Charging station recommendation method and device based on charging cloud platform, equipment and storage medium

Abstract

The invention provides a charging station recommendation method, device and equipment based on a charging cloud platform and a storage medium, and the method is applied to the charging cloud platform, and comprises the steps: determining a to-be-charged vehicle set according to a scheduling request of a to-be-charged vehicle, and determining a corresponding charging station set; based on the vehicle information of each to-be-charged vehicle in the to-be-charged vehicle set and the charging station information of each charging station in the charging station set, constructing a target function by taking the minimum charging total cost of all to-be-charged vehicles in the to-be-charged vehicle set as a target; then solving the target function to obtain an optimal solution; and finally, based on the optimal solution, performing charging station recommendation on each to-be-charged vehicle in the to-be-charged vehicle set. The charging demands of multiple vehicles are comprehensively considered, and the charging station recommendation scheme is determined by taking the minimum charging total cost as the target, so that the vehicle charging experience of multiple users can be ensured in a balanced manner, the charging cost of the users is reduced, the balancing of the charging station load is realized, and the resource configuration is optimized.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle charging, and in particular to a charging station recommendation method, device, equipment and storage medium based on a charging cloud platform. Background Art

[0002] With the popularity of new energy vehicles such as electric vehicles, the construction of charging infrastructure is also increasing. Users need to plan their charging behavior reasonably during driving to meet their travel needs and reduce costs. At the same time, charging station operators also hope to reasonably allocate charging resources, improve the utilization rate and operating efficiency of charging stations, and optimize user charging experience.

[0003] Existing charging station recommendations only focus on charging recommendations for a single vehicle, which can easily lead to large differences in utilization and load rates between charging stations and an inability to reasonably allocate charging resources. It can also easily lead to problems such as charging congestion, long waiting times, and high costs, resulting in a poor charging experience for users. Summary of the invention

[0004] The embodiments of the present invention provide a charging station recommendation method, device, equipment and storage medium based on a charging cloud platform to solve the problems of poor user charging experience and unreasonable allocation of charging resources.

[0005] In a first aspect, an embodiment of the present invention provides a charging station recommendation method based on a charging cloud platform, which is applied to the charging cloud platform. The method includes:

[0006] Determine a set of vehicles to be charged and a corresponding set of charging stations according to a scheduling request of one or more vehicles to be charged;

[0007] According to the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations, an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged;

[0008] Solving the objective function to obtain an optimal solution; wherein the optimal solution is a solution that minimizes the objective function;

[0009] Based on the optimal solution, a charging station is recommended for each vehicle to be charged in the set of vehicles to be charged.

[0010] In a possible implementation, the objective function is constructed based on the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations, with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged, including:

[0011] For each vehicle to be charged, the charging cost of the vehicle to be charged when charging at each charging station is determined according to the vehicle information corresponding to the vehicle to be charged and the information of each charging station in the charging station set; wherein the charging cost is determined according to the charging time cost, the charging fee cost and the cost influencing factor;

[0012] Based on each charging cost corresponding to each vehicle to be charged, an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged.

[0013] In a possible implementation, the vehicle information includes state of charge information, battery capacity information, vehicle speed information, and vehicle location information; the charging station information includes charging price information, charging power information, service fee information, location information, point of interest score information, and satisfaction information; the cost influencing factor includes point of interest score information and satisfaction information;

[0014] The step of determining the charging cost of the vehicle to be charged at each charging station according to the vehicle information corresponding to the vehicle to be charged and the information of each charging station in the charging station set includes:

[0015] Determine a first time cost of charging the vehicle to be charged at each charging station according to the vehicle location information and vehicle speed information of the vehicle to be charged and the location information of each charging station, and determine a second time cost of charging the vehicle to be charged at each charging station according to the state of charge information and battery capacity information of the vehicle to be charged and the charging power information of each charging station;

[0016] Determine the charging cost of the vehicle to be charged at each charging station according to the state of charge information and battery capacity information of the vehicle to be charged, as well as the charging unit price information of each charging station and the service fee information;

[0017] The charging cost of the vehicle to be charged at each charging station is obtained according to the corresponding first time cost, second time cost, charging fee cost, and interest point score information and satisfaction information of the charging station.

[0018] In a possible implementation, obtaining the charging cost of the vehicle to be charged at each charging station according to the corresponding first time cost, second time cost, charging cost, and interest point score information and satisfaction information of the charging station includes:

[0019] According to L ij =(T ij1 +T ij2 +M ij -R j,Correctpoi )(1-λ j ), and obtain the charging cost; wherein, Lij is the charging cost of vehicle i when charging at charging station j, where j is a charging station in the charging station set, T ij1 The first time cost of charging vehicle i at charging station j, T ij2 is the second time cost of charging vehicle i at charging station j, M ij The charging cost of vehicle i when charging at charging station j, R j,Correctpoi is the interest point score information of charging station j, λ j is the satisfaction rate information of charging station j.

