Method and system for deploying an electric vehicle battery swapping station and allocating battery requests

Abstract

The present invention provides a method and system for deploying an electric vehicle battery swapping station and allocating battery requests, which relates to the technical field of battery swapping station site selection. The present invention obtains data of the battery swapping network, where the data of the battery swapping network includes battery swapping station data, battery swapping request data, and long-distance subsidy data; based on the data of the battery swapping network, an optimization model for deploying the battery swapping station and allocating battery requests is constructed with the goal of minimizing the total social cost, where the total social cost is the sum of the deployment cost of the battery swapping station, the driving cost of the electric vehicle, and the long-distance subsidy cost; the optimization model for deploying the battery swapping station and allocating battery requests is solved to obtain the number of optimally deployed battery swapping stations, the deployment locations of the battery swapping stations, and the set Nm of battery swapping requests allocated to the battery swapping station deployed at location m. When constructing the optimization model for deploying the battery swapping station and allocating battery requests, the present invention takes into account the deployment cost, the driving cost, and the long-distance subsidy cost, which can effectively reduce the total social cost of battery swapping, improve the user experience, and provide a friendly battery swapping service at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of battery swapping station site selection, and particularly to a method and system for deploying an electric vehicle battery swapping station and allocating battery requests. Background Art

[0002] The popularization of electric vehicles has attracted wide attention and is regarded as the most promising solution to the energy crisis and environmental pollution. Currently, the electric vehicle industry is developing rapidly, has become popular in many countries in the past few years, and will occupy an important market share in the next few decades.

[0003] The popularity of electric vehicles has led to a strong demand for power supply in the current transportation system. However, it is difficult to meet the energy demand in a timely and sufficient manner only by using traditional battery charging facilities. The long battery charging time (usually more than 1 hour) and the limitation of the number of available charging stations will lead to a poor user experience and cannot relieve the user's range anxiety. Therefore, building a battery swapping station (BSS) has become a new paradigm to overcome the above disadvantages - replacing a depleted battery with a fully charged battery, and this process only takes a few minutes. There have been some related studies on the deployment of battery swapping stations and the allocation of battery requests. Such as minimizing the total daily operating cost of the charging and battery replacement network, comprehensively considering battery purchase and charging decisions, or maximizing profits based on the location, size of the battery swapping station, and the customer's battery swapping point requests, etc.

[0004] However, the existing research on the deployment of battery swapping stations and the allocation of battery requests does not jointly consider the site selection and construction of battery swapping stations and the allocation of user battery swapping requests with long-distance subsidies, resulting in a high total social cost of battery swapping. Summary of the Invention

[0005] (I) Technical Problems to be Solved

[0006] In view of the deficiencies of the prior art, the present invention provides a method and system for deploying an electric vehicle battery swapping station and allocating battery requests, and solves the technical problem that the existing methods for deploying battery swapping stations and allocating battery requests result in a high total social cost of battery swapping.

[0007] (II) Technical Solutions

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] In a first aspect, the present invention provides a method for deploying an electric vehicle battery swapping station and allocating battery requests, including:

[0010] S1. Obtain data of the battery swapping network, where the data of the battery swapping network includes battery swapping station data, battery swapping request data, and long-distance subsidy data;

[0011] S2. Based on the data of the battery swapping network, an optimization model for battery swapping station deployment and battery request allocation is constructed with the goal of minimizing the total social cost, where the total social cost is the sum of the deployment cost of the battery swapping station, the driving cost of the electric vehicle, and the long-distance subsidy cost;

[0012] S3. Solve the optimization model for battery swapping station deployment and battery request allocation to obtain the number of battery swapping stations for optimized deployment, the deployment locations of the battery swapping stations, and the set N of battery swapping requests allocated to the battery swapping station deployed at location m m 。

[0013] Preferably, the optimization model for battery swapping station deployment and battery request allocation includes an objective function and constraint conditions, and their expressions are as follows:

[0014]

[0015]

[0016] Among them,

[0017]

[0018] In the formula, c total represents the total social cost for all deployed battery swapping stations; represents the total social cost of the battery swapping station deployed at location m; N represents the set of all battery swapping requests; N m is the set of battery swapping requests served by the battery swapping station deployed at location m, N m′ is the set of battery swapping requests served by the battery swapping station deployed at location m'; M represents the set of locations where battery swapping stations are deployed, m, m' ∈ M, m ≠ m'; ; R max represents the maximum service range of the battery swapping station; L m represents the maximum number of batteries stored in the battery swapping station;

[0019] represents the driving cost for the owner who issues battery swapping request n to drive to the battery swapping station deployed at location m; p n represents the driving cost per kilometer for the owner who issues battery swapping request n; d mn represents the distance between the battery swapping station deployed at location m and the location of the owner who issues battery swapping request n;

[0020] represents the driving subsidy for the owner who issues battery swapping request n to drive to the battery swapping station deployed at location m; represents the maximum tolerable distance for an electric vehicle driver to drive to a remote battery swapping station; w represents the subsidy cost per additional kilometer driven;

[0021] f m represents the total economic cost of the battery swapping station deployed at location m; represents the construction cost; represents the land cost; G represents the set of building location coordinates;

[0022] The optimization variables of the objective function are M, N m and G; The constraint conditions (6a) and (6b) ensure that the battery swapping requests of all electric vehicles are satisfied and each battery swapping request can only be served by one battery swapping station. The constraint condition (6c) indicates that the number of battery swapping requests that a battery swapping station can serve should be less than its maximum battery number L m .

