A Method for Accessibility Analysis of Electric Vehicle Charging Stations Based on the LEVCD-2SFCA Model
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHONGQING UNIV OF POSTS & TELECOMM
- Filing Date
- 2022-12-15
- Publication Date
- 2026-05-26
Smart Images
Figure CN116245299B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of urban infrastructure planning and analysis, and relates to a method for analyzing the accessibility of electric vehicle charging stations based on the LEVCD-2SFCA model. Background Technology
[0002] Electric vehicle charging infrastructure is a crucial supporting infrastructure for the development of the electric vehicle industry, and its planning and construction are important topics in the field of electric vehicle research. Currently, due to the lack of a comprehensive approach to electric vehicle planning and layout, only a few cities have begun constructing charging facilities, which hinders the development of the electric vehicle industry.
[0003] The rational planning and placement of electric vehicle charging facilities is conducive to the large-scale promotion of electric vehicles and the convenience of charging while driving. Active exploration and research into charging facility planning can provide theoretical and technical support for the construction of charging infrastructure, optimize the location of charging facilities, thereby saving construction costs and improving the service quality of charging infrastructure. In the planning of electric vehicle charging stations, accessibility is a crucial factor in assessing the equitable distribution of electric vehicle charging services and an important indicator of whether charging service resources are excessive or insufficient. Simultaneously, accessibility is a means of analyzing the current spatial configuration of urban electric vehicle charging stations and a prerequisite for optimizing urban resource allocation, improving energy efficiency, enhancing social benefits, and improving transportation convenience, providing data support for the site selection of charging stations.
[0004] In accessibility analysis of charging stations, geographic information system (GIS) accessibility analysis techniques are typically used, such as network analysis and two-step mobile search. However, network analysis cannot fully reflect the relationship between supply and demand; the two-step mobile search method suffers from problems such as difficulty in obtaining charging demand points, poor accuracy, a fixed and unfounded search radius, unrealistic distance considerations, and failure to take into account competition among charging stations. Summary of the Invention
[0005] In view of this, the purpose of this invention is to provide an accessibility analysis method for electric vehicle charging stations based on the LEVCD-2SFCA model, which combines the competition and weights of charging stations and uses the actual road distance instead of the physical circular radius distance to achieve a more realistic accessibility analysis.
[0006] To achieve the above objectives, the present invention provides the following technical solution:
[0007] A method for accessibility analysis of electric vehicle charging stations based on the LEVCD-2SFCA model is proposed. The LEVCD algorithm is a search algorithm for electric vehicle charging demand points proposed based on different electric vehicle trajectory data, and 2SFCA is a Gaussian two-step move search method. This method includes the following steps:
[0008] S1. Collect electric vehicle trajectory data and POI point data of driving factors affecting the selection of charging station sites within a certain period of time in the study area, perform data preprocessing, and form a dataset;
[0009] S2. Analyze electric vehicle trajectory data using a charging demand algorithm to obtain historical charging demand points for electric vehicles;
[0010] S3. Analyze the attributes of the driving factor data that affect the selection of charging station sites in step S1, classify the levels according to the attribute information, generate kernel density maps corresponding to different driving factors using ArcGIS software, normalize the kernel density data of POI point data, and obtain the fitness F of each location in the study area by weight superposition and summation.
[0011] S4. Based on the fitness F obtained in step S3, the fitness of the points falling within the grid is represented by the grid fitness. The maximum demand of the study area is matched with the minimum service radius that the study area hopes to achieve, and the service radius of the charging station at the point to be determined is calculated.
[0012] S5. Perform 2SFCA reachability analysis based on the data obtained in steps S1 to S4.
[0013] S6. Based on the analysis results of step S5, obtain quantitative accessibility data for all charging stations, form an accessibility map, evaluate the accessibility of charging stations based on the accessibility map, and use it as a reference for charging station site selection.
[0014] Furthermore, in step S1, the electric vehicle trajectory data includes: the vehicle's unique identifier, remaining battery power, latitude and longitude, vehicle status, charging status, passenger status, speed, and time;
[0015] The driving factor POI data includes: schools, hospitals, shopping malls, roads, and parking lots.
[0016] The data preprocessing includes: correcting, supplementing and reconstructing outliers and missing values in electric vehicle trajectory data; analyzing the attribute information of POI point data; classifying and grading POI point data; and using a word frequency weighting algorithm to obtain the weights of POI point data.
[0017] Furthermore, in step S2, the corresponding charging demand algorithm is selected according to the different types of vehicles to obtain the historical electric vehicle charging demand points.
