Shared EV adjustable capacity calculation method and system based on historical GIS data analysis

Abstract

A shared EV adjustable capacity calculation method based on historical GIS data analysis comprises the following steps that modeling is conducted on a traffic road network based on a historical GIS data set of a shared fuel automobile, and space grids are divided for a selected research area; calculating a correlation coefficient and a similarity coefficient of any two adjacent space grid travel volumes, and carrying out functional region clustering; based on historical GIS data, extracting a change track of a traffic starting point and a traffic ending point of a single shared fuel automobile within one day, and solving a shortest path of the traffic starting point and the traffic ending point based on a Dijkstra algorithm; estimating the power consumption of the ith shared EV in the single travel mileage if the travel demand and the traffic condition of the shared vehicle selected by the user are kept unchanged; calculating the guaranteed electric quantity by considering the loss of the DR on the battery and preventing the over-discharge of the battery; and calculating the adjustable capacity value of the shared EV of the certain functional area at the t moment. The method provided by the invention is high in operability and can be used for training and verifying the space-time prediction model of the EV adjustable capacity.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicle charging and discharging, and more specifically, relates to a method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis. Background Art

[0002] In the power market environment, the gradual popularization of shared electric vehicles and the development and maturity of related technologies such as smart grids have expanded the role scope of shared electric vehicle operators, making it possible for them to play the role of distributed power supply providers. The dispatch ownership of shared EVs belongs to their operators. The grid dispatch department signs a demand response (DR) contract with the operator containing information such as the discharge amount and discharge time of shared EVs, and purchases dispatch control rights, so that it can control them to provide instantaneous response to the grid through V2G (vehicle-to-grid) and coordinate the supply and demand balance of the power system.

[0003] However, due to the privacy issues and business principles of shared electric vehicle user travel data, there is currently no public data to support research on their participation in vehicle-grid interaction. This paper proposes a method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis, through which a sample data set of shared EV adjustable capacity can be established, providing data support for research on shared EVs participating in vehicle-grid interaction. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and system for calculating the adjustable capacity of a shared EV based on historical GIS data analysis.

[0005] The present invention aims to solve the problem of calculating the adjustable capacity of shared EVs. Limited by the privacy issues of shared EV user travel data and business principles, there is currently no public data to support research on shared EVs participating in grid demand response and other aspects. The present invention assumes that the travel needs and traffic conditions of urban users who choose shared transportation tools remain unchanged, and uses the longitude and latitude coordinates of the parking spaces of shared fuel vehicles and POI (points of interest) data to calculate the adjustable capacity of shared EVs. The above data information does not involve the privacy issues of shared electric vehicle user travel data and is easy to obtain.

[0006] The present invention adopts the following technical solution.

[0007] A first aspect of the present invention provides a method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis, the method comprising the following steps:

[0008] Step 1: Based on the shared fuel vehicle historical GIS dataset, the transportation network is modeled and the spatial grid is divided for the selected study area;

[0009] Step 2, calculate the Person correlation coefficient and Jaccard similarity coefficient of the travel volume of any two adjacent spatial grids, and perform functional area clustering;

[0010] Step 3: Based on the clustering results and historical GIS data, the changing trajectory of the traffic start and end points of a single shared fuel vehicle in one day is extracted, and the shortest path of the traffic start and end points is solved based on the Dijkstra shortest path algorithm;

[0011] Step 4: Based on the obtained path information, assuming that the user's travel demand and traffic conditions for shared transportation remain unchanged, estimate the power consumption of the i-th shared EV for a single trip;

[0012] Step 5: Based on the estimated result, taking into account the loss of the battery caused by DR and preventing the battery from over-discharging, calculate the minimum power;

[0013] Step 6: Calculate the adjustable capacity value of the shared EV in a functional area at time t based on the calculation basis of the guaranteed power.

[0014] Preferably, in step 1, modeling the traffic network based on the shared fuel vehicle historical GIS dataset and dividing the selected research area into spatial grids specifically includes:

[0015] Step 1.1, obtain the shared fuel vehicle historical GIS dataset and perform data preprocessing;

[0016] Step 1.2: Determine the study area based on the longitude and latitude coordinate data in the historical GIS data and perform grid modeling.