[0023] Preferably, the S3 specifically includes:

[0024] S301. Reduce the search space of the candidate deployment locations of the battery swapping station from infinite to finite through the neighbor generation algorithm, and obtain the candidate location set for the deployment of the battery swapping station;

[0025] S302. According to the candidate location set for the deployment of the battery swapping station, introduce two decision variables a m and b mn to perform an equivalent transformation on the optimization model of the battery swapping station deployment and battery request allocation, and obtain the joint optimization model;

[0026] S303. Solve the joint optimization model through the greedy algorithm to obtain the number of the battery swapping stations with optimized deployment, the deployment locations of the battery swapping stations, and the set N of the battery swapping requests allocated to the battery swapping station deployed at location m m .

[0027] Preferably, the S301 includes:

[0028] Initialize the candidate location set M c as an empty set;

[0029] For each battery swapping request, find all neighbors within the maximum coverage range to create a request bundle; For each request bundle, call the MinDisk algorithm to find the minimum enclosing circle with the coordinates G m as the center and radius r; where, the request bundle refers to the set of battery swapping requests served by the same battery swapping station;

[0030] Obtain the center of the minimum enclosing circle of each request bundle as the candidate location set M c .

[0031] Preferably, the joint optimization model of the battery swapping station deployment and battery request allocation includes:

[0032]

[0033]

[0034] Among them, a m ∈ {0, 1}, indicating whether to deploy a battery swapping station at position m; b mn ∈ {0, 1}, indicating whether to assign the battery swapping request n to the battery swapping station deployed at position m; M c represents the set of candidate locations.

[0035] Preferably, the S303 includes:

[0036] Initialize the deployment location set M as an empty set, a m = 0, b mn = 0;

[0037] Obtain the generated set of candidate locations M c ;

[0038] For each candidate location m in the set of candidate locations M c , use the greedy idea to find the location with the minimum average deployment cost, which is expressed by the formula

[0039] Repeat to find the final deployed location set M based on the greedy formula until all battery swapping requests can be satisfied, and obtain the number of optimized deployed battery swapping stations, the deployment locations of the battery swapping stations, and the set N of battery swapping requests assigned to the battery swapping station deployed at position m m .

[0040] Preferably, the S303 includes:

[0041] For the set M0 of already deployed battery swapping stations, for the battery swapping station m deployed at position m in the set, judge whether the shortest distance d mn from the owner who issues the battery swapping request n exceeds 1.5 times the maximum service range R max . If it exceeds, skip this battery swapping station; if it does not exceed, find the number |N m | of all battery swapping requests within the coverage range of the battery swapping station deployed at position m;

[0042] Use the greedy strategy to find the battery swapping station with the minimum average cost, and obtain the set M exist of the locations of the battery swapping stations actually providing battery swapping services and the set N' of already served battery swapping requests. The calculation formula is as follows:

[0043] For all sets N\N' of unserved battery swapping requests, all candidate battery swapping station locations M c \M, compare each candidate location m, and use the greedy idea to find the location with the minimum average deployment cost, which is expressed by the formula Repeat the process of finding the finally deployed location set M based on the greedy formula until all battery swapping requests can be satisfied; obtain the number of optimized deployed battery swapping stations, the deployment locations of the battery swapping stations, and the set N of battery swapping requests assigned to the battery swapping station deployed at location m m 。

[0044] In a third aspect, the present invention provides a system for deploying electric vehicle battery swapping stations and allocating battery requests, including:

[0045] A data acquisition module for acquiring data of the battery swapping network, where the data of the battery swapping network includes battery swapping station data, battery swapping request data, and long-distance subsidy data;

[0046] A model construction module for constructing an optimization model for deploying battery swapping stations and allocating battery requests based on the data of the battery swapping network with the goal of minimizing the total social cost, where the total social cost is the sum of the deployment cost of the battery swapping stations, the driving cost of electric vehicles, and the long-distance subsidy cost;

[0047] A solving module for solving the optimization model for deploying battery swapping stations and allocating battery requests to obtain the number of optimized deployed battery swapping stations, the deployment locations of the battery swapping stations, and the set N of battery swapping requests assigned to the battery swapping station deployed at location m m 。

[0048] In a third aspect, the present invention provides a computer-readable storage medium storing a computer program for deploying electric vehicle battery swapping stations and allocating battery requests, where the computer program causes a computer to execute the method for deploying electric vehicle battery swapping stations and allocating battery requests as described above.

[0049] In a fourth aspect, the present invention provides an electronic device, including: one or more processors, a memory, and one or more programs, where the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the deployment of electric vehicle battery swapping stations and the allocation of battery requests as described above.