[0018] When the electric vehicle is used as a taxi, the charging demand algorithm is as follows:
[0019] 1) For each new energy vehicle, select points with a speed of 0 and a passenger load of 0;
[0020] 2) Determine whether the points selected in 1) are within a circular area with a radius of 50m of the charging station;
[0021] 3) Place the points within the range into the set, and find the segments in the trajectory of the point that have a velocity of 0 and a duration of 40 to 120 minutes;
[0022] 4) Find the nearest point in the segment obtained in 3) where the passenger load is 1, and use it as the charging demand point;
[0023] 5) Traverse the trajectory of each new energy vehicle and collect all points that meet the requirements.
[0024] When the electric vehicle is a regular passenger vehicle, the charging demand algorithm is as follows;
[0025] 1) Determine the charging time and location based on the time it takes for the electric vehicle's remaining battery power to rise, the parking location, and the duration of parking;
[0026] 2) Analyze the frequency of remaining battery power during historical charging for each vehicle, and use the battery power with the highest frequency as the charging threshold for each vehicle;
[0027] 3) The location where the vehicle's remaining battery power reaches the charging threshold is designated as the charging demand point.
[0028] Furthermore, in step S3, the latitude and longitude information of the POI point data is linked with the x and y fields of the ArcGIS software data, and the corresponding coordinate system is specified; the kernel density of different POI point data is calculated by the kernel density analysis tool in ArcGIS software, and the kernel density of each POI point data is normalized and weighted and superimposed to obtain the fitness F of different locations.
[0029] Furthermore, in step S4, the reference radius is calculated as follows;
[0030] Using fitness F as a reference, the maximum F and the minimum service radius R in the study area are compared. min By solving the simultaneous equations, the service radius of the charging station at the desired location can be obtained:
[0031] R = F1 ÷ F × R min
[0032] In the formula, R represents the service radius of charging stations and charging demand points within the study area, F1 represents the maximum fitness, and F represents the fitness at the location of the point to be determined.
[0033] Furthermore, step S5 includes the following steps:
[0034] S51. Take a charging station j on road K as an example, and form a linear domain centered on j with d0 as the road radius. Use trajectory data to obtain the charging power of electric vehicles corresponding to each demand point. Take the center point of the road as the location of the road, and sum the charging power of all charging demand points falling on the road as the demand power of the road. For each road k located in the linear domain, use the Gaussian equation to assign weights, and sum the weighted roads k to obtain the potential charging demand of charging station j.
[0035] S52. Considering the competition from other charging stations within the spatial domain, demand averaging is required when calculating the potential users of charging station j: P k / n, where P k Let represent the total charging power demand of road k within the spatial domain of charging station j, and n represent the number of charging stations within the spatial domain; calculate the supply-demand ratio based on the average demand calculation results:
[0036]
[0037] In the formula, S j d represents the total charging power that the charging station can provide. kj Let G(k) represent the road distance from road k to charging station j, and G() represent the Gaussian equation considering spatial friction.
[0038] S53. For each road i, a spatial range is formed by a given spatial distance threshold d0. The supply-demand ratio R for each charging station l within the spatial range is... l The Gaussian equation is used to assign weights, and then the weighted supply and demand ratios are summed to obtain the charging station accessibility A for each road i. i :
[0039]
[0040] In the formula, d il represents the road distance from road i to charging station l, and m represents the weighting coefficient of the charging station.
[0041] Furthermore, in step S6, the accessibility coefficient of each road is interpolated and visualized to form an accessibility map. The accessibility of charging stations on each road is evaluated based on the accessibility map, providing a reference for future charging station site selection.
[0042] The beneficial effects of this invention are as follows: By studying and analyzing the trajectory data of electric vehicles in the study area, this invention accurately determines the charging demand points of electric vehicles; by dynamically determining the service radius of charging stations through POI kernel density, different service radii can be determined according to different study areas; at the same time, in the two-step moving search method, the traditional fixed radius is abandoned and the actual road distance is introduced, making this invention more realistic; in addition, the competition and differences between charging stations are taken into account, making the relationship between supply and demand more accurate.
[0043] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description
[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:
[0045] Figure 1 This is a flowchart of the method of the present invention;
[0046] Figure 2 Flowchart of the algorithm for extracting charging demand points. Detailed Implementation
[0047] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.
[0048] This embodiment uses Beijing as the study area and applies the present invention to perform accessibility analysis of electric vehicle charging stations. The process is as follows: Figure 1 As shown. The study utilizes electric vehicle trajectory data and Points of Interest (POI) data from charging stations, schools, hospitals, shopping malls, roads, and parking lots. The trajectory data comes from the 2020 Digital Car Competition, the POI data was obtained through a web crawler from Gaode Maps, the road vector data is from the geographic data platform of the School of Urban and Environmental Sciences at Peking University, and the study area map was obtained using Omap software.
[0049] First, the electric vehicle trajectory data is preprocessed, with outliers and missing values removed and added. Then, a charging demand algorithm is used to determine all charging demand points within the study area, selecting different algorithms based on the characteristics of the electric vehicles.