[0017] Preferably, in step 1.1, the data set includes the latitude and longitude coordinates and POI information of the shared fuel vehicle parking point with a 5-minute resolution:

[0018] C t ={t,(LON i，t , LAT i，t ), T i，t,POI}

[0019] Among them, t∈[0,96], i∈[1,M], M is the total number of shared fuel vehicles, LON i,t is the longitude coordinate of the i-th shared fuel car at time t, LAT i,t is the latitude coordinate of the i-th shared fuel vehicle at time t, T i,t,POI is the POI type of the i-th shared fuel car at time t;

[0020] The data preprocessing specifically includes:

[0021] Eliminate data such as fields that are all empty or all zero, data with abnormal longitude and latitude, delete duplicate data and irrelevant data, and convert historically collected data into high-quality, in-depth historical GIS data for analysis.

[0022] Preferably, step 1.2 specifically includes:

[0023] Step 1.2.1, map the latitude and longitude coordinate data in the historical GIS data after data preprocessing to the spatial area of ​​grid modeling, and determine the study area, including:

[0024] Take α=max(LON i,t ), β=max(LAT i,t ), γ=min(LON i,t ),δ=min(LAT i,t ); A=(α, β), B=(α, δ), C=(γ, β), D=(γ, δ), and connect the four points ABCD in sequence as the boundaries of the study area, that is, the selected study area is a rectangular area with AB and AC as the sides.

[0025] Convert latitude and longitude coordinates to Euclidean distance:

[0026]

[0027]

[0028] Among them, R is the radius of the earth, which is 6371km;

[0029] Step 1.2.2, divide the selected study area into spatial grids, including:

[0030]

[0031] Where n is the number of starting points in the historical GIS data set, and N*N spatial grids are obtained after division;

[0032] Step 1.2.3, determine the POI type to which the spatial grid belongs, specifically including:

[0033] G(N) POI =max(count((LON i,t ,LAT i,t ) N,POI,j ))

[0034] Among them, G(N) POI is the POI type of the spatial grid G(N), (LON i,t ,LAT i,t ) N,POI,jis the POI type of all coordinate points in the spatial grid G(N), j∈{residential area, industrial area, commercial area, leisure area, public service area}.

[0035] Preferably, in step 2, the calculation of the Person correlation coefficient and the Jaccard similarity coefficient of the travel volume of any two adjacent spatial grids to perform functional area clustering specifically includes:

[0036] Step 2.1, calculate the Person correlation coefficient of the travel volume under the time series changes of any two adjacent grids G(m) and G(n):

[0037]

[0038] in, represents the travel volume of the grid G(m) in period t, is the starting point travel volume, that is, the number of vehicles whose travel starting point is located in the grid G(m) and are in driving state, is the terminal trip volume, that is, the number of vehicles whose trip terminal point is located in the grid G(m) and was in the driving state at the last moment; represents the travel volume of the grid G(n) in period t, is the starting point travel volume, that is, the number of vehicles whose travel starting point is located in the grid G(n) and are in driving state, is the terminal trip volume, that is, the number of vehicles whose trip terminal point is located in the grid G(n) and was in the driving state at the last moment;

[0039] Step 2.2, calculate the Jaccard coefficient of the travel volume under the time series changes of any two adjacent grids G(m) and G(n):

[0040]

[0041] Step 2.3, determine the correlation between any two adjacent grids G(m) and G(n):

[0042]

[0043] Among them, R(G(m), G(n)) is the correlation coefficient between any two adjacent grids G(m) and G(n). When R(G(m), G(n))<0.7, the similarity of the adjacent grids is considered to be 0, and they cannot be clustered, that is, they do not belong to the same functional area; when γ mn When ≥0.7, it is considered that the adjacent grids can be clustered, that is, they belong to the same functional area.

[0044] Preferably, in step 3, based on the historical GIS data, the changing trajectory of the traffic start and end points of a single shared fuel vehicle in one day is extracted, and the shortest path of the traffic start and end points is solved based on the Dijkstra shortest path algorithm, which specifically includes:

[0045] In step 3, the shared EV starting and ending point coordinate set N is established p,q , p and q are the starting point and the end point of a trip, p,q∈N p,q , including:

[0046] Step 3.1, initialize the distances of all nodes to infinity and the time spent at the starting point to 0;

[0047] Step 3.2, create an unlabeled node set and add the starting point to the set;

[0048] Step 3.3, when the set is not empty, select the node closest to the starting point from the set as the starting point and mark it;

[0049] Step 3.4, update the distance from the starting point to all unmarked nodes adjacent to the marked nodes. If it is found that the distance to an unmarked node through a marked node is shorter, update the distance to the new distance;

[0050] Step 3.1, repeat steps 3.3 and 3.4 until the set is empty, and select the shortest path S between the starting point and the end point of the trip p,q .

