Competition-considered electric vehicle charging reachability analysis method, system and device

Abstract

The invention belongs to the technical field of traffic, and particularly relates to a competition-considered electric vehicle charging reachability analysis method, system and device. The method comprises the following steps: rasterizing and coding a map of a target area, marking grids containing charging stations, and generating a starting point set and an ending point set; defining a path that a user drives a vehicle from a starting point to any charging station and then drives out in a non-driving mode as a charging trip chain; and generating all candidate charging trip chains from the specified starting point to the specified terminal point through the optional charging station, finally, calculating the charging accessibility between any two points in the target area by combining the weighted average generalized charging process cost of the vehicle between any two points and the competitive attraction of the charging station to the vehicle, and generating a corresponding thermodynamic diagram. According to the invention, the problems that the space distribution of the actual travel demand of the user is not considered in the space layout of the existing charging station, the charging reachability distribution among different places in the area is unbalanced, and the charging experience of the user is poor are solved.

Description

Technical Field

[0001] The present invention belongs to the field of traffic technology, and specifically relates to a charging accessibility analysis method for electric vehicles taking competition into consideration, a corresponding system and device, and a method for optimizing the layout of charging piles in a region. Background Art

[0002] Compared with traditional fuel vehicles, electric vehicles can reduce people's travel costs and expand people's travel needs. However, as the market ownership of electric vehicles continues to increase, the charging needs 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 needs of the majority of car owners is becoming a top priority for charging station operators and urban planners.

[0003] Traditional charging station spatial layout plans are mainly aimed at expanding the spatial coverage of charging stations. In these plans, the charging service radius that each charging station can support is defined in combination with the amount of charging services that it can provide, and then charging stations are set up in segments within the region to ensure that car owners can quickly find nearby charging stations and enjoy charging services at any location in the target area. In the past era of electric vehicle increments, these traditional plans can indeed help to optimize the site selection of charging stations and improve the service level of the region. However, in today's era of electric vehicle stock, unbalanced and insufficient charging service capabilities are gradually becoming the new main contradiction. Some users said that in some areas with concentrated travel demand, even if there are multiple charging stations in the area, the waiting time for the charging process is still high. Although charging services can be obtained in the end, the experience of the process is not good. In some areas with low travel demand frequency, even if the number of charging stations is small, users will not have charging anxiety.

[0004] Therefore, how to evaluate the charging accessibility between different locations in the region based on the actual travel needs of users, and use this as a basis for decision-making on optimizing the spatial layout of charging stations, is becoming a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] In order to solve the problem that the spatial layout of existing charging stations fails to take into account the spatial distribution of users' actual travel needs, which leads to uneven distribution of charging accessibility among different locations in the region and poor charging experience for users, the present invention provides an electric vehicle charging accessibility analysis method considering competition and its corresponding system, device and method for optimizing the layout of charging piles in the region.

[0006] The present invention is implemented by the following technical solutions: A charging accessibility analysis method for electric vehicles considering competition, comprising: The map of the target area is rasterized and encoded according to the preset spatial intervals, the grids containing the charging stations are marked, the user's travel needs are collected and analyzed, and then a starting point set with the grid location code as the element is generated. O and the endpoint set D .

[0007] A charging travel chain is defined as a path in which a user starts from any grid, drives the vehicle to a charging station, and then uses a non-driving method to reach any end point.

[0008] Generate all candidate charging travel chains from the specified starting point to the specified end point via the optional charging station, and calculate the number of any starting point in the target area by the following formula: o With the end point d Charging accessibility A od , and generate the corresponding heat map: , In the above formula, C ode For vehicles of category e, starting from the starting point o To the end d The weighted average generalized charging process cost; A ke is the competitive attractiveness of charging station k to current vehicles; e Indicates the type of vehicle, e = 1 means private car, e = 2 means bus; and Respectively C ode and A ke Related constant parameters.

[0009] As a further improvement of the present invention, C ode The calculation method is: First, calculate the following formula to get the value of the charging chain selected by the user: s The total cost required : , In the above formula, For users from the starting point o To the charging station k The generalized driving cost when For users at charging stations k The generalized charging cost of charging; For users from charging stations k To the end d The generalized driving cost.

[0010] Then the Dogit model based on path utility is used to represent any charging travel chain s The path selection probability , and then calculate the vehicle from the starting point by the following formula o To the end d The weighted average generalized charging process cost C ode : , In the above formula, S ode express e Class vehicles from the starting point o To the end d All candidate charging travel chains s A collection of .

