CN117172861A - Mobile charging dynamic pricing method based on user load difference and space constraint - Google Patents
Mobile charging dynamic pricing method based on user load difference and space constraint Download PDFInfo
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Abstract
The invention provides a mobile charging dynamic pricing method based on user load difference and space constraint, which comprises the following steps: step S1: carrying out equilateral hexagon division on an operation area of the mobile charging service; step S2: acquiring track data of a vehicle in an operation area and carrying out data preprocessing; step S3: map matching is carried out on the processed network vehicle track data to obtain traffic flow distribution in each hexagonal area, and the traffic flow distribution in each area is converted based on the processed track data to obtain charging demand distribution in each area; step S4: the method comprises the steps of constructing a mobile charging dynamic pricing model by collecting charging demand distribution and idle mobile charging vehicle quantity of each area; on the basis, a multi-objective optimization model is established, wherein the multi-objective optimization model is optimized with the optimal satisfaction degree of the mobile charging service operation benefit maximization and the user charging service. The invention can effectively determine the optimal pricing of each region, thereby adjusting the pricing and improving the supply and demand relationship among the regions.
Description
Technical Field
The invention belongs to the technical field of electric automobile charging, and particularly relates to a mobile charging dynamic pricing method based on user load difference and space constraint.
Background
Electric Vehicles (EVs) are considered as effective tools for replacing traditional fuel-oil vehicles in the future due to the characteristics of environmental protection, high efficiency and low use cost. However, with the rapid development of the electric automobile industry, the problem of difficult electric automobile charging is also attracting attention.
Currently, there are two main charging modes: firstly, charging an electric vehicle by using a fixed charging pile; and (II) the driver arrives at the battery exchange station to replace the battery. The former is problematic in that the charging time is excessively long, and when the power grid is in a peak period of electricity consumption, a greater burden is caused to the power grid; while the latter can shorten the charging time by replacing the battery, this charging mode requires a large installation space, and many problems still remain to be solved by the related art.
In this case, many companies begin to strongly popularize mobile charging modes. Unlike a fixed charging pile, a mobile charging user can send information such as personal positioning, expected electric quantity, expected charging time and the like to a system center through a mobile phone APP. After receiving the information, the system sends a command to the mobile charging vehicle. Although mobile charging can effectively make up for the deficiencies of the traditional charging method, during actual operation, due to free floating of the electric vehicle and frequent aggregation of some hot spots, this will lead to a mismatch between the charging demand and the actual charging capacity that the area can provide during peak charging, i.e. a problem of unbalanced supply and demand. Users in these areas sometimes need to wait a long time to charge, even because the waiting time is too long and have to discard the mobile charging service.
Therefore, there is a need for a pricing method that can take into account the space-time difference of charging demands in a charging service area to alleviate the operating pressure of mobile charging services during peak hours.
Disclosure of Invention
In view of the above technical problems, the present invention provides a mobile charging dynamic pricing method based on user load difference and space constraint, the method comprising:
step S1: carrying out equilateral hexagon division on an operation area of the mobile charging service;
step S2: acquiring track data of a vehicle in an operation area and carrying out data preprocessing;
step S3: map matching is carried out on the processed network vehicle track data to obtain traffic flow distribution in each hexagonal area, and the traffic flow distribution in each area is converted based on the processed track data to obtain charging demand distribution in each area;
step S4: the method comprises the steps of constructing a mobile charging dynamic pricing model by collecting charging demand distribution and idle mobile charging vehicle quantity of each area; on the basis, a multi-objective optimization model is established, wherein the multi-objective optimization model is optimized by comprehensively considering the maximization of mobile charging service operation income and the satisfaction degree of user charging service;
step S5: and solving the multi-target optimization model by adopting a multi-target co-evolution algorithm, and obtaining a feasible solution of multi-target optimization through continuous evolution, splicing, sequencing and screening of the population.
