Electric energy planning device and method for electric vehicle

Abstract

The invention provides an electric energy planning device and method for an electric vehicle, and relates to the technical field of charging pile planning, and the electric energy planning device comprises a region division module, a data collection module, an analysis module, a comprehensive analysis module and a comparison module. According to the method, data acquisition and analysis are carried out on public charging piles in an existing layout in a region for the first time, whether the charging piles in a to-be-evaluated region need to be rectified or not is evaluated from three aspects of public charging pile charging time use uniformity, public charging pile charging waiting time and public charging pile setting satisfaction, and a to-be-rectified confidence coefficient is output; and generating a public charging pile replanning evaluation index, outputting the confidence coefficient to be adjusted of the public charging pile, if the confidence coefficient is low, indicating that the public charging pile is reasonable in layout and does not need rectification, and if the confidence coefficient is high, indicating that the public charging pile is unreasonable in layout and needs rectification, thereby improving the utilization rate of social resources.

Description

Technical Field

[0001] This invention relates to the field of charging pile planning technology, specifically to an electric vehicle power planning device and method. Background Technology

[0002] Public charging stations for electric vehicles are facilities that provide charging services for electric vehicles. They are typically located in public places such as parking lots, shopping malls, hotels, gas stations, and roadsides. These charging stations allow electric vehicle users to charge their vehicles, thereby extending their driving range.

[0003] However, after the initial planning and construction of public charging stations for electric vehicles, they often fail to meet actual usage needs, resulting in an unreasonable distribution of public charging stations. This leads to situations where some public charging stations require queuing for charging, while others remain idle and unused, resulting in a waste of social resources. Therefore, it is necessary to determine whether public charging stations in a region need to be replanned and rebuilt.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an electric vehicle power planning device and method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An electric vehicle power planning device and method, the specific steps of which include:

[0008] S1. Divide the area to be evaluated into n sub-regions of equal area and number them;

[0009] S2. Collect the number of public charging piles in each sub-area and number them, and collect the charging time of each public charging pile in the previous year.

[0010] S3. Preprocess the charging time of each public charging pile in the previous year to generate the total charging time of public charging piles in each sub-area, and further process it to generate the overall average charging time of public charging piles, the average charging time of charging piles in sub-areas, and the charging pile usage frequency coefficient.

[0011] S4. Collect electric vehicle frequency data in the area to be evaluated, preprocess the electric vehicle frequency data to generate the frequency percentage of the sub-area, preprocess the total charging time of the public charging piles in the sub-area to generate the charging time percentage of the public charging piles in the sub-area.

[0012] S5. Conduct a correlation analysis on the percentage of charging time and the percentage of frequency of public charging piles in the sub-area to generate a user waiting value evaluation coefficient.

[0013] S6. Collect basic data on public charging piles, preprocess the basic data on public charging piles, and generate a regional satisfaction evaluation index.

[0014] S7. Preprocess the regional satisfaction evaluation index, user waiting value evaluation coefficient, and charging pile usage frequency coefficient to generate a public charging pile replanning evaluation index. Compare the public charging pile replanning evaluation index with the threshold and output the public charging pile adjustment confidence level.

[0015] Furthermore, in S1-S2, the sub-regions are numbered 1, 2, ..., j, ..., n, and the number of public charging piles in the j-th sub-region is k. j The subscript j represents the j-th sub-region, and the charging time of the i-th public charging pile in the j-th sub-region in the previous year was... The unit is hours.