[0050] (III) Beneficial effects

[0051] The present invention provides a method and a system for deploying electric vehicle battery swapping stations and allocating battery requests. Compared with the prior art, the following beneficial effects are achieved:

[0052] The present invention first obtains data of the battery swapping network, where the data of the battery swapping network includes data of battery swapping stations, battery swapping request data, and long-distance subsidy data; then, based on the data of the battery swapping network, an optimization model for battery swapping station deployment and battery request allocation is constructed with the goal of minimizing the total social cost, where the total social cost is the sum of the deployment cost of the battery swapping stations, the driving cost of electric vehicles, and the long-distance subsidy cost; finally, the optimization model for battery swapping station deployment and battery request allocation is solved to obtain the number of optimally deployed battery swapping stations, the deployment locations of the battery swapping stations, and the set N of battery swapping requests allocated to the battery swapping station deployed at location m m When constructing the optimization model for battery swapping station deployment and battery request allocation, the present invention takes into account the deployment cost, the driving cost, and the long-distance subsidy cost, which can effectively reduce the total social cost of battery swapping, improve the user experience, and provide friendly battery swapping services at the same time. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0054] Figure 1 It is a block diagram of a method for deploying an electric vehicle battery swapping station and allocating battery requests according to an embodiment of the present invention

[0055] Figure 2 It is a schematic diagram of the candidate locations and coverage ranges of battery swapping stations during the verification process;

[0056] Figure 3a 、 3b 3c are respectively the number |N| of battery swapping requests, the number L of batteries stored in each battery swapping station, m and the maximum service range R max of the impact on the annual total social cost;

[0057] Figure 4a 、 4b 4c are respectively the number |N| of battery swapping requests, the number L of batteries stored in each battery swapping station, m and the maximum service range R max of the impact on the annual total social cost. Detailed Embodiments

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] By providing a method and system for deploying an electric vehicle battery swapping station and allocating battery requests, the embodiments of the present application solve the technical problem that the existing methods for deploying battery swapping stations and allocating battery requests result in high total social costs for battery swapping.

[0060] The overall idea of the technical solutions in the embodiments of the present application to solve the above technical problems is as follows:

[0061] Most of the existing research on battery swapping stations considers the optimal deployment location and customer order allocation to reduce the total system cost, lacking the joint consideration of the site selection and construction of battery swapping stations and the allocation of battery swapping requests for users with long-distance subsidies. In view of the deficiencies of the prior art, the embodiments of the present invention study the "site deployment and request allocation" problem. The neighbor generation algorithm reduces the search space of the candidate deployment locations of battery swapping stations from infinite to finite, and allocates battery swapping requests through a greedy algorithm, solving the problem complexity of infinite candidate locations and strong coupling between optimization variables, minimizing the total social cost while considering construction costs, driving costs, and subsidy costs, effectively reducing the total social cost of battery swapping, and at the same time improving the user experience and providing a friendly battery swapping service.

[0062] To better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0063] The embodiments of the present invention provide a method for deploying an electric vehicle battery swapping station and allocating battery requests, as Figure 1 shown, the method includes:

[0064] S1. Obtain data of the battery swapping network, where the data of the battery swapping network includes battery swapping station data, battery swapping request data, and long-distance subsidy data;

[0065] S2. Based on the data of the battery swapping network, construct an optimization model for deploying battery swapping stations and allocating battery requests with the goal of minimizing the total social cost, where the total social cost is the sum of the deployment cost of the battery swapping station, the driving cost of the electric vehicle, and the long-distance subsidy cost;

[0066] S3. Solve the optimization model for deploying battery swapping stations and allocating battery requests to obtain the number of battery swapping stations with optimized deployment, the deployment locations of the battery swapping stations, and the set N of battery swapping requests allocated to the battery swapping station deployed at location m m .

[0067] In the embodiment of the present invention, when constructing an optimization model for the deployment of battery swapping stations and the allocation of battery requests, the deployment cost, driving cost, and long-distance subsidy cost are considered, which can effectively reduce the total social cost of battery swapping, improve the user experience, and provide a friendly battery swapping service at the same time.

[0068] The following is a detailed description of each step:

[0069] In step S1, data of the battery swapping network is obtained, where the data of the battery swapping network includes battery swapping station data, battery swapping request data, and long-distance subsidy data. The specific implementation process is as follows:

[0070] The battery swapping station data includes:

[0071] The set M0 of existing battery swapping station locations; the set M of deployed battery swapping station locations; the set M of locations where battery swapping stations can be deployed c ; the construction cost of the battery swapping station deployed at location m The land cost of location m The maximum service range R of the battery swapping station max and the maximum number of batteries L stored in the battery swapping station m .

[0072] The battery swapping request data includes:

[0073] The set N of battery swapping requests; the set N of battery swapping requests served by the battery swapping station deployed at location m m ; the distance d between the battery swapping station deployed at location m and the location of the vehicle owner who issues the battery swapping request n mn ; the driving cost p per kilometer of the vehicle owner who issues the battery swapping request n n and the driving cost of the vehicle owner who issues request n to the battery swapping station deployed at location m

[0074] The long-distance subsidy data includes:

[0075] The subsidy cost w per additional kilometer traveled and the maximum tolerance distance of the electric vehicle driver to the battery swapping station far away

[0076] In step S2, based on the data of the battery swapping network, an optimization model for the deployment of battery swapping stations and the allocation of battery requests is constructed with the goal of minimizing the total social cost, where the total social cost is the sum of the deployment cost of the battery swapping station, the driving cost of the electric vehicle, and the long-distance subsidy cost. The specific implementation process is as follows:

[0077] Let N m represent the set of battery replacement requests served by the battery swapping station deployed at location m, and define the total social cost of the battery swapping station deployed at location m as follows:

[0078]

[0079] Among them, f m is the total economic cost of the battery swapping station deployed at location m, that is, the deployment cost of the battery swapping station. When deploying a battery swapping station at location m, two types of economic costs are considered, including construction cost and land cost The construction cost is usually fixed and often involves human resources, building materials, etc. The land cost is related to the building location coordinates G m =(x m , y m ) applicable to a specific price distribution within the urban area.