[0051] Preferably, in step 4, assuming that the user's travel demand for shared transportation and traffic conditions remain unchanged, the estimating of the power consumption of a single trip of the i-th shared EV specifically includes:

[0052] Step 4.1, calculate air conditioning energy consumption:

[0053] E ac,te,p,q =E te / 100*S p,q

[0054] Among them, E ac,te,p,2 At te temperature, S p,q Air conditioning energy consumption of the route, E te is the air conditioning energy consumption per 100 kilometers at te temperature;

[0055] Step 4.2, calculate driving energy consumption:

[0056] E drive,p,q =F p,q *S p,q

[0057]

[0058]

[0059] Among them, E drive,p,q For section S p,q Driving energy consumption, F p,q For shared EV passing through road section S p,q The force, λ is the traction coefficient, The mileage in Δt time is S p,q The average speed of the vehicle is expressed in km / h, m is the vehicle mass, θ p,q is the road slope;

[0060] E p,q =∑E ac,te,p,q +E drive,p,q Step 4.3, calculate the total energy consumption:

[0061] E p,q =∑E ac,te，p，q +E drive，p，q

[0062] Among them, E p,q For S p,q The total energy consumption of the route in kWh / km.

[0063] Preferably, in step 5, taking into account the loss of the battery caused by DR and preventing the battery from being over-discharged, the calculation of the guaranteed minimum power specifically includes:

[0064] Considering the loss of battery caused by DR and preventing battery over-discharge, the discharge should be stopped when the battery capacity is less than 20%, and the minimum power E at time t is defined as G (t) The minimum amount of electricity that needs to be reserved to prevent over-discharge and ensure the travel needs of users in a functional area at time (t+1):

[0065] E G (t) = 20% E N ×Q-max(E j,t+1 )

[0066] Among them, E G (t) is the guaranteed minimum power at time t, E N is the battery energy of a single shared EV, Q is the total number of shared EV vehicles in a functional area at time t, max(E j，t+1 ) is the maximum power consumption of the historical travel trajectory of a functional area at time (t+1).

[0067] Preferably, in step 6, the calculation of the adjustable capacity SOC of a shared EV in a functional area at time t is j,t Values, including:

[0068] Assuming that shared EVs are only charged at night and only discharged during the day, the "adjustable capacity" of shared EVs at time t is defined as the total remaining power of shared EVs parked at the current time minus the guaranteed power E G (t) to ensure that the battery health of electric EVs and the travel needs of electric EV users are prioritized, namely:

[0069]

[0070] Among them, SOC j,t is the adjustable capacity value of functional area j at time t, U is the total number of electric vehicles parked in the functional area at time t, It is the sum of the power consumption of all trips before time t on the same day.

[0071] The second aspect of the present invention provides a system for calculating the adjustable capacity of a shared EV based on historical GIS data analysis, including: data collection, data preprocessing, spatial grid modeling, functional area clustering, route planning, energy consumption estimation, battery management, and adjustable capacity calculation; and runs a method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis as described in any one of claims 1 to 9:

[0072] Data collection is used to systematically collect historical GIS data, including the location of shared EVs, road network information, parking spots, and POI information;

[0073] Data preprocessing is used to clean, filter, remove duplicates, and transform the collected data to prepare high-quality data for subsequent analysis;

[0074] Spatial grid modeling is used to divide the study area into spatial grids, usually using rectangular or other shaped grids to represent geographic space;

[0075] Functional area clustering is used to calculate the correlation between adjacent spatial grids and cluster them into different functional areas;

[0076] Route planning is used to extract the changing trajectories of the traffic start and end points of a single shared EV within a day based on historical GIS data, and use the shortest path algorithm to plan the best route;

[0077] Energy consumption estimation is used to estimate the energy consumption of each shared EV in a single trip, including air conditioning energy consumption and driving energy consumption;

[0078] Battery management is used to take into account battery health and prevent over-discharge, and calculate the minimum power to ensure battery availability;

[0079] Adjustable capacity calculation is used to calculate the adjustable capacity of each functional area at a specific moment to meet user travel needs and battery management.