[0020] In a possible implementation, the scheduling request includes charging demand information of the vehicle to be charged, and the charging demand information includes an expected charging time and an expected charging area;

[0021] The step of determining a set of vehicles to be charged according to the scheduling request of one or more vehicles to be charged includes:

[0022] Based on the charging demand information, the vehicles to be charged are divided into corresponding sets of vehicles to be charged; wherein the expected charging time and the expected charging area of ​​the vehicles to be charged in the set of vehicles to be charged are the same or similar;

[0023] The determining the corresponding charging station set includes:

[0024] The charging stations in the expected charging area whose real-time load rates are less than a preset threshold are screened as recommended charging stations; wherein the charging station set includes one or more recommended charging stations.

[0025] In a possible implementation, solving the objective function to obtain an optimal solution includes:

[0026] Obtaining constraints of the objective function; wherein the constraints include a load rate constraint and a congestion rate constraint of each charging station in the charging station set;

[0027] Based on the particle swarm algorithm, the objective function is solved under the constraints of the constraints to obtain the optimal solution; wherein the dimension of the particles in the particle swarm is the number of vehicles to be charged in the set of vehicles to be charged, the position of the particle is the feasible solution of the objective function, and the search space of the particle is the set of charging stations.

[0028] In a possible implementation, the particle swarm algorithm is used to solve the objective function type under the constraint of the constraint condition to obtain the optimal solution, including:

[0029] Under the constraints of the constraint conditions, the position and velocity of each particle in the particle swarm are initialized, and the fitness value of each particle is calculated according to the objective function to determine the individual optimal position of each particle;

[0030] According to the individual optimal position of each particle, the global optimal position of the particle group is determined;

[0031] If the current number of iterations reaches the maximum number of iterations, the calculation is stopped, and the global optimal position is determined to be the optimal solution;

[0032] If the current number of iterations does not reach the maximum number of iterations, the speed and position of the particle are updated, and the steps of calculating the fitness value of each particle according to the objective function and determining the individual optimal position of each particle are repeated until the current number of iterations reaches the maximum number of iterations, and the current global optimal position is determined as the optimal solution.

[0033] In a second aspect, an embodiment of the present invention provides a charging station recommendation device based on a charging cloud platform, which is applied to the charging cloud platform and includes:

[0034] A first processing unit, configured to determine a set of vehicles to be charged and a corresponding set of charging stations according to a scheduling request of one or more vehicles to be charged;

[0035] A second processing unit is used to construct an objective function with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged according to the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations;

[0036] A solution unit, used to solve the objective function to obtain an optimal solution; wherein the optimal solution is a solution that makes the objective function obtain a minimum value;

[0037] The third processing unit is used to recommend a charging station for each vehicle to be charged in the set of vehicles to be charged based on the optimal solution.

[0038] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0040] The embodiment of the present invention provides a charging station recommendation method, device, equipment and storage medium based on a charging cloud platform, which is applied to the charging cloud platform. First, according to the scheduling request of one or more vehicles to be charged, a set of vehicles to be charged is determined, and a corresponding set of charging stations is determined; then, based on the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations, an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged; then the objective function is solved to obtain an optimal solution; finally, based on the optimal solution, a charging station is recommended for each vehicle to be charged in the set of vehicles to be charged. The present invention comprehensively considers the charging needs of multiple vehicles, and determines a charging station recommendation plan with the goal of minimizing the total charging cost, which can ensure the balanced vehicle charging experience of multiple users, reduce the charging time cost and expense cost of users, and can also achieve load balancing of charging stations and optimize resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 It is a flow chart of an implementation method of a charging station recommendation method based on a charging cloud platform provided by an embodiment of the present invention;

[0043] Figure 2 is a flowchart of another charging station recommendation method based on a charging cloud platform provided by an embodiment of the present invention;

[0044] Figure 3 It is a structural schematic diagram of a charging station recommendation device based on a charging cloud platform provided by an embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention. SUMMARY OF THE INVENTION

[0048] With the popularity of new energy vehicles such as electric vehicles, the construction of charging infrastructure is also increasing. Users need to plan their charging behavior reasonably during driving to meet their travel needs and reduce costs. At the same time, charging station operators also hope to reasonably allocate charging resources, improve the utilization rate and operating efficiency of charging stations, and optimize user charging experience.

[0049] After research, the inventor found that the existing charging station recommendations only focus on charging recommendations for a single vehicle, which can easily lead to large differences in utilization and load rates between charging stations, and there is a problem of unreasonable allocation of charging resources. It can also easily cause problems such as crowded charging, long waiting time, and high costs for users, resulting in a poor charging experience for users.

[0050] In order to optimize the user charging experience and balance the load of charging stations, in the implementation of the present application, a charging cloud platform is used to implement charging station recommendations. First, according to the scheduling request of one or more vehicles to be charged, a set of vehicles to be charged is determined, and a corresponding set of charging stations is determined; then, based on the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations, an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged; then the objective function is solved to obtain the optimal solution; finally, based on the optimal solution, a charging station is recommended for each vehicle to be charged in the set of vehicles to be charged. The present invention uses a charging cloud platform to comprehensively consider the charging needs of multiple vehicles, and with the goal of minimizing the total charging cost, a charging station recommendation plan is determined, which can ensure the balanced charging experience of multiple users, reduce the user's charging time cost and expense cost, and can also achieve the balancing of the charging station load and optimize resource allocation.