[0080]

[0081] is the driving cost for the vehicle owner who issues the battery swapping request n to drive to the battery swapping station deployed at location m. Since electric vehicle owners usually send a battery swapping request n from a fixed location far away from the battery swapping station, the location where the request is sent is denoted as G n =(x n , y n ). According to the Euclidean formula, the distance between the battery swapping station deployed at location m and the vehicle owner who issues the battery swapping request n can be expressed as

[0082]

[0083] Among them, p n is the driving cost per kilometer of the vehicle owner who issues the battery swapping request n.

[0084] is the long-distance subsidy cost for the vehicle owner who issues the battery swapping request n to drive to the target battery swapping station deployed at location m. It is assumed that electric vehicle drivers have a certain tolerance for the maximum distance to a remote battery swapping station, denoted as the maximum tolerance distance If the distance d mn between the battery swapping request n and the target battery swapping station is greater than it will greatly increase the risk of users experiencing annoyance and frustration, which will reduce the long-term profit of the battery swapping service. Therefore, it is crucial to provide a user-friendly service experience. Providing additional subsidies when the tolerance distance is exceeded can help relieve user anxiety and reduce user complaints.

[0085]

[0086] For the set M of all deployed battery swapping stations, the total social cost can be represented by c total as follows:

[0087]

[0088] The goal of the embodiment of the present invention is to minimize the total social cost under the battery capacity constraint. The objective function and constraint conditions of the optimization model for swap station deployment and battery request allocation can be expressed in the following form:

[0089]

[0090] The optimization variables are M, N m and G. Constraint conditions (6a) and (6b) ensure that all the battery swapping requests of electric vehicles are satisfied and each battery swapping request can only be served by one swap station. Constraint condition (6c) indicates that the number of battery swapping requests that a swap station can serve should be less than its maximum number of batteries L m .

[0091] In step S3, solve the optimization model for swap station deployment and battery request allocation to obtain the number of swap stations with optimized deployment, the deployment locations of the swap stations, and the set N of battery swapping requests allocated to the swap station deployed at location m m . The specific implementation process is as follows:

[0092] In the optimization process of problem P1, it is necessary to optimize the set M of deployed swap stations and the location G simultaneously. At the same time, it is also necessary to optimize the set N of battery swapping requests allocated to the swap station deployed at location m m . First, it is difficult to determine the number of swap stations to be deployed. Sparsely deploying swap stations can reduce the deployment cost, but it will increase the driving cost of users and the subsidy cost increased due to long-distance compensation. On the contrary, densely deploying swap stations can reduce the driving cost, but the cumulative deployment cost of all swap stations may increase significantly. Second, even if the number of swap stations to be deployed is given, it is very difficult to determine the best deployment location of the swap stations in the continuous space because the candidate locations of the swap stations are infinite. Third, the deployment of swap stations and the allocation of battery swapping requests are tightly coupled, making this problem more complex.

[0093] In the embodiment of the present invention, to solve the above-mentioned problem P1, first, a neighbor generation algorithm is proposed to reduce the candidate deployment locations of the swap stations from infinite to finite. Next, through equivalent optimization variables A, B, problem P1 is transformed into problem P2. Then, a greedy algorithm is used to solve the joint swap station deployment and battery request allocation problem. Step S2 specifically includes:

[0094] S301. Reduce the search space of the candidate deployment locations of the swap stations from infinite to finite through the neighbor generation algorithm to obtain the set of candidate locations for swap station deployment. Specifically:

[0095] It is difficult to deploy battery swapping stations in a continuous space to serve all battery swapping requests. Therefore, embodiments of the present invention attempt to use a neighbor generation algorithm to reduce the search space from infinite to finite. First, the following definitions are given:

[0096] Definition 1. A request bundle is a set of battery swapping requests served by the same battery swapping station. Naturally, the service bundle can be covered by the battery swapping station providing the battery swapping service. Obviously, the number of battery swapping stations should be as small as possible, but at the same time sufficient to satisfy serving all battery swapping requests.

[0097] Embodiments of the present invention define the maximum service range of a battery swapping station as R max , which is equal to the maximum user tolerance distance Then, the ideal service area is a circular coverage area with a radius of R max , and its center is the candidate location for deploying the battery swapping station. Next, embodiments of the present invention propose a neighbor generation algorithm using the classic MinDisk algorithm as a subroutine.

[0098] The MinDisk algorithm is a classic algorithm. Given a set of randomly deployed nodes in a two-dimensional space, it can calculate the smallest enclosing disk (SED) covering these points. The output of the algorithm is the radius and center of the circle. Based on this, embodiments of the present invention propose a corresponding neighbor generation algorithm, and the specific algorithm runs as follows:

[0099] First, initialize the candidate location set M c ; Secondly, for each battery swapping request, first find all neighbors within the maximum coverage range to create a "request bundle". Then, for each bundle, call the MinDisk algorithm to find the smallest enclosing disk with coordinates G m as the center and radius r. Finally, obtain the set of all candidate locations where the battery swapping station can be deployed, that is, the candidate location set for deploying the battery swapping station.