Further, the specific method of the step S1 is as follows:
and determining an operation area of the mobile charging service, and performing equilateral hexagon division, namely dividing the whole operation area into a plurality of equilateral hexagons.
Further, the specific size of the equilateral hexagon is determined according to the formula as follows:
wherein P is u (i)，Q u (i) Respectively representing the number and the effective order of effective OD points in the ith grid on the spatial scale u, m represents the number of hexagons after region division, R u Indicating the size of the utilization of the effective OD point.
Further, the specific method of step S2 is as follows:
s21, acquiring network vehicle operation track data, cleaning the data, screening unreasonable data, and adding a limited range to the data according to the range of an operation area of mobile charging service, wherein the network vehicle operation track data comprises: starting point data, track GPS positioning data, real-time speed information, course angle information and the like of each network vehicle;
s22, converting the network vehicle operation track data from the GCJ-02 coordinate system to obtain related data under the WSG-84 coordinate system.
Further, the specific method of the step S3 is as follows:
s31, dividing the operation time of the mobile charging service into n mutually independent time periods;
s32, map mapping is carried out based on the running track of the net-bound vehicle and the divided hexagonal areas so as to describe traffic flow in different hexagonal areas in different time periods, namely traffic flow distribution conditions;
and S33, converting traffic flow according to the storage quantity of the network bus and the electric automobile in the city according to the proportion, and obtaining the distribution condition of the charging demands in each area.
Further, the specific method of step S4 is as follows:
s41, obtaining the number of electric vehicles waiting to be charged in each hexagonal area according to the charging demand distribution and the idle mobile charging vehicle distribution in the area by using a queuing theory, and expressing the number by the following formula:
Q i,t ＝max{0,Q i,t-1 +λ i,t -V i,t -L i,t }，
wherein Q is i,t-1 And Q i,t Queue lengths, V, for region i in t-1 and t time periods, respectively i,t The number of the mobile charging vehicles is empty in the time interval t; l (L) i,t Indicating the number of electric vehicles that have completed charging during this time interval;
s42, after the number of electric vehicles waiting for charging service in each area in each time interval is collected, judging the supply and demand conditions of each area so as to formulate a proper charging price and guide a user to transfer among different areas;
wherein, for region i, the mobile charging dynamic pricing model for each time period is expressed by the following formula:
wherein the method comprises the steps ofRepresenting the initial charge price of region i, +.>The real-time charging price of the area i determined according to the mobile charging dynamic pricing model is represented, and alpha represents a specific proportion of price adjustment according to the queue length.
Further, the charging price obeys the following constraint:
wherein P is max And P min The highest and lowest price charged allowed by the system during dynamic pricing, respectively.
Further, on the basis of the mobile charging dynamic pricing model, an objective function of the system is constructed, wherein the objective function comprises maximization of operator income and optimal user satisfaction;
wherein the objective function of the operator revenue is:
wherein Profit represents the final total revenue for the operator, C m Refers to a basic cost that is required to be spent when a mobile charging car provides a charging service, which respectively includes: electric quantity cost, labor cost, daily maintenance cost, C m Set to a fixed constant, n means thatThe whole duration of the system for providing the charging service is divided into N time periods, N i Representing the initial number of mobile charging cars the system initially sets in each hexagonal area c 0 Then represents the base cost of each mobile charging vehicle per day;
wherein the objective function of user satisfaction is:
wherein Qos refers to the integrated satisfaction of all users in the final system, c p Refers to penalty factor for price, c w Is a penalty factor with respect to time, c o Is a penalty factor for the user's willingness to transfer.
