Analysis Method, System and Device for Electric Vehicle Charging Accessibility Considering Competition

Abstract

The present invention belongs to the field of transportation technologies, and particularly relates to a method, a system and a device for analyzing the charging accessibility of electric vehicles considering competition. The method includes rasterizing and encoding a map of a target area, marking the grids containing charging stations therein, and generating a set of starting points and a set of ending points; defining a path where a user drives a vehicle from a starting point to any charging station and then exits in a non-driving manner as a charging trip chain; generating all candidate charging trip chains from a specified starting point through optional charging stations to a specified ending point, and finally, combining the weighted average generalized charging process cost between any two points of the vehicle and the competitive attraction of the charging station to the vehicle, calculating the charging accessibility between any two points in the target area, and generating a corresponding heat map. The present invention solves the problems that the spatial layout of existing charging stations fails to consider the spatial distribution of the actual travel needs of users, the charging accessibility distribution between different locations in the area is unbalanced, and the charging experience of users is poor.

Description

Technical Field

[0001] The present invention belongs to the technical field of transportation, and specifically relates to a method for analyzing the charging accessibility of electric vehicles considering competition, as well as a corresponding system, device, and an optimization method for the layout of charging piles within a region. Background Art

[0002] Compared with traditional fuel vehicles, electric vehicles can reduce people's travel costs and then expand people's travel demands. However, with the continuous increase in the market ownership of electric vehicles, the charging demands of electric vehicle owners are also increasing. On this basis, how to increase the number of charging stations and optimize the spatial layout of charging stations to meet the charging demands of the majority of vehicle owners has become an urgent task for charging station operators and urban planners.

[0003] Traditional spatial layout schemes for charging stations mainly aim to expand the spatial coverage of charging stations. In these schemes, the charging service radius that can be supported is delimited by combining the amount of charging services provided by each charging station, and then charging stations are set up in slices within the region to ensure that vehicle owners can quickly find a nearby charging station at any location within the target region and enjoy charging services. In the past era of increasing electric vehicle numbers, these traditional schemes did help to optimize the location selection of charging stations and improve the service level of the region. However, in today's era of electric vehicle stock, the unbalanced and insufficient charging service capabilities are gradually becoming the new main contradiction. Some users said that in some areas with concentrated travel demands, even though there are already multiple charging stations in the region, the waiting time during the charging process is still relatively high, and although they can ultimately obtain charging services, the experience during the process is not good. In some areas with low travel demand frequencies, even if the number of charging stations is small, users do not have charging anxiety.

[0004] Therefore, how to evaluate the charging accessibility between different locations within a region in combination with the actual travel demands of users and use this as a decision-making basis for optimizing the spatial layout of charging stations has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] In order to solve the problem that the spatial layout of existing charging stations fails to consider the spatial distribution of the actual travel demands of users, which in turn leads to unbalanced distribution of charging accessibility between different locations within the region and poor charging experience for users, the present invention provides a method for analyzing the charging accessibility of electric vehicles considering competition, as well as a corresponding system, device, and an optimization method for the layout of charging piles within a region.

[0006] The present invention is implemented by adopting the following technical solutions:

[0007] A method for analyzing the charging accessibility of electric vehicles considering competition, which includes:

[0008] Rasterize and encode the map of the target area at a preset spatial interval, mark the grids containing charging stations, collect and analyze the travel demands of users, and then generate a set of starting points with the position codes of the grids as elements O and an end set D .

[0009] Define a charging travel chain as a path where a user starts from any grid, drives a vehicle to a charging station, and then reaches any end point by non-driving means

[0010] Generate all candidate charging travel chains from a specified starting point through optional charging stations to a specified end point, and calculate the charging reachability between any starting point o and end point d in the target area by the following formula A od , and generate a corresponding heat map

[0011] ,

[0012] In the above formula, C ode is the weighted average generalized charging process cost for class e vehicles from the starting point o to the end point d ; A ke is the competitive attraction of charging station k to the current vehicle e represents the type of vehicle, e = 1 indicates a private car, e = 2 indicates a bus and are respectively constants related to C ode and A ke parameters

[0013] As a further improvement of the present invention, C ode is calculated as follows

[0014] First, calculate the total cost s required for the user to select any charging travel chain by the following formula

[0015] ,

[0016] In the above formula, is the generalized driving cost for the user from the starting point o to the charging station k ; is the cost for the user at the charging station kThe generalized charging cost for charging For the user from the charging station k to the end point d The generalized driving cost.

[0017] Then, the Dogit model based on path utility is adopted to represent the path selection probability s of any charging travel chain , and then the weighted average generalized charging process cost of the vehicle from the starting point o to the end point d is calculated through the following formula C ode :

[0018] ,

[0019] In the above formula, S ode represents e the set of all candidate charging travel chains of type o vehicles from the starting point d to the end point s .