[0051] In one or more embodiments of the present invention, the execution subject may be an electronic device equipped with a charging cloud platform.

[0052] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0053] Figure 1 The present invention provides a flowchart of a charging station recommendation method based on a charging cloud platform, which is applied to the charging cloud platform and is described in detail as follows:

[0054] Step 101: Determine a set of vehicles to be charged and a corresponding set of charging stations according to a scheduling request of one or more vehicles to be charged.

[0055] Among them, the vehicle to be charged is a vehicle with charging demand.

[0056] The charging cloud platform in this embodiment is a comprehensive platform built based on cloud computing, Internet of Things, big data and other technologies, which is used to manage and optimize the operation and services of charging facilities.

[0057] Exemplarily, after this embodiment uses the charging cloud platform to receive scheduling requests from one or more vehicles to be charged, this embodiment can determine a set of vehicles to be charged and a corresponding set of charging stations based on the scheduling requests from one or more vehicles to be charged after certain trigger conditions are met.

[0058] In a feasible implementation manner, the trigger condition may be a time trigger condition or a quantity trigger condition of scheduling requests.

[0059] Step 102 , constructing an objective function with the goal of minimizing the total charging cost of all vehicles in the set of vehicles to be charged based on the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations.

[0060] Among them, this embodiment obtains the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations, wherein the vehicle information can be carried in the scheduling request or retrieved again after receiving the scheduling request. The charging station information can be pre-stored, retrieved in real time, or partially pre-stored and partially retrieved in real time, which is not limited in this embodiment.

[0061] After obtaining the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations, this embodiment processes and calculates the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations based on a predetermined processing logic, and constructs an objective function with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged.

[0062] In one example, the vehicle information in this embodiment may include energy information, driving information, etc. of the vehicle, and the charging station information may include service information, charging information, and geographic location information of the charging station, etc.

[0063] Step 103, solving the objective function to obtain an optimal solution; wherein the optimal solution is a solution that minimizes the objective function.

[0064] Exemplarily, in this embodiment, the objective function may be solved by using algorithms such as particle swarm algorithm, genetic algorithm, simulated annealing algorithm, etc. The objective function may also be solved by using a linear programming method.

[0065] Step 104 : Based on the optimal solution, a charging station is recommended for each vehicle to be charged in the set of vehicles to be charged.

[0066] Among them, the optimal solution in this embodiment is to take all the vehicles to be charged in the set of vehicles to be charged as the research objects and recommend a charging station plan with the minimum total cost.

[0067] Exemplarily, this embodiment may recommend a charging station for each vehicle to be charged in the set of vehicles to be charged based on the determined optimal charging station recommendation scheme, for example, by sending recommendation information to the vehicles to be charged through the charging cloud platform.

[0068] Among them, this embodiment can recommend the same or different charging stations to multiple vehicles to be charged.

[0069] In one example, when recommending a charging station, this embodiment can send charging station information for the charging station to the vehicle to be charged, including the location information, distance information, charging unit price, charging service fee, current load information, etc. of the recommended charging station for the user to confirm and select.

[0070] In summary, the embodiments of the present invention provide a charging station recommendation method, device, equipment and storage medium based on a charging cloud platform. First, according to the scheduling request of one or more vehicles to be charged, a set of vehicles to be charged is determined, and a corresponding set of charging stations is determined; then, based on the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations, an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged; then the objective function is solved to obtain an optimal solution; finally, based on the optimal solution, a charging station is recommended for each vehicle to be charged in the set of vehicles to be charged. The present invention comprehensively considers the charging needs of multiple vehicles, and determines a charging station recommendation plan with the goal of minimizing the total charging cost. It can evenly guarantee the vehicle charging experience of multiple users, reduce the charging time cost and expense cost of users, and can also achieve load balancing of charging stations and optimize resource allocation.

[0071] In order to better implement charging recommendations and improve the accuracy of recommendations, the expected charging time and expected charging area of ​​each vehicle to be charged in the set of vehicles to be charged in this embodiment are the same or similar, and the charging station set is determined according to the expected charging area corresponding to the set of vehicles to be charged, and the recommended charging stations are screened and filtered according to the real-time load rate of the charging station, thereby ensuring the user's charging experience and realizing the load balancing of the charging station; the present embodiment comprehensively considers the vehicle information and the charging station information, calculates the charging time cost and the charging fee cost, and then determines the objective function based on the charging time cost, the charging fee cost and the cost influencing factor corresponding to the charging station when each vehicle to be charged is charged at each charging station, and solves the charging recommendation plan based on the objective function, which helps to save user expenses, reduce user charging time, and optimize user charging experience; in addition, the present embodiment also uses a particle swarm algorithm to solve the objective function, which has a fast convergence speed and can quickly obtain the optimal solution, thereby improving the recommendation speed and optimizing the user experience.