[0100] The code of the neighbor generation algorithm is as follows:

[0101]

[0102] S302. According to the candidate location set M c for deploying the battery swapping station, introduce two decision variables a m and b mn to equivalently transform the optimization model for battery swapping station deployment and battery request allocation, and obtain a joint optimization model. a m ∈ {0, 1}, indicating whether to deploy a battery swapping station at location m;; b mn ∈ {0, 1}, indicating whether to allocate the battery swapping request n to the battery swapping station deployed at location m; specifically:

[0103] The objective function and constraints of the optimization model for swapping station deployment and battery request allocation can be equivalently re-expressed as a joint optimization model, and the specific expression is as follows:

[0104]

[0105] S303. Solve the joint optimization model through the greedy algorithm to obtain the number of swapping stations with optimized deployment, the deployment locations of the swapping stations, and the set N of swapping requests allocated to the swapping station deployed at location m m . The specific implementation process is as follows:

[0106] Since problem P2 involves jointly optimizing swapping station deployment and swapping request allocation. The embodiment of the present invention proposes an efficient and simple algorithm - the greedy algorithm, and the specific algorithm runs as follows:

[0107] First, initialize the deployment location set M as an empty set, a m = 0, b mn = 0;

[0108] Second, run the neighbor generation algorithm to generate the candidate location set M c ; In the specific implementation process, since the candidate location set has been generated in the previous step, it can be directly obtained and used in this step without calling the neighbor generation algorithm again.

[0109] Then, for each candidate location m, use the greedy idea to find the location with the minimum average deployment cost, which is expressed by the formula as

[0110] Keep repeating to find the finally deployed location set M based on the greedy formula until all swapping requests can be satisfied. Obtain the number of swapping stations with optimized deployment, the deployment locations of the swapping stations, and the set N of swapping requests allocated to the swapping station deployed at location m m .

[0111] The code of the greedy algorithm is as follows:

[0112]

[0113] The main idea of the above greedy algorithm is to select the most cost-saving location in the candidate location set M c under the maximum capacity constraint, which can serve as many requests as possible until all requests are satisfied. Obviously, for each m ∈ M c \M, it is hoped that under the constraint of the maximum battery capacity of L m , the number of requests for which swapping services can be provided can be maximized, that is, |N m | ≤ L m .

[0114] Let \(M\) be the set of locations where battery swapping stations have been selected for deployment, and \(N'\) be the set of battery swapping requests that have been satisfied. Initially, both \(M\) and \(N'\) are empty sets. In each iteration, first calculate the total social cost of all unselected candidate locations Then select the location with the lowest cost as the most cost-saving location.

[0115]

[0116] It should be noted that in the specific implementation process, when building a new battery swapping station where there already exists a battery swapping station, the same method can also be used, denoted by \(M_0\), where \(|M_0|\geq1\).

[0117] Similarly, the candidate deployment locations of the battery swapping stations are reduced from infinite to finite, and the neighbor generation algorithm still applies.

[0118] After executing the neighbor generation algorithm, a set \(M\) of candidate battery swapping station locations is obtained c . The specific algorithm runs as follows:

[0119] First, initialize the set \(M\) of candidate locations c ;

[0120] Secondly, for each battery swapping request, first find all neighbors within the maximum coverage range to create a "request bundle".

[0121] Then, for each bundle, call the MinDisk algorithm to find the minimum enclosing circle with the coordinate \(G\) m as the center and radius \(r\).

[0122] Finally, obtain the set of all candidate locations where the battery swapping station can be selected for deployment, that is, the set \(M\) of candidate locations for battery swapping station deployment c .

[0123] To solve the variant problem involving the existing battery swapping stations \(M_0\), the embodiments of the present invention improve the above greedy algorithm. Naturally, it is hoped that the existing battery swapping stations can serve as many battery swapping requests as possible.

[0124] The code of the improved greedy algorithm is as follows:

[0125]

[0126] The specific process is as follows:

[0127] First, for the set \(M_0\) of already deployed battery swapping stations, for each battery swapping station \(m\) in the set, judge whether the distance \(d\) mn to the nearest vehicle owner who issues the battery swapping request \(n\) exceeds 1.5 times the maximum service range \(R\) max , that is, \(\frac{3}{2}R\) maxIf it exceeds, skip this battery swapping station; if not, find the number of all battery swapping requests within the coverage of the battery swapping station deployed at location m, denoted as |N|. m |.

[0128] Secondly, use the greedy strategy to find the battery swapping station with the minimum average cost, which is calculated by the formula, thus obtaining the set M of battery swapping stations actually providing battery swapping services exist and the set N'' of battery swapping requests that have been served;

[0129] Then, for all sets of battery swapping requests N\N' that have not been served and all candidate battery swapping station locations M c \M; compare each candidate location m, and use the greedy idea to find the location with the minimum average deployment cost, which is expressed by the formula Keep repeating to find the finally deployed location set M based on the greedy formula until all battery swapping requests are satisfied. The final location set of the battery swapping stations is M exist and the union of M, obtaining the number of optimized deployed battery swapping stations, the deployment locations of the battery swapping stations, and the set N of battery swapping requests assigned to the battery swapping station deployed at location m m .

[0130] The core idea of the above improved greedy algorithm is to use the greedy strategy to arrange battery swapping requests to existing battery swapping stations. Select the most cost-saving candidate locations from M c that can serve as many battery swapping requests as possible under the maximum battery capacity constraint until all battery swapping requests are satisfied. When selecting candidate locations in M c , if the distance from an existing battery swapping station to serve battery swapping request n is short and candidate battery swapping station m is the most cost-saving, the service provider for battery swapping request n can be replaced from the existing battery swapping station to the battery swapping station deployed at candidate location m.

[0131] Let M be the set of locations where battery swapping stations have been selected for deployment, and N' be the set of battery swapping requests that have been served. Initially, both M and N' are empty sets. Arrange the car owners who send battery swapping requests to drive to existing battery swapping stations. In each iteration, calculate the overall social cost of all unselected candidate locations Select the most cost-saving location.