Further, the specific method of step S5 is as follows:
s51, initializing two populations, namely a pricing population and a quantity population distributed by mobile charging vehicles in different areas;
s52, randomly generating a certain number of initial individuals aiming at two populations, randomly sequencing the priorities of the initial individuals, and marking the individuals with the highest priorities as optimal individuals;
s53, selecting individuals with higher priority as parents by using a tournament selection mechanism for each population, and performing crossover and mutation operations on the parents to generate offspring;
s54, combining each individual in the population with the optimal individual in the other population to form a complete solution;
s55, for each population, determining the feasibility and non-dominance level of the individuals according to the unsatisfied requirements and target values, dividing the individuals into different levels, and prioritizing the individuals in the same level, thereby determining the priority order of all the individuals, and updating the optimal individuals;
s56, adopting elite selection strategy to each population, keeping elite individuals with higher priority from father and filial generation, and eliminating inferior individuals to keep the population within a preset scale;
step S57, determining whether the iteration times meet the suspension conditions, and if so, suspending the algorithm.
Further: the method further comprises the steps of:
when a user generates a charging demand, personal charging information is sent to a system terminal through a mobile phone APP;
the system feeds back to the user, wherein the charging price comprises the current hexagonal area and the current neighborhood of the user; wherein the personal charging information includes: current location, current remaining power, expected supplemental power, and expected charging time.
The invention provides a mobile charging dynamic pricing method based on user load difference and space constraint, which has the following beneficial effects:
1) In the past, many studies on electric vehicles have selected to divide a region into a square or a triangle. However, the method and the device divide the areas by adopting the equilateral hexagons, so that not only can the statistical accuracy of inflow and outflow of the electric vehicle among the areas be improved, but also the problem about the mobile cost setting of the user during the transfer among the areas can be simplified, the solving difficulty of the system can be effectively reduced, and the final solving accuracy of the result can be improved.
2) The traditional pricing method using a single charging station as the minimum pricing unit is not applicable to mobile charging service, because a plurality of mobile charging vehicles are often generated in the service process, the services of the mobile charging vehicles are uniformly distributed by the system, and if the single mobile charging vehicle is used as the pricing unit, not only is large calculation amount caused, but also the inefficiency is unfavorable for the system to feed back to the user in real time. However, the invention introduces a dynamic pricing method with the area as the minimum pricing unit, which can effectively adapt to the characteristics of flexible movement of the mobile charging vehicle and taking the individual as the service initiator.
3) Compared with the existing heuristic algorithm, the multi-objective co-evolution algorithm provided by the invention can avoid subjective errors caused by introducing a weighted summation mode when the highest user satisfaction and the maximization of operator benefits are comprehensively considered. Meanwhile, the multi-target co-evolution algorithm provided by the invention is combined with a multi-target optimization model, so that the optimal pricing of each region can be effectively determined, and the supply and demand relationship among the regions can be further improved by price adjustment.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, a brief description will be given below of the drawings required for the embodiments or the prior art descriptions, and it is obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a mobile charging dynamic pricing method based on user load differences and space constraints provided by an embodiment of the invention;
fig. 2 is a schematic diagram of simple modeling of an inspection object in a three-dimensional scene of the urban power inspection path planning method according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the prior art, the dynamic charging pricing research of the electric automobile is developed based on the traditional fixed charging station, and the intelligent pricing research aiming at the characteristics of mobile charging service, independence from the power grid load and the like is lacking.
The invention provides a mobile charging dynamic pricing method based on user load difference and space constraint, which comprises the steps of carrying out equilateral hexagon division on a mobile charging service area, and counting traffic flow distribution conditions in each hexagon area after the division. And converting the traffic flow data into the charging demand distribution of the electric automobile according to the distribution proportion of various types of vehicles. And obtaining the waiting charging time of each area based on a queuing theory, and counting the supply and demand relation between the charging demand in each area and the idle mobile charging vehicle. And constructing a mobile charging dynamic pricing model based on the user load difference, and realizing dynamic pricing of each region in different time intervals. And finally, solving the objective function by utilizing a multi-objective collaborative optimization algorithm. The dynamic pricing method provided by the invention can effectively influence the charging behavior of the user, guide the user to transfer between different areas, finally achieve the purpose of balancing the charging supply and demand among the areas and increasing the service benefit of a charging operator, and comprises the following specific processes:
as shown in fig. 1, the method includes:
step S1: the operation area of the mobile charging service is meshed (divided into a plurality of equilateral hexagons).
