarchy process; the first judgment matrix is obtained by constructing evaluation index data of at least one criterion layer related to the evaluation index data of the target layer; and determining a weight vector of the evaluation index data of the target layer according to the first judgment matrix.
According to the scheme, the judgment matrix of the evaluation index data of the target layer can be determined based on the scale principle of the analytic hierarchy process, and then the weight vector of the evaluation index data of the target layer is determined according to the judgment matrix. The specific implementation process of obtaining the weight vector according to the judgment matrix can refer to the foregoing description.
Thereby, a first judgment matrix of evaluation index data of the target layer is determined by the analytic hierarchy process; and determining a weight vector of the evaluation index data of the target layer according to the first judgment matrix. Reliable data support can be provided for further determining the target layer evaluation vector and finally determining the energy efficiency evaluation grade of the electric vehicle charging station.
S240, determining an objective layer evaluation vector according to the first fuzzy relation matrix and the weight vector of the evaluation index data of the objective layer, and determining the grade of the electric vehicle charging station according to the objective layer evaluation vector.
Illustratively, the target layer evaluation vector E is determined by a fuzzy relation matrix of the evaluation index data of the target layer and a weight vector of the evaluation index data of the target layer:
In this embodiment, optionally, determining the rating of the electric vehicle charging station according to the target layer evaluation vector includes: determining the grade of the electric vehicle charging station according to the target layer evaluation vector and the standard membership degree based on a fuzzy comprehensive evaluation method; and determining the grade of the score according to the corresponding relation between the score and the grade of the score.
Specifically, the standard degree of membership J ═ 54321]Using the target layer evaluation vector E ═ E (E)1,e2,…,em) The weight is formed by the elements in the Chinese sentence, the total score is obtained after the scores of all the comment grades are weighted and averaged, and the corresponding relation between the score and the score grade is as follows: [4.5,5]Is excellent, [3.5, 4.5 ]]Is good, [2.5, 3.5 ]]Is medium, [1.5, 2.5]Is qualified, [1, 1.5 ]]Is not qualified.
For example, the score G of the electric vehicle charging station is determined based on the formula:
Therefore, the grade of the electric vehicle charging station is determined according to the target layer evaluation vector and the standard membership degree based on a fuzzy comprehensive evaluation method; the grading grade is determined according to the corresponding relation between the grading and the grading grade, so that the energy efficiency of the electric vehicle charging station can be quantitatively evaluated, and the energy efficiency of the electric vehicle charging station can be scientifically and effectively evaluated.
The technical scheme provided by the embodiment of the invention comprises the steps of determining evaluation index data of the electric vehicle charging station, establishing an evaluation index system according to the evaluation index data, determining a first fuzzy relation matrix of the evaluation index data of a target layer according to the evaluation index system, determining a weight vector of the evaluation index data of the target layer, determining an evaluation vector of the target layer according to the fuzzy relation matrix of the evaluation index data of the target layer and the weight vector of the evaluation index data of the target layer, and determining a grading grade of the electric vehicle charging station according to the evaluation vector of the target layer. Through executing the scheme, the efficient and rapid energy efficiency evaluation tool for the electric vehicle charging station can be realized, the high energy efficiency of the charging station is favorably ensured, and the energy conservation and emission reduction of charging facilities are promoted.
The popularization of the electric automobile industry aims to develop energy conservation and emission reduction, so that the energy efficiency of related supporting facilities needs to be evaluated, and the realization of the energy conservation and emission reduction functions is ensured. The charging facility is one of the matching electric automobile industry main bodies, and the energy efficiency in the construction, operation and management determines the contribution of the electric automobile charging facility to energy conservation and emission reduction.
In the prior art, the energy efficiency of the electric automobile charging pile is evaluated from a hardware facility level, and the defects that an analysis program is complex, the calculated amount is large, the difference exists among the internal power electronic devices of the charging piles of different models, and a hardware metering point is required to be set for acquiring electric energy data exist.
Fig. 3 is a flowchart of an energy efficiency evaluation method for an electric vehicle charging station according to an embodiment of the present invention, and in order to more clearly express the technical solution of the present invention, as shown in fig. 3, the technical solution according to the embodiment of the present invention may include the following steps:
(1) And normalizing the judgment matrix.
Normalization formula:
(2) and adding the normalized judgment matrixes according to rows.
(3) And normalizing the vector to obtain the weight vector.
Using a formula
(4) And calculating the maximum characteristic root of the judgment matrix.
(5) And (5) checking the consistency of the judgment matrix.
