Electric vehicle optimal charging strategy generation method

Abstract

The invention discloses an electric vehicle optimal charging strategy generation method, which belongs to the technical field of electric vehicle charging, and comprises the following steps: S100, obtaining the real-time electric quantity, real-time geographic information and historical charging information of a vehicle, and the real-time electricity price and geographic information of a charging station; s200, coordinating and integrating the geographic information to obtain a coordinate geographic set; sorting the substation charging times of the vehicle based on the historical charging information of the vehicle to obtain a charging time set; s300, calculating a charging station preference score based on the charging frequency set; the charging cost of the vehicle is calculated based on the real-time electric quantity and the real-time electricity price of the vehicle; virtual distances from the vehicle to different charging stations are speculated according to the coordinate geographic set; s400, setting an objective function of a Deckstra algorithm according to the charging station preference score, the charging cost and the virtual distance, and solving the objective function to obtain an optimal charging strategy of the vehicle; the problem that the charging cost is high due to the fact that the change of real-time electricity prices of different charging stations is not considered in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to a method for generating an optimal charging strategy for an electric vehicle. Background Art

[0002] With the development of renewable energy technology and microgrid technology, more and more new energy electric vehicles are participating in public transportation. However, the battery capacity of electric vehicles is limited, resulting in a shorter driving range than conventional power vehicles. In addition, the geographical location and real-time electricity prices of different charging stations are different, resulting in great differences in the cost of charging electric vehicles and the distance to the charging station. Therefore, it is necessary to propose a method to find a charging station with a suitable distance and the lowest cost when the electric vehicle is low on power.

[0003] Chinese patent, publication number: CN117990121A, publication date: May 7, 2024, discloses a navigation charging path planning method, device, electronic device and readable storage medium, including: obtaining the current vehicle remaining power and current navigation information of the vehicle, the navigation information including the navigation starting point and the navigation end point; obtaining the vehicle's historical unit energy consumption, charging habit information and map information; based on the map information, planning the navigation path corresponding to the navigation information; determining the predicted power consumption according to the path information, the navigation path and the historical unit energy consumption; comparing the predicted power consumption and the current vehicle remaining power to obtain a comparison result; determining the preset charging pile on the path information based on the comparison result, the charging pile location point and the navigation path, and selecting the target charging pile that meets the charging habit information from the preset charging piles; planning the target navigation charging path including the target charging pile based on the map information; and when the electric vehicle is insufficiently charged, the invention does not take into account the charging cost changes caused by the real-time electricity prices of different charging stations, resulting in high charging costs for electric vehicles. Summary of the invention

[0004] The purpose of the present invention is to address the problem that the existing technology does not take into account the charging cost changes caused by the real-time electricity prices of different charging stations, resulting in the electric vehicle being unable to find the charging station with the shortest path and the lowest charging cost when the power is insufficient; a method for generating an optimal charging strategy for an electric vehicle is proposed, which calculates the drivable distance of the current vehicle with the remaining power and the virtual distances of the current vehicle to different charging stations according to the current real-time power of the vehicle, the real-time geographic information, and the geographic information of the charging station, calculates the charging cost of the current vehicle according to the current real-time power of the vehicle and the real-time electricity price of the charging station, calculates the preference scores of different charging stations through the historical charging information of the vehicle, and obtains the superior charging stations with superior geographical locations and good services, and finally the owner of the current vehicle selects a suitable charging station to charge the current vehicle according to the optimal charging strategy planned by the Dykstra algorithm with the objective function set based on the preference score of the charging station, the charging cost and the virtual distance, thereby significantly reducing the comprehensive cost of charging the current vehicle.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is a method for generating an optimal charging strategy for an electric vehicle, comprising the following steps: S100, obtaining the real-time power, real-time geographic information and historical charging information of the vehicle, and obtaining the real-time electricity price and geographic information of the charging station; The vehicle historical charging information includes the current vehicle historical charging information and other vehicle historical charging information; S200, coordinate and integrate the current real-time geographic information of the vehicle and the geographic information of the charging station to obtain a coordinate geographic set; Synchronously, extracting the number of times the current vehicle was charged at different charging stations from the current vehicle's historical charging information and the number of times other vehicles were charged at different charging stations from the other vehicles' historical charging information and integrating them to obtain a charging number set; S300, calculating a charging station preference score for the current vehicle based on the charging number set combined with a preference formula; Simultaneously, the charging fee of the current vehicle is calculated based on the current real-time power and real-time electricity price of the vehicle in combination with the charging cost formula; simultaneously, the virtual distance of the current vehicle to different charging stations is estimated according to the coordinate geographic set and the virtual distance formula; S400: setting and solving an objective function of the Dykstra algorithm according to the charging station preference score, charging cost and virtual distance to obtain an optimal charging strategy for the current vehicle.