[0132] The following verifies the effectiveness of the greedy algorithm proposed in the embodiments of the present invention through comparative experiments:

[0133] A. Evaluation settings

[0134] In this verification process, it is assumed that all battery swapping requests and battery swapping stations are distributed within a 20km×20km area. The maximum number of batteries L stored in the batteries at the battery swapping stations mThe variation range is from 9 to 15. The driving cost per kilometer is p n = 3.65. The subsidy cost per additional kilometer driven is w = 7. For long-distance driving, the maximum tolerance distance of the driver is The construction cost f of the battery swapping station m is location-dependent and ranges from 40 to 60. The number of battery swapping requests ranges from 200 to 400. The maximum service range is from 1 km to 3 km. The number of existing battery swapping stations is 0 or 5.

[0135] B. Comparison settings

[0136] During the optimization process, three benchmark algorithms for comparison are designed in this verification process, named Grid Generation Algorithm (GGA), Random Algorithm (RAN), and Optimization Algorithm (OPT) respectively:

[0137] RAN: After the neighbor generation algorithm in the embodiment of the present invention gives the candidate locations, randomly select locations from them to deploy battery swapping stations to serve as many battery swapping requests as possible under the maximum battery swapping request constraint until all requests are satisfied.

[0138] GGA: Uniformly divide the area into multiple grids, regard each grid as a neighbor, and then use the greedy algorithm in the embodiment of the present invention for the deployment of battery swapping stations and the allocation of battery swapping requests.

[0139] OPT: Inspired by the brute-force algorithm, after obtaining the candidate locations by using the neighbor generation algorithm in the embodiment of the present invention, enumerate all possible combinations in the candidate location set to obtain the optimal solution.

[0140] When the number of existing battery swapping stations is 0, the algorithm of the embodiment of the present invention is named BGA, and when the number of existing battery swapping stations is 0, the algorithm of the embodiment of the present invention is named ABGA, C. Evaluation results and analysis

[0141] During the verification process, first Figure 2 gives the candidate locations and coverage range of the battery swapping stations (the execution result of the neighbor generation algorithm). This verification will analyze the influence of different variables on the proposed algorithm.

[0142] (1) Influence of the number of battery swapping requests N: Figure 3aIt is shown that the annual total social cost increases as N increases. Because more battery swapping requests will result in more driving costs and they require more battery swapping stations to ensure that all requests can be served. The greedy algorithm proposed in the embodiments of the present invention is superior to GGA and RAN on average, with improvements of 35.6% and 104.4% respectively; compared with the optimal solution, the greedy algorithm proposed in the embodiments of the present invention can reach at most 110.3% of the optimal solution, with an average of 112.0%.

[0143] When there are 5 battery swapping stations, Figure 4a It is shown that the annual total social cost increases as N increases. The above discussion still holds. The improved greedy algorithm proposed in the embodiments of the present invention is superior to GGA and RAN on average, with improvements of 47.8% and 90.9% respectively; compared with the optimal solution, our algorithm 4 can reach at most 107.5% of the optimal solution, with an average of 111.7%.

[0144] (2) The maximum number of batteries L stored in each battery swapping station m The influence of: Figure 3b It is shown that the annual total social cost decreases as L m increases. The reason is that the more batteries a battery swapping station can store, the relatively fewer battery swapping stations need to be deployed. The greedy algorithm proposed in the embodiments of the present invention is superior to GGA and RAN on average, with improvements of 32.5% and 116.8% respectively; compared with the optimal solution, our algorithm 3 can reach at most 102.4% of the optimal solution, with an average of 108.9%.

[0145] When there are 5 battery swapping stations, Figure 4b It is shown that the annual total social cost decreases as L m increases. The above discussion still holds. The improved greedy algorithm proposed in the embodiments of the present invention is superior to GGA and RAN on average, with improvements of 46.3% and 108.4% respectively; compared with the optimal solution, our algorithm 4 can reach at most 107.2% of the optimal solution, with an average of 110.9%.

[0146] (3) The maximum service range R max The influence of: Figure 3c It is shown that as R max increases, the total social cost decreases. This is because with a larger neighbor radius, more battery swapping requests can be merged into the same neighbor, thus potentially reducing the number of candidate battery swapping stations. The greedy algorithm proposed in the embodiments of the present invention is superior to GGA and RAN on average, with improvements of 30.4% and 90.4% respectively. The results calculated by the greedy algorithm proposed in the embodiments of the present invention are very close to the optimal solution, and can reach at most 112.4%, with an average of 117.1% of the optimal social cost.

[0147] When there are 5 battery swapping stations, Figure 4c It shows that the total annual social cost decreases with the increase of Rmax. The above discussion still holds. The improved greedy algorithm proposed in the embodiments of the present invention is superior to GGA and RAN on average, with improvements of 51.2% and 86.9% respectively. The results calculated by the improved greedy algorithm proposed in the embodiments of the present invention are also very close to the minimum value calculated by OPT, reaching up to 104.7% at most and 108.7% on average of the optimal social cost.