[0072] Figure 2 Another implementation flow chart of a charging station recommendation method based on a charging cloud platform provided in an embodiment of the present invention, the method is applied to the charging cloud platform, and is described in detail as follows:

[0073] Step 201 : determining a set of vehicles to be charged and a corresponding set of charging stations according to a scheduling request of one or more vehicles to be charged.

[0074] In a feasible implementation manner, the scheduling request includes charging demand information of the vehicle to be charged, and the charging demand information includes the expected charging time and the expected charging area; step 201 includes:

[0075] Based on the charging demand information, the vehicles to be charged are divided into corresponding sets of vehicles to be charged; wherein the expected charging times and expected charging areas of the vehicles to be charged in the set of vehicles to be charged are the same or similar.

[0076] The charging stations whose real-time load rates in the expected charging area are less than a preset threshold are screened as recommended charging stations; wherein the charging station set includes one or more recommended charging stations.

[0077] Among them, the similar expected charging times of the vehicles to be charged may mean that the difference between the expected charging times is within a preset time threshold, and the similar expected charging areas may mean that the distance between the expected charging areas is within a preset distance threshold.

[0078] In a feasible implementation, in this embodiment, the expected charging areas of the vehicles to be charged in the same set of vehicles to be charged are the same or similar, or the expected charging times of the vehicles to be charged in the same set of vehicles to be charged are the same or similar, or the expected charging times and expected charging areas of the vehicles to be charged in the same set of vehicles to be charged are the same or similar.

[0079] Exemplarily, this embodiment determines a corresponding set of charging stations based on the determined set of vehicles to be charged, and the area to which the vehicles to be charged in the corresponding set of charging stations belong covers the expected charging area of ​​each vehicle to be charged in the set of vehicles to be charged, or the similarity between the area to which the vehicles to be charged in the charging station set belong and the expected charging area of ​​each vehicle to be charged in the set of vehicles to be charged is greater than a preset similarity threshold.

[0080] The similarity calculation can be obtained based on the location of the region and the distance between the regions.

[0081] In a feasible implementation, the present embodiment further screens the charging stations based on their load rate information, which may include the real-time load rate and the predicted load rate within a preset time period in the future. For example, if the real-time load rate is greater than a preset threshold, the charging station is listed as a not recommended charging station, that is, the charging station is not included in the charging station set; or, if both the real-time load rate and the predicted load rate within a preset time period in the future are greater than the preset threshold, the charging station is listed as a not recommended charging station; or, if one of the real-time load rate and the predicted load rate within a preset time period in the future is greater than the preset threshold, the charging station is listed as a not recommended charging station.

[0082] In one example, this embodiment can analyze and obtain the predicted load rate within a future preset time period based on historical data, or can use an algorithm model to obtain the predicted load rate within a future preset time period based on historical data.

[0083] Step 202, for each vehicle to be charged, the charging cost of the vehicle to be charged when charging at each charging station is determined based on the vehicle information corresponding to the vehicle to be charged and the information of each charging station in the charging station set; wherein the charging cost is determined based on the charging time cost, the charging fee cost and the cost influencing factor.

[0084] In one example, the vehicle information includes state of charge information, battery capacity information, vehicle speed information, and vehicle location information; the charging station information includes charging price information, charging power information, service fee information, location information, point of interest score information, and satisfaction information; the cost influencing factor includes point of interest score information and satisfaction information; step 202 includes the following steps:

[0085] The first time cost of charging the vehicle to be charged at each charging station is determined based on the vehicle position information and vehicle speed information of the vehicle to be charged and the position information of each charging station, and the second time cost of charging the vehicle to be charged at each charging station is determined based on the charge state information and battery capacity information of the vehicle to be charged and the charging power information of each charging station.

[0086] The charging cost of the vehicle to be charged at each charging station is determined based on the charge state information and battery capacity information of the vehicle to be charged, as well as the charging unit price information and service fee information of each charging station.

[0087] The charging cost of the vehicle to be charged at each charging station is obtained according to the corresponding first time cost, second time cost, charging fee cost, and interest point score information and satisfaction information of the charging station.

[0088] In a feasible implementation manner, this embodiment is based on L ij =(T ij1 +T ij2 +M ij -R j,Correctpoi )(1-λ j ), and get the charging cost; where L ij is the charging cost of vehicle i when charging at charging station j, j is a charging station in the charging station set, T ij1 The first time cost of charging vehicle i at charging station j, T ij2 is the second time cost of charging vehicle i at charging station j, M ij The charging cost of vehicle i when charging at charging station j, R j,Correctpoi is the interest point score information of charging station j, λ j is the satisfaction rate information of charging station j.

[0089] Wherein, i is a non-zero integer, i=1...h, h represents the total number of vehicles to be charged in the set of vehicles to be charged; j is a non-zero integer, j=1...n, n represents the total number of charging stations in the set of charging stations.