[0006] In this solution, the real-time power of the current vehicle, the real-time geographic information, and the geographic information of the charging station are used to calculate, find the charging station that the current vehicle can reach before the power is exhausted, estimate the virtual distance of the current vehicle to the charging station, and select the appropriate charging station; synchronously, the real-time power of the current vehicle and the real-time electricity price of the charging station are used to calculate, obtain the charging cost required for the current vehicle to be fully charged in the appropriate charging station, and at the same time, the preference degree of different charging stations selected by the owner is calculated according to the historical charging information of the current vehicle and the historical charging information of other vehicles, and the preference score of the charging station is obtained. A large amount of data on the subjective selection of charging stations by the owner of the current vehicle and other vehicles is used to screen out many secondary charging stations with poor geographical location and poor service, and find the charging station preferred by the owner of the current vehicle among the superior charging stations; finally, the objective function of the Dykstra algorithm is set and solved according to the charging station preference score, charging cost and virtual distance, and the optimal charging strategy of the current vehicle is obtained. At the same time, the owner of the current vehicle selects a suitable charging station according to the optimal charging strategy to fully charge the energy storage battery of the current vehicle at the most economical charging cost.

[0007] Preferably, in S200, the specific process of coordinateizing and integrating the current vehicle real-time geographic information and the charging station geographic information to obtain the coordinate geographic set is: S211, integrating the current real-time geographic information of the vehicle and the geographic information of the charging station in the form of an image to obtain a map containing at least the current vehicle location, the current vehicle destination location, and the charging station location; S212. Establish a two-dimensional coordinate system in the map with the current vehicle position as the origin, obtain the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates, and organize the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates to obtain a coordinate geographic set.

[0008] In this solution, the current vehicle real-time geographic information and the charging station geographic information are integrated into the same map and coordinated, and the coordinates are used to intuitively represent the location of the current vehicle, the current vehicle destination, and the charging station to support subsequent steps; wherein, the current vehicle real-time geographic information and the charging station geographic information can also be directly obtained according to existing map software, without spending a lot of time to generate them yourself.

[0009] Preferably, in S200, the specific process of extracting and integrating the number of times the current vehicle has been charged at different charging stations from the historical charging information of the current vehicle and the number of times other vehicles have been charged at different charging stations from the historical charging information of other vehicles to obtain the charging number set is: S221, extracting the number of times the current vehicle has been charged at different charging stations to establish a current vehicle number subset, and extracting the number of times other vehicles have been charged at different charging stations to establish other vehicle number subsets; Simultaneously, a corresponding factor is established according to the probability of the current vehicle and other vehicles charging historically at the same charging station; S222, extracting the number of times the current vehicle and other vehicles have been charged historically at the same charging station from the current vehicle frequency subset and other vehicle frequency subsets based on the corresponding factor to obtain the same frequency subset; S223, integrating the current vehicle frequency subset, other vehicle frequency subsets, and the same frequency subsets to obtain a charging frequency set; The corresponding factor represents the association relationship between the current vehicle and other vehicles.

[0010] In this solution, the number of times the current vehicle and other vehicles have been charged in different charging stations is counted, and corresponding factors are established based on the probability of the current vehicle and other vehicles being charged in the same charging station in history. The number of times the current vehicle and other vehicles have been charged in the same charging station in history is counted based on the corresponding factors, and the connection between the current vehicle and other vehicles is established, so that the popularity of different charging stations can be intuitively reflected in digital form, which supports the subsequent calculation of charging station preference scores.