[0020] As a further improvement of the present invention, the calculation formula of

[0021] ,

[0022] In the above formula, is e the utility of type s vehicles selecting the charging travel chain o from the starting point d to the end point, which reflects the user's preferences for charging cost, charging time, charging convenience, and charging station capacity; is a constant parameter characterizing the user's sensitivity to utility; is a parameter characterizing the user's loyalty to the charging travel chain s .

[0023] As a further improvement of the present invention, is calculated through the following effect function:

[0024] ,

[0025] In the above formula, is e the actual utility value of type s vehicles selecting the charging travel chain o from the starting point d to the end point; is the e type of vehicle from the starting pointo To the end d The total number of optional charging travel chains; Is a random error term subject to a certain independent extreme value distribution.

[0026] As a further improvement of the present invention, the costs of the three processes included in any charging travel chain , And Are both composed of two parts: economic cost and time cost.

[0027] Construct the following value coefficient function to exponentially compress the actual probability of an event, and then generate the monetary value weight related to the economic cost w + ( p ) and the time value weight related to the time cost w - ( p );

[0028] ,

[0029] In the above formula, p Is the actual probability of the event occurring; w ( p ) Is the corrected probability; , Is the curvature control parameter.

[0030] As a further improvement of the present invention, the charging travel chain is divided into three stages: entering the station, charging, and leaving the station.

[0031] In the entering the station stage, the economic cost spent by the user includes the driving cost consumed when driving along the optimal planned path; the time cost spent is the driving time.

[0032] In the charging stage, the economic cost spent by the user includes the charging cost, service cost, and parking cost; the time cost spent includes the waiting time and service time.

[0033] In the leaving the station stage, the economic cost spent by the user depends on the travel mode adopted by the user; the time cost spent is the driving time.

[0034] As a further improvement of the present invention, the competitive charging attractiveness A ke Of charging station k for the current vehicle is calculated as follows:

[0035] Combining the valence attributes of the charging station and the user, calculate the absolute attractiveness k Of any charging station f ( k ).

[0036] Assume that there is a replaceable charging station in any charging trip chain k 2, then k Take 2 as k the competitor of 1, and calculate the attractive force of charging station 1 when there is a competing charging station 2 through the following formula k When there is charging station k 1 and take it as k the competitive attraction of 1:

[0037] ,

[0038] In the above formula, represents the potential charging attraction scale of charging station k for type e vehicles; represents charging station k 1 and k the distance between 2.

[0039] An optimization method for the layout of charging piles in a region, which includes:

[0040] I. Grid and encode the map of the target region at a preset spatial interval, with the positions of each charging pile in the region as decision variables; maximize the charging accessibility between any starting point and ending point in the region as the optimization goal, and set constraint conditions including the number of charging stations constraint in combination with the actual scenario, and then construct a single-objective optimization model.

[0041] II. Use a genetic algorithm or other optimization algorithms to solve the single-objective optimization model to obtain the optimization result.

[0042] Among them, during the iterative optimization process of the algorithm, the value of the objective function is updated through the method of analyzing the charging accessibility of electric vehicles considering competition as described above.

[0043] III. Generate a spatial layout map of new charging stations according to the final iterative optimization result.

[0044] The present invention also includes a system for analyzing the charging accessibility of electric vehicles considering competition, which uses the method of analyzing the charging accessibility of electric vehicles considering competition as described above to generate the charging accessibility between any two locations in the target region. The system for analyzing the charging accessibility of electric vehicles includes: a scenario modeling unit, a path planning unit, a cost update unit, an attraction quantification unit, and an accessibility calculation unit.

[0045] Among them, the scenario modeling unit is used to grid and encode the map of the target region at a preset spatial interval, mark the grids containing charging stations among them, and analyze the travel demands of the collected users, and then generate a starting point set and an ending point set with the position codes of the grids as elements.

[0046] The path planning unit is used to combine the scenario information provided by the scenario modeling unit to generate all candidate charging travel chains from the specified starting point to the specified end point via the optional charging station. The cost update unit is used to dynamically update the weighted average generalized charging process cost of each vehicle from any starting point to the end point in combination with the charging travel chain. The attractiveness quantification unit is used to consider the competitive relationship between charging stations and generate the charging attractiveness of a specified charging station to any vehicle.

[0047] The accessibility calculation unit is used to combine the outputs of the scenario modeling unit, the path planning unit, the cost updating unit, and the attraction quantification unit, and calculate the charging accessibility between any starting point o and end point d in the target area using the following formula: A od :

[0048] .

[0049] The present invention also includes a device for analyzing the charging accessibility of electric vehicles with consideration of competition, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the aforementioned method for analyzing the charging accessibility of electric vehicles with consideration of competition is implemented, thereby generating a weighted average generalized charging process cost of any vehicle from a specified starting point to a specified end point in a target area.