[0080] Compared with the prior art, the beneficial effects of the present invention include at least: the present invention innovatively proposes a method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis. According to this method, the adjustable capacity of shared EVs in different functional areas at any time can be obtained; limited by the privacy issues of shared EV user travel data and business principles, there is currently no public data to support research on shared EVs participating in grid demand response and other aspects. The present invention assumes that the travel needs and traffic conditions of urban users who choose shared transportation tools remain unchanged, and uses the longitude and latitude coordinates of parking spaces and POI (points of interest) data of shared fuel vehicles to calculate the adjustable capacity of shared EVs. The above data information does not involve the privacy issues of shared electric vehicle user travel data and is easy to obtain. Therefore, the method proposed in the present invention is highly operational; this method can establish a sample data set of shared EV adjustable capacity, which can be used for training and verification of spatiotemporal prediction models for EV adjustable capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 The figure is a flow chart of the method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis of the present invention. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] The purpose of the present invention is to provide a method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis. The proposed method for calculating the adjustable capacity of a shared EV takes into account the privacy issues of user travel data and business principles, uses the longitude and latitude coordinates of the parking spaces of shared fuel vehicles and POI (point of interest) data to calculate the adjustable capacity of a shared EV, and considers that the city plans its power supply by grids, and adjacent grids are most likely powered by the same distribution network outgoing line. When conducting a study on the calculation of the dispatchable capacity value of a shared EV, vehicles parked in multiple adjacent grids but belonging to the same functional area are aggregated, which has strong operability and is easy to promote.

[0085] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0086] Figure 1 FIG. 1 is a flow chart of a method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis according to the present invention. Figure 1 As shown, the method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis provided by the present invention includes the following steps:

[0087] S1, based on the shared fuel vehicle historical GIS dataset, the transportation network is modeled and the spatial grid is divided for the selected study area.

[0088] S2, calculate the Person correlation coefficient and Jaccard similarity coefficient of the travel volume of any two adjacent spatial grids, and perform functional area clustering.

[0089] S3, based on historical GIS data, extracts the changing trajectory of the traffic start and end points of a single shared fuel vehicle within a day, and solves the shortest path of the traffic start and end points based on the Dijkstra shortest path algorithm.

[0090] S4, assuming that the user's travel demand and traffic conditions for shared transportation remain unchanged, estimate the power consumption of the i-th shared EV for a single trip.

[0091] S5, taking into account the loss of battery caused by DR and preventing battery over-discharge, calculates the minimum power.

[0092] S6, calculating the adjustable capacity value of the shared EV in a functional area at time t.

[0093] In a preferred but non-limiting embodiment of the present invention, in step S1, the traffic network is modeled based on the shared fuel vehicle historical GIS dataset, and the selected research area is divided into spatial grids, specifically including:

[0094] S101, obtaining a shared fuel vehicle historical GIS dataset and performing data preprocessing, specifically including:

[0095] The data set includes the latitude and longitude coordinates and POI information of shared fuel vehicle parking points with a 5-minute resolution:

[0096] C t ={t,(LON i,t ,LAT i,t ),T i，t，POI}

[0097] Among them, t∈[0,96], i∈[1,M], M is the total number of shared fuel vehicles, LON i，tis the longitude coordinate of the i-th shared fuel car at time t, LAT i，t is the latitude coordinate of the i-th shared fuel vehicle at time t, T i,t,POI is the POI type of the i-th shared fuel car at time t.

[0098] The data preprocessing specifically includes:

[0099] Eliminate data such as fields that are all empty or all zero, data with abnormal longitude and latitude, delete duplicate data and irrelevant data, and convert historically collected data into high-quality, in-depth historical GIS data for analysis.

[0100] In a preferred but non-limiting embodiment of the present invention, S102, determining the research area based on the latitude and longitude coordinate data in the historical GIS data and performing grid modeling specifically includes:

[0101] S1021, mapping the latitude and longitude coordinate data in the historical GIS data after data preprocessing to the spatial area of ​​the grid modeling, and determining the research area, specifically including:

[0102] Take α=max(LON i,t ), β=max(LAT i,t ), γ=min(LON i,t ),δ=min(LAT i,t ); A=(α, β), B=(α, δ), C=(γ, β), D=(γ, δ), and connect the four points ABCD in sequence as the boundaries of the study area, that is, the selected study area is a rectangular area with AB and AC as the sides.