[0011] Preferably, in S300, the specific process of calculating the charging station preference score based on the charging number set combined with the preference formula is: S311, performing similarity calculation based on the number of times the current vehicle has been charged historically at different charging stations and the number of times other vehicles have been charged historically at different charging stations in the charging number set, to obtain similarities of the different charging stations; S312, inputting the similarity and the number of times the vehicle has been charged at different charging stations in the charging frequency set into a preference formula to calculate a charging station preference score; The vehicles include the current vehicle and other vehicles.

[0012] In this solution, similarity calculation is performed based on the number of times the current vehicle has been charged in different charging stations in history and the number of times other vehicles have been charged in different charging stations in history, so that the similarity of different charging stations can be obtained, and the possible preference of the current vehicle owner for different charging stations can be inferred based on the similarity and preference formula.

[0013] Preferably, the preference formula in S312 is specifically as follows: In the formula, Q i is the preference score of charging station i, is the total number of times the vehicle is charged at charging station i, is the total number of times the vehicle is charged at charging station j, R i is the number of times the current vehicle is charged at charging station i, and sim(i,j) is the similarity between charging station i and charging station j; wherein the similarity sim(i,j) between charging station i and charging station j is one of the N optimal similarities selected according to the K nearest neighbor principle.

[0014] Preferably, in S300, the specific process of calculating the current vehicle charging fee based on the current vehicle real-time power and real-time electricity price combined with the charging cost formula is: Constructing a charging cost formula according to the proportional relationship between the charging cost, the power consumption, and the electricity price, and inputting the current vehicle real-time power consumption and the real-time electricity price into the constructed charging cost formula to calculate the current vehicle charging fee; The charging cost formula is as follows: C c =(1-C s )×C i,t ; In the formula, C c is the charging cost, C s is the real-time power, C i,t is the real-time electricity price of charging station i at time t.

[0015] In this solution, a charging cost formula is constructed based on the proportional relationship between charging cost, power consumption, and electricity price. The charging cost required to fully charge the current vehicle can be obtained based on the changes in real-time electricity prices and the current changes in power consumption of the vehicle. At the same time, the charging cost formula can be used as one of the restriction conditions for screening charging stations.

[0016] Preferably, in S300, the specific process of estimating the virtual distances from the current vehicle to different charging stations according to the coordinate geographic set and the virtual distance formula is as follows: S321, extracting the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates from the coordinate geographic set as inputs of the deviation angle formula for calculation, to obtain the deviation angle coefficient between the current vehicle and the charging station; Synchronously, the current vehicle coordinates and the charging station coordinates are input into a straight-line distance formula to obtain a straight-line distance between the current vehicle and the charging station; Simultaneously, calculating the drivable distance of the current vehicle according to the real-time power of the current vehicle and the proportional relationship between the power of the current vehicle and the drivable distance; S322, input the deviation angle coefficient and the straight-line distance into a virtual distance calculation formula to obtain a virtual distance set; simultaneously, filter the virtual distance set according to the drivable distance to obtain a virtual distance.

[0017] In this solution, the straight-line distance between the current vehicle and the charging station is calculated based on the current vehicle coordinates and the charging station coordinates, and the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates in the coordinate geographic set are extracted as inputs of the deviation angle formula, and the deviation angle coefficient between the current vehicle and the charging station is calculated to clarify the driving direction of the current vehicle and the direction of the charging station. Subsequently, the straight-line distance and the deviation angle coefficient are input into the virtual distance calculation formula for calculation to obtain a virtual distance set; at the same time, the drivable distance of the current vehicle is calculated based on the real-time power of the current vehicle and the proportional relationship between the power of the current vehicle and the driving distance, and the drivable distance is used as a restriction condition to filter the virtual distance set to obtain a virtual distance based on the straight-line distance from the current vehicle to the charging station and modified by the current vehicle driving direction and the charging station direction.

[0018] Preferably, the deviation angle formula in S321 is specifically as follows: Where ρ is the deviation angle coefficient between the current vehicle and the charging station, and cosθ is calculated based on the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates, as shown below: Among them, m 0 is the horizontal coordinate of the current vehicle, n 0 is the ordinate of the current vehicle, m 1 is the horizontal coordinate of the charging station, n 1 is the ordinate of the charging station, m 2 is the horizontal coordinate of the current vehicle destination, n 2 It is the vertical coordinate of the current vehicle destination.