[0050] The technical solution provided by the present invention has the following beneficial effects:

[0051] The present invention proposes a new method for analyzing the charging accessibility of various locations in a region based on the charging travel chain in combination with the user's travel information. The heat map obtained by this method can not only reflect the coverage of charging stations in the region, but also fully consider the travel needs of people in different regions. The solution of the present invention also fully considers the various economic costs and time costs contained in different strategies when evaluating the charging accessibility between regions, and more fully considers the multiple factors that affect human decision-making. Therefore, the final heat map obtained by this method contains richer information, which can provide a scientific basis for the site selection planning of charging stations within the region and has higher practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 This is a typical scenario of a user performing charging behavior described in Example 1 of the present invention.

[0054] Figure 2 This is the map information of the rasterized area in Example 1 of the present invention.

[0055] Figure 3 It is a schematic diagram showing the non - homogeneity of time value in Embodiment 1 of the present invention.

[0056] Figure 4 It is an image reflecting the relationship between the driving travel time, the walking time of a person and their probability density in Embodiment 1 of the present invention.

[0057] Figure 5 It is an image reflecting the relationship between the time of driving travel and walking and the opportunity time cost in Embodiment 1 of the present invention.

[0058] Figure 6 is Figure 2 The heat map corresponding to the finally generated area, which characterizes the charging accessibility at each location within the area.

[0059] Figure 7 It is a step - flow chart of the optimization method for the charging pile layout within the area provided in Embodiment 2 of the present invention.

[0060] Figure 8 It is a module schematic diagram of the electric vehicle charging accessibility analysis system provided in Embodiment 3 of the present invention. Detailed implementation manners

[0061] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] Embodiment 1

[0063] In order to accurately reflect whether the number of charging stations in a region is sufficient and whether the spatial distribution of each charging station is reasonable, a quantifiable evaluation index is provided in this embodiment, that is, the charging accessibility of electric vehicles. The theory of charging accessibility proposed in this embodiment follows the following three basic assumptions: First, for the resident travel events that only consider charging, users will follow the principle of proximity when charging. Second, during the charging process, the vehicle owner is more likely to choose a point of interest nearby to carry out travel activities instead of staying in place until the charging is completed. Third, after the charging is completed, the vehicle owner will return to the original position along the return route to pick up the vehicle. Based on the above assumptions, this embodiment believes that the typical scenarios of users' charging behaviors are as Figure 1 shown:

[0064] First, the user starts from their place of residence or work and drives a motor vehicle with insufficient battery power to look for a charging pile (step 1). After the user arrives at the charging station, they connect to the charging pile and start charging. While the vehicle is charging, the user will look for an activity point and carry out activities such as work / entertainment / rest by walking, taking public transportation or a taxi, and using a shared bicycle. After the vehicle is fully charged or the activity the user participates in ends, the user will return to the charging station, pick up the vehicle and drive it back to the original place of residence or work.

[0065] Combined with the above detailed description of the scenario when the user has a charging behavior, this embodiment further proposes the concept of a charging travel chain. Considering that the return journey after the user finishes charging and the previous journey are inverse processes, this embodiment further simplifies the concept of the charging travel chain to that within a designated area, the user drives from point A (the starting point) to the charging station, leaves the vehicle at the charging station for charging, and then reaches point B by walking or other means of transportation to carry out other activities. During this process, if the charging station layout within a certain area can fully meet the user's needs to realize the above charging travel chain, it means that the spatial layout of the charging stations in this area is better, that is, the charging accessibility of electric vehicles between different locations within this area is higher; otherwise, it means that the charging accessibility of electric vehicles between different locations in this area is lower.

[0066] Furthermore, in order to quantify the charging accessibility between various locations within a certain area, this embodiment provides an analysis method for the charging accessibility of electric vehicles considering competition, and this method includes the following process:

[0067] I. Scenario Modeling

[0068] The map of the target area is rasterized and encoded at a preset spatial interval, the grids containing charging stations are marked, the travel demands of users are collected and analyzed, and then a starting point set with the position codes of the grids as elements is generated O and an end point set D .

[0069] As Figure 2 shown, in order to simplify the processing process, for any area that needs to conduct charging accessibility analysis, this embodiment needs to first obtain the GIS information of this area and perform rasterized segmentation processing on the map of this area at a preset spatial interval. In order to distinguish the geographical locations of each grid obtained after segmentation, this embodiment sets a position code for each grid. In addition, in order to simplify the calculation, this embodiment can also use the coordinates of the geographical center as the spatial coordinates of this area.