[0011] As a further improvement of the present invention, The calculation formula is: , In the above formula, for e Type of vehicle chooses charging travel chain s From the starting point o To the end d Utility, which reflects user preferences for charging cost, charging time, charging convenience, and charging station capacity; is a constant parameter that represents the user's sensitivity to utility; A characterization of the user's charging travel chain s parameters of loyalty.

[0012] As a further improvement of the present invention, The effect function is calculated as follows: , 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.

[0013] As a further improvement of the present invention, the cost of the three processes included in any charging chain is , and Both are composed of two parts: economic cost and time cost.

[0014] The following value coefficient function is constructed to exponentially compress the actual probability of an event, thereby generating a monetary value weight related to the economic cost. w + ( p ) and the time value weight associated with time cost w - ( p ); , In the above formula, p is the actual probability of the event occurring; w ( p ) is the modified probability; , is the curvature control parameter.

[0015] 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.

[0016] During the station entry stage, the economic cost incurred by the user includes the driving cost consumed when driving according to the optimal planned route; the time cost incurred is the driving time.

[0017] During the charging stage, the economic costs incurred by users include charging fees, service fees and parking fees; the time costs incurred include waiting time and service time.

[0018] During the departure stage, the economic cost incurred by the user depends on the travel mode adopted by the user; the time cost incurred is the travel time.

[0019] As a further improvement of the present invention, the competitive charging attraction of charging station k to the current vehicle A ke The calculation method is as follows: Combining the valence attributes of charging stations and users, we can calculate the k The absolute attraction f ( k ).

[0020] Assume that there is a replaceable charging station in any charging trip chain k 2, then k 2 as k 1's competitors, and calculate the existence of competing charging stations by the following formula k 2 hour charging station k 1. Attraction , and use it as k 1. Competitive appeal: , In the above formula, represents the potential charging attraction scale of charging station k for class e vehicles; Indicates charging station k 1 and k The distance between 2.

[0021] A method for optimizing the layout of charging piles in a region, comprising: First, the map of the target area is rasterized and encoded according to the preset spatial intervals, and the location of each charging pile in the area is used as the decision variable; the maximization of charging accessibility between any starting point and end point in the area is taken as the optimization goal, and the constraints including the number of charging stations are set in combination with the actual scenario, and then a single-objective optimization model is constructed.

[0022] 2. Use genetic algorithms or other optimization algorithms to solve the single-objective optimization model and obtain the optimization results.

[0023] Among them, in the iterative optimization process of the algorithm, the value of the objective function is updated through the electric vehicle charging accessibility analysis method considering competition as mentioned above.

[0024] 3. Generate a new spatial layout diagram of the charging station based on the final iterative optimization results.

[0025] The present invention also includes a competitive electric vehicle charging accessibility analysis system, which uses the competitive electric vehicle charging accessibility analysis method as described above to generate charging accessibility between any two locations in a target area. The electric vehicle charging accessibility analysis system includes: a scenario modeling unit, a path planning unit, a cost updating unit, an attractiveness quantification unit, and an accessibility calculation unit.

[0026] The scenario modeling unit is used to rasterize and encode the map of the target area according to the preset spatial interval, mark the grids containing the charging stations, and analyze the collected travel needs of users to generate a starting point set and a destination set with the grid location code as the element.

[0027] 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. 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: Aod : .

[0028] 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.

[0029] The technical solution provided by the present invention has the following beneficial effects: 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

[0030] 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: Figure 1 This is a typical scenario of a user performing charging behavior described in Example 1 of the present invention.

[0031] Figure 2 This is the map information of the rasterized area in Example 1 of the present invention.

[0032] Figure 3 Schematic diagram showing the non-homogeneity of time value in Example 1 of the present invention.

[0033] Figure 4 This is an image reflecting the relationship between the driving time of a car trip and the walking time of a person and their probability density in Example 1 of the present invention.

[0034] Figure 5 This is an image reflecting the relationship between the time and opportunity time cost of driving and walking in Example 1 of the present invention.

[0035] Figure 6 for Figure 2 The corresponding area finally generates a heat map representing the charging accessibility of various locations in the area.

[0036] Figure 7 This is a flowchart of the steps of the method for optimizing the layout of charging piles in a region provided in Example 2 of the present invention.