[0019] Preferably, the virtual distance calculation formula in S322 is specifically as follows: s i =L i ×(1+ρ i ); In the formula, s i represents the virtual distance from the current vehicle to charging station i, L i represents the straight-line distance from the current vehicle to charging station i, ρ i Represents the deviation angle coefficient between the current vehicle and charging station i.

[0020] Preferably, in S400, the Dykstra algorithm objective function set according to the charging station preference score, charging cost and virtual distance is specifically as follows: minf=λ 1 ×c 1 ×s i +λ2 ×c 2 ×(1-C s )+λ 3 ×P i ; In the formula, λ 1 is the virtual distance weight coefficient, λ 2 is the charging cost weight coefficient, λ 3 is the preference score weight coefficient, c 1 is the distance cost corresponding to the virtual distance, c 2 is the real-time electricity price cost corresponding to the charging fee, P i is the preference score of charging station i, C s is the real-time power, s i is the virtual distance, w j represents the frequency of users using charging station j, sim(i,j) is the similarity between charging station i and charging station j, Q j is the preference score of charging station j.

[0021] The present invention has at least the following substantial effects: The present application calculates based on the real-time power of the current vehicle, the real-time geographic information, and the geographic information of the charging station, finds the charging station that the current vehicle can reach before the power is exhausted, estimates the virtual distance of the current vehicle to the charging station, and selects the appropriate charging station; synchronously, the real-time power of the current vehicle and the real-time electricity price of the charging station are calculated to obtain the charging cost required for the current vehicle to be fully charged in the appropriate charging station, and at the same time, the preference degree of different charging stations selected by the owner is calculated based on the current vehicle's historical charging information and other vehicles' historical charging information to obtain the preference score of the charging station, and a large amount of data on the subjective selection of charging stations by the current vehicle owner and other vehicle owners are used to select many secondary charging stations with poor geographical location and poor service, and find the charging station preferred by the current vehicle owner among the superior charging stations; finally, the objective function of the Dykstra algorithm is set and solved according to the charging station preference score, charging cost and virtual distance to obtain the optimal charging strategy of the current vehicle, and the owner of the current vehicle selects a suitable charging station according to the optimal charging strategy to fully charge the energy storage battery of the current vehicle at the most economical charging cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0023] Figure 1 This is a flow chart of a method for generating an optimal charging strategy for an electric vehicle according to the present invention; Figure 2 Schematic diagram of the collaborative filtering mechanism in the present invention. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0026] Embodiment 1: like Figure 1 As shown, this embodiment provides a method for generating an optimal charging strategy for an electric vehicle, comprising the following steps: S100, obtaining the real-time power, real-time geographic information and historical charging information of the vehicle, and obtaining the real-time electricity price and geographic information of the charging station; The vehicle historical charging information includes the current vehicle historical charging information and other vehicle historical charging information.

[0027] S200, coordinate and integrate the current real-time geographic information of the vehicle and the geographic information of the charging station to obtain a coordinate geographic set; Synchronously, the number of times the current vehicle was historically charged at different charging stations in the current vehicle historical charging information and the number of times other vehicles were historically charged at different charging stations in the other vehicles historical charging information are extracted and integrated to obtain a charging number set.

[0028] In one embodiment, the current real-time geographic information of the vehicle and the geographic information of the charging station are integrated in the form of coordinates, which specifically includes the following sub-steps: S211, integrating the current real-time geographic information of the vehicle and the geographic information of the charging station in the form of an image to obtain a map containing at least the current vehicle location, the current vehicle destination location, and the charging station location; S212. Establish a two-dimensional coordinate system in the map with the current vehicle position as the origin, obtain the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates, and organize the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates to obtain a coordinate geographic set.

[0029] In this embodiment, the current real-time geographic information of the vehicle and the geographic information of the charging station can also be directly obtained according to the navigation map software, and the two-dimensional coordinate system is established with the help of the navigation map software.