[0090] Exemplarily, in this embodiment, the time cost of charging the vehicle to be charged at each charging station may be calculated according to the following formula, including the first time cost and the second time cost:

[0091]

[0092] In this embodiment, the charging cost of the vehicle to be charged at each charging station can be calculated according to the following formula:

[0093] M ij =(SOC 2,i-SOC 1,i )Q i x j +c j

[0094] Among them, T ij represents the time cost of charging vehicle i at charging station j, M ij represents the charging cost of vehicle i when charging at charging station j, d ij represents the distance from the charging vehicle i to the charging station j, v i represents the average speed of vehicle i to be charged, SOC 1,i Indicates the current state of charge of the battery of the vehicle to be charged, SOC 2,i Indicates the expected state of charge of the battery after the charging of the vehicle is completed, Q i Indicates the battery capacity of the vehicle to be charged, p j represents the charging power of the charging pile in charging station j, x j represents the charging unit price of charging station j, c j represents the service fee in charging station j.

[0095] The point of interest (POI) score information of the charging station can affect the recommendation of the charging station to a certain extent, so the present invention introduces a weighted POI score, where the considered POIs include food, hotel accommodation, shopping, leisure and entertainment, sports and fitness, medical care, etc.

[0096] The formula for defining the POI score of charging station j is:

[0097]

[0098] Wherein, b = {0, 1, 2, ..., a} represents the number of POI types around charging station j, a = {0, 1, 2, ..., 10} represents the maximum interval of b. In the present invention, the total number of POIs around each charging station is considered not to exceed 10, x represents the number of POIs of a certain type around the charging station, and W represents the weight coefficient of the corresponding type of POI, which can be set according to the preliminary survey results. When a = 0, R j,poi =0.

[0099] Since the simple rating cannot reflect the difference between the current charging stations and the average level, the above ratings are revised:

[0100]

[0101] represents the average POI score of all charging stations. If the station score is higher than the average, R j,Correctpoi>0, the charging station recommendation plays a positive role. The present invention believes that the charging cost of the charging vehicle will be reduced accordingly. If the station score is lower than the average, then R j,Correctpoi <0, which has a negative effect on the recommendation of this charging station. The present invention believes that the charging cost of the charging vehicle will increase accordingly. If the score of the station is exactly equal to the average value, then R j,Correctpoi =0, it will not affect the recommendation of the charging station, nor will it affect the charging cost of the charging vehicle.

[0102] λ j represents the satisfaction rate of the jth charging station, which can be expressed as:

[0103]

[0104] where g j The number of people who indicated satisfaction with the charging station in the historical survey, g j,all represents the total number of people surveyed historically, λ j ∈[0,1] is equivalent to a weight. A high satisfaction rate will reduce the current objective function value, and vice versa.

[0105] In summary, this embodiment can obtain L ij =(T ij1 +T ij2 +M ij -R j,Correctpoi )(1-λ j ) to calculate the charging cost of vehicle i at charging station j.

[0106] Step 203 , based on each charging cost corresponding to each vehicle to be charged, an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged.

[0107] Exemplarily, in this embodiment, each charging cost corresponding to each vehicle to be charged is added together to obtain the total charging cost of all vehicles to be charged, and an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged.

[0108] In one example, the objective function minL obtained in this embodiment is as follows:

[0109]

[0110] Among them, I represents the set of vehicles to be charged, and J represents the set of charging stations.

[0111] Step 204, obtaining the constraint conditions of the objective function; wherein the constraint conditions include the load rate constraint and the congestion rate constraint of each charging station in the charging station set.

[0112] Exemplarily, the constraint condition of the objective function in this embodiment can be expressed by the following formula.

[0113] The load rate constraint of charging station j can be expressed as:

[0114]

[0115] The congestion rate constraint of charging station j can be expressed as:

[0116]

[0117] Among them, P j represents the real-time load of the vehicle to be charged after being recommended to charging station j, P j,max represents the maximum load of charging station j, thr1 represents the charging station load rate threshold; D k,j It represents the number of vehicles to be charged after being recommended to charging station j and calculated in real time with the charging station as the center radius kkm, D k,max represents the maximum number of cars in historical statistics, and thr2 represents the congestion rate threshold near the charging station.

[0118] Step 205, based on the particle swarm algorithm, under the constraints of the constraints, the objective function is solved to obtain the optimal solution; wherein the dimension of the particle in the particle swarm is the number of vehicles to be charged in the set of vehicles to be charged, the position of the particle is the feasible solution of the objective function, and the search space of the particle is the set of charging stations.

[0119] In one example, step 205 includes the following steps:

[0120] Under the constraints, the position and velocity of each particle in the particle swarm are initialized, and the fitness value of each particle is calculated according to the objective function to determine the individual optimal position of each particle.

[0121] According to the individual optimal position of each particle, the global optimal position of the particle swarm is determined.

[0122] If the current number of iterations reaches the maximum number of iterations, the calculation is stopped and the global optimal position is determined as the optimal solution.

[0123] If the current number of iterations does not reach the maximum number of iterations, the speed and position of the particle are updated, and the steps of calculating the fitness value of each particle according to the objective function and determining the individual optimal position of each particle are repeated until the current number of iterations reaches the maximum number of iterations and the current global optimal position is determined as the optimal solution.