[0037] Figure 8 This is a module schematic diagram of the electric vehicle charging accessibility analysis system provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0039] Example 1 In order to accurately reflect whether the number of charging stations in an area is sufficient and whether the spatial distribution of each charging station is reasonable, this embodiment provides a quantifiable evaluation indicator, namely, the charging accessibility of electric vehicles. The charging accessibility theory proposed in this embodiment follows the following three basic assumptions: 1. For resident travel events that only consider charging, users will follow the principle of proximity when charging. 2. During the charging process, car owners are more likely to choose nearby points of interest to carry out travel activities, and will not stay in place until charging is completed. 3. After charging is completed, the car owner will return to the original location along the return route to pick up the car. Based on the above assumptions, this embodiment believes that typical scenarios for users to carry out charging behavior are as follows: Figure 1 As shown: First, the user starts from his residence or workplace, drives a low-powered vehicle to find a charging station (step 1). After arriving at the charging station, the user connects to the charging station and starts charging. While the vehicle is charging, the user will find an activity point by walking, taking public transportation or taxis, or sharing bicycles, and carry out work / entertainment / rest activities. After the vehicle is fully charged or the activity the user is attending is over, the user will return to the charging station, pick up the vehicle, and drive the vehicle back to the original residence or workplace.

[0040] Combined with the above detailed description of the scenario when the user performs charging behavior, this embodiment further proposes the concept of a charging travel chain. Considering that the return trip after the user finishes charging is a reverse process of the previous trip, this embodiment further simplifies the concept of a charging travel chain to a designated area where the user drives from place A (starting point) to the charging station, leaves the vehicle in the charging station to charge, and then walks or uses other means of transportation to reach place B to carry out other activities. In this process, if the layout of charging stations in a certain area can fully meet the user's needs to realize the above-mentioned 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 in this area is higher; otherwise, it means that the charging accessibility of electric vehicles between different locations in this area is lower.

[0041] Furthermore, in order to quantify the charging accessibility between various locations in a certain area, this embodiment provides an electric vehicle charging accessibility analysis method considering competition, which includes the following process: 1. Scene Modeling The map of the target area is rasterized and encoded according to the preset spatial intervals, the grids containing the charging stations are marked, the user's travel needs are collected and analyzed, and then a starting point set with the grid location code as the element is generated. O and the endpoint set D .

[0042] like Figure 2 As shown, in order to simplify the processing process, for any area that needs to be analyzed for charging accessibility, this embodiment needs to first obtain the GIS information of the area, and rasterize the map of the area according to the preset spatial interval. In order to distinguish the geographical location of each grid obtained after segmentation, this embodiment sets a location code for each grid. In addition, in order to simplify the calculation, this embodiment can also use the coordinates of the geographic center as the spatial coordinates of the area.

[0043] Considering that the spatial position of the charging station in the grid map remains fixed, this embodiment pre-marks the grid containing the charging station. At this time, the spatial position of the charging station can be characterized by the position code of the grid. In addition, for different regions, the travel needs of the crowd are different, and the spatial distribution of users' living, working, and entertainment behaviors is also diverse. In order to accurately describe the various possible travel behaviors of users, this embodiment selects to collect and analyze the travel needs of users, and classifies the starting point and end point of each travel behavior of the user, and finally obtains a location set containing all starting points, namely the starting point set O; and a set containing all end points, namely the end point set D. In this embodiment, the elements contained in the starting point set and the end point set are the position codes of the grid.

[0044] 2. Numerical Quantization Based on the information from the above scenario modeling, this embodiment combines the path planning results between different locations, the economic cost and time cost of the user's travel behavior, and the user's personal preferences to evaluate the feasibility of whether the user can better carry out daily activities in accordance with the charging travel chain mode between any two points.

[0045] Specifically, in the technical solution provided by this embodiment, we first define a charging travel chain in which the user starts from any grid, drives the vehicle to the charging station, and then uses a non-driving method to reach the grid corresponding to any destination. Then, we sequentially generate all candidate charging travel chains in which the user starts from the specified starting point, passes through any optional charging station, and finally reaches the specified destination. Finally, we calculate the number of charging paths from any starting point in the target area using the following formula: o With the end point d Charging accessibility A od , and generate the corresponding heat map: , In the above formula, C ode For vehicles of category e, starting from the starting point o To the end d The weighted average generalized charging process cost; A ke is the competitive attractiveness of charging station k to current vehicles; e Indicates the type of vehicle, e = 1 indicates a private vehicle (PEV), e = 2 means bus (BE); and Respectively C ode and A ke Related constant parameters.