[0030] In a real-time example, the historical charging times of vehicles at different charging stations are counted, which specifically includes the following sub-steps: In S200, the specific process of extracting and integrating the number of times the current vehicle has been charged at different charging stations from the current vehicle's historical charging information and the number of times other vehicles have been charged at different charging stations from the other vehicles' historical charging information to obtain a charging number set is as follows: S221, extracting the number of times the current vehicle has been charged at different charging stations to establish a current vehicle number subset, and extracting the number of times other vehicles have been charged at different charging stations to establish other vehicle number subsets; Simultaneously, a corresponding factor is established according to the probability of the current vehicle and other vehicles charging historically at the same charging station; S222, extracting the number of times the current vehicle and other vehicles have been charged historically at the same charging station from the current vehicle frequency subset and other vehicle frequency subsets based on the corresponding factor to obtain the same frequency subset; S223, integrating the current vehicle frequency subset, other vehicle frequency subsets, and the same frequency subsets to obtain a charging frequency set; The corresponding factor represents the association relationship between the current vehicle and other vehicles.

[0031] In this embodiment, the other vehicles are selected from the vehicle charging information of the charging station according to a preset quantity threshold, and the charging station is selected according to the current vehicle. Specifically, a circle is constructed on the map with the preset distance threshold as the radius and the current vehicle as the center, and the charging point covered by the circle on the map is set as the selected charging station. Secondly, the historical charging information of the vehicle is extracted from the historical charging data of the charging station. The preset distance threshold can be set according to the calculated drivable distance of the current vehicle, that is, the calculation step of the drivable distance of the current vehicle is advanced, and the drivable distance is set as the preset distance threshold for screening charging stations in the map.

[0032] S300, calculating a charging station preference score for the current vehicle based on the charging number set combined with a preference formula; Simultaneously, the charging cost of the current vehicle is calculated based on the current vehicle real-time power and real-time electricity price combined with the charging cost formula; simultaneously, the virtual distance of the current vehicle to different charging stations is estimated according to the coordinate geographic set and the virtual distance formula.

[0033] In one embodiment, in order to understand the current vehicle owner's preference for selecting charging stations and screen out superior charging stations, this embodiment adopts a preference scoring scheme, which specifically includes the following sub-steps: S311, performing similarity calculation based on the number of times the current vehicle has been charged historically at different charging stations and the number of times other vehicles have been charged historically at different charging stations in the charging number set, to obtain similarities of the different charging stations; S312, inputting the similarity and the number of times the vehicle has been charged at different charging stations in the charging frequency set into a preference formula to calculate a charging station preference score; The vehicles include the current vehicle and other vehicles.

[0034] In one embodiment, the preference formula is specifically as follows: In the formula, Q i is the preference score of charging station i, is the total number of times the vehicle is charged at charging station i, is the total number of times the vehicle is charged at charging station j, R i is the number of times the current vehicle is charged at charging station i, and sim(i,j) is the similarity between charging station i and charging station j; wherein the similarity sim(i,j) between charging station i and charging station j is one of the N optimal similarities selected according to the K nearest neighbor principle.

[0035] In this embodiment, the historical charging data of any charging station is mainly used to determine the connection between the car owner and a specific charging station, and the specific charging station is recommended as a potential preferred charging station for other car owners with the same preference. Figure 2 The collaborative filtering mechanism shown establishes a preference model for car owners to charging stations, analyzes the degree of preference of car owners to charging stations, and indicates that the same type of charging stations have the same appeal to car owners with the same preferences; wherein the similarity calculation is performed using the Pearson correlation coefficient method, and the calculation formula is as follows: In the formula, sim(i,j) represents the similarity between charging station i and charging station j, R n,i represents the number of times user n selects charging station i, Rn,j represents the number of times user n selects charging station j, represents the total number of times charging station i is selected by users, represents the total number of times charging station j is selected by users; after the similarity calculation is completed, according to the K nearest neighbor principle, the set of N objects with the highest similarity is found, and the set is input into the preference formula to calculate the preference.

[0036] In one embodiment, in order to calculate the cost of fully charging the current vehicle, this embodiment adopts a charging cost formula scheme, which specifically includes the following sub-steps: Constructing a charging cost formula according to the proportional relationship between the charging cost, the power consumption, and the electricity price, and inputting the current vehicle real-time power consumption and the real-time electricity price into the constructed charging cost formula to calculate the current vehicle charging fee; The charging cost formula is as follows: C c =(1-C s )×C i,t ; In the formula, C c is the charging cost, C s is the real-time power, C i,t is the real-time electricity price of charging station i at time t.