[0124] Exemplarily, in this embodiment, the number of particle swarms is set to m, the maximum number of iterations is set to K, and the dimension of each particle is defined as the number of charging vehicles h in I. The solution process of this embodiment may include the following steps:

[0125] Step 1: Initialize the speed and position of each particle in the particle swarm. In the search space J, the kth (k = 1, 2, ..., m) particle in the particle swarm is described as follows:

[0126] The current position of the particle, i.e. the selected charging station position, is:

[0127]

[0128] The current velocity vector of the particle, i.e. the search direction, is:

[0129]

[0130] Step 2: Evaluate each particle according to the fitness function, which is the objective function recommended by the charging station in this method, and obtain the historical optimal position of the kth particle:

[0131]

[0132] By comparing all particles, the global optimal position of the entire particle group is obtained as:

[0133]

[0134] Step 3: Check the current number of iterations. If the current number of iterations is less than the set maximum number of iterations, execute step 4; otherwise, jump to step 8.

[0135] Step 4: Update the velocity v of each particle k and position x k .

[0136] The particle velocity update formula is:

[0137] v k+1 =ω×v k +c1×rand()×(p k -x k )+c2×rand()×(p g -x k )

[0138] The particle position update formula is:

[0139] x k+1 =x k +v k+1

[0140] Wherein ω represents the inertia factor, whose value is negative, rand() is a random number between (0,1), c1c2 represents the learning factor, and in the present invention, c1=c2=2 is set.

[0141] Step 5: Evaluate the function fitness value L(k) of each particle.

[0142] Step 6: Update the historical optimal position p of each particle k .

[0143] Step 7: Update the global optimal position p of the group g , skip to step 3.

[0144] Step 8: Output the global optimal position p of the group g , that is, the optimal solution ends.

[0145] Step 206 : Based on the optimal solution, a charging station is recommended for each vehicle to be charged in the set of vehicles to be charged.

[0146] Exemplarily, this step refers to step 104 and will not be described in detail.

[0147] In summary, in the present embodiment, the expected charging time and expected charging area of ​​each vehicle to be charged in the set of vehicles to be charged are the same or similar, and the charging station set is determined according to the expected charging area corresponding to the set of vehicles to be charged, and the present embodiment screens and filters the recommended charging stations according to the real-time load rate of the charging station, thereby ensuring the user's charging experience and realizing the load balancing of the charging station; the present embodiment comprehensively considers the vehicle information and the charging station information, calculates the charging time cost and the charging fee cost, and then determines the objective function based on the charging time cost, the charging fee cost and the cost influencing factor corresponding to the charging station when each vehicle to be charged is charged at each charging station, and solves the charging recommendation plan based on the objective function, which helps to save user expenses, reduce user charging time, and optimize user charging experience; in addition, the present embodiment also uses the particle swarm algorithm to solve the objective function, which has a fast convergence speed, can quickly obtain the optimal solution, improve the recommendation speed, and optimize the user experience.

[0148] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0149] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0150] Figure 3 The structure diagram of the charging station recommendation device based on the charging cloud platform provided by an embodiment of the present invention is shown, which is applied to the charging cloud platform. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0151] like Figure 3As shown, the charging station recommendation device 3 based on the charging cloud platform includes:

[0152] The first processing unit 301 is used to determine a set of vehicles to be charged and a corresponding set of charging stations according to a scheduling request of one or more vehicles to be charged.

[0153] The second processing unit 302 is used to construct an objective function with the goal of minimizing the total charging cost of all vehicles in the set of vehicles to be charged according to the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations.

[0154] The solving unit 303 is used to solve the objective function to obtain an optimal solution; wherein the optimal solution is a solution that minimizes the objective function.

[0155] The third processing unit 304 is configured to recommend a charging station to each vehicle to be charged in the set of vehicles to be charged based on the optimal solution.

[0156] In a possible implementation, the second processing unit 302 is specifically configured to:

[0157] For each vehicle to be charged, the charging cost of the vehicle to be charged when charging at each charging station is determined based on the vehicle information corresponding to the vehicle to be charged and the information of each charging station in the charging station set; wherein the charging cost is determined based on the charging time cost, the charging fee cost and the cost influencing factor.

[0158] Based on each charging cost corresponding to each vehicle to be charged, an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged.

[0159] In a possible implementation, the vehicle information includes state of charge information, battery capacity information, vehicle speed information, and vehicle location information; the charging station information includes charging price information, charging power information, service fee information, location information, point of interest score information, and satisfaction information; the cost influencing factor includes point of interest score information and satisfaction information; the second processing unit 302 is specifically used to:

[0160] The first time cost of charging the vehicle to be charged at each charging station is determined based on the vehicle position information and vehicle speed information of the vehicle to be charged and the position information of each charging station, and the second time cost of charging the vehicle to be charged at each charging station is determined based on the charge state information and battery capacity information of the vehicle to be charged and the charging power information of each charging station.