[0046] Combining the above formula, we can see that the weighted average generalized charging process cost between the two locations is C ode , as well as the competitive attractiveness of the charging stations included in the adopted travel chain to the vehicle, are the key factors affecting the final evaluation value of charging accessibility. The following is a detailed description of each indicator of the above chain: 1. Weighted average generalized charging process cost In this embodiment, the weighted average generalized charging process cost is a comprehensive result that includes all possible costs incurred by the user in the process of completing vehicle charging from one location and reaching another location. C odeThe calculation method is as follows: First, each charging travel chain can be divided into three stages: 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 to be provided and finally connects the vehicle's charging port to the charging pile. In the departure stage, the user uses a non-driving method to reach the end point from the charging station and carry out personal interest activities.

[0047] The above three stages will generate different costs respectively. Therefore, the following formula is used to calculate the cost of a user choosing any charging chain: s The total cost required : , In the above formula, For users from the starting point o To the charging station k The generalized driving cost when For users at charging stations k The generalized charging cost of charging; For users from charging stations k To the end d The generalized driving cost.

[0048] In this embodiment, the cost of the three processes included in any charging chain is , and In order 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 the event, thereby generating a monetary value weight related to the economic cost: w + ( p ) and the time value weight associated with time cost w - ( p ); , In the above formula, p is the actual probability of the event occurring; w ( p ) is the modified probability; , is the curvature control parameter.

[0049] The generalized driving cost C generated at any stage can be calculated by the following formula:

[0050] In the above formula, C1 and C2 represent the driving cost and time cost of the corresponding stage respectively.

[0051] Among them, the travel cost can often be directly quantified into a specific monetary value, while the time cost is a value that is difficult to quantify. In this embodiment, considering that the time value of urban residents is heterogeneous, travel time constraints have a significant impact on time value. For travelers with strict travel time constraints, such as Figure 3 As shown, the time value of arriving at the destination at time t0 is the highest, the time value rises sharply in a relatively short period of time before this time point, and the time value of arriving after this time point drops rapidly (see Figure 3 The A curve in the figure shows that this rule is particularly true for basic and necessary travel.

[0052] In addition, in order to quantify the unit value of time cost, this embodiment can select the following time value model in macroeconomics or microeconomics to determine the unit value V of travel time: T : The macroeconomic method is also called the production method. It is generally used to calculate the time value of work travel. The corresponding time value model is as follows:

[0053] In the above formula, GDP is the gross national product; M is the average number of employed persons per year; N is the average number of working hours per year.

[0054] The microeconomic method is also called the income method. It is generally used to calculate the time value of non-work travel. The corresponding time value model is as follows:

[0055] in, Q is the gross national product.

[0056] (1) Costs of the entry stage In the solution of this embodiment, the user starts from the starting point o To the charging station k The generalized driving cost It includes driving cost (economic cost) and time cost. The calculation formula is as follows: , In the above formula, is the shortest path distance from the starting point o to the charging station k of the e-th EV, which can be obtained by the Dijkstra algorithm; R is the average driving distance of EV on urban roads (km); is the average unit driving cost of EV on urban roads (yuan / km); is the parameter used to capture the “marginal effect”; n is the number of people in the vehicle; V T is the unit value of travel time; v is the average driving speed of EV on urban roads (km / h); w + ( p )and w - ( p ) are the monetary value weight and time value weight determined by prospect theory respectively.

[0057] (2) Cost of charging stage In the solution of this embodiment, the user is at the charging station k Generalized charging cost for charging The calculation formula is as follows: , In the above formula, is the economic cost of the charging process, The congestion cost is the time cost of the charging process. Both can cover various costs incurred during the period from the vehicle arriving at the charging station to leaving the charging station fully charged, including charging cost, parking cost and time cost. The congestion cost involves the time value of the group.

[0058] Specifically, the economic cost of the charging stage in the station is calculated by the following formula:

[0059] In the above formula, For choosing a charging station k Charging fees to be paid for charging; For choosing a charging station k Operator service fees required for charging; For choosing a charging station k Parking fees apply for charging. Indicates that charging fee is charged per unit of charging volume; A service fee is charged to represent the unit charge amount; To indicate the parking fee per unit time (yuan / h); and Respectively represent the actual parking time and free parking time; Q + Indicates the charge level of the vehicle in the charging station.