Determination formula of score:
according to the technical scheme provided by the embodiment of the invention, energy efficiency evaluation is carried out from the whole aspect of the electric vehicle charging station, an evaluation method based on an analytic hierarchy process and a fuzzy comprehensive evaluation method is adopted, an energy efficiency evaluation system and various index weights of the electric vehicle charging station are formulated by combining expert experience, the energy efficiency evaluation indexes of the electric vehicle charging station are divided into different hierarchy dimensions from the whole aspect, qualitative evaluation of different experts is converted into quantitative energy efficiency evaluation values by adopting a fuzzy mathematic mode, the defect that the energy efficiency evaluation is carried out from the whole aspect of the electric vehicle charging station in the prior art is overcome, the knowledge and practical experience of different experts are fully combined, and compared with the prior art, the analysis program is simpler and the calculation amount is small. The method and the device provide a tool for energy efficiency evaluation of the charging station, the charging station to be built can be ensured to keep high energy efficiency after being built and operated, and improvement measures can be provided for the built charging station through an evaluation result, so that the high energy efficiency of the charging station is ensured, and charging facilities are promoted to finish energy conservation and emission reduction tasks.
Fig. 4 is a schematic diagram of an energy efficiency evaluation apparatus for an electric vehicle charging station, which may be configured in an electronic device for evaluating energy efficiency of an electric vehicle charging station according to an embodiment of the present invention. As shown in fig. 4, the apparatus includes:
an evaluation index system establishing module 410, configured to determine evaluation index data of the electric vehicle charging station, and establish an evaluation index system according to the evaluation index data; the evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer;
a fuzzy relation matrix determining module 420, configured to determine a first fuzzy relation matrix of the evaluation index data of the target layer according to the evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector;
and the grading level determination module 430 is configured to determine a target layer evaluation vector according to the first fuzzy relation matrix, and determine a grading level of the electric vehicle charging station according to the target layer evaluation vector.
Optionally, the apparatus further includes a weight vector determination module, configured to determine a weight vector of evaluation index data of a target layer before determining an evaluation vector of the target layer according to the first fuzzy relation matrix; correspondingly, the scoring level determining module 430 is specifically configured to determine an evaluation vector of the target layer according to the first fuzzy relation matrix and the weight vector of the evaluation index data of the target layer.
Optionally, the weight vector determination module is specifically configured to determine a first determination matrix of the evaluation index data of the target layer based on an analytic hierarchy process; the first judgment matrix is obtained by constructing evaluation index data of at least one criterion layer related to the evaluation index data of the target layer; and determining a weight vector of the evaluation index data of the target layer according to the first judgment matrix.
Optionally, the determining process of the criterion layer evaluation vector includes: determining a second fuzzy relation matrix of each evaluation index data of the criterion layer according to the evaluation index system based on a fuzzy comprehensive evaluation method; the second fuzzy relation matrix is obtained by constructing evaluation index data of at least one index layer related to the evaluation index data of the criterion layer; determining a weight vector of each evaluation index data of the criterion layer based on an analytic hierarchy process; and determining a criterion layer evaluation vector according to the second fuzzy relation matrix and the weight vector of the evaluation index data of the criterion layer.
Optionally, the process of determining the weight vector of the evaluation index data of the criterion layer includes: determining a second judgment matrix of the evaluation index data of the criterion layer based on an analytic hierarchy process; the second judgment matrix is obtained by constructing evaluation index data of at least one index layer related to the evaluation index data of the criterion layer; and carrying out normalization processing on the second judgment matrix to obtain a weight vector of the evaluation index data of the criterion layer.
Optionally, the scoring level determining module 430 is specifically configured to determine, based on a fuzzy comprehensive evaluation method, a scoring of the electric vehicle charging station according to the target layer evaluation vector and the standard membership degree; and determining the grade of the score according to the corresponding relation between the score and the grade of the score.
Optionally, the evaluation index data of the criterion layer includes at least one of a power supply system, a charging device, and a monitoring system; the evaluation index data of the index layer comprises at least one of a distribution transformer, a power distribution cabinet, a metering device, a harmonic treatment device, a rectifying device, a direct current charging device, a security monitoring system, a charging and discharging monitoring system and an intelligent charging and discharging monitoring device.
The device provided by the embodiment can execute the energy efficiency evaluation method of the electric vehicle charging station provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 5, the electronic device includes:
one or more processors 510, one processor 510 being illustrated in FIG. 5;
a memory 520;
the apparatus may further include: an input device 530 and an output device 540.
The processor 510, the memory 520, the input device 530 and the output device 540 of the apparatus may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The memory 520 is a non-transitory computer-readable storage medium, and can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the method for evaluating the energy efficiency of an electric vehicle charging station according to the embodiment of the present invention. The processor 510 executes various functional applications and data processing of the computer device by executing the software programs, instructions and modules stored in the memory 520, so as to implement the energy efficiency evaluation method of the electric vehicle charging station according to the above method embodiment, that is:
determining evaluation index data of the electric vehicle charging station, and establishing an evaluation index system according to the evaluation index data; the evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer;
determining a first fuzzy relation matrix of evaluation index data of a target layer according to the evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector;
and determining a target layer evaluation vector according to the first fuzzy relation matrix, and determining a grading level of the electric vehicle charging station according to the target layer evaluation vector.