[0037] In one embodiment, the current vehicle's drivable distance is used to select charging stations to which the current vehicle can travel, specifically including the following sub-steps: S321, extracting the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates from the coordinate geographic set as inputs of the deviation angle formula for calculation, to obtain the deviation angle coefficient between the current vehicle and the charging station; Synchronously, the current vehicle coordinates and the charging station coordinates are input into a straight-line distance formula to obtain a straight-line distance between the current vehicle and the charging station; Simultaneously, calculating the drivable distance of the current vehicle according to the real-time power of the current vehicle and the proportional relationship between the power of the current vehicle and the drivable distance; S322, input the deviation angle coefficient and the straight-line distance into a virtual distance calculation formula to obtain a virtual distance set; simultaneously, filter the virtual distance set according to the drivable distance to obtain a virtual distance.

[0038] In one embodiment, the deviation angle formula in S321 is specifically as follows: Where ρ is the deviation angle coefficient between the current vehicle and the charging station, and cosθ is calculated based on the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates, as shown below: Among them, m 0 is the horizontal coordinate of the current vehicle, n 0 is the ordinate of the current vehicle, m 1 is the horizontal coordinate of the charging station, n 1 is the ordinate of the charging station, m 2 is the horizontal coordinate of the current vehicle destination, n 2 It is the vertical coordinate of the current vehicle destination.

[0039] In one embodiment, the virtual distance calculation formula in S322 is specifically as follows: s i =L i ×(1+ρ i ); In the formula, s i represents the virtual distance from the current vehicle to charging station i, L i represents the straight-line distance from the current vehicle to charging station i, ρ i Represents the deviation angle coefficient between the current vehicle and charging station i.

[0040] In this embodiment, the relationship between the straight-line distance and the travel distance is evaluated by the least squares regression method, and the detour index is 1.417 and the correlation index is 0.94, which means that the two are equal to a great extent. Based on this, this embodiment uses the deviation angle to adjust the straight-line distance to calculate the actual travel distance from the current vehicle to different charging stations, that is, the virtual distance. The calculation formula of the straight-line distance is as follows: In the formula, m 0 is the horizontal coordinate of the current vehicle, n 0 is the ordinate of the current vehicle, m 1 is the horizontal coordinate of the charging station, n 1 is the ordinate of the charging station; After the straight-line distance calculation is completed, the deviation angle is calculated according to the current vehicle's driving direction, the destination location and the charging station location to determine whether the current vehicle's direction of reaching the charging station is the same as the direction of the destination. At the same time, the straight-line distance is adjusted according to the calculated deviation angle to obtain the actual driving distance from the current vehicle to the charging station, that is, the virtual distance; At the same time, the real-time power of the current vehicle is used as a basis to calculate its drivable distance, which is used to filter the virtual distance. The calculation formula of the drivable distance is as follows: x=x 0 -a 1 ×(1-C s )×100; Where x is the vehicle's remaining battery distance, i.e., the drivable distance, and the remaining battery is the real-time battery; 0 is the driving distance of the vehicle when it is fully charged; a 1 The distance traveled by the vehicle for every 1% of its electricity; C s The real-time power level of the vehicle.

[0041] S400: setting and solving an objective function of the Dykstra algorithm according to the charging station preference score, charging cost and virtual distance to obtain an optimal charging strategy for the current vehicle.

[0042] In one embodiment, the objective function of the Dykstra algorithm is specifically as follows: minf=λ 1 ×c 1 ×s i +λ 2 ×c 2 ×(1-C s )+λ 3 ×P i ; In the formula, λ 1 is the virtual distance weight coefficient, λ 2 is the charging cost weight coefficient, λ 3 is the preference score weight coefficient, c 1 is the distance cost corresponding to the virtual distance, c 2 is the real-time electricity price cost corresponding to the charging fee, P i is the preference score of charging station i, C s is the real-time power, s i is the virtual distance, w j represents the frequency of users using charging station j, sim(i,j) is the similarity between charging station i and charging station j, Q j is the preference score of charging station j.

[0043] In this embodiment, a compromise solution of the corresponding virtual distance, real-time electricity price and charging station preference score is calculated through the objective function of the Dykstra algorithm, and the optimal charging station with the minimum comprehensive cost is obtained through the compromise solution.