[0060] When an electric car arrives at a charging station to charge, it not only has to pay various fees for charging, but also has to bear the 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 incurred by the vehicle during the charging stage should include waiting time and service time. The time cost of the vehicle charging process The calculation formula is: , In the above formula, k is the number of working days per month; r is the wage rate coefficient; w i The monthly income of the traveler; For vehicles at charging stations k The generalized charging time consumed; For vehicles at charging stations k The time required to fully charge, i.e. the service time, depends on the charge amount and the power of the charging equipment; For vehicles at charging stations k Queuing time due to congestion.

[0061] Specifically, the waiting time It can be calculated by the following formula: , In the above formula, Indicates the charging capacity of the electric vehicle; Indicates the rated power of the charging station (KW); represents the average waiting time in the queue in period i, i =1, 2, …, 4; represents the average arrival rate of electric vehicles in time period i.

[0062] Service Hours The calculation is performed by the following formula:

[0063] In the above formula, Indicates the rated power of the charging pile (KW); Indicates charging efficiency; (3) Costs of the off-site phase During the process of users traveling from the charging station to the destination, the time when the car owner or passenger arrives at the destination is often uncertain because the charging station may be congested, causing users to queue up to charge, or need to find another charging station to charge. During this process, the car owner and passengers may have the following two activities: 1. Wait for the PEV to be fully charged and then drive to the destination (home, shopping mall or company); 2. Do not wait for the PEV to be fully charged, start charging and walk to the destination. Since the probability of activity 1 is small, only activity 2 is considered in the calculation here.

[0064] Among them, a person's walking time is affected by many factors, such as speed, road conditions, and charging time, and these factors are normally distributed. Assume that a person's walking time The probability density of Figure 4 The normal distribution shown is:

[0065] in, The expected walking time for the vehicle owner or passenger to reach the destination. Represents the probability density of the owner or passenger walking to the destination.

[0066] Furthermore, the opportunity time cost of walking is Figure 5 In particular, because buses are usually charged in the early morning when they are not in service, there are no passengers on the buses during charging, so the opportunity time cost loss of the buses is not considered in this process.

[0067] In summary, the time cost spent by users in the off-site phase is calculated by the following formula:

[0068] In the above formula, It represents the time cost consumed by the user when leaving the station when he chooses to walk; Represents the cumulative distribution function of the user's walking time during the departure phase.

[0069] Based on the cost of each charging travel chain calculated in the above process, this embodiment uses the Dogit model based on path utility to represent any charging travel chain. s The path selection probability , and then calculate the vehicle from the starting point by the following formula o To the end d The weighted average generalized charging process cost C ode : , In the above formula, S ode express eClass vehicles from the starting point o To the end d All candidate charging travel chains s A collection of .

[0070] Specifically, any charging travel chain s The path selection probability The following calculation formula is used to derive: , In the above formula, for e Type of vehicle chooses charging travel chain s From the starting point o To the end d Utility, which reflects user preferences for charging cost, charging time, charging convenience, and charging station capacity; is a constant parameter that represents the user's sensitivity to utility; A characterization of the user's charging travel chain s The loyalty parameters. The effect function is calculated as follows: , 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.

[0071] 2. Competitive attractiveness of charging stations 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: (1) Combine the valence attributes of charging stations and users to calculate the k The absolute attraction f ( k ); specifically include: 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: , 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); , , , Parameters of the four error terms included in the model were estimated for absolute attractiveness.

[0072] Among them, the cumulative number of opportunities for charging station k to be selected is v 1 satisfies the following formula: ; in, O k is the number of facilities within reach; is a conditional function, determined by: , in, is the time it takes to walk to the activity destination. t 0 is the threshold value where no distance attenuation occurs, which is 5 minutes. The function is the walking travel attenuation function obtained by segmented fitting based on the 5-minute, 10-minute, and 15-minute living circle thresholds.

[0073] (2) Assume that there is a replaceable charging station in any charging trip chain k 2, then k 2 as k 1's competitors, and calculate the existence of competing charging stations by the following formula k 2 hour charging station k 1. Attraction , and use it as k 1. Competitive appeal: , In the above formula, represents the potential charging attraction scale of charging station k for class e vehicles; Indicates charging station k 1 and k The distance between 2.

[0074] by Figure 2 Taking the distribution of charging stations in the area shown in the figure as an example, the final heat map representing the charging accessibility of each area is as follows Figure 6 shown.