[0050] 1. When the electric vehicle is used as a taxi, the steps of its charging demand algorithm are as follows: Figure 2 As shown, the details are as follows:
[0051] 1) For each new energy vehicle, select points with a speed of 0 and a passenger load of 0;
[0052] 2) Determine whether the points selected in 1) are within a circular area with a radius of 50m of the charging station;
[0053] 3) Place the points within the range into the set, and find the segments in the trajectory of the point that have a velocity of 0 and a duration of 40 to 120 minutes;
[0054] 4) Find the nearest point in the segment obtained in 3) where the passenger load is 1, and use it as the charging demand point;
[0055] 5) Traverse the trajectory of each new energy vehicle and collect all points that meet the requirements.
[0056] 2. When the electric vehicle is a regular passenger vehicle, the charging demand algorithm is as follows:
[0057] 1) Determine the charging time and location based on the time it takes for the electric vehicle's remaining battery power to rise, the parking location, and the duration of parking;
[0058] 2) Analyze the frequency of remaining battery power during historical charging for each vehicle, and use the highest frequency of remaining battery power as the charging threshold for each vehicle.
[0059] 3) The location where the vehicle's remaining battery power reaches the charging threshold is designated as the charging demand point.
[0060] After the charging demand points are extracted, the POI data other than charging stations are analyzed. The data are classified and graded according to their attribute information. Weights are obtained through a word frequency weighting algorithm. The POI data other than charging stations are imported into ArcGIS software. Kernel density analysis is performed on each type of data using adaptive bandwidth. The kernel density of each driving factor is normalized. Finally, the kernel density is weighted and summed to obtain the fitness F of the current location.
[0061] Using fitness F as a reference, the maximum F and the minimum service radius R in the study area are compared. minBy combining the minimum service radius, which is given by the charging station guidelines for each region (e.g., the minimum service radius in key areas of Beijing is 900m), and combining this with the minimum service radius desired for the current study area, the service radius of the charging station at the desired location is obtained:
[0062] R = F1 ÷ F × R min
[0063] In the formula, R represents the service radius of charging stations and charging demand points within the study area, F1 represents the maximum fitness, and F represents the fitness at the location of the point to be determined.
[0064] Perform 2SFCA reachability analysis:
[0065] 1. Take a charging station j on road K as an example, and form a linear domain centered on j with d0 as the road radius. Use trajectory data to obtain the charging power of electric vehicles corresponding to each demand point. Take the center point of the road as the location of the road, and sum the charging power of all charging demand points falling on the road as the demand power of the road. For each road k located in the linear domain, use the Gaussian equation to assign weights, and sum the weighted roads k to obtain the potential charging demand of charging station j.
[0066] Considering the competition from other charging stations within the spatial domain, demand averaging is required when calculating the potential users of charging station j: P k / n, to reconstruct the race condition, where P k Let represent the total charging power demand of road k within the spatial domain of charging station j, and n represent the number of charging stations within the spatial domain; calculate the supply-demand ratio based on the average demand calculation results:
[0067]
[0068] In the formula, S j d represents the total charging power that the charging station can provide. kj Let G(k) represent the road distance from road k to charging station j, and G() represent the Gaussian equation considering spatial friction. The calculation method of the model after introducing the Gaussian equation is shown in the following formula:
[0069]
[0070] 2. For each road i, a spatial range is formed by a given spatial distance threshold d0. The supply-demand ratio R for each charging station l within the spatial range is determined. l The Gaussian equation is used to assign weights (the weights can be obtained by analyzing the number of charging piles, power rating, etc. in the charging station information of POI data), and then the weighted supply and demand ratios are summed to obtain the charging station accessibility A for each road i. i :
[0071]
[0072] In the formula, d il Let represent the road distance from road i to charging station l, and m represent the weighting coefficient of the charging station. For A i The value can be understood as the amount of charging station capacity occupied by road i in a certain neighborhood.
[0073] After obtaining the accessibility data, outlier processing is performed, and then the data is visualized through interpolation. The goal is to quantitatively describe the current accessibility of each road to the charging station, providing a basis for subsequent charging station site selection and planning, such as placing more charging stations in areas with low accessibility.