[0148] The embodiments of the present invention also provide a system for electric vehicle battery swapping station deployment and battery request allocation, which includes:

[0149] A data acquisition module, configured to acquire data of the battery swapping network, where the data of the battery swapping network includes battery swapping station data, battery swapping request data, and long-distance subsidy data;

[0150] A model construction module, configured to construct an optimization model for electric vehicle battery swapping station deployment and battery request allocation based on the data of the battery swapping network with the goal of minimizing the total social cost, where the total social cost is the sum of the deployment cost of the battery swapping station, the driving cost of the electric vehicle, and the long-distance subsidy cost;

[0151] A solving module, configured to solve the optimization model for electric vehicle battery swapping station deployment and battery request allocation to obtain the number of battery swapping stations for optimized deployment, the deployment locations of the battery swapping stations, and the set N of battery swapping requests allocated to the battery swapping station deployed at location m m .

[0152] It can be understood that the system for electric vehicle battery swapping station deployment and battery request allocation provided by the embodiments of the present invention corresponds to the method for electric vehicle battery swapping station deployment and battery request allocation described above. The explanations, examples, beneficial effects, etc. of the relevant content can refer to the corresponding content in the method for electric vehicle battery swapping station deployment and battery request allocation, which will not be elaborated here.

[0153] The embodiments of the present invention also provide a computer-readable storage medium, which stores a computer program for electric vehicle battery swapping station deployment and battery request allocation, where the computer program enables a computer to execute the method for electric vehicle battery swapping station deployment and battery request allocation as described above.

[0154] The embodiments of the present invention also provide an electronic device, including:

[0155] One or more processors;

[0156] A memory; and

[0157] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs include methods for performing the deployment of electric vehicle battery swapping stations and battery request allocation as described above.

[0158] In summary, compared with the prior art, the following beneficial effects are achieved:

[0159] 1. When constructing the optimization model for the deployment of battery swapping stations and battery request allocation in the embodiments of the present invention, the deployment cost, driving cost, and long-distance subsidy cost are considered, which can effectively reduce the total social cost of battery swapping, and at the same time improve the user experience and provide a friendly battery swapping service.

[0160] 2. In the embodiments of the present invention, the search space of the candidate deployment locations of the battery swapping stations is reduced from infinite to finite through the neighbor generation algorithm, solving the problem complexity of infinite candidate locations and strong coupling between optimization variables, and improving the solving efficiency.

[0161] 3. In the embodiments of the present invention, the greedy algorithm is used to allocate battery swapping requests, solving the problem of complex coupling relationships in battery swapping request allocation and improving the solving efficiency.

[0162] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0163] 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for deploying electric vehicle battery swap stations and allocating battery requests, characterized in that: include: S1. Obtaining data on the battery swap network, where the data includes battery swap station data, battery swap request data, and long-distance subsidy data; S2. Based on the data from the battery swap network, an optimization model for battery swap station deployment and battery request allocation is constructed with the goal of minimizing the total social cost, where the total social cost is the sum of the deployment cost of the battery swap station, the driving cost of the electric vehicle, and the long-distance subsidy cost; S3. Solve the battery swap station deployment and battery request allocation optimization model to obtain the number of optimally deployed battery swap stations, the deployment locations of the battery swap stations, and the battery swap request set N allocated to the battery swap station deployed at location m. m ; The battery swap station deployment and battery request allocation optimization model includes an objective function and constraints, which are expressed as follows: in, where c total represents the total social cost for all battery swapping stations deployed; represents the total social cost of the battery swapping station deployed at location m; N represents the set of all battery swapping requests; N m is the set of battery swapping requests served by the battery swapping station deployed at location m, N m′ is the set of battery swapping requests served by the battery swapping station deployed at location m′, M represents the set of locations where battery swapping stations are deployed, m, m′ ∈ M, m ≠ m′; R max represents the maximum service range of the battery swapping station; L m represents the maximum number of batteries stored in the battery swapping station; represents the driving cost for the vehicle owner who issues battery swapping request n to drive to the battery swapping station deployed at location m; p n represents the driving cost per kilometer for the vehicle owner who issues battery swapping request n; d mn represents the distance between the battery swapping station deployed at location m and the location of the vehicle owner who issues battery swapping request n; represents the driving subsidy for the vehicle owner who issues battery swapping request n to drive to the battery swapping station deployed at location m; represents the maximum tolerable distance for an electric vehicle driver to drive to a remote battery swapping station; w represents the subsidy cost per additional kilometer driven; f m represents the total economic cost of the battery swapping station deployed at location m; represents the construction cost; represents the land cost; G represents the set of building location coordinates; The optimization variables of the objective function are M and N m and G; Constraints (6a) and (6b) ensure that all electric vehicle battery swap requests are met and each battery swap request can only be served by one battery swap station. Constraint (6c) indicates that the number of battery swap requests that a battery swap station can serve should be less than its maximum number of batteries L m .

2. The method for deploying an electric vehicle battery swapping station and allocating battery requests according to claim 1, wherein, The S3 specifically includes: S301. Reduce the search space of candidate deployment locations of the battery swap station from infinite to finite using a neighbor generation algorithm to obtain a set of candidate locations for the battery swap station deployment; S302: Based on the candidate location set for battery swap station deployment, two decision variables a are introduced. m and b mn The battery swap station deployment and battery request allocation optimization models are equivalently transformed to obtain a joint optimization model; S303. Solve the joint optimization model through the greedy algorithm to obtain the number of optimized deployed battery swapping stations, the deployment locations of the battery swapping stations, and the battery swapping requests N assigned to battery swapping station m m .