[0016] Furthermore, the charging time of the i-th public charging pile in the j-th sub-region in the previous year... Correlation analysis is performed to generate the total charging time for public charging stations in the j-th sub-region, based on the following formula:

[0017]

[0018] Among them, ZCS j The total charging time for the public charging piles in the j-th sub-area;

[0019] The total charging time ZCS for the public charging pile in the j-th sub-area j The number of public charging stations k contained in the j-th sub-region j Correlation analysis was performed to generate the overall average charging time of public charging stations and the average charging time of charging stations in the j-th sub-region. The formula used was:

[0020]

[0021] Where JZ represents the average charging time of all public charging stations, and JC represents the average charging time of all public charging stations. j The average charging time for charging piles in the j-th sub-region;

[0022] Average charging time JZ for the overall public charging piles and average charging time JC for the charging piles in the j-th sub-area. j Correlation analysis was performed to generate the charging pile usage frequency coefficient, based on the following formula:

[0023]

[0024] Wherein, CSX is the charging pile usage frequency coefficient;

[0025] The average charging time of the public charging piles reflects the average charging time of the public charging piles in the area to be evaluated. The average charging time of the charging piles in the j-th sub-area reflects the average charging time of the public charging piles in each sub-area. The charging pile usage frequency coefficient reflects the smoothness of the charging usage rate of the public charging piles in the area to be evaluated. The larger the charging pile usage frequency coefficient, the more uneven the charging usage rate of the public charging piles in the area to be evaluated.

[0026] Furthermore, the frequency of electric vehicles appearing at intersections in the j-th sub-region is M. j Correlation analysis is performed on the frequency of electric vehicles appearing at intersections in the j-th sub-region to generate the frequency M of electric vehicles appearing at intersections in the region to be evaluated. The formula used is as follows:

[0027] Furthermore, in step S4, the electric vehicle frequency data for the area to be evaluated includes the frequency M of electric vehicles appearing at intersections in the area to be evaluated and the frequency M of electric vehicles appearing at intersections in the j-th sub-area. j The frequency M of electric vehicles at intersections in the evaluation area and the frequency M of electric vehicles at intersections in the j-th sub-area are respectively determined. j Perform correlation analysis to generate the frequency percentage T of the j-th sub-region. j The formula used is:

[0028] Furthermore, the total charging time ZCS for the public charging piles in the j-th sub-area is... j Correlation analysis was performed to generate the total charging time (CT) of public charging stations in the area to be evaluated, based on the following formula:

[0029] The total charging time CT of the public charging piles in the evaluation area and the total charging time ZCS of the public charging piles in the j-th sub-area. j Correlation analysis was performed to generate the percentage S of charging time for public charging piles in the j-th sub-region. j The formula used is:

[0030] Furthermore, the percentage of charging time S for the public charging piles in the j-th sub-area j The percentage of frequency in the j-th sub-region T j Correlation analysis was performed to generate the user wait value rating coefficient YDX, based on the following formula:

[0031]

[0032] Among them, the user waiting value evaluation coefficient YDX reflects the user waiting situation of the public charging pile in the j-th sub-area.

[0033] Furthermore, the basic data of the public charging piles includes the maximum traffic flow that the public charging piles can serve in the area to be evaluated, G1; the cost of the charging piles in the area to be evaluated, G2; and the network loss value of the power distribution system in the area to be evaluated, G3.

[0034] Correlation analysis was performed on the basic data of public charging piles to generate the vehicle flow satisfaction index ω(G) in the area to be evaluated. x ), Charging pile cost satisfaction index ω(Z0) in the area to be evaluated, and power distribution system network loss value satisfaction index ω(G) in the area to be evaluated. min The waiting value satisfaction index ω(R) within the region to be evaluated is based on the following formula:

[0035]

[0036] Where G4 represents the user waiting weighted valuation coefficient, ω(G x (G) represents the traffic flow satisfaction index. x Z0 is the maximum traffic flow weight index, and G is the charging pile cost weight index. min R is the network loss weighting index of the power distribution system, and R is the user waiting weighting index.