[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for analyzing the accessibility of an electric vehicle charging station based on a LEVCD-2SFCA model, characterized in that: The method includes the following steps: S1. Collect electric vehicle trajectory data and POI point data of driving factors affecting the selection of charging station sites within the study area over one year, perform data preprocessing, and form a dataset. S2. Analyze electric vehicle trajectory data using a charging demand algorithm to obtain historical charging demand points for electric vehicles; S3. Analyze the attributes of the driving factor data that affect the selection of charging station sites in step S1, classify the levels according to the attribute information, generate kernel density maps corresponding to different driving factors using ArcGIS software, normalize the kernel density data of POI point data, and obtain the fitness F of each location in the study area by weight superposition and summation. S4. Based on the fitness F obtained in step S3, the fitness of the points falling within the grid is represented by the grid fitness. The maximum demand of the study area is matched with the minimum service radius that the study area hopes to achieve, and the service radius of the charging station at the point to be determined is calculated. S5. Perform 2SFCA reachability analysis based on the data obtained in steps S1 to S4: S51, at a charging station on Road K. j ,by As the road radius forms a... j The linear domain is defined by the center point of the road. Trajectory data is used to obtain the charging power of the electric vehicle corresponding to each demand point. The center point of the road is used as the road's location, and the charging power of all charging demand points falling on that road is summed to obtain the road's demand power. For each road k within the linear domain, a Gaussian equation is used to assign weights, and the weighted roads k are summed to obtain the charging station's power. j Potential charging demand; S52. Considering the competition from other charging stations within the spatial domain, therefore, when calculating charging stations... j When considering potential users, it is necessary to calculate the average demand: ,in Indicates charging station j Roads within the spatial domain k The total charging power required. n This indicates the number of charging stations within the spatial domain; the supply-demand ratio is calculated based on the average demand. In the formula, This indicates the total charging power that the charging station can provide. Indicates road k To the charging station j The road distance, G ( ) represents the Gaussian equation considering spatial friction; S53, for each road i Given a spatial distance threshold This forms a spatial domain, and for each charging station within that domain... l supply and demand ratio The Gaussian equation is used to assign weights, and then the weighted supply and demand ratios are summed to obtain the results for each road. i Charging station accessibility : In the formula, Indicates road i To the charging station l The road distance, m This represents the weighting coefficient of the charging station; S6. Based on the analysis results of step S5, obtain quantitative accessibility data for all charging stations, form an accessibility map, evaluate the accessibility of charging stations based on the accessibility map, and use it as a reference for charging station site selection.
2. The reachability analysis method according to claim 1, characterized in that: In step S1, the electric vehicle trajectory data includes: the vehicle's unique identifier, remaining battery power, latitude and longitude, vehicle status, charging status, passenger status, speed, and time; The driving factor POI data includes: schools, hospitals, shopping malls, roads, and parking lots.
3. The reachability analysis method according to claim 1, characterized in that: In step S1, the data preprocessing is as follows: correcting, supplementing and reconstructing outliers and missing values in the electric vehicle trajectory data; analyzing the attribute information of the POI point data; classifying and grading the POI point data; and using a word frequency weighting algorithm to obtain the weight of the POI point data.
4. The reachability analysis method according to claim 1, characterized in that: In step S2, the corresponding charging demand algorithm is selected according to the different types of vehicles to obtain the historical electric vehicle charging demand points.
5. The reachability analysis method according to claim 4, characterized in that: When an electric vehicle is used as a taxi, the charging demand is calculated as follows: 1) For each new energy vehicle, select points with a speed of 0 and a passenger load of 0; 2) Determine whether the points selected in 1) are within a circular area with a radius of 50m of the charging station; 3) Place the points within the range into the set, and find the segments in the trajectory of the point that have a velocity of 0 and a duration of 40~120min; 4) Find the nearest point in the segment obtained in 3) where the passenger load is 1, and use it as the charging demand point; 5) Traverse the trajectory of each new energy vehicle and collect all points that meet the requirements.
6. The reachability analysis method according to claim 4, characterized in that: When the electric vehicle is a regular passenger vehicle, the charging demand algorithm is as follows; 1) Determine the charging time and location based on the time it takes for the electric vehicle's remaining battery power to rise, the parking location, and the duration of parking; 2) Analyze the frequency of remaining battery power during historical charging for each vehicle, and use the remaining battery power with the highest frequency as the charging threshold for each vehicle; 3) The location where the vehicle's remaining battery power reaches the charging threshold is designated as the charging demand point.
7. The reachability analysis method according to claim 1, characterized in that: In step S3, the latitude and longitude information of the POI point data is linked with the x and y fields of the ArcGIS software data, and the corresponding coordinate system is specified; the kernel density of different POI point data is calculated by the kernel density analysis tool in ArcGIS software, and the kernel density of each POI point data is normalized and weighted and superimposed to obtain the fitness F of different locations.
8. The reachability analysis method according to claim 1, characterized in that: In step S4, the reference service radius is calculated as follows; Using fitness F as a reference, the maximum F is compared with the minimum service radius. By solving the simultaneous equations, the service radius of the charging station at the desired location can be obtained: In the formula, R This indicates the service radius of charging stations and charging demand points within the study area. Indicates maximum fitness. F This represents the fitness at the location of the point to be determined.