[0161] The charging cost of the vehicle to be charged at each charging station is determined based on the charge state information and battery capacity information of the vehicle to be charged, as well as the charging unit price information and service fee information of each charging station.

[0162] The charging cost of the vehicle to be charged at each charging station is obtained according to the corresponding first time cost, second time cost, charging fee cost, and interest point score information and satisfaction information of the charging station.

[0163] In a possible implementation, the second processing unit 302 is further configured to:

[0164] According to L ij =(T ij1 +T ij2 +M ij -R j,Correctpoi )(1-λ j ), and get the charging cost; where L ij is the charging cost of vehicle i when charging at charging station j, j is a charging station in the charging station set, T ij1 The first time cost of charging vehicle i at charging station j, T ij2 is the second time cost of charging vehicle i at charging station j, M ij The charging cost of vehicle i when charging at charging station j, R j,Correctpoi is the interest point score information of charging station j, λ j is the satisfaction rate information of charging station j.

[0165] In a possible implementation, the scheduling request includes charging demand information of the vehicle to be charged, and the charging demand information includes an expected charging time and an expected charging area; the first processing unit 301 is specifically used to:

[0166] Based on the charging demand information, the vehicles to be charged are divided into corresponding sets of vehicles to be charged; wherein the expected charging times and expected charging areas of the vehicles to be charged in the set of vehicles to be charged are the same or similar.

[0167] The first processing unit 301 is further specifically configured to:

[0168] The charging stations whose real-time load rates in the expected charging area are less than a preset threshold are screened as recommended charging stations; wherein the charging station set includes one or more recommended charging stations.

[0169] In a possible implementation, the solving unit 303 is specifically used for:

[0170] Obtain constraints of the objective function; wherein the constraints include a load rate constraint and a congestion rate constraint of each charging station in the charging station set.

[0171] Based on the particle swarm algorithm, the objective function is solved under the constraints of the constraints to obtain the optimal solution; wherein, the dimension of the particle in the particle swarm is the number of vehicles to be charged in the set of vehicles to be charged, the position of the particle is the feasible solution of the objective function, and the search space of the particle is the set of charging stations.

[0172] In a possible implementation, the solving unit 303 is further configured to:

[0173] Under the constraints, the position and velocity of each particle in the particle swarm are initialized, and the fitness value of each particle is calculated according to the objective function to determine the individual optimal position of each particle;

[0174] According to the individual optimal position of each particle, the global optimal position of the particle swarm is determined.

[0175] If the current number of iterations reaches the maximum number of iterations, the calculation is stopped and the global optimal position is determined as the optimal solution.

[0176] If the current number of iterations does not reach the maximum number of iterations, the speed and position of the particle are updated, and the steps of calculating the fitness value of each particle according to the objective function and determining the individual optimal position of each particle are repeated until the current number of iterations reaches the maximum number of iterations and the current global optimal position is determined as the optimal solution.

[0177] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0178] Figure 4 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 40 and a memory 41. The memory 41 is used to store a computer program 42, and the processor 40 is used to call and run the computer program 42 stored in the memory 41 to perform the steps in the above-mentioned charging station recommendation method embodiments based on the charging cloud platform, such as Figure 1 Alternatively, the processor 40 is used to call and run the computer program 42 stored in the memory 41 to implement the functions of each module / unit in the above-mentioned device embodiments, such as Figure 3 Functionality of modules / units 301 to 304 is shown.

[0179] Exemplarily, the computer program 42 may be divided into one or more modules / units, one or more modules / units are stored in the memory 41 and executed by the processor 40 to complete the present application. One or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the electronic device 4. For example, the computer program 42 may be divided into Figure 3 Modules / units 301 to 304 are shown.

[0180] The electronic device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0181] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0182] The memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 41 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used to store computer programs and other programs and data required by the electronic device. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0183] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0184] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0185] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0186] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0187] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0188] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0189] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned charging station recommendation method embodiment based on the charging cloud platform can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunications signals.

[0190] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A charging station recommendation method based on a charging cloud platform, characterized in that: The method is applied to a charging cloud platform, and the method comprises: Determine a set of vehicles to be charged and a corresponding set of charging stations according to a scheduling request of one or more vehicles to be charged; According to the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations, an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged; Solving the objective function to obtain an optimal solution; wherein the optimal solution is a solution that minimizes the objective function; Based on the optimal solution, a charging station is recommended for each vehicle to be charged in the set of vehicles to be charged.

2. The charging station recommendation method based on the charging cloud platform according to claim 1 is characterized in that: The objective function is constructed based on the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations, with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged, including: For each vehicle to be charged, the charging cost of the vehicle to be charged when charging at each charging station is determined according to the vehicle information corresponding to the vehicle to be charged and the information of each charging station in the charging station set; wherein the charging cost is determined according to the charging time cost, the charging fee cost and the cost influencing factor; Based on each charging cost corresponding to each vehicle to be charged, an objective function is constructed with the goal of minimizing the total charging cost of all vehicles to be charged.