[0070] Considering that the spatial location of the charging station in the grid map remains fixed, in this embodiment, the grid containing the charging station is pre-marked. At this time, the spatial location of the charging station can be characterized by the position encoding of the grid. In addition, for different regions, the travel demands of the population are different, and the spatial distribution of users' behaviors such as residence, work, and entertainment is also diverse. In this embodiment, in order to accurately describe various possible travel behaviors of users, the travel demands of users are collected and analyzed, and the starting and ending points of each travel behavior of users are classified, and finally a set of positions containing all starting points, that is, the starting point set O, and a set containing all ending points, that is, the ending point set D, are obtained. In this embodiment, each element contained in the starting point set and the ending point set is the position encoding of the grid.

[0071] II. Numerical Quantification

[0072] Based on the information of the above scenario modeling, in this embodiment, the feasibility of whether users can better carry out daily activities according to the charging travel chain mode between any two points is evaluated by combining the path planning results between different locations, the economic cost and time cost required for the travel behaviors carried out by users, and the personal preferences of users.

[0073] Specifically, in the technical solution provided in this embodiment, first, a path in which a user starts from any grid, drives a vehicle to a charging station, and then reaches the grid corresponding to any ending point by a non-driving method is defined as a charging travel chain. Then, all candidate charging travel chains for the user to start from a specified starting point, pass through any optional charging station, and finally reach a specified ending point are generated in sequence. Finally, the charging accessibility o between the starting point d and the ending point A od in the target area is calculated, and a corresponding heat map is generated:

[0074] ,

[0075] In the above formula, C ode is the weighted average generalized charging process cost of class e vehicle from the starting point o to the ending point d ; A ke is the competitive attraction of charging station k to the current vehicle; e represents the type of vehicle, e = 1 represents a private car (PEV), e = 2 represents a bus (BE); and are respectively related to C ode andA ke Relevant constant parameters.

[0076] Combined with the above formula, it can be seen that the weighted average generalized charging process cost between two places C ode , and the competitive attraction of the charging stations included in the adopted travel chain to the vehicle are the key factors affecting the evaluation value of the final charging accessibility. The following will separately elaborate on each of the above chain indicators in detail:

[0077] (I) Weighted average generalized charging process cost

[0078] In this embodiment, the weighted average generalized charging process cost is a comprehensive result that includes all possible costs incurred by the user during the process of completing vehicle charging at one place and arriving at another. Specifically, the weighted average generalized charging process cost C ode is calculated as follows:

[0079] First, each charging travel chain can be divided into three stages, namely the entry stage, the charging stage, and the departure stage. In the entry stage, the user drives from the starting point to the selected charging station. In the charging stage, the user waits for the charging service and finally connects the charging port of the vehicle to the charging pile. In the departure stage, the user uses a non-driving method to reach the end point from the charging station to carry out personal interest activities.

[0080] The above three stages will respectively generate different costs. Therefore, the total cost required for the user to select any charging travel chain s is calculated through the following formula :

[0081] ,

[0082] In the above formula, is the generalized driving cost for the user from the starting point o to the charging station k ; is the generalized charging cost for the user to charge at the charging station k ; is the generalized driving cost for the user from the charging station k to the end point d .

[0083] In this embodiment, the costs of the three processes included in any charging travel chain , and Both consist of two parts: economic cost and time cost. To reasonably allocate the weights of the two types of costs in the final cost, this embodiment constructs the following value coefficient function to exponentially compress the actual probability of an event, and then generates a monetary value weight related to the economic cost w + ( p ) and a time value weight related to the time cost w - ( p );

[0084] ,

[0085] In the above formula, p is the actual probability of the event occurring; w ( p ) is the corrected probability; , is the curvature control parameter.

[0086] Then the generalized travel cost C generated at any stage can be calculated by the following formula:

[0087]

[0088] In the above formula, C1 and C2 respectively represent the travel cost and time cost of the corresponding stage.

[0089] Among them, the travel cost can often be directly quantified as a specific monetary value, while the time cost is a value quantity that is difficult to quantify. In this embodiment, considering that the time value of urban residents is heterogeneous, the travel time constraint has a significant impact on the time value. For travelers with relatively strict travel time constraints, such as Figure 3 shown, the time value of arriving at the destination at time t0 is the highest, the time value rises steeply within a relatively short period of time before this time point, and the time value of arriving after this time point drops rapidly (see curve A in Figure 3 ), and the basic necessary travel especially conforms to this law.

[0090] In addition, in order to quantify the unit value quantity of the time cost, this embodiment can select the following time value model in the macroeconomic law or microeconomic law to determine the unit value V of the travel time T :

[0091] The macroeconomic law is also called the production method, which is generally used for calculating the time value of work travel. Its corresponding time value model is as follows:

[0092]

[0093] In the above formula, GDP is the gross national product; Mis the average annual employment N is the average annual working hours.