[0037] Traffic flow satisfaction index ω(G) within the evaluation area x ), Charging pile cost satisfaction index ω(Z0) in the area to be evaluated, and power distribution system network loss value satisfaction index ω(G) in the area to be evaluated. min Correlation analysis was performed on the waiting value satisfaction index ω(R) within the region to be evaluated to generate the regional satisfaction assessment index ω, based on the following formula:

[0038]

[0039] Where, ω max and ω min The formula used is:

[0040] Furthermore, in step S7, a correlation analysis is performed on the regional satisfaction evaluation index, user wait time evaluation coefficient, and charging pile usage frequency coefficient to generate the public charging pile replanning evaluation index GGZ, based on the following formula:

[0041]

[0042] Wherein, α is the weight of the annual usage hours of the public charging pile, and the value range of α is [1000, 8765].

[0043] The threshold is θ. When GGZ ≥ θ, the confidence level for adjusting the public charging piles is 95%-100%, indicating that the location of the public charging piles in the evaluation area is unreasonable and needs adjustment; when... When the confidence level for adjusting public charging stations is 60%-95%, it indicates that the location of public charging stations in the area to be evaluated is relatively reasonable and can be adjusted; when The confidence level for public charging piles to be adjusted is 0%-60%, indicating that the location of public charging piles in the area to be evaluated is reasonable and no adjustment is needed.

[0044] The present invention also provides an electric vehicle power planning device for executing an electric vehicle power planning method, comprising:

[0045] A region division module, which is used to divide the region to be evaluated into sub-regions;

[0046] Data acquisition module A is used to collect the number of public charging piles in the sub-area and the charging time of each public charging pile in the previous year.

[0047] Analysis module A is used to preprocess the charging time of each public charging pile in the previous year to generate the total charging time of public charging piles in each sub-region, and further process to generate the overall average charging time of public charging piles, the average charging time of charging piles in sub-regions, and the charging pile usage frequency coefficient.

[0048] Data acquisition module B is used to collect electric vehicle frequency data in the area to be evaluated.

[0049] Analysis module B is used to preprocess electric vehicle frequency data to generate sub-region frequency percentage, preprocess the total charging time of public charging piles in sub-regions to generate sub-region public charging time percentage, perform correlation analysis on sub-region public charging pile charging time percentage and sub-region frequency percentage, and generate user wait value evaluation coefficient.

[0050] Data acquisition module C, which is used to collect basic data of public charging piles;

[0051] Analysis module C is used to preprocess the basic data of public charging piles and generate a regional satisfaction evaluation index.

[0052] The comprehensive analysis module is used to preprocess the regional satisfaction evaluation index, user waiting value evaluation coefficient, and charging pile usage frequency coefficient output by analysis modules C, B, and A to generate a public charging pile replanning evaluation index.

[0053] The comparison module is used to compare the public charging pile replanning evaluation index output by the comprehensive analysis module with the threshold, and output the confidence level of the public charging pile to be adjusted.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] This invention is based on;

[0056] This invention is the first to collect and analyze data on existing public charging stations within a region. It evaluates whether charging stations in the evaluated area need improvement based on three aspects: the evenness of charging time usage, waiting time, and satisfaction with the installation of public charging stations. The invention outputs a confidence level indicating the need for improvement. By collecting data on the usage of public charging stations, the frequency of electric vehicles at intersections in the area, and basic data on public charging stations, it analyzes and generates a charging station usage frequency coefficient, a user waiting time evaluation coefficient, and a regional satisfaction assessment index. This generates a public charging station replanning evaluation index and outputs a confidence level indicating the need for adjustment. A low confidence level indicates a reasonable layout of public charging stations requiring no improvement, while a high confidence level indicates an unreasonable layout requiring improvement, thereby improving the utilization rate of social resources. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0058] Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0060] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0061] Example:

[0062] Please see Figure 1 The present invention provides a technical solution:

[0063] An electric vehicle power planning device and method, when planning the power supply for existing electric vehicles, needs to consider the usage of charging piles in various areas, and then relocate or modify public charging piles accordingly. This requires a comprehensive evaluation of the usage of public charging piles in the area. This invention comprehensively considers the following three aspects: the uniformity of charging time usage of public charging piles, the waiting time for charging at public charging piles, and the satisfaction with the installation of public charging piles to evaluate whether the charging piles in the area to be evaluated need to be rectified, and provides a confidence level for rectification. The specific steps include:

[0064] Step 1: Divide the area to be evaluated into n sub-regions of equal area and number them;

[0065] Step 2: Collect the number of public charging piles in each sub-area and number them, and collect the charging time of each public charging pile in the previous year;

[0066] The sub-regions are numbered 1, 2, ..., j, ..., n, and the j-th sub-region contains k public charging stations. j The subscript j represents the j-th sub-region, and the charging time of the i-th public charging pile in the j-th sub-region in the previous year was... The unit is hours;

[0067] Step 3: Preprocess the charging time of each public charging pile in the previous year to generate the total charging time of public charging piles in each sub-area, and further process it to generate the overall average charging time of public charging piles, the average charging time of charging piles in sub-areas, and the charging pile usage frequency coefficient.

[0068] Charging time of the i-th public charging station in the j-th sub-region of the previous year Correlation analysis is performed to generate the total charging time for public charging stations in the j-th sub-region, based on the following formula:

[0069]

[0070] Among them, ZCS j The total charging time for the public charging piles in the j-th sub-area;

[0071] The total charging time ZCS for the public charging pile in the j-th sub-area j The number of public charging stations k contained in the j-th sub-region j Correlation analysis was performed to generate the overall average charging time of public charging stations and the average charging time of charging stations in the j-th sub-region. The formula used was:

[0072]

[0073] Where JZ represents the average charging time of all public charging stations, and JC represents the average charging time of all public charging stations. j The average charging time for charging piles in the j-th sub-region;

[0074] Average charging time JZ for the overall public charging piles and average charging time JC for the charging piles in the j-th sub-area. j Correlation analysis was performed to generate the charging pile usage frequency coefficient, based on the following formula:

[0075]

[0076] Wherein, CSX is the charging pile usage frequency coefficient;

[0077] The average charging time of the public charging piles reflects the average charging time of the public charging piles in the area to be evaluated. The average charging time of the charging piles in the j-th sub-area reflects the average charging time of the public charging piles in each sub-area. The charging pile usage frequency coefficient reflects the smoothness of the charging usage rate of the public charging piles in the area to be evaluated. The larger the charging pile usage frequency coefficient, the more uneven the charging usage rate of the public charging piles in the area to be evaluated.

[0078] Step 4: Collect electric vehicle frequency data in the area to be evaluated, preprocess the electric vehicle frequency data to generate the frequency percentage of the sub-area, preprocess the total charging time of the public charging piles in the sub-area to generate the charging time percentage of the public charging piles in the sub-area.

[0079] The frequency of electric vehicles at the intersection in the j-th sub-region is M. j .

[0080] In step S4, the electric vehicle frequency data for the area to be evaluated includes the frequency M of electric vehicles appearing at intersections in the area to be evaluated and the frequency M of electric vehicles appearing at intersections in the j-th sub-area. j Correlation analysis is performed on the frequency of electric vehicles appearing at intersections in the j-th sub-region to generate the frequency M of electric vehicles appearing at intersections in the region to be evaluated. The formula used is as follows:

[0081] The frequency M of electric vehicles appearing at intersections in the evaluation area and the frequency M of electric vehicles appearing at intersections in the j-th sub-area are discussed. j Perform correlation analysis to generate the frequency percentage T of the j-th sub-region. j The formula used is:

[0082] The total charging time ZCS for the public charging pile in the j-th sub-area j Correlation analysis was performed to generate the total charging time (CT) of public charging stations in the area to be evaluated, based on the following formula:

[0083] The total charging time CT of the public charging piles in the evaluation area and the total charging time ZCS of the public charging piles in the j-th sub-area. j Correlation analysis was performed to generate the percentage S of charging time for public charging piles in the j-th sub-region. j The formula used is:

[0084] Step 5: Perform a correlation analysis on the percentage of charging time and the percentage of frequency of public charging stations in the sub-area to generate a user waiting value evaluation coefficient;

[0085] Combining steps 4 and 5, the electric vehicle frequency data in the area to be evaluated refers to the number of vehicles passing through the intersection in a day. At each intersection, the more vehicles passing through per unit time, the greater the traffic flow. Since traffic flow is directly proportional to charging demand, the charging demand can be calculated through traffic flow. Therefore, the waiting situation for users to charge at public charging stations can be evaluated by comparing the data overflow.

[0086] The percentage of charging time for the public charging piles in the j-th sub-area is S. j The percentage of frequency in the j-th sub-region T j Correlation analysis was performed to generate the user wait value rating coefficient YDX, based on the following formula:

[0087]

[0088] Among them, the user waiting value evaluation coefficient YDX reflects the user waiting situation of the public charging pile in the j-th sub-area.

[0089] Step 6: Collect basic data on public charging piles, preprocess the data, and generate a regional satisfaction evaluation index.

[0090] The basic data of the public charging piles include the maximum traffic flow that the public charging piles can serve in the area to be evaluated, G1; the cost of the charging piles in the area to be evaluated, G2; and the network loss value of the power distribution system in the area to be evaluated, G3.

[0091] Correlation analysis was performed on the basic data of public charging piles to generate the vehicle flow satisfaction index ω(G) in the area to be evaluated. x ), Charging pile cost satisfaction index ω(Z0) in the area to be evaluated, and power distribution system network loss value satisfaction index ω(G) in the area to be evaluated. min The waiting value satisfaction index ω(R) within the region to be evaluated is based on the following formula:

[0092]

[0093] Where G4 represents the user waiting weighted valuation coefficient, ω(G x The vehicle flow satisfaction index reflects the ability of public charging stations to provide services to electric vehicles in the evaluated area. The charging station cost satisfaction index reflects the proportion of the construction budget for charging stations in the evaluated area. The power distribution system network loss value satisfaction index reflects the acceptance level of power distribution system losses in the evaluated area. The waiting time satisfaction index reflects the user satisfaction with the waiting time at charging stations in the evaluated area; the higher the value, the higher the user satisfaction with the waiting time. x Z0 is the maximum traffic flow weight index, and G is the charging pile cost weight index. min R is the network loss weighting index of the power distribution system, and R is the user waiting weighting index.

[0094] Traffic flow satisfaction index ω(G) within the evaluation area x ), Charging pile cost satisfaction index ω(Z0) in the area to be evaluated, and power distribution system network loss value satisfaction index ω(G) in the area to be evaluated. min Correlation analysis was performed on the waiting value satisfaction index ω(R) within the region to be evaluated to generate the regional satisfaction assessment index ω, based on the following formula:

[0095]

[0096] Where, ω max and ω min The formula used is:

[0097] Step 7: Preprocess the regional satisfaction evaluation index, user waiting value evaluation coefficient, and charging pile usage frequency coefficient to generate a public charging pile replanning evaluation index. Compare the public charging pile replanning evaluation index with the threshold and output the public charging pile adjustment confidence level.

[0098] A correlation analysis was conducted on the regional satisfaction evaluation index, user wait time evaluation coefficient, and charging pile usage frequency coefficient to generate the public charging pile replanning evaluation index GGZ. The formula used is as follows:

[0099]

[0100] Wherein, α is the weight of the annual usage hours of the public charging pile, and the value range of α is [1000, 8765].