[0103] Convert latitude and longitude coordinates to Euclidean distance:

[0104]

[0105]

[0106] Among them, R is the radius of the earth, which is 6371km.

[0107] S1022, dividing the selected research area into spatial grids, specifically including:

[0108]

[0109] Among them, n is the number of starting points in the historical GIS dataset, and N*N spatial grids are obtained after division.

[0110] S1023, determining the POI type to which the spatial grid belongs, specifically including:

[0111] G(N) POI=max(count((LON i,t ,LAT i,t ) N,POI,j ))

[0112] Among them, G(N) POI is the POI type of the spatial grid G(N), (LON i,t ,LAT i,t ) N,POI,j is the POI type of all coordinate points in the spatial grid G(N), j∈{residential area, industrial area, commercial area, leisure area, public service area}.

[0113] In a preferred but non-limiting embodiment of the present invention, in step S2, the Person correlation coefficient and Jaccard similarity coefficient of the travel volume of any two adjacent spatial grids are calculated to perform functional area clustering, specifically including:

[0114] S201, calculate the Person correlation coefficient of the travel volume under the time series changes of any two adjacent grids G(m) and G(n):

[0115]

[0116] in, represents the travel volume of the grid G(m) in period t, is the starting point travel volume, that is, the number of vehicles whose travel starting point is located in the grid G(m) and are in driving state, is the terminal trip volume, that is, the number of vehicles whose trip terminal point is located in the grid G(m) and was in the driving state at the last moment; represents the travel volume of the grid G(n) in period t, is the starting point travel volume, that is, the number of vehicles whose travel starting point is located in the grid G(n) and are in driving state, is the terminal travel volume, that is, the number of vehicles whose travel terminal point is located in the grid G(n) and was in the driving state at the previous moment.

[0117] S202, calculate the Jaccard coefficient of the travel volume under the time series change of any two adjacent grids G(m) and G(n):

[0118]

[0119] S203, determine the correlation between any two adjacent grids G(m) and G(n):

[0120]

[0121] J(G(m) POI ,G(n) POI)=where R(G(m),G(n)) is the correlation coefficient between any two adjacent grids G(m) and G(n). When R(G(m),G(n))<0.7, the similarity of the adjacent grids is considered to be 0 and cannot be clustered, that is, they do not belong to the same functional area; and when γ mn When ≥0.7, it is considered that the adjacent grids can be clustered, that is, they belong to the same functional area.

[0122] In step S3, based on historical GIS data, the changing trajectory of the traffic start and end points of a single shared fuel vehicle in one day is extracted, and the shortest path of the traffic start and end points is solved based on the Dijkstra shortest path algorithm, which specifically includes:

[0123] Establish a shared EV start and end point coordinate set N p,q , p and q are the starting point and the end point of a trip, p,q∈N p,q ;

[0124] S301, initialize the distances of all nodes to infinity, and initialize the time spent at the starting point to 0;

[0125] S302, creating an unmarked node set and adding the starting point to the set;

[0126] S303, when the set is not empty, select a node from the set that is closest to the starting point as the starting point and mark it;

[0127] S304, update the distance from the starting point to all unmarked nodes adjacent to the marked node. If it is found that the distance to an unmarked node via a marked node is shorter, update the distance to the new distance;

[0128] S305, repeat S303 and S304 until the set is empty, and select the shortest path S between the starting point and the end point of the trip p,q .

[0129] In a preferred but non-limiting embodiment of the present invention, in step S4, assuming that the user's travel demand for shared transportation and traffic conditions remain unchanged, estimating the power consumption of a single trip of the i-th shared EV specifically includes:

[0130] S401, calculate air conditioning energy consumption:

[0131] E ac,te,p,q =E te / 100*S p,q

[0132] Among them, E ac,te,p,q At te temperature, S p,q Air conditioning energy consumption of the route, E teis the air conditioning energy consumption per 100 kilometers at te temperature.