Step S2: and collecting network vehicle-restraining data for preprocessing, namely acquiring track data of the network vehicle-restraining in the operation area and preprocessing the data.
Step S3: map matching is carried out on the processed network vehicle track data to obtain traffic flow distribution in each hexagonal area, and the traffic flow distribution in each area is converted based on the processed track data to obtain charging demand distribution in each area.
Step S4: a mobile charging dynamic pricing model (hereinafter referred to as dynamic pricing model) is built by collecting the charging demand distribution and the number of idle mobile charging vehicles in each area; on this basis, a multi-objective optimization model is established that comprehensively considers the maximization of mobile charging service operator benefits (i.e., the maximization of operator benefits in fig. 1) and the optimization of user charging service satisfaction (i.e., the maximization of user satisfaction in fig. 1).
Step S5: and solving the multi-target optimization model by adopting a multi-target co-evolution algorithm, and continuously evolving, splicing, sequencing and screening the population to obtain a feasible solution of multi-target optimization, so that the dynamic pricing method for improving the income and the user satisfaction of operators can be simultaneously satisfied.
The detailed procedure of steps S1 to S5 of the present invention will be described below.
1. Step S1
In some embodiments of the present invention, the specific method of step S1 is as follows:
and determining an operation area of the mobile charging service, and performing equilateral hexagon division, namely dividing the whole operation area into a plurality of equilateral hexagons.
The specific size of the equilateral hexagon is determined according to the following formula:
wherein P is u (i)，Q u (i) Respectively representing the number and the effective orders of effective OD points (namely starting points and ending points) in the ith grid on the spatial scale u, wherein m represents the number of hexagons after region division, R u The utilization size of the effective OD point is represented. The division mode capable of maximizing the effective data quantity of each unit grid in the spatial scale can be found through solving, and therefore the utilization rate of the data is improved.
2. Step S2
In some embodiments of the present invention, the specific method of step S2 is as follows:
step S21: and acquiring the running track data of the net vehicle by adopting related data provided by a drop cover sub-data opening plan.
In order to eliminate noise interference existing in the original data, the data needs to be cleaned, unreasonable data is screened out, and a limited range is added to the data according to the range of an operation area of mobile charging service, wherein the network vehicle operation track data comprises: starting point data, track GPS positioning data, real-time speed information, course angle information and the like of each network vehicle.
Step S22:
(1) Data cleaning: the data set is processed from a practical point of view, for unreasonable situations, such as: the order distance is less than 500 meters, the passengers are in idle state suddenly in a state of continuously bearing the passengers, and the like, and the data are removed to realize the first step of data processing, namely, the singular points in the data transmission and recording process are deleted, the data set field is empty, the order travel distance is less than 500 meters, the order travel time is 0 minute, and the like.
(2) Coordinate conversion: considering that the network vehicle operation track data is set based on the GCJ-02 coordinate system, namely the dripping data is set based on the GCJ-02 coordinate system, if the network vehicle operation track data is projected according to the data, the network vehicle operation track data is required to be converted from the GCJ-02 coordinate system to obtain related data under the WSG-84 coordinate system, and finally map matching is carried out according to the converted data, so that the traffic flow condition and the movement track of the vehicle in different time periods are obtained.
After the processing, map matching is conveniently carried out, and the running track condition of the network vehicle is obtained.
3. Step S3
In some embodiments of the present invention, the specific method of step S3 is as follows:
step S31: according to the time distribution of the travel demands of users in the data set, the operation time of the mobile charging service is divided into n mutually independent time periods (the operation time of one day is divided into 24 time periods in the invention).