[0075] Example 2 On the basis of the scheme in Example 1, Figure 7 As shown, this embodiment also provides a method for optimizing the layout of charging piles in a region, which includes: First, the map of the target area is rasterized and encoded according to the preset spatial intervals, and the location of each charging pile in the area is used as the decision variable; the maximization of charging accessibility between any starting point and end point in the area is taken as the optimization goal, and the constraints including the number of charging stations are set in combination with the actual scenario, and then a single-objective optimization model is constructed.

[0076] In the optimization model of this embodiment, the number of charging piles can be appropriately increased or decreased according to the actual situation. When adjusting the location of the charging station, you can choose to adjust only the added charging stations, or you can choose to re-plan the locations of all charging stations. When setting constraints, you can set various constraints related to cost, distance between stations, charging power, etc. in combination with the actual situation. Of course, the set constraints should include the number of charging stations.

[0077] The optimization goal set in this embodiment is to optimize the overall charging accessibility between various locations in the region, that is, users can choose to charge at any charging station and reach nearby places to carry out work, study and other activities.

[0078] 2. Use genetic algorithms or other optimization algorithms to solve the single-objective optimization model and obtain the optimization results.

[0079] 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, etc. In the iterative optimization process of the algorithm, the value of the objective function is updated by the electric vehicle charging accessibility analysis method considering competition as in Example 1.

[0080] 3. Generate a new spatial layout diagram of the charging station based on the final iterative optimization results.

[0081] Example 3 Based on the solution of Example 1, this Example 1 also includes a competitive electric vehicle charging accessibility analysis system, which uses the competitive electric vehicle charging accessibility analysis method of Example 1 to generate charging accessibility between any two locations in the target area. Figure 8 As shown, the electric vehicle charging accessibility analysis system includes: a scenario modeling unit, a path planning unit, a cost updating unit, an attraction quantification unit, and an accessibility calculation unit.

[0082] The scenario modeling unit is used to rasterize and encode the map of the target area according to the preset spatial interval, mark the grids containing the charging stations, and analyze the collected travel needs of users to generate a starting point set and a destination set with the grid location code as the element.

[0083] 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. 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 : .

[0084] Example 4 Based on the scheme of Example 1, this embodiment further provides an electric vehicle charging accessibility analysis device 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, the electric vehicle charging accessibility analysis method considering competition as in Example 1 is implemented, and then the weighted average generalized charging process cost of any vehicle from a specified starting point to a specified end point in a target area is generated.

[0085] The electric vehicle charging accessibility analysis device considering competition provided in this embodiment is essentially a computer device for implementing the method in Embodiment 1. In actual application, the computer device can be an intelligent terminal capable of executing a program, a tablet computer, a laptop 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.

[0086] The computer device indicated in this embodiment includes at least but is not limited to: a memory and a processor that can be connected to each other through a system bus. Among them, the memory (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory can be an internal storage unit of a computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory can also be an external storage device of a computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store an operating system and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various types of data that have been output or are to be output.

[0087] 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 in some embodiments. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run program codes stored in a memory or process data.

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing charging accessibility of electric vehicles considering competition, characterized in that: It includes: The map of the target area is rasterized and encoded according to the preset spatial intervals, the grids containing the charging stations are marked, the user's travel needs are collected and analyzed, and then a starting point set with the grid location code as the element is generated. O and the endpoint set D ; A charging travel chain is defined as a path where a user starts from any grid, drives the vehicle to a charging station, and then uses a non-driving method to reach any destination. Generate all candidate charging trip chains from the specified starting point to the specified end point via the optional charging station, and calculate the number of any starting point in the target area by the following formula: o With the end point d Charging accessibility A od , and generate the corresponding heat map: ， In the above formula, C ode For vehicles of category e, starting from the starting point o To the end d The weighted average generalized charging process cost; A ke is the competitive attractiveness of charging station k to current vehicles; e Indicates the type of vehicle, e = 1 means private car, e = 2 means bus; and Respectively C ode and A ke Related constant parameters.

2. The method for analyzing charging accessibility of electric vehicles considering competition according to claim 1, characterized in that: Weighted average generalized charging process cost C ode The calculation method is: First, calculate the following formula to get the value of the charging chain selected by the user: s The total cost required : ， In the above formula, For users from the starting point o To the charging station k The generalized driving cost when For users at charging stations k The generalized charging cost of charging; For users from charging stations k To the end d The generalized driving cost; Then the Dogit model based on path utility is used to represent any charging travel chain s The path selection probability , and then calculate the vehicle from the starting point by the following formula o To the end d The weighted average generalized charging process cost C ode : ， In the above formula, S ode express e Class vehicles from the starting point o To the end d All candidate charging travel chains s A collection of .