The memory 520 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 520 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 520 may optionally include memory located remotely from processor 510, which may be connected to a terminal device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 530 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the computer apparatus. The output device 540 may include a display device such as a display screen.
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements an energy efficiency assessment method for an electric vehicle charging station, according to an embodiment of the present invention:
determining evaluation index data of the electric vehicle charging station, and establishing an evaluation index system according to the evaluation index data; the evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer;
determining a first fuzzy relation matrix of evaluation index data of a target layer according to the evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector;
and determining a target layer evaluation vector according to the first fuzzy relation matrix, and determining a grading level of the electric vehicle charging station according to the target layer evaluation vector.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An energy efficiency evaluation method for an electric vehicle charging station is characterized by comprising the following steps:
determining evaluation index data of the electric vehicle charging station, and establishing an evaluation index system according to the evaluation index data; the evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer;
determining a first fuzzy relation matrix of evaluation index data of a target layer according to the evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector;
and determining a target layer evaluation vector according to the first fuzzy relation matrix, and determining a grading level of the electric vehicle charging station according to the target layer evaluation vector.
2. The method of claim 1, prior to determining a target layer evaluation vector from the first fuzzy relation matrix, comprising:
determining a weight vector of the evaluation index data of the target layer;
correspondingly, determining a target layer evaluation vector according to the first fuzzy relation matrix comprises the following steps:
and determining an evaluation vector of the target layer according to the first fuzzy relation matrix and the weight vector of the evaluation index data of the target layer.
3. The method of claim 2, wherein determining a weight vector for evaluation index data of the target layer comprises:
determining a first judgment matrix of evaluation index data of the target layer based on an analytic hierarchy process; the first judgment matrix is obtained by constructing evaluation index data of at least one criterion layer related to the evaluation index data of the target layer;
and determining a weight vector of the evaluation index data of the target layer according to the first judgment matrix.
4. The method of claim 1, wherein the determining of the criterion-level evaluation vector comprises:
determining a second fuzzy relation matrix of each evaluation index data of the criterion layer according to the evaluation index system based on a fuzzy comprehensive evaluation method; the second fuzzy relation matrix is obtained by constructing evaluation index data of at least one index layer related to the evaluation index data of the criterion layer;
determining a weight vector of each evaluation index data of the criterion layer based on an analytic hierarchy process;
and determining a criterion layer evaluation vector according to the second fuzzy relation matrix and the weight vector of the evaluation index data of the criterion layer.
5. The method according to claim 4, wherein the determining process of the weight vector of the evaluation index data of the criterion layer comprises:
determining a second judgment matrix of the evaluation index data of the criterion layer based on an analytic hierarchy process; the second judgment matrix is obtained by constructing evaluation index data of at least one index layer related to the evaluation index data of the criterion layer;
and carrying out normalization processing on the second judgment matrix to obtain a weight vector of the evaluation index data of the criterion layer.
6. The method of claim 1, wherein determining a rating for an electric vehicle charging station based on the target layer evaluation vector comprises:
determining the grade of the electric vehicle charging station according to the target layer evaluation vector and the standard membership degree based on a fuzzy comprehensive evaluation method;
and determining the grade of the score according to the corresponding relation between the score and the grade of the score.
7. The method according to claim 1, wherein the evaluation index data of the criterion layer includes at least one of a power supply system, a charging device, and a monitoring system;
the evaluation index data of the index layer comprises at least one of a distribution transformer, a power distribution cabinet, a metering device, a harmonic treatment device, a rectifying device, a direct current charging device, a security monitoring system, a charging and discharging monitoring system and an intelligent charging and discharging monitoring device.
8. An electric vehicle charging station energy efficiency assessment device, comprising:
the evaluation index system establishing module is used for determining evaluation index data of the electric vehicle charging station and establishing an evaluation index system according to the evaluation index data; the evaluation index system comprises a target layer, a criterion layer and an index layer, wherein the target layer, the criterion layer and the index layer are respectively composed of at least one evaluation index data; the evaluation index data of the criterion layer is related to the evaluation index data of at least one index layer;
the fuzzy relation matrix determining module is used for determining a first fuzzy relation matrix of the evaluation index data of the target layer according to the evaluation index system; wherein the first fuzzy relation matrix is determined by at least one criterion layer evaluation vector;
and the grading level determination module is used for determining a target layer evaluation vector according to the first fuzzy relation matrix and determining the grading level of the electric vehicle charging station according to the target layer evaluation vector.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the electric vehicle charging station energy efficiency assessment method of any of claims 1-7.
10. A computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the electric vehicle charging station energy efficiency assessment method according to any one of claims 1-7.
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