Step S32: map mapping is carried out based on the running track of the net-bound vehicle and the divided hexagonal areas so as to describe traffic flow in different hexagonal areas in different time periods, namely traffic flow distribution conditions;
step S33: in general, the larger the traffic flow, the more the charging demand is, and the general traffic flow is a mixed traffic flow, namely, the fuel-oil vehicle, the electric vehicle, the hybrid electric vehicle and the like, which are generally formed according to a certain proportion. It can be assumed that the higher the traffic flow, the higher the number of electric vehicles. Meanwhile, the remaining power of the electric vehicle may be considered to be gaussian-shaped, so that the greater the traffic flow, the greater the potential charging demand, and the greater the demand of the area corresponding thereto for mobile charging service.
Therefore, the traffic flow is converted based on the calculated storage quantity of the city network bus and the electric automobile according to the proportion, and the charging demand distribution condition in each hexagonal area is further obtained.
4. Step S4
In some embodiments of the present invention, a service area-based dynamic pricing model is built according to the charging demand data of each area, and thus, the specific method of step S4 is as follows:
step S41: according to the distribution of charging demands in the areas and the distribution of idle mobile charging vehicles, the number of electric vehicles waiting to be charged in each hexagonal area is obtained by using a queuing theory, and the number is expressed by the following formula:
Q i,t ＝max{0,Q i,t-1 +λ i,t -V i,t -L i,t }
wherein Q is i,t-1 And Q i,t Queue lengths, V, for region i in t-1 and t time periods, respectively i,t The number of the mobile charging vehicles is empty in the time interval t; l (L) i,t Indicating the number of electric vehicles that have completed charging during this time interval.
Step S42: after the system collects the number of electric vehicles waiting for charging service in each time period, the supply and demand conditions of each area can be judged according to the number so as to formulate a proper charging price and guide a user to transfer among different areas.
Wherein for region i, the dynamic pricing model for each time period is as follows:
wherein the method comprises the steps ofRepresenting the initial charge price of region i, +.>The real-time charging and charging price of the area i determined according to the dynamic pricing model is represented, and alpha represents a specific proportion of price adjustment according to the queue length.
In addition, the basic charging cost and the highest charging price acceptable in the market need to be considered when making the charging price. Thus, in this embodiment, the dynamic pricing model should obey the following constraints:
wherein P is max And P min The highest and lowest price charged allowed by the system during dynamic pricing, respectively.
When the user selects the charging area, it is assumed that the user considers only their current area and the adjacent areas of the area, which is set based on the target user of the mobile charging service. Since the mobile charging mode is still currently a complementary form of the conventional fixed charging mode, the charging tariff is generally higher than the fixed charging. When the owner of the electric vehicle selects the mobile charging service, it may be because the remaining amount of the electric vehicle is insufficient to move too far or the user does not want to move too far to obtain the power supplement.
The electric automobile owner can be considered to select the service area to be the result under the comprehensive action of multiple factors, and the electric automobile user can select the service area to be the result under the comprehensive action of multiple factors, namely the user needs to consider not only the charging cost, but also the moving cost, the time cost and the like among different areas. Therefore, users can transfer among areas only when the price difference among different areas is larger than the time cost and the moving cost of the users among the different areas, and the distribution condition of the charging demands in each area is affected.
The charging price can influence the charging demand rate of each area, and the specific charging demand rate of different areas is determined by the following formula:
represents the estimated charging demand of region i statistically derived from historical data, lambda i,t Is updated final charging demand influenced by the price between the regions,/->Representing the intended user flowing out of region i, +.>Representing the demand of an intended user flowing into the area from the vicinity of the area;
wherein,the solution formula of (2) is as follows:
refers to the charge price corresponding to the region i having the lowest charge price among all the neighborhood j of the region. And p is 0 By the lowest price difference the user moves between the different areas, that is to say, the user will have a tendency to transfer only if the price difference between the areas is greater than this, otherwise the user will only choose to charge in the area where he was originally located. />And solving by a dynamic pricing model. />Is obtained by summing the outflow from all the zones.