3. The method for deploying an electric vehicle battery swapping station and allocating battery requests according to claim 2, wherein, The S301 includes: Initialize the candidate location set M c is an empty set; For each battery swap request, find all neighbors within the maximum coverage area to create a request bundle; for each request bundle, call the MinDisk algorithm to find the neighbors with coordinates G m is the minimum enclosing circle with a center and radius r; wherein the request bundle refers to a set of battery swap requests served by the same battery swap station; Obtain the centers of the minimum enclosing circles of each request bundle as the candidate location set M c .

4. The method for deploying electric vehicle battery swap stations and allocating battery requests as claimed in claim 2, characterized in that: The combined battery swap station deployment and battery request allocation optimization model includes: Among them, the minimum total social cost in P1 Equivalently expressed as P2, a m ∈{0,1}, indicating whether a battery swap station is deployed at location m; b mn ∈{0,1} indicates whether the battery swap request n is assigned to the battery swap station deployed at location m; M c Represents a set of candidate locations.

5. The method for deploying electric vehicle battery swap stations and allocating battery requests as claimed in claim 4, characterized in that: The S303 includes: Initialize the set M of deployment locations to be an empty set, a m = 0, b mn = 0; Get the candidate location set M c ; For each candidate location m in the set M of candidate locations c find the location with the minimum average deployment cost using the greedy approach, which is expressed by the formula Repeat finding the set M of the finally deployed locations based on the greedy formula until all battery swapping requests can be satisfied, and obtain the number of optimized deployed battery swapping stations, the deployment locations of the battery swapping stations, and the set N of battery swapping requests assigned to the battery swapping station deployed at location m m .

6. The method for deploying an electric vehicle battery swapping station and allocating battery requests according to claim 4, characterized in that, The S303 includes: For the set M0 of already deployed battery swapping stations, for the battery swapping station deployed at location m in the set, obtain the number |N of all battery swapping requests within the coverage range m |; For the set M0 of already deployed battery swapping stations, for the battery swapping station deployed at location m in the set, determine the shortest distance d from the vehicle owner who issued the battery swapping request n mn Whether it exceeds 1.5 times the maximum coverage range R max If it exceeds, skip this battery swapping station; if it does not exceed, find all the battery swapping request quantities |N m | within the coverage range of the battery swapping station deployed at location m; Use the greedy strategy to find the battery swap station with the minimum average cost, and obtain the set of battery swap stations M that actually provide battery swap services exist And the battery swap requests N′ that have been served are calculated as follows: For all sets of battery swapping requests \(N\setminus N'\) that have not been served, and all candidate battery swapping station locations \(M\) c \M, compare each candidate location \(m\), and use the greedy approach to find the location with the minimum average deployment cost, which can be expressed by the formula Repeat the process of finding the finally deployed location set \(M\) based on the greedy formula until all battery swapping requests are satisfied; obtain the number of optimized deployed battery swapping stations, the deployment locations of the battery swapping stations, and the set of battery swapping requests assigned to the battery swapping station deployed at location \(m\), \(N\) m .

7. A system for deploying electric vehicle battery swap stations and allocating battery requests, characterized in that: include: A data acquisition module is used to obtain data on the battery swap network, where the battery swap network data includes battery swap station data, battery swap request data, and long-distance subsidy data; A model building module is used to build an optimization model for battery swap station deployment and battery request allocation based on battery swap network data, with the goal of minimizing the total social cost, where the total social cost is the sum of the deployment cost of the battery swap station, the cost of electric vehicle driving, and the cost of long-distance subsidies; A solution module, which is used to solve the optimization model of swapping station deployment and battery request allocation, and obtain the number of swapping stations with optimized deployment, the deployment locations of the swapping stations, and the set N of battery swapping requests allocated to the swapping station deployed at location m m ; The battery swap station deployment and battery request allocation optimization model includes an objective function and constraints, which are expressed as follows: in, Where c total represents the total social cost of all deployed battery swap stations; represents the total social cost of the battery swap station deployed at location m; N represents the set of all battery swap requests; N m is the set of battery swap requests served by the battery swap station deployed at location m, N m′ is the set of battery swap requests served by the battery swap station deployed at location m′, where M represents the set of locations where battery swap stations are deployed, m,m′∈M,m≠m′; R max Indicates the maximum service range of the battery swap station; L m Indicates the maximum number of batteries that can be stored in the battery swap station; represents the driving cost of a car owner who issues a battery swap request n to travel to the battery swap station deployed at location m; p n represents the driving cost per kilometer for the owner who issues a battery replacement request n; d mn represents the distance between the battery swap station deployed at position m and the location of the vehicle owner who issued the battery swap request n; The driving subsidy for a car owner with a battery swap request n to travel to a battery swap station at location m; represents the maximum distance that electric vehicle drivers can tolerate when driving to a distant battery swap station; w represents the subsidy cost for each additional kilometer traveled; f m represents the total economic cost of the battery swap station deployed at location m; represents the construction cost; represents the land cost; G represents the building location coordinate set; The optimization variables of the objective function are M and N m and G; Constraints (6a) and (6b) ensure that all electric vehicle battery swap requests are met and each battery swap request can only be served by one battery swap station. Constraint (6c) indicates that the number of battery swap requests that a battery swap station can serve should be less than its maximum number of batteries L m .

8. A computer-readable storage medium, characterized in that It stores a computer program for deploying electric vehicle battery swap stations and allocating battery requests, wherein the computer program enables a computer to execute the method for deploying electric vehicle battery swap stations and allocating battery requests as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the electric vehicle battery swap station deployment and battery request allocation as described in any one of claims 1 to 6.

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