[0133] S402, calculating driving energy consumption:

[0134] E drive,p,q =F p,q *S p,q

[0135]

[0136]

[0137] Among them, E drive,p,q For section S p,q Driving energy consumption, F p,q For shared EV passing through road section S p,q The force, λ is the traction coefficient, The mileage in Δt time is S p,q The average speed of the vehicle is expressed in km / h, m is the vehicle mass, θ p,q is the road slope;

[0138] S403, calculate total energy consumption:

[0139] E p,q =∑E ac,te,p,q +E drive,p,q

[0140] Among them, E p,q For S p,q Total energy consumption of the route, in kWh / km;

[0141] E p,q =∑E ac,te,p,q +E drive,p,q In step S5, taking into account the loss of the battery caused by DR and preventing the battery from over-discharging, the minimum power is calculated, which specifically includes:

[0142] Considering the loss of battery caused by DR and preventing battery over-discharge, the discharge should be stopped when the battery capacity is less than 20%, and the minimum power E at time t is defined as G (t) The minimum amount of electricity that needs to be reserved to prevent over-discharge and ensure the travel needs of users in a functional area at time (t+1):

[0143] E G (t) = 20% E N ×Q-max(E j,t+1 )

[0144] Among them, E G (t) is the guaranteed minimum power at time t, E Nis the battery energy of a single shared EV, Q is the total number of shared EV vehicles in a functional area at time t, max(E j,t+1 ) is the maximum power consumption of the historical travel trajectory of a functional area at time (t+1).

[0145] In step S6, the adjustable capacity SOC of a shared EV in a functional area at time t is calculated. j,t Values, including:

[0146] Assuming that shared EVs are only charged at night and only discharged during the day, the "adjustable capacity" of shared EVs at time t is defined as the total remaining power of shared EVs parked at the current time minus the guaranteed power E G (t) to ensure that the battery health of electric EVs and the travel needs of electric EV users are prioritized, namely:

[0147]

[0148] Among them, SOC j,t is the adjustable capacity value of functional area j at time t, U is the total number of electric vehicles parked in the functional area at time t, It is the sum of the power consumption of all trips before time t on the same day.

[0149] The method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis provided by the present invention models the traffic network based on the historical GIS data set of shared fuel vehicles, and divides the selected research area into spatial grids; calculates the Person correlation coefficient and Jaccard similarity coefficient of the travel volume of any two adjacent spatial grids, and performs functional area clustering; based on historical GIS data, extracts the changing trajectory of the traffic start and end points of a single shared fuel vehicle within a day, and solves the shortest path of the traffic start and end points based on the Dijkstra shortest path algorithm; assuming that the travel demand and traffic conditions of users who choose shared transportation tools remain unchanged, estimates the power consumption of a single trip mileage of the i-th shared EV; takes into account the loss of DR to the battery and prevents the battery from being over-discharged, calculates the minimum power; and calculates the adjustable capacity value of the shared EV in a certain functional area at time t.

[0150] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

[0151] The beneficial effects of the present invention include at least: the present invention innovatively proposes a method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis. According to this method, the adjustable capacity of shared EVs in different functional areas at any time can be obtained; limited by the privacy issues of shared EV user travel data and business principles, there is currently no public data to support research on shared EVs participating in grid demand response and other aspects. The present invention assumes that the travel needs and traffic conditions of urban users who choose shared transportation tools remain unchanged, and uses the longitude and latitude coordinates of parking spaces and POI (points of interest) data of shared fuel vehicles to calculate the adjustable capacity of shared EVs. The above data information does not involve the privacy issues of shared electric vehicle user travel data and is easy to obtain. Therefore, the method proposed in the present invention is highly operational; this method can establish a sample data set of shared EV adjustable capacity, which can be used for training and verification of spatiotemporal prediction models for EV adjustable capacity.

[0152] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis, comprising the steps of: Step 1: Based on the historical GIS dataset of shared fuel vehicles, the traffic network is modeled and the selected research area is divided into spatial grids; Step 2: The Person correlation coefficient and Jaccard similarity coefficient of the travel volume of any two adjacent spatial grids are calculated to cluster the functional areas; Step 3: Based on the clustering results and according to the historical GIS data, the changing trajectory of the traffic start and end points of a single shared fuel vehicle within a day is extracted, and the shortest path of the traffic start and end points is solved based on the Dijkstra shortest path algorithm; Step 4: Based on the obtained path information, assuming that the travel demand and traffic conditions of users who choose shared transportation tools remain unchanged, the power consumption of a single trip mileage of the i-th shared EV is estimated; Step 5: Based on the obtained estimation results, the minimum power is calculated taking into account the loss of DR to the battery and preventing the battery from over-discharging; Step 6: Based on the calculation basis of the minimum power, the adjustable capacity value of the shared EV in a functional area at time t is calculated.