3. The charging station recommendation method based on the charging cloud platform according to claim 2 is characterized in that: The vehicle information includes state of charge information, battery capacity information, vehicle speed information and vehicle location information; the charging station information includes charging price information, charging power information, service fee information, location information, point of interest score information and satisfaction information; the cost influencing factor includes point of interest score information and satisfaction information; The step of determining the charging cost of the vehicle to be charged at each charging station according to the vehicle information corresponding to the vehicle to be charged and the information of each charging station in the charging station set includes: Determine a first time cost of charging the vehicle to be charged at each charging station according to the vehicle location information and vehicle speed information of the vehicle to be charged and the location information of each charging station, and determine a second time cost of charging the vehicle to be charged at each charging station according to the state of charge information and battery capacity information of the vehicle to be charged and the charging power information of each charging station; Determine the charging cost of the vehicle to be charged at each charging station according to the state of charge information and battery capacity information of the vehicle to be charged, as well as the charging unit price information of each charging station and the service fee information; The charging cost of the vehicle to be charged at each charging station is obtained according to the corresponding first time cost, second time cost, charging fee cost, and interest point score information and satisfaction information of the charging station.

4. The charging station recommendation method based on the charging cloud platform according to claim 3 is characterized in that: The method of obtaining the charging cost of the vehicle to be charged at each charging station according to the corresponding first time cost, second time cost, charging cost, and interest point score information and satisfaction information of the charging station includes: According to L ij =(T ij1 +T ij2 +M ij -R j,Correctpoi )(1-λ j ), and obtain the charging cost; wherein, L ij is the charging cost of vehicle i when charging at charging station j, where j is a charging station in the charging station set, T ij1 The first time cost of charging vehicle i at charging station j, T ij2 M is the second time cost of charging vehicle i at charging station j, ij The charging cost of vehicle i when charging at charging station j, R j,Correctpoi is the interest point score information of charging station j, λ j is the satisfaction rate information of charging station j.

5. The charging station recommendation method based on the charging cloud platform according to any one of claims 1 to 4, characterized in that: The scheduling request includes charging demand information of the vehicle to be charged, and the charging demand information includes an expected charging time and an expected charging area; The step of determining a set of vehicles to be charged according to the scheduling request of one or more vehicles to be charged includes: Based on the charging demand information, the vehicles to be charged are divided into corresponding sets of vehicles to be charged; wherein the expected charging time and the expected charging area of ​​the vehicles to be charged in the set of vehicles to be charged are the same or similar; The determining the corresponding charging station set includes: The charging stations in the expected charging area whose real-time load rates are less than a preset threshold are screened as recommended charging stations; wherein the charging station set includes one or more recommended charging stations.

6. The charging station recommendation method based on the charging cloud platform according to any one of claims 1 to 4, characterized in that: The step of solving the objective function to obtain an optimal solution includes: Obtaining constraints of the objective function; wherein the constraints include a load rate constraint and a congestion rate constraint of each charging station in the charging station set; Based on the particle swarm algorithm, the objective function is solved under the constraints of the constraints to obtain the optimal solution; wherein the dimension of the particles in the particle swarm is the number of vehicles to be charged in the set of vehicles to be charged, the position of the particle is the feasible solution of the objective function, and the search space of the particle is the set of charging stations.

7. The charging station recommendation method based on the charging cloud platform according to claim 6 is characterized in that: The particle swarm algorithm is used to solve the objective function type under the constraint of the constraint condition to obtain the optimal solution, including: Under the constraints of the constraint conditions, the position and velocity of each particle in the particle swarm are initialized, and the fitness value of each particle is calculated according to the objective function to determine the individual optimal position of each particle; According to the individual optimal position of each particle, the global optimal position of the particle group is determined; If the current number of iterations reaches the maximum number of iterations, the calculation is stopped, and the global optimal position is determined to be the optimal solution; If the current number of iterations does not reach the maximum number of iterations, the speed and position of the particle are updated, and the steps of calculating the fitness value of each particle according to the objective function and determining the individual optimal position of each particle are repeated until the current number of iterations reaches the maximum number of iterations, and the current global optimal position is determined as the optimal solution.

8. A charging station recommendation device based on a charging cloud platform, characterized in that: The device is applied to a charging cloud platform, and the device comprises: A first processing unit, configured to determine a set of vehicles to be charged and a corresponding set of charging stations according to a scheduling request of one or more vehicles to be charged; A second processing unit is used to construct an objective function with the goal of minimizing the total charging cost of all vehicles to be charged in the set of vehicles to be charged according to the vehicle information of each vehicle to be charged in the set of vehicles to be charged and the charging station information of each charging station in the set of charging stations; A solution unit, used to solve the objective function to obtain an optimal solution; wherein the optimal solution is a solution that makes the objective function obtain a minimum value; The third processing unit is used to recommend a charging station for each vehicle to be charged in the set of vehicles to be charged based on the optimal solution.

9. An electronic device, characterized in that: It comprises a processor and a memory, the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the charging station recommendation method based on the charging cloud platform as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the charging station recommendation method based on the charging cloud platform as described in any one of claims 1 to 7 above are implemented.

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