[0094] The microeconomic method is also called the income method, which is generally used for calculating the time value of non-work trips. The corresponding time value model is as follows:

[0095]

[0096] Wherein, Q is the gross national product.

[0097] (1)Cost in the entering station stage

[0098] In the solution of this embodiment, the generalized driving cost o when the user travels from the starting point k to the charging station includes the driving cost (belonging to the economic cost) and the time cost. Its calculation formula is as follows:

[0099] ,

[0100] In the above formula, is the shortest path distance from the starting point o to the charging station k for the e-th EV, which can be obtained by the Dijkstra algorithm; R is the average driving distance (km) of EVs on urban roads; is the average unit driving cost (yuan / km) of EVs on urban roads; is a parameter used to capture the "marginal effect"; n is the number of passengers in the vehicle; V T is the unit value of travel time; v is the average driving speed (km / h) of EVs on urban roads; w + ( p ) and w - ( p ) are the monetary value weight and the time value weight determined by the prospect theory respectively.

[0101] (2)Cost in the charging stage

[0102] In the solution of this embodiment, the generalized charging cost k when the user charges at the charging station is calculated as follows:

[0103] ,

[0104] In the above formula, is the economic cost during the charging process, is the time cost of the charging process. The two can cover various costs generated during the period from when the vehicle arrives at the charging station to when it leaves the charging station after being fully charged, including charging costs, parking costs, and time costs. The congestion cost involves the time value of the group.

[0105] Specifically, the economic cost part of the in-station charging stage is calculated by the following formula:

[0106]

[0107] In the above formula, is the charging fee to be paid for choosing to charge at the charging station k ; is the operator service fee to be paid for choosing to charge at the charging station k ; is the parking fee to be paid for choosing to charge at the charging station k ; represents the charging fee charged per unit of charging amount; is the service fee charged per unit of charging amount; is the parking fee charged per unit of time (yuan / h); and respectively represent the actual parking duration and the free parking duration; Q + represents the charging amount of the vehicle in the charging station.

[0108] When an electric vehicle arrives at the charging station for charging, it not only has to pay various fees caused by charging, but also bears the queuing waiting time caused by congestion at the charging station. This part calculates the time delay cost caused by the charging event. Therefore, the actual time cost generated by the vehicle during the charging stage should include two parts: waiting time and service time. The time cost of the vehicle charging process is calculated by the formula:

[0109] ,

[0110] In the above formula, k is the number of working days per month; r is the wage rate coefficient; w i is the monthly income of the traveler; is the generalized charging time consumed by the vehicle in the charging station k ; is the time required for the vehicle to be fully charged in the charging station k , that is, the service time, which depends on the charging amount and the power of the charging equipment; is the queuing time caused by congestion of the vehicle in the charging station k .

[0111] Specifically, the queuing time It can be calculated by the following formula:

[0112] ,

[0113] In the above formula, represents the charging capacity of the electric vehicle; represents the rated power (KW) of the charging station; represents the average queuing waiting time within period i, i i = 1, 2,..., 4; represents the average arrival rate of electric vehicles within period i.

[0114] Service time Then it is calculated by the following formula:

[0115]

[0116] In the above formula, represents the rated power (KW) of the charging pile; represents the charging efficiency;

[0117] (3) Cost during the departure stage

[0118] During the process of the user traveling from the charging station to the destination, due to possible congestion at the charging station resulting in the user queuing for charging, or the need to find another charging station for charging, etc., the time for the car owner or passenger to reach the travel destination often faces great uncertainty. During this process, the car owner and passenger may have the following two activities: 1. Wait for the PEV to be fully charged and then drive to the travel destination (home, shopping mall or company); 2. Without waiting for the PEV to be fully charged, start charging and then walk to the travel destination. Since the occurrence probability of activity 1 is relatively small, only the case of activity 2 is considered in the calculation here.

[0119] Among them, the walking time of people is affected by various factors, such as speed, road conditions and charging time, and these factors follow a normal distribution. Assume that the walking time of Figure 4 follows the normal distribution as shown in

[0120]

[0121] Among them, is the expected walking time for the car owner or passenger to walk to the destination. represents the probability density of the car owner or passenger walking to the destination.

[0122] And the opportunity time cost of walking is as shown in Figure 5As shown. In particular, since buses generally charge when they stop operating in the early morning and there are no passengers on the bus during charging, the opportunity time cost loss of the bus is not considered during this process.

[0123] In summary, the time cost spent by the user during the off-station stage is calculated by the following formula:

[0124]

[0125] In the above formula, represents the time cost consumed by the user during the off-station stage when choosing to walk; represents the cumulative distribution function of the walking time of the user during the off-station stage.