2. The method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis according to claim 1 is characterized in that: Step 1 includes: Step 1.1, obtain the shared fuel vehicle historical GIS dataset and perform data preprocessing; Step 1.2: Determine the study area based on the longitude and latitude coordinate data in the historical GIS data and perform grid modeling.

3. The method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis according to claim 2 is characterized in that: In step 1.1, the data set includes the latitude and longitude coordinates and POI information of the shared fuel car parking points with a 5-minute resolution: C t ={t,(LON i,t ,LAT i,t ),T i,t,POI } Among them, t∈[0,96], i∈[1,M], M is the total number of shared fuel vehicles, LON i,t is the longitude coordinate of the i-th shared fuel car at time t, LAT i,t is the latitude coordinate of the i-th shared fuel vehicle at time t, T i,t,POI is the POI type of the i-th shared fuel car at time t; The data preprocessing specifically includes: Eliminate data such as fields that are all empty or all zero, data with abnormal longitude and latitude, delete duplicate data and irrelevant data, and convert historically collected data into high-quality, in-depth historical GIS data for analysis.

4. The method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis according to claim 2 is characterized in that: Step 1.2 specifically includes: Step 1.2.1, map the latitude and longitude coordinate data in the historical GIS data after data preprocessing to the spatial area of ​​grid modeling, and determine the study area, including: Take α=max(LON i,t ), β=max(LAT i,t ), γ=min(LON i,t ),δ=min(LAT i,t ); A=(α, β), B=(α, δ), C=(γ, β), D=(γ, δ), connect the four points ABCD in sequence as the boundaries of the study area, that is, the selected study area is a rectangular area with AB and AC as the sides; Convert latitude and longitude coordinates to Euclidean distance: Among them, R is the radius of the earth, which is 6371km; Step 1.2.2, divide the selected study area into spatial grids, including: Where n is the number of starting points in the historical GIS data set, and N*N spatial grids are obtained after division; Step 1.2.3, determine the POI type to which the spatial grid belongs, specifically including: G(N) POI =max(count((LON i,t ,LAT i,t ) N,POI,j )) Among them, G(N) POI is the POI type of the spatial grid G(N), (LON i,t ,LAT i,t ) N,POI,j is the POI type of all coordinate points in the spatial grid G(N), j∈{residential area, industrial area, commercial area, leisure area, public service area}.

5. The method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis according to claim 1 is characterized in that: In step 2, the calculation of the Person correlation coefficient and the Jaccard similarity coefficient of the travel volume of any two adjacent spatial grids to perform functional area clustering specifically includes: Step 2.1, calculate the Person correlation coefficient of the travel volume under the time series changes of any two adjacent grids G(m) and G(n): in, represents the travel volume of the grid G(m) in period t, is the starting point travel volume, that is, the number of vehicles whose travel starting point is located in the grid G(m) and are in driving state, is the terminal trip volume, that is, the number of vehicles whose trip terminal point is located in the grid G(m) and was in the driving state at the last moment; represents the travel volume of the grid G(n) in period t, is the starting point travel volume, that is, the number of vehicles whose travel starting point is located in the grid G(n) and are in driving state, is the terminal trip volume, that is, the number of vehicles whose trip terminal point is located in the grid G(n) and was in the driving state at the last moment; Step 2.2, calculate the Jaccard coefficient of the travel volume under the time series changes of any two adjacent grids G(m) and G(n): Step 2.3, determine the correlation between any two adjacent grids G(m) and G(n): Among them, R(G(m), G(n)) is the correlation coefficient between any two adjacent grids G(m) and G(n). When R(G(m), G(n))<0.7, the similarity of the adjacent grids is considered to be 0, and they cannot be clustered, that is, they do not belong to the same functional area; when γ mn When ≥0.7, it is considered that the adjacent grids can be clustered, that is, they belong to the same functional area.

6. The method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis according to claim 1, characterized in that: In step 3, based on the historical GIS data, the changing trajectory of the traffic start and end points of a single shared fuel vehicle in one day is extracted, and the shortest path of the traffic start and end points is solved based on the Dijkstra shortest path algorithm, which specifically includes: In step 3, the shared EV starting and ending point coordinate set N is established p,q , p and q are the starting point and the end point of a trip, p,q∈N p,q , including: Step 3.1, initialize the distances of all nodes to infinity and the time spent at the starting point to 0; Step 3.2, create an unlabeled node set and add the starting point to the set; Step 3.3, when the set is not empty, select the node closest to the starting point from the set as the starting point and mark it; Step 3.4, update the distance from the starting point to all unmarked nodes adjacent to the marked nodes. If it is found that the distance to an unmarked node through a marked node is shorter, update the distance to the new distance; Step 3.1, repeat steps 3.3 and 3.4 until the set is empty, and select the shortest path S between the starting point and the end point of the trip p,q .