[0101] The threshold is θ, where θ = 36.48. When GGZ ≥ θ, the confidence level for adjusting the public charging piles is 95%-100%, indicating that the location of the public charging piles in the evaluation area is unreasonable and needs adjustment; when... When the confidence level for adjusting public charging stations is 60%-95%, it indicates that the location of public charging stations in the area to be evaluated is relatively reasonable and can be adjusted; when The confidence level for public charging piles to be adjusted is 0%-60%, indicating that the location of public charging piles in the area to be evaluated is reasonable and no adjustment is needed.

[0102] Reference Figure 2 The present invention also provides an electric vehicle power planning device for executing an electric vehicle power planning method, comprising:

[0103] A region division module, which is used to divide the region to be evaluated into sub-regions;

[0104] Data acquisition module A is used to collect the number of public charging piles in the sub-area and the charging time of each public charging pile in the previous year.

[0105] Analysis module A is used to preprocess the charging time of each public charging pile in the previous year to generate the total charging time of public charging piles in each sub-region, and further process to generate the overall average charging time of public charging piles, the average charging time of charging piles in sub-regions, and the charging pile usage frequency coefficient.

[0106] Data acquisition module B is used to collect electric vehicle frequency data in the area to be evaluated.

[0107] Analysis module B is used to preprocess electric vehicle frequency data to generate sub-region frequency percentage, preprocess the total charging time of public charging piles in sub-regions to generate sub-region public charging time percentage, perform correlation analysis on sub-region public charging pile charging time percentage and sub-region frequency percentage, and generate user wait value evaluation coefficient.

[0108] Data acquisition module C, which is used to collect basic data of public charging piles;

[0109] Analysis module C is used to preprocess the basic data of public charging piles and generate a regional satisfaction evaluation index.

[0110] The comprehensive analysis module is used to preprocess the regional satisfaction evaluation index, user waiting value evaluation coefficient, and charging pile usage frequency coefficient output by analysis modules C, B, and A to generate a public charging pile replanning evaluation index.

[0111] The comparison module is used to compare the public charging pile replanning evaluation index output by the comprehensive analysis module with the threshold, and output the confidence level of the public charging pile to be adjusted.

[0112] When GGZ≥θ, the confidence level for adjusting public charging piles is 95%-100%, indicating that the location of public charging piles in the evaluation area is unreasonable and needs adjustment; when When the confidence level for adjusting public charging stations is 60%-95%, it indicates that the location of public charging stations in the area to be evaluated is relatively reasonable and can be adjusted; when A confidence level of 0%-60% for public charging piles to be adjusted indicates that the location of public charging piles in the area to be evaluated is reasonable and no adjustment is needed. The lower the value of the public charging pile replanning evaluation index GGZ, the lower the confidence level of public charging piles to be adjusted. When the value of the public charging pile replanning evaluation index GGZ is 0, the confidence level of public charging piles to be adjusted is 0%.

[0113] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0114] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0116] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for electric vehicle power planning, characterized in that, The specific steps include: S1. Divide the area to be evaluated into n sub-regions of equal area and number them; S2. Collect the number of public charging piles in each sub-area and number them, and collect the charging time of each public charging pile in the previous year. S3. Preprocess the charging time of each public charging pile in the previous year to generate the total charging time of public charging piles in each sub-area, and further process it to generate the overall average charging time of public charging piles, the average charging time of charging piles in sub-areas, and the charging pile usage frequency coefficient. S4. Collect electric vehicle frequency data in the area to be evaluated, preprocess the electric vehicle frequency data to generate the frequency percentage of the sub-area, preprocess the total charging time of the public charging piles in the sub-area to generate the charging time percentage of the public charging piles in the sub-area. S5. Conduct a correlation analysis on the percentage of charging time and the percentage of frequency of public charging piles in the sub-area to generate a user waiting value evaluation coefficient. S6. Collect basic data on public charging piles, preprocess the basic data on public charging piles, and generate a regional satisfaction evaluation index. S7. Preprocess the regional satisfaction evaluation index, user waiting value evaluation coefficient, and charging pile usage frequency coefficient to generate a public charging pile replanning evaluation index. Compare the public charging pile replanning evaluation index with the threshold and output the public charging pile adjustment confidence level.