3. The method for analyzing charging accessibility of electric vehicles considering competition as claimed in claim 2, characterized in that: The expression is: ， In the above formula, for e Type of vehicle chooses charging travel chain s From the starting point o To the end d The utility of charging, which reflects the user's preference for charging cost, charging time, charging convenience, and charging station capacity; is a constant parameter that represents the user's sensitivity to utility; A characterization of the user's charging travel chain s A constant parameter of loyalty.

4. The method for analyzing charging accessibility of electric vehicles considering competition as claimed in claim 3, characterized in that: The effect function is calculated as follows: ， 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.

5. The method for analyzing charging accessibility of electric vehicles considering competition as claimed in claim 2, characterized in that: The costs of the three processes in any charging travel chain , and Both are composed of two parts: economic cost and time cost; The following value coefficient function is constructed to exponentially compress the actual probability of an event, thereby generating a monetary value weight related to the economic cost. w + ( p ) and the time value weight associated with time cost w - ( p ); ， In the above formula, p is the actual probability of the event occurring; w ( p ) is the modified probability; , is the curvature control parameter.

6. The method for analyzing charging accessibility of electric vehicles considering competition as claimed in claim 5, characterized in that: The charging travel chain is divided into three stages: entry, charging and departure; During the station entry phase, the economic cost incurred by the user includes the driving cost consumed when driving along the optimal planned route; the time cost consumed is the driving time; During the charging phase, the economic costs incurred by users include charging fees, service fees, and parking fees; the time costs incurred include waiting time and service time; During the departure stage, the economic cost incurred by the user depends on the travel mode adopted by the user; the time cost incurred is the travel time.

7. The method for analyzing charging accessibility of electric vehicles considering competition as claimed in claim 6, characterized in that: Charging station k competitive charging attractiveness for current vehicles A ke The calculation method is as follows: Combining the valence attributes of charging stations and users, we can calculate the k The absolute attraction f ( k ); Assume that there is a replaceable charging station in any charging trip chain k 2, then k 2 as k 1's competitors, and calculate the existence of competing charging stations by the following formula k 2 hour charging station k 1. Attraction , and use it as k 1. Competitive appeal: ， In the above formula, Indicates charging station k 1. The potential scale of charging attraction for e-type vehicles; Indicates charging station k 1 and k The distance between 2.

8. A method for optimizing the layout of charging piles in a region, characterized in that: It includes:

1. Rasterize and encode the map of the target area according to the preset spatial interval, and use the location of each charging pile in the area as the decision variable; The optimization goal is to maximize the charging accessibility between any starting point and end point in the region, and the constraints including the number of charging stations are set in combination with the actual scenario, thus constructing a single-objective optimization model.

2. Using a genetic algorithm or other optimization algorithm to solve the single-objective optimization model to obtain an optimization result; Wherein, in the iterative optimization process of the algorithm, the value of the objective function is updated by the competitive electric vehicle charging accessibility analysis method as described in any one of claims 1 to 7; 3. Generate a new spatial layout diagram of the charging station based on the final iterative optimization results.

9. An electric vehicle charging accessibility analysis system considering competition, characterized in that: It uses the electric vehicle charging accessibility analysis method considering competition as described in any one of claims 1 to 7 to generate charging accessibility between any two locations in the target area; The electric vehicle charging accessibility analysis system comprises: A scenario modeling unit, which is used to rasterize and encode the map of the target area according to a preset spatial interval, mark the grids containing the charging stations, and analyze the collected travel needs of users to generate a start point set and a destination set with the grid position code as an element; A path planning unit, which is used to generate all candidate charging travel chains from a specified starting point to a specified end point via an optional charging station 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 the end point in combination with the charging travel chain; An attractiveness quantification unit, which is used to consider the competitive relationship between charging stations and generate the charging attractiveness of a specified charging station for any vehicle; 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 the end point d in the target area using the following formula: A od : 。 10. A device for analyzing 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 competitive electric vehicle charging accessibility analysis method as described in any one of claims 1 to 7, and then generates a weighted average generalized charging process cost of any vehicle from a specified starting point to a specified end point in a target area.

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