[0044] This embodiment has at least the following substantial effects: This embodiment calculates according to the real-time power of the current vehicle, the real-time geographic information, and the geographic information of the charging station, finds the charging station that the current vehicle can reach before the power is exhausted, estimates the virtual distance of the current vehicle to the charging station, and selects the appropriate charging station; synchronously, according to the real-time power of the current vehicle and the real-time electricity price of the charging station, calculates the charging cost required for the current vehicle to be fully charged in the appropriate charging station, and at the same time calculates the preference degree of different charging stations selected by the owner according to the historical charging information of the current vehicle and the historical charging information of other vehicles, and obtains the preference score of the charging station, and uses a large amount of data of the current vehicle owner and other vehicle owners subjectively selecting charging stations to select many secondary charging stations with poor geographical location and poor service, and finds the charging station preferred by the current vehicle owner among the superior charging stations; finally, the objective function of the Dykstra algorithm is set and solved according to the charging station preference score, charging cost and virtual distance, and the optimal charging strategy of the current vehicle is obtained. At the same time, the owner of the current vehicle selects a suitable charging station according to the optimal charging strategy to fully charge the energy storage battery of the current vehicle at the most economical charging cost.

[0045] The above specific embodiments are preferred embodiments of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A method for generating an optimal charging strategy for an electric vehicle, characterized in that: The method comprises the following steps: S100, obtaining the real-time power, real-time geographic information and historical charging information of the vehicle, and obtaining the real-time electricity price and geographic information of the charging station; The vehicle historical charging information includes the current vehicle historical charging information and other vehicle historical charging information; S200, coordinate and integrate the current real-time geographic information of the vehicle and the geographic information of the charging station to obtain a coordinate geographic set; Synchronously, extracting the number of times the current vehicle was charged at different charging stations from the current vehicle's historical charging information and the number of times other vehicles were charged at different charging stations from the other vehicles' historical charging information and integrating them to obtain a charging number set; S300, calculating a charging station preference score for the current vehicle based on the charging number set combined with a preference formula; Simultaneously, the charging fee of the current vehicle is calculated based on the current real-time power and real-time electricity price of the vehicle in combination with the charging cost formula; simultaneously, the virtual distance of the current vehicle to different charging stations is estimated according to the coordinate geographic set and the virtual distance formula; S400: setting and solving an objective function of the Dykstra algorithm according to the charging station preference score, charging cost and virtual distance to obtain an optimal charging strategy for the current vehicle.

2. The method for generating an optimal charging strategy for an electric vehicle according to claim 1, characterized in that: In S200, the specific process of coordinateizing and integrating the current vehicle real-time geographic information and the charging station geographic information to obtain the coordinate geographic set is as follows: S211, integrating the current real-time geographic information of the vehicle and the geographic information of the charging station in the form of an image to obtain a map containing at least the current vehicle location, the current vehicle destination location, and the charging station location; S212. Establish a two-dimensional coordinate system in the map with the current vehicle position as the origin, obtain the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates, and organize the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates to obtain a coordinate geographic set.

3. A method for generating an optimal charging strategy for an electric vehicle according to claim 1 or 2, characterized in that: In S200, the specific process of extracting and integrating the number of times the current vehicle has been charged at different charging stations from the current vehicle's historical charging information and the number of times other vehicles have been charged at different charging stations from the other vehicles' historical charging information to obtain a charging number set is as follows: S221, extracting the number of times the current vehicle has been charged at different charging stations to establish a current vehicle number subset, and extracting the number of times other vehicles have been charged at different charging stations to establish other vehicle number subsets; Simultaneously, a corresponding factor is established according to the probability of the current vehicle and other vehicles charging historically at the same charging station; S222, extracting the number of times the current vehicle and other vehicles have been charged historically at the same charging station from the current vehicle frequency subset and other vehicle frequency subsets based on the corresponding factor to obtain the same frequency subset; S223, integrating the current vehicle frequency subset, other vehicle frequency subsets, and the same frequency subsets to obtain a charging frequency set; The corresponding factor represents the association relationship between the current vehicle and other vehicles.

