Highway charging load simulation method and system based on multi-period traffic balance

Abstract

The invention discloses a highway charging load simulation method and system based on multi-period traffic balance. The method comprises the following steps: acquiring highway network topological parameters, charging station distribution parameters, user travel origin and destination demand data and electric vehicle battery parameters; the method comprises the following steps of: aiming at the characteristics of long road section distance and large driving time span of an expressway, establishing a multi-period space-time network model of the expressway, and constructing a space-time topological structure comprising a space-time driving arc and a virtual staying arc; based on a traffic balance principle, constructing a highway multi-period traffic distribution model comprising the main problem and the sub-problems; and solving the highway multi-period traffic distribution model in the step 3 by adopting an iterative algorithm, and mapping the converged space-time path flow into a charging demand so as to obtain space-time load distribution of each charging station of the highway. According to the method, the defect that a traditional single-period traffic model cannot accurately describe the long-distance cross-period driving characteristics and the queuing congestion effect of the expressway can be overcome.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power system and transportation system operation optimization, and particularly relates to a highway charging load simulation method and system based on multi-period traffic equilibrium. BACKGROUND

[0002] With the acceleration of global energy transformation and the wide popularity of electric vehicles, the demand for intercity travel has surged, and the construction and operation of highway charging infrastructure are facing great challenges. Compared with the complex urban road network, the highway scenario has the characteristics of long road section distance and large travel time span, and the travel time between adjacent nodes of vehicles cannot be ignored. This long-distance travel feature makes the charging demand of electric vehicles present a complex strong coupling relationship in time and space distribution.

[0003] However, the existing charging load simulation technology mostly follows the single-period static traffic model, which is difficult to capture the cross-period dynamic characteristics of vehicles in long-distance travel, cannot accurately describe the hysteresis effect of traffic flow on the time axis, and is difficult to reflect the path selection changes and load space-time transfer effect caused by congestion and queuing during holidays or peak periods. Therefore, there is an urgent need for a simulation method that can integrate time and space dimensions and accurately reflect the strong coupling relationship between highway traffic flow and charging load to improve the accuracy and practicality of load simulation. SUMMARY

[0004] The purpose of the present application is to provide a highway charging load simulation method and system based on multi-period traffic equilibrium to make up for the deficiency of traditional single-period traffic models that cannot accurately depict the long-distance cross-period travel characteristics and queuing congestion effect of highways.

[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: The highway charging load simulation method based on multi-period traffic equilibrium comprises the following steps: Step 1: Obtain data, including highway road network topology parameters, charging station distribution parameters, user trip origin-destination demand data, and electric vehicle battery parameters; Step 2: In view of the characteristics of long road section distance and large travel time span of highways, a multi-period space-time network model of highways is established in combination with the obtained data, the spatial nodes of the original traffic road network are expanded along the time dimension, and a space-time topology structure containing space-time travel arcs and virtual stay arcs is constructed; Step three: on the basis of the established multi-period highway space-time network model, a multi-period highway traffic distribution model containing a main problem and a sub-problem is constructed based on the traffic equilibrium principle, the main problem is to distribute space-time flow in the space-time topology structure by minimizing the total travel cost of all users, and the sub-problem is to solve the single-vehicle optimal space-time path in the space-time topology structure considering the mileage anxiety under the given traffic state; Step four: the multi-period highway traffic distribution model is solved by using an iterative algorithm, the path flow in the space-time topology structure obtained after convergence is mapped as the charging demand, and the space-time load distribution of each charging station on the highway is obtained.

[0006] The further improvement of the application is that in step two, in view of the characteristics of long distance and large span of driving time of the highway section, a multi-period highway space-time network model is established combined with the obtained data, including: The single-period traffic network directed graph considering the spatial characteristics is expanded to a space-time network graph in the time dimension , wherein the node set represents all nodes in the traffic network, the charging station set S is a subset of the node set , is a road set connecting the nodes in , is an expanded node set , and is an expanded road set ; Specifically, in the space-time network, the expanded node is expanded to the expanded node , wherein is a time period, is the maximum time period in the research range; the charging station set S is a subset of the node set , and is expanded to the expanded charging station set , the charging station of the first time period , wherein is the original charging station node; the starting point and the ending point are , the original traffic road of is expanded to the expanded road of each time period , and the cross-period virtual stay road of the same node is established , at this time, the vehicle can only pass through in one direction along the time period growth direction.

[0007] The further improvement of the present application is that in step three, the main problem in the multi-period traffic assignment model of the expressway carries out the space-time traffic distribution in the whole traffic network, and the objective function (1) is to minimize the total travel cost of all EV users under the user equilibrium condition; formula (2) calculates the total travel cost, including the travel time cost of the expressway section, the queuing waiting time cost of the service area charging station and the charging cost; formula (3) is the user equilibrium condition; (1) (2) (3) wherein, is the minimum travel cost of OD pair ; is the travel demand of OD pair ; is the total travel cost of OD pair on the extended space-time path , including the travel time, the queuing time and the charging cost; is the conversion coefficient of "time-cost"; and respectively represent the traffic flow on the road d and the queuing number of the charging station k in the period t; and are the corresponding travel time and queuing time respectively; the indication and are used to represent the association of the path and the charging station / road; and are the fast charging price and the fast charging quantity respectively; is the traffic flow of OD pair on the extended space-time path ; is the set of feasible extended paths of OD pair ; is the set of OD pairs.