2. The electric vehicle power planning method according to claim 1, characterized in that: In S1-S2, the sub-regions are numbered 1, 2, ..., j, ..., n, and the number of public charging piles in the j-th sub-region is k. j The subscript j represents the j-th sub-region, and the charging time of the i-th public charging pile in the j-th sub-region in the previous year was... The unit is hours.

3. The electric vehicle power planning method according to claim 2, characterized in that: In S3, the charging time of the i-th public charging pile in the j-th sub-region of the previous year is... Correlation analysis is performed to generate the total charging time for public charging stations in the j-th sub-region, based on the following formula: Among them, ZCS j The total charging time for the public charging piles in the j-th sub-area; The total charging time ZCS for the public charging piles in the j-th sub-area j and the number of public charging piles k contained in the j-th sub-region j Correlation analysis was performed to generate the overall average charging time of public charging stations and the average charging time of charging stations in the j-th sub-region. The formula used was: Where JZ represents the average charging time of all public charging stations, and JC represents the average charging time of all public charging stations. j The average charging time for charging piles in the j-th sub-region; Average charging time JZ for the overall public charging piles and average charging time JC for the charging piles in the j-th sub-area. j Correlation analysis was performed to generate the charging pile usage frequency coefficient, based on the following formula: Wherein, CSX is the charging pile usage frequency coefficient; The average charging time of the public charging piles reflects the average charging time of the public charging piles in the area to be evaluated. The average charging time of the charging piles in the j-th sub-area reflects the average charging time of the public charging piles in each sub-area. The charging pile usage frequency coefficient reflects the smoothness of the charging usage rate of the public charging piles in the area to be evaluated. The larger the charging pile usage frequency coefficient, the more uneven the charging usage rate of the public charging piles in the area to be evaluated.

4. The electric vehicle power planning method according to claim 3, characterized in that: In step S4, the electric vehicle frequency data for the area to be evaluated includes the frequency M of electric vehicles appearing at intersections in the area to be evaluated and the frequency M of electric vehicles appearing at intersections in the j-th sub-area. j Correlation analysis is performed on the frequency of electric vehicles appearing at intersections in the j-th sub-region to generate the frequency M of electric vehicles appearing at intersections in the region to be evaluated. The formula used is as follows: The frequency M of electric vehicles appearing at intersections in the evaluation area and the frequency M of electric vehicles appearing at intersections in the j-th sub-area are discussed. j Perform correlation analysis to generate the frequency percentage T of the j-th sub-region. j The formula used is:

5. The electric vehicle power planning method according to claim 4, characterized in that: The intersection is a junction of three or more branching roads.

6. The electric vehicle power planning method according to claim 5, characterized in that: The total charging time ZCS for the public charging piles in the j-th sub-area j Correlation analysis was performed to generate the total charging time (CT) of public charging stations in the area to be evaluated, based on the following formula: The total charging time CT of the public charging piles in the evaluation area and the total charging time ZCS of the public charging piles in the j-th sub-area. j Correlation analysis was performed to generate the percentage S of charging time for public charging piles in the j-th sub-region. j The formula used is:

7. The electric vehicle power planning method according to claim 6, characterized in that: The percentage of charging time for the public charging piles in the j-th sub-area is S. j The percentage of frequency in the j-th sub-region T j Correlation analysis was performed to generate the user wait value rating coefficient YDX, based on the following formula: Among them, the user waiting value evaluation coefficient YDX reflects the user waiting situation of the public charging pile in the j-th sub-area.