And constructing an objective function of the system on the basis of the mobile charging dynamic pricing model. The objective function includes two parts in total: the operator benefit is maximized and the user satisfaction is optimal.
Wherein the objective function of the operator revenue is:
wherein Profit represents the final total revenue for the operator, C m Refers to a basic cost that is required to be spent when a mobile charging car provides a charging service, which respectively includes: cost of electricity, labor cost, daily maintenance cost, etc., in the present embodiment, for the purpose of reducing the amount of computation, C m Is set to a fixed constant. N refers to dividing the whole duration of the system for providing charging service into N time periods, N i Representing the initial number of mobile charging cars the system initially sets in each hexagonal area c 0 Represents the basic cost of each mobile charging car used every day, including daily maintenance costs, daily use costs, etc.
Wherein the objective function of user satisfaction is:
wherein Qos refers to the comprehensive satisfaction of all users in the final system, and the satisfaction of general users comprises three parts, including: user price satisfaction, user time satisfaction, and user mobile intent satisfaction. Too high a charge price, too long a waiting time, and a choice of zone transfer due to priceThe user satisfaction may be reduced. c p Refers to penalty factor for price, c w Is a penalty factor with respect to time, c o Is a penalty factor for the user's willingness to transfer.
Constructing a multi-objective optimization model of the mobile charging service system on the basis of the two objective functions:
max E＝σ 1 Profit+σ 2 Qos
wherein sigma 1 ，σ 2 And the operation benefits and the user service satisfaction degree weights are respectively obtained, so that the importance degree of the mobile charging service operator on the operation benefits and the user satisfaction degree is embodied.
5. Step S5
In some embodiments of the present invention, the objective model is solved by using the multi-objective co-evolution algorithm to obtain an optimal solution for the system, so, as shown in fig. 2, the specific method of step S5 is as follows:
step S51: initializing two populations, namely a pricing population and a distribution population (distribution quantity population) of mobile charging vehicles in different areas;
step S52: randomly sequencing the populations and marking the optimal body, namely randomly generating a certain number of initial individuals aiming at the two populations, randomly sequencing the priorities of the initial individuals, and marking the individuals with the highest priorities as the optimal individuals;
step S53: the evolution operation, namely selecting individuals with higher priority as parents by using a tournament selection mechanism for each population, and performing crossover and mutation operations on the parents so as to generate offspring;
step S54: complete solution splicing, namely combining each individual in a population with the optimal individual in another population to form a complete solution;
step S55: individual prioritization, i.e., for each population, determining the feasibility and non-dominance level of the individuals according to the unsatisfied demand and target values, and classifying the individuals into different levels, and prioritizing the individuals in the same level, thereby determining the priority order of all the individuals, and updating the best individuals;
step S56: elite individual screening, namely, adopting an elite selection strategy for each population, retaining elite individuals with higher priority from father and offspring, and removing inferior individuals to keep the population within a preset scale;
step S57: and determining whether the iteration times meet the suspension conditions, if so, suspending the algorithm, obtaining an optimal solution set, and if not, returning to the step S53 to iterate again.
In some embodiments of the invention: the method further comprises the steps of:
when a user generates a charging demand, personal charging information is sent to a system terminal through a mobile phone APP, wherein the personal charging information comprises: current location, current remaining power, expected charge time, and the like. At this time, the system terminal will feed back to the user, including the charging prices of the hexagonal area and the neighborhood where the user is currently located.
The user selects among all the selectable charging areas according to the personal situation. The process of selecting the charging area by the user is a process of adjusting the charging supply and demand relationship among the areas by the system through adjusting the charging price. Under the influence of price, users in areas with heavy charging load can transfer to other areas, and corresponding mobile charging vehicles in idle areas can be utilized.