4. The method for generating an optimal charging strategy for an electric vehicle according to claim 1, characterized in that: In S300, the specific process of calculating the charging station preference score based on the charging number set combined with the preference formula is: S311, performing similarity calculation based on the number of times the current vehicle has been charged historically at different charging stations and the number of times other vehicles have been charged historically at different charging stations in the charging number set, to obtain similarities of the different charging stations; S312, inputting the similarity and the number of times the vehicle has been charged at different charging stations in the charging frequency set into a preference formula to calculate a charging station preference score; The vehicles include the current vehicle and other vehicles.

5. The method for generating an optimal charging strategy for an electric vehicle according to claim 4, characterized in that: The preference formula in S312 is specifically as follows: In the formula, Q i is the preference score of charging station i, is the total number of times the vehicle is charged at charging station i, is the total number of times the vehicle is charged at charging station j, R i is the number of times the current vehicle is charged at charging station i, and sim(i,j) is the similarity between charging station i and charging station j; wherein the similarity sim(i,j) between charging station i and charging station j is one of the N optimal similarities selected according to the K nearest neighbor principle.

6. A method for generating an optimal charging strategy for an electric vehicle according to claim 1 or 4, characterized in that: In S300, the specific process of calculating the current vehicle charging fee based on the current vehicle real-time power and real-time electricity price combined with the charging cost formula is: Constructing a charging cost formula according to the proportional relationship between the charging cost, the power consumption, and the electricity price, and inputting the current vehicle real-time power consumption and the real-time electricity price into the constructed charging cost formula to calculate the current vehicle charging fee; The charging cost formula is as follows: C c =(1-C s )×C i,t ; In the formula, C c is the charging cost, C s is the real-time power, C i,t is the real-time electricity price of charging station i at time t.

7. The method for generating an optimal charging strategy for an electric vehicle according to claim 6, characterized in that: In S300, the specific process of estimating the virtual distances from the current vehicle to different charging stations according to the coordinate geographic set and the virtual distance formula is as follows: S321, extracting the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates from the coordinate geographic set as inputs of the deviation angle formula for calculation, to obtain the deviation angle coefficient between the current vehicle and the charging station; Synchronously, the current vehicle coordinates and the charging station coordinates are input into a straight-line distance formula to obtain a straight-line distance between the current vehicle and the charging station; Simultaneously, calculating the drivable distance of the current vehicle according to the real-time power of the current vehicle and the proportional relationship between the power of the current vehicle and the drivable distance; S322, inputting the deviation angle coefficient and the straight-line distance into a virtual distance calculation formula for calculation to obtain a virtual distance set; Simultaneously, the virtual distance set is screened according to the drivable distance to obtain the virtual distance.

8. The method for generating an optimal charging strategy for an electric vehicle according to claim 7, characterized in that: The deviation angle formula in S321 is specifically as follows: Where ρ is the deviation angle coefficient between the current vehicle and the charging station, and cosθ is calculated based on the current vehicle coordinates, the current vehicle destination coordinates, and the charging station coordinates, as shown below: Among them, m0 is the horizontal coordinate of the current vehicle, n0 is the vertical coordinate of the current vehicle, m1 is the horizontal coordinate of the charging station, n1 is the vertical coordinate of the charging station, m2 is the horizontal coordinate of the current vehicle destination, and n2 is the vertical coordinate of the current vehicle destination.

9. The method for generating an optimal charging strategy for an electric vehicle according to claim 7, characterized in that: The virtual distance calculation formula in S322 is specifically as follows: s i =L i ×(1+ρ i ); In the formula, s i represents the virtual distance from the current vehicle to charging station i, L i represents the straight-line distance from the current vehicle to charging station i, ρ i Represents the deviation angle coefficient between the current vehicle and charging station i.

10. The method for generating an optimal charging strategy for an electric vehicle according to claim 1, characterized in that: In S400, the Dykstra algorithm objective function set according to the charging station preference score, charging cost and virtual distance is specifically as follows: minf=λ1×c1×s i +λ2×c2×(1-C s )+λ3×P i ; Where λ1 is the virtual distance weight coefficient, λ2 is the charging cost weight coefficient, λ3 is the preference score weight coefficient, c1 is the distance cost corresponding to the virtual distance, c2 is the real-time electricity price cost corresponding to the charging cost, P i is the preference score of charging station i, C s is the real-time power, s i is the virtual distance, w j represents the frequency of users using charging station j, sim(i,j) is the similarity between charging station i and charging station j, Q j is the preference score of charging station j.

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