8. The electric vehicle power planning method according to claim 1, characterized in that: The basic data of the public charging piles include the maximum traffic flow that the public charging piles can serve in the area to be evaluated, G1; the cost of the charging piles in the area to be evaluated, G2; and the network loss value of the power distribution system in the area to be evaluated, G3. Correlation analysis was performed on the basic data of public charging piles to generate a vehicle flow satisfaction index ω(G) for the area to be evaluated. x The evaluation index includes the charging pile cost satisfaction index ω(Z0) and the power distribution system network loss value satisfaction index ω(G) within the evaluation area. min The waiting value satisfaction index ω(R) within the region to be evaluated is based on the following formula: Where G4 represents the user waiting weighted valuation coefficient, ω(G x The vehicle flow satisfaction index reflects the ability of public charging stations to provide services to electric vehicles in the evaluated area. The charging station cost satisfaction index reflects the proportion of the construction budget for charging stations in the evaluated area. The power distribution system network loss value satisfaction index reflects the acceptance level of power distribution system losses in the evaluated area. The waiting time satisfaction index reflects the user satisfaction level with the waiting time for charging stations in the evaluated area. x Z0 is the maximum traffic flow weight index, and G is the charging pile cost weight index. min R is the network loss weighting index of the power distribution system, and R is the user waiting weighting index. Traffic flow satisfaction index ω(G) within the evaluation area x The evaluation index includes the charging pile cost satisfaction index ω(Z0) and the power distribution system network loss value satisfaction index ω(G) within the evaluation area. min Correlation analysis was performed on the waiting value satisfaction index ω(R) within the region to be evaluated to generate the regional satisfaction assessment index ω, based on the following formula: Where, ω max and ω min The formula used is:

9. The electric vehicle power planning method according to claims 2-8, characterized in that: In step S7, a correlation analysis is performed on the regional satisfaction evaluation index, user waiting value evaluation coefficient, and charging pile usage frequency coefficient to generate the public charging pile replanning evaluation index GGZ, based on the following formula: Where α is the weight of the annual usage hours of the public charging pile, and the value range of α is [1000, 8765]. The threshold is θ. When GGZ ≥ θ, the confidence level for adjusting the public charging piles is 95%-100%, indicating that the location of the public charging piles in the evaluation area is unreasonable and needs adjustment; when... When the confidence level for adjusting public charging stations is 60%-95%, it indicates that the location of public charging stations in the area to be evaluated is relatively reasonable and can be adjusted; when The confidence level for public charging piles to be adjusted is 0%-60%, indicating that the location of public charging piles in the area to be evaluated is reasonable and no adjustment is needed.

10. An electric vehicle power planning device, used to execute the electric vehicle power planning method according to claim 1, characterized in that, include: A region division module, which is used to divide the region to be evaluated into sub-regions; Data acquisition module A is used to collect the number of public charging piles in the sub-area and the charging time of each public charging pile in the previous year. Analysis module A is used to preprocess the charging time of each public charging pile in the previous year to generate the total charging time of public charging piles in each sub-region, and further process to generate the overall average charging time of public charging piles, the average charging time of charging piles in sub-regions, and the charging pile usage frequency coefficient. Data acquisition module B is used to collect electric vehicle frequency data in the area to be evaluated. Analysis module B is used to preprocess electric vehicle frequency data to generate sub-region frequency percentage, preprocess the total charging time of public charging piles in sub-regions to generate sub-region public charging time percentage, perform correlation analysis on sub-region public charging pile charging time percentage and sub-region frequency percentage, and generate user wait value evaluation coefficient. Data acquisition module C, which is used to collect basic data of public charging piles; Analysis module C is used to preprocess the basic data of public charging piles and generate a regional satisfaction evaluation index. The comprehensive analysis module is used to preprocess the regional satisfaction evaluation index, user waiting value evaluation coefficient, and charging pile usage frequency coefficient output by analysis modules C, B, and A to generate a public charging pile replanning evaluation index. The comparison module is used to compare the public charging pile replanning evaluation index output by the comprehensive analysis module with the threshold, and output the confidence level of the public charging pile to be adjusted.

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