[0126] Based on the cost consumed by each charging travel chain calculated through the above process, in this embodiment, the Dogit model based on path utility is further used to represent the path selection probability s of any charging travel chain , and then the weighted average generalized charging process cost o from the starting point d to the ending point C ode of the vehicle is calculated through the following formula:

[0127] ,

[0128] In the above formula, S ode represents e the set of all candidate charging travel chains o from the starting point d to the ending point s of class

[0129] Specifically, the path selection probability s of any charging travel chain is deduced using the following calculation formula:

[0130] ,

[0131] In the above formula, is the utility of class e vehicles choosing the charging travel chain s from the starting point o to the ending point d , which reflects the user's preferences for charging cost, charging time, charging convenience, and charging station capacity; is a constant parameter representing the user's sensitivity to utility; is a parameter representing the user's loyalty to the charging travel chain s . Among them, is calculated through the following effect function:

[0132] ,

[0133] In the above formula, for e Type of vehicle chooses charging travel chain s From the starting point o To the end d The actual utility value of For the e Class vehicles from the starting point o To the end d The total number of optional charging mobility chains; is a random error term that obeys an independent extreme value distribution.

[0134] 2. Competitive attractiveness of charging stations

[0135] When generating a charging travel chain from the starting point to the end point, multiple paths may be obtained due to the selection of different charging stations. At this time, it is necessary to evaluate the charging attractiveness of each charging station to each user. This embodiment uses competitive attractiveness to evaluate the attractiveness of each charging station to the user needs of the specified starting point and end point. Specifically, under the premise that there are multiple charging stations to choose from, any charging station in this embodiment k right e Charging attractiveness of vehicles A ke The calculation process is as follows:

[0136] (1) Combine the valence attributes of charging stations and users to calculate the k The absolute attraction f ( k ); specifically include:

[0137] Assume that each power station has N valence attributes , each user uses the weights of N attributes To represent the valence attribute, in this embodiment, N is 3. Then the absolute attractiveness of charging station k to class e vehicles is f ke Satisfy the following formula:

[0138] ,

[0139] In the above formula, v 1 is the cumulative number of opportunities for charging station k to be selected; v 2 is the evaluation result of the service level of charging station k based on the number of charging piles, the types of charging piles it owns, and the occupancy of charging piles during peak hours; v 3 is the average charging price of charging station k during peak hours (including electricity price and power station service fee); , , , are the 4 error term parameters included in the absolute attractiveness evaluation model.

[0140] Among them, the cumulative number of opportunities v 1 for which charging station k is selected satisfies the following formula:

[0141] ;

[0142] Among them, O k is the number of facilities within the reachable range; is a conditional function determined by the following formula:

[0143] ,

[0144] Among them, is the time consumed for walking to the activity destination, t 0 is the threshold value without distance decay effect, that is, 5 min. This function is a walking travel attenuation function obtained by piecewise fitting according to the 5 min, 10 min, and 15 min living circle threshold values.

[0145] (2) Assume that there is a replaceable charging station k 2 in any charging travel chain, then k 2 is used as k 1's competitor, and the attractiveness k 2 of charging station k 1 when there is a competing charging station is calculated by the following formula and used as k 1's competitive attractiveness:

[0146] ,

[0147] In the above formula, represents the potential charging attractiveness scale of charging station k for class e vehicles; represents the distance between charging stations k 1 and k 2.

[0148] Taking the distribution of charging stations in the area shown by Figure 2 as an example, the final heat map representing the charging accessibility of each area is as shown in Figure 6 .

[0149] Embodiment 2

[0150] On the basis of the solution of Embodiment 1, as shown in Figure 7As shown in the figure, this embodiment also provides an optimization method for the layout of charging piles in a region, which includes:

[0151] 1. Grid and encode the map of the target region at a preset spatial interval, using the positions of each charging pile in the region as decision variables; taking the maximization of charging accessibility between any starting point and ending point in the region as the optimization goal, and combining the actual scenario to set constraint conditions including the constraint on the number of charging stations, thereby constructing a single-objective optimization model.

[0152] In the optimization model established in this embodiment, according to the actual situation, the number of charging piles can be appropriately increased or decreased. When adjusting the positions of the charging stations, it is possible to choose to only adjust the newly added charging stations, or to re-plan the positions of all charging stations. When setting the constraint conditions, various constraint conditions related to costs, distances between stations, charging power, etc. can be set in combination with the actual situation. Of course, the constraint conditions should include the constraint on the number of charging stations.

[0153] The optimization goal set in this embodiment is to make the overall charging accessibility between various locations in the region optimal, that is, users can choose to charge at any charging station and reach nearby places to carry out activities such as work and study.

[0154] 2. Use a genetic algorithm or other optimization algorithms to solve the single-objective optimization model to obtain the optimization result.