In summary, the invention considers that the mobile charging service as a novel charging mode may have uneven charging demand distribution in the process of operation, which not only causes the low utilization rate of the mobile charging vehicle, but also reduces the satisfaction degree of users. Therefore, the service area is divided on the basis of considering the real-time distribution of the charging demands, and dynamic pricing is performed based on the divided hexagonal area. The price influences the selection of the charging area and the charging behavior of the user, so that the user is guided to move from the charging demand high-load area to the charging demand low-load area, and the dynamic balance of the supply-demand relationship is further facilitated.
Meanwhile, in order to avoid the problems of user satisfaction reduction and potential user loss caused by excessive pricing, a multi-objective optimization model is constructed. Meanwhile, the problem of operator income and user satisfaction is considered, and a multi-target co-evolution algorithm is introduced to solve a multi-target model, so that the algorithm can be used for guiding dynamic pricing about the divided areas and helping a decision maker to decide initial mobile charging vehicle proportions of different hexagonal areas in a strategic stage.
The present invention is not limited to the above-mentioned embodiments, but is not limited to the above-mentioned embodiments, and any simple modification, equivalent changes and modification made to the above-mentioned embodiments according to the technical matters of the present invention can be made by those skilled in the art without departing from the scope of the present invention.
Claims (10)
1. A method for dynamic pricing of mobile charging based on user load differences and space constraints, the method comprising:
step S1: carrying out equilateral hexagon division on an operation area of the mobile charging service;
step S2: acquiring track data of a vehicle in an operation area and carrying out data preprocessing;
step S3: map matching is carried out on the processed network vehicle track data to obtain traffic flow distribution in each hexagonal area, and the traffic flow distribution in each area is converted based on the processed track data to obtain charging demand distribution in each area;
step S4: the method comprises the steps of constructing a mobile charging dynamic pricing model by collecting charging demand distribution and idle mobile charging vehicle quantity of each area; on the basis, a multi-objective optimization model is established, wherein the multi-objective optimization model is optimized by comprehensively considering the maximization of mobile charging service operation income and the satisfaction degree of user charging service;
step S5: and solving the multi-target optimization model by adopting a multi-target co-evolution algorithm, and obtaining a feasible solution of multi-target optimization through continuous evolution, splicing, sequencing and screening of the population.
2. The mobile charging dynamic pricing method based on user load difference and space constraint according to claim 1, wherein the specific method of step S1 is as follows:
and determining an operation area of the mobile charging service, and performing equilateral hexagon division, namely dividing the whole operation area into a plurality of equilateral hexagons.
3. The mobile charging dynamic pricing method based on user load differences and space constraints of claim 1, wherein the specific size of the equilateral hexagon is determined according to the formula:
wherein P is u (i)，Q u (i) Respectively representing the number and the effective order of effective OD points in the ith grid on the spatial scale u, m represents the number of hexagons after region division, R u Indicating the size of the utilization of the effective OD point.
4. The mobile charging dynamic pricing method based on user load difference and space constraint according to claim 1, wherein the specific method of step S2 is as follows:
s21, acquiring network vehicle operation track data, cleaning the data, screening unreasonable data, and adding a limited range to the data according to the range of an operation area of mobile charging service, wherein the network vehicle operation track data comprises: starting point data, track GPS positioning data, real-time speed information, course angle information and the like of each network vehicle;
s22, converting the network vehicle operation track data from the GCJ-02 coordinate system to obtain related data under the WSG-84 coordinate system.
5. The mobile charging dynamic pricing method based on user load difference and space constraint according to claim 1, wherein the specific method of step S3 is as follows:
s31, dividing the operation time of the mobile charging service into n mutually independent time periods;
s32, map mapping is carried out based on the running track of the net-bound vehicle and the divided hexagonal areas so as to describe traffic flow in different hexagonal areas in different time periods, namely traffic flow distribution conditions;
and S33, converting traffic flow according to the storage quantity of the network bus and the electric automobile in the city according to the proportion, and obtaining the distribution condition of the charging demands in each area.