7. The method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis according to claim 1 is characterized in that: In step 4, assuming that the user's travel demand and traffic conditions for using shared transportation remain unchanged, the estimated power consumption of the i-th shared EV for a single trip includes: Step 4.1, calculate air conditioning energy consumption: E ac,te,p,q =E te / 100*S p,q Among them, E ac,te,p,q At te temperature, S p,q Air conditioning energy consumption of the route, E te is the air conditioning energy consumption per 100 kilometers at te temperature; Step 4.2, calculate driving energy consumption: E drive,p,q =F p,q *S p,q Among them, E drive,p,q For section S p,q Driving energy consumption, F p,q For shared EV passing through road section S p,q The force, λ is the traction coefficient, The mileage in Δt time is S p,q The average speed of the vehicle is expressed in km / h, m is the vehicle mass, θ p，q is the road slope; Step 4.3, calculate the total energy consumption: AND p，q =∑E ac,te，p,q +E drive,p,q Among them, E p,q For S p,q The total energy consumption of the route in kWh / km.

8. The method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis according to claim 1 is characterized by: In step 5, taking into account the loss of the battery caused by DR and preventing the battery from being over-discharged, the calculation of the guaranteed power specifically includes: Considering the loss of battery caused by DR and preventing battery over-discharge, the discharge should be stopped when the battery capacity is less than 20%, and the minimum power E at time t is defined as G (t) The minimum amount of electricity that needs to be reserved to prevent over-discharge and ensure the travel needs of users in a functional area at time (t+1): E G (t)=20%E N ×Q-max(E j,t+1 ) Among them, E G (t) is the guaranteed minimum power at time t, E N is the battery energy of a single shared EV, Q is the total number of shared EV vehicles in a functional area at time t, max(E j,t+1 ) is the maximum power consumption of the historical travel trajectory of a functional area at time (t+1).

9. The method for calculating the adjustable capacity of shared EVs based on historical GIS data analysis according to claim 1, characterized in that: In step 6, the adjustable capacity SOC of a shared EV in a functional area at time t is calculated j,t Values, including: Assuming that shared EVs are only charged at night and only discharged during the day, the "adjustable capacity" of shared EVs at time t is defined as the total remaining power of shared EVs parked at the current time minus the guaranteed power E G (t) to ensure that the battery health of electric EVs and the travel needs of electric EV users are prioritized, namely: Among them, SOC j，t is the adjustable capacity value of functional area j at time t, U is the total number of electric vehicles parked in the functional area at time t, It is the sum of the power consumption of all trips before time t on the same day.

10. A shared EV adjustable capacity calculation system based on historical GIS data analysis, comprising: Data collection, data preprocessing, spatial grid modeling, functional area clustering, route planning, energy consumption estimation, battery management, and adjustable capacity calculation; A method for calculating the adjustable capacity of a shared EV based on historical GIS data analysis as claimed in any one of claims 1 to 9 is run, characterized in that: Data collection is used to systematically collect historical GIS data, including the location of shared EVs, road network information, parking spots, and POI information; Data preprocessing is used to clean, filter, remove duplicates, and transform the collected data to prepare high-quality data for subsequent analysis; Spatial grid modeling is used to divide the study area into spatial grids, usually using rectangular or other shaped grids to represent geographic space; Functional area clustering is used to calculate the correlation between adjacent spatial grids and cluster them into different functional areas; Route planning is used to extract the changing trajectories of the traffic start and end points of a single shared EV within a day based on historical GIS data, and use the shortest path algorithm to plan the best route; Energy consumption estimation is used to estimate the energy consumption of each shared EV in a single trip, including air conditioning energy consumption and driving energy consumption; Battery management is used to take into account battery health and prevent over-discharge, and calculate the minimum power to ensure battery availability; Adjustable capacity calculation is used to calculate the adjustable capacity of each functional area at a specific moment to meet user travel needs and battery management.

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