[0155] In this embodiment, after establishing the mathematical model corresponding to the above optimization problem, various existing optimization algorithms can be selected to solve it. Typical optimization algorithms that can solve such problems include genetic algorithms, particle swarm optimization algorithms, sparrow search algorithms, and so on. Among them, in the iterative optimization process of the algorithm, the value of the objective function is updated through the method of analyzing the charging accessibility of electric vehicles considering competition as in Embodiment 1.

[0156] 3. Generate a new spatial layout map of the charging stations according to the final iterative optimization result.

[0157] Embodiment 3

[0158] Based on the solution of Embodiment 1, this Embodiment 1 also includes a system for analyzing the charging accessibility of electric vehicles considering competition, which uses the method of analyzing the charging accessibility of electric vehicles considering competition as in Embodiment 1 to generate the charging accessibility between any two locations in the target region. As Figure 8 shown, the system for analyzing the charging accessibility of electric vehicles includes: a scenario modeling unit, a path planning unit, a cost update unit, an attraction quantification unit, and an accessibility calculation unit.

[0159] Among them, the scenario modeling unit is used to rasterize and encode the map of the target area at a preset spatial interval, mark the grids containing charging stations, and analyze the travel demands of the collected users, and then generate a starting point set and an ending point set with the position codes of the grids as elements.

[0160] The path planning unit is used to generate all candidate charging travel chains from a specified starting point, through optional charging stations, to a specified ending point by combining the scenario information provided by the scenario modeling unit. The cost update unit is used to dynamically update the weighted average generalized charging process cost of each vehicle from any starting point to the ending point by combining the charging travel chains. The attraction quantification unit is used to generate the charging attraction of a specified charging station for any vehicle by considering the competition relationship between charging stations.

[0161] The accessibility calculation unit is used to calculate the charging accessibility between any starting point o and ending point d in the target area by combining the outputs of the scenario modeling unit, the path planning unit, the cost update unit, and the attraction quantification unit, using the following formula A od :

[0162] .

[0163] Embodiment 4

[0164] Based on the solution of Embodiment 1, this embodiment further provides an analysis device for electric vehicle charging accessibility considering competition, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, it implements the method for analyzing electric vehicle charging accessibility considering competition as in Embodiment 1, and then generates the weighted average generalized charging process cost of any vehicle from a specified starting point to a specified ending point in the target area.

[0165] The analysis device for electric vehicle charging accessibility considering competition provided in this embodiment is essentially a computer device for implementing the method in Embodiment 1. In actual application, this computer device can be an intelligent terminal, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including an independent server, or a server cluster composed of multiple servers), etc. that can execute programs.

[0166] The computer device pointed out in this embodiment includes at least, but is not limited to, a memory and a processor that can communicate with each other through a system bus. Among them, the memory (i.e., the readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the computer device. Of course, the memory may also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the memory is generally used to store the operating system and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various data that have been output or will be output.

[0167] In some embodiments, the processor may be a central processing unit (CPU), a graphics processing unit (GPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data.

[0168] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for analyzing the charging accessibility of electric vehicles considering competition, characterized in that, It includes: Rasterize and encode the map of the target area at a preset spatial interval, mark the grids containing charging stations, collect and analyze the travel demands of users, and then generate a set of starting points with the position codes of the grids as elements O and a set of end points D ; Define a charging trip chain as a path where a user starts from any grid, drives a vehicle to a charging station, and then reaches any end point by non-driving means. Generate all candidate charging travel chains from a specified starting point, passing through optional charging stations, to a specified ending point, and calculate the charging reachability between any starting point o and ending point d in the target area, and generate a corresponding heat map: ​ ， In the above formula, C ode is the weighted average generalized charging process cost of class e vehicles from the starting point o to the ending point d ; A ke is the competitive attraction of charging station k to the current vehicle; e represents the type of the vehicle, e when e = 1, it represents a private car, and are respectively C ode and A ke related constant parameters; C ode The calculation formula is as follows: ， In the above formula, is any charging travel chain represented by the Dogit model based on path utility s 's path selection probability; represents the total cost required when the user selects any charging travel chain s . The costs of the three processes included in the total cost of any charging travel chain are all composed of two parts: economic cost and time cost; S ode represents e all candidate charging travel chains o from the starting point d to the ending point s of class A ke The calculation method is as follows: Calculate the absolute attractiveness of any charging station by combining the valence attributes of the charging station and the user k ; f ( k ) Assume that there is a replaceable charging station in any charging travel chain k 2, then k 2 is used as k a competitor of 1; Calculate the existence of competing charging stations using the following formula k charging station at 2 k attractiveness of 1 , and use it as k competitive attractiveness of 1: ， In the above formula, represents a charging station k x the potential charging attraction scale for Class e vehicles; represents a charging station k x and k the distance between 2.