6. The mobile charging dynamic pricing method based on user load difference and space constraint according to claim 1, wherein the specific method of step S4 is as follows:
s41, obtaining the number of electric vehicles waiting to be charged in each hexagonal area according to the charging demand distribution and the idle mobile charging vehicle distribution in the area by using a queuing theory, and expressing the number by the following formula:
Q i,t ＝max{0,Q i,t-1 +λ i,t -V i,t -L i,t }，
wherein Q is i,t-1 And Q i,t Queue lengths, V, for region i in t-1 and t time periods, respectively i,t The number of the mobile charging vehicles is empty in the time interval t; l (L) i,t Indicating the number of electric vehicles that have completed charging during this time interval;
s42, after the number of electric vehicles waiting for charging service in each area in each time interval is collected, judging the supply and demand conditions of each area so as to formulate a proper charging price and guide a user to transfer among different areas;
wherein, for region i, the mobile charging dynamic pricing model for each time period is expressed by the following formula:
wherein the method comprises the steps ofRepresenting the initial charge price of region i, +.>The real-time charging price of the area i determined according to the mobile charging dynamic pricing model is represented, and alpha represents a specific proportion of price adjustment according to the queue length.
7. The mobile charging dynamic pricing method based on user load differences and space constraints of claim 6, wherein the charging price is subject to the following constraints:
wherein P is max And P min The highest and lowest price charged allowed by the system during dynamic pricing, respectively.
8. The mobile charging dynamic pricing method based on user load difference and space constraint of claim 6, wherein an objective function of the system is constructed based on the mobile charging dynamic pricing model, wherein the objective function comprises operator profit maximization and user satisfaction optimization;
wherein the objective function of the operator revenue is:
wherein Profit represents the final total revenue for the operator, C m Means when the mobile charging vehicle provides chargingThe basic costs spent in service include: electric quantity cost, labor cost, daily maintenance cost, C m Set to a fixed constant, N is the total duration of the system for providing charging service divided into N time periods, N i Representing the initial number of mobile charging cars the system initially sets in each hexagonal area c 0 Then represents the base cost of each mobile charging vehicle per day;
wherein the objective function of user satisfaction is:
wherein Qos refers to the integrated satisfaction of all users in the final system, c p Refers to penalty factor for price, c w Is a penalty factor with respect to time, c o Is a penalty factor for the user's willingness to transfer.
9. The mobile charging dynamic pricing method based on user load difference and space constraint according to claim 1, wherein the specific method of step S5 is as follows:
s51, initializing two populations, namely a pricing population and a quantity population distributed by mobile charging vehicles in different areas;
s52, randomly generating a certain number of initial individuals aiming at two populations, randomly sequencing the priorities of the initial individuals, and marking the individuals with the highest priorities as optimal individuals;
s53, selecting individuals with higher priority as parents by using a tournament selection mechanism for each population, and performing crossover and mutation operations on the parents to generate offspring;
s54, combining each individual in the population with the optimal individual in the other population to form a complete solution;
s55, for each population, determining the feasibility and non-dominance level of the individuals according to the unsatisfied requirements and target values, dividing the individuals into different levels, and prioritizing the individuals in the same level, thereby determining the priority order of all the individuals, and updating the optimal individuals;
s56, adopting elite selection strategy to each population, keeping elite individuals with higher priority from father and filial generation, and eliminating inferior individuals to keep the population within a preset scale;
step S57, determining whether the iteration times meet the suspension conditions, and if so, suspending the algorithm.
10. The mobile charging dynamic pricing method based on user load differences and space constraints of claim 1, wherein: the method further comprises the steps of:
when a user generates a charging demand, personal charging information is sent to a system terminal through a mobile phone APP;
the system feeds back to the user, wherein the charging price comprises the current hexagonal area and the current neighborhood of the user; wherein the personal charging information includes: current location, current remaining power, expected supplemental power, and expected charging time.
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