2. The method for analyzing the charging accessibility of electric vehicles considering competition according to claim 1, wherein Weighted average generalized charging process cost C ode The calculation method is as follows: First, calculate the total cost required when the user selects any one of the charging travel chains through the following formula s :​ In the above formula, is the generalized driving cost when the user travels from the starting point o to the charging station k ; is the generalized charging cost when the user charges at the charging station k ; is the generalized driving cost when the user travels from the charging station k to the end point d . Then, the Dogit model based on path utility is adopted to represent the path selection probability of any charging trip chain s and further calculate the weighted average generalized charging process cost of the vehicle from the starting point to the ending point o d C ode .​​ 3. The method for analyzing the charging accessibility of electric vehicles considering competition according to claim 2, characterized in that: The expression is: ， In the above formula, is e the utility of the vehicle type's choice of the charging travel chain s from the origin o to the destination d , which reflects the user's preferences for charging cost, charging time, charging convenience, and charging station capacity; is a constant parameter representing the user's sensitivity to the utility; is a constant parameter representing the user's loyalty to the charging travel chain s .

4. The method for analyzing the charging accessibility of electric vehicles considering competition according to claim 3, wherein: Calculated by the following utility function: ， In the above formula, is e the actual utility value of the charging travel chain selected by Class s vehicles from the starting point o to the end point d ; is the e total number of optional charging travel chains for Class o vehicles from the starting point d to the end point ; and is a random error term subject to a certain independent extreme value distribution.

5. The method for analyzing the charging accessibility of electric vehicles considering competition according to claim 2, characterized in that: The costs of the three processes included in any charging travel chain are both composed of two parts: economic cost and time cost, and the proportion of economic cost and time cost in the total cost is balanced through the monetary value weight and the time value weight ; Construct a value coefficient function as follows to exponentially compress the actual probability of an event, and then generate a monetary value weight related to economic costs and a time value weight related to time costs ; ， In the above formula, p is the actual probability of the event occurring; w ( p ) is the corrected probability; , is the curvature control parameter.

6. The method for analyzing the charging accessibility of electric vehicles considering competition according to claim 5, characterized in that: The charging trip chain is divided into three stages: entering the station, charging, and leaving the station. In the stage of entering the station, the economic cost incurred by the user includes the driving cost consumed when driving along the optimal planned path; the time cost incurred is the driving time. In the charging stage, the economic cost incurred by the user includes the charging cost, service cost, and parking cost; the time cost incurred includes the waiting time and service time. In the stage of leaving the station, the economic cost incurred by the user depends on the travel mode adopted by the user; the time cost incurred is the driving time.

7. An optimization method for the layout of charging piles within a region, characterized in that, It includes: First, rasterize and encode the map of the target area at a preset spatial interval, and use the positions of each charging pile in the area as decision variables. Take the maximization of the charging accessibility between any starting point and end point in the area as the optimization goal, and set constraints including the number of charging station constraints in combination with the actual scenario, and then construct a single-objective optimization model. Second, use a genetic algorithm or other optimization algorithms to solve the single-objective optimization model to obtain the optimization result. Among them, in the iterative optimization process of the algorithm, update the value of the objective function through the method for analyzing the charging accessibility of electric vehicles considering competition as described in any one of claims 1-6. Third, generate a new spatial layout map of the charging station according to the final iterative optimization result.

8. A system for analyzing the charging accessibility of electric vehicles considering competition, characterized in that, It adopts the method for analyzing the charging accessibility of electric vehicles considering competition as described in any one of claims 1-6 to generate the charging accessibility between any two locations in the target area. The electric vehicle charging accessibility analysis system includes: A scenario modeling unit, which is used to rasterize and encode the map of the target area at a preset spatial interval, mark the grids containing charging stations, and analyze the travel demands of the collected users, and then generate a starting point set and an end point set with the position codes of the grids as elements. A path planning unit, which is used to generate all candidate charging trip chains from a specified starting point through an optional charging station to a specified end point in combination with the scenario information provided by the scenario modeling unit. A cost update unit, which is used to dynamically update the weighted average generalized charging process cost of each vehicle from any starting point to an end point in combination with the charging trip chain. An attraction quantification unit, which is used to generate the charging attraction of a specified charging station to any vehicle considering the competition relationship between charging stations. A reachability calculation unit, which is configured to calculate the charging reachability between any starting point o and ending point d within a target area by using the following formula in combination with the outputs of the scenario modeling unit, path planning unit, cost update unit, and attraction quantification unit : 。 9. An analysis device for the charging accessibility of electric vehicles considering competition, comprising a memory, a processor, and a computer program stored in the memory and running in the processor, characterized in that: When the processor executes the computer program, it implements the method for analyzing the charging accessibility of electric vehicles considering competition as described in any one of claims 1-6, and then generates the weighted average generalized charging process cost of any vehicle from a specified starting point to a specified end point in the target area.

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