[0008] The further improvement of the present application is that in step three, the main problem carries out the space-time traffic distribution in the whole traffic network, and the constraint condition is represented as follows: (4) (5) (6) (7) (8) (9) (10) Constraint (4) is the relationship between spatiotemporal path and travel demand; constraint (5) ensures that the flow is non-negative; constraint (6) maps the three-dimensional spatiotemporal path flow to the two-dimensional spatial path flow; constraints (7)–(8) give the queuing time and travel time functions; constraints (9)–(10) obtain the flow of each charging station and each road segment. in, Time period Internal OD Take a spatial path Traffic; Indicates extended path In time period Spatial path The correspondence; and This refers to the free-flow driving time and the inherent service time of the charging station. and For road capacity and charging station service capacity; and OD pairs respectively path Will it reach a fast charging station? With roads The indicated quantity; For OD The set of feasible paths in the spatial dimension; It is a set of discrete time periods; For road segment sets; For fast charging stations.

[0009] A further improvement of this invention is that, in step three, considering the long distance of highway sections, the sub-problem performs the selection of the user's optimal path taking into account mileage anxiety under the current traffic conditions given by the main problem. The mathematical model is as follows. (11) (12) (13) (14) (15) (16) (17) Formula (11) is an objective function for minimizing travel time, charging queuing time and fast charging cost; formula (12)-formula (17) are constraint conditions, wherein constraint condition (12) is an electric vehicle charging constraint; constraint condition (13) is a maximum charging power constraint; constraint condition (14) is a relaxation constraint whether a road section is passed; constraint condition (15) is a user charging decision constraint; constraint condition (16) is an initial state of charge constraint; and constraint condition (17) is a user range anxiety constraint; wherein an indication of an extended arc in a time period whether corresponding road arcs ; represent an OD pair whether an arc is selected ; is an auxiliary variable of whether fast charging is performed at a fast charging station , a time point ; represent a SoC at a location , a time point ; is a spatial distance of a road arc (u, v); is a unit distance energy consumption; is a relaxation variable, and is allowed to be non-zero only when the road arc is not selected; is a maximum fast charging power; is an initial SOC of an OD pair , and is set to ; represents a lower limit of SoC set due to range anxiety; is a set of road arcs of an extended network; is a set of all nodes with fast charging capability.

[0010] The further improvement of the present application is that, in step four, an iterative algorithm is used to solve the multi-period traffic distribution model of the expressway, comprising: After the optimal flow distribution is obtained, the extended space-time path flow and the corresponding charging amount are used to calculate the charging amount of each charging station, as shown in formula (18), so as to determine the space-time distribution of the charging load of the expressway; (18) wherein, represent an OD pair in a time period path an indication variable of whether fast charging is performed at a fast charging station ; is a fast charging station in a time period Total fast charging load.

[0011] A highway charging load simulation system based on multi-time period traffic equilibrium includes: Data acquisition unit: Acquires data, including highway network topology parameters, charging station distribution parameters, user travel origin and destination demand data, and electric vehicle battery parameters; The first model building unit: In view of the characteristics of long distances and long travel time spans of highways, and in combination with the acquired data, a multi-time spatiotemporal network model of highways is established. The spatial nodes of the original traffic network are extended along the time dimension to construct a spatiotemporal topology structure that includes spatiotemporal travel arcs and virtual stop arcs. The second model building unit: Based on the established multi-period spatiotemporal network model of the highway, a multi-period traffic allocation model of the highway containing a main problem and sub-problems is constructed based on the principle of traffic balance. The main problem is to allocate spatiotemporal traffic with the goal of minimizing the total travel cost of all users in the spatiotemporal topology. The sub-problems are to solve the optimal spatiotemporal path of a single vehicle in the spatiotemporal topology considering mileage anxiety under a given traffic condition. Model Solving Unit: An iterative algorithm is used to solve the multi-period traffic assignment model of the highway. The path flow in the spatiotemporal topology obtained after convergence is mapped to the charging demand, and the spatiotemporal load distribution of each charging station on the highway is obtained.

[0012] A further improvement of this invention lies in that, in the first model building unit, considering the characteristics of long highway segments and large travel time spans, a multi-time-spatial network model of the highway is established based on the acquired data, including: Directed graph of single-time traffic network considering spatial characteristics Extending to the time dimension to form a spatiotemporal network diagram , where the node set Let S represent all nodes in the transportation network, and let S be the set of nodes. A subset, For connection The road set of the middle node It is an expanded node set. It is an extended road set; Specifically, in spatiotemporal networks, Expand into an extended node ,in For a period of time, The maximum time period within the research scope; the charging station set S is the node set. A subset, similarly extended to an extended charging station set. , No. Charging stations during certain hours ,in The original charging station node is a starting point and an ending point The original traffic road is expanded into an expanded road in each time period Meanwhile, a cross-time period virtual parking road of the node is established At this time, the vehicle can only pass in one direction along the time period growth direction.

[0013] The second model establishing unit is further improved, in which the main problem in the multi-time period traffic distribution model of the expressway performs space-time flow distribution in the whole traffic network, the objective function (1) is to minimize the total travel cost of all EV users under the user equilibrium condition, the formula (2) calculates the total travel cost, including the expressway section travel time cost, the service area charging station queuing waiting time cost and the charging cost, and the formula (3) is the user equilibrium condition. (1) (2) (3) Wherein, is the minimum travel cost of the OD pair ; is the travel demand of the OD pair ; is the total travel cost of the OD pair on the expanded space-time path , including the travel time, the queuing time and the charging cost; is the conversion coefficient of ''time-cost''; and respectively represent the traffic flow on the road d in the time period t and the queuing number of the charging station k; and respectively are the corresponding travel time and queuing time; the indication quantity and are used for representing the association of the path and the charging station / road; and respectively are the fast charging price and the fast charging quantity; is the traffic flow of the OD pair on the expanded space-time path ; is the feasible expanded path set of the OD pair ; is the OD pair set.

[0014] The second model establishing unit is further improved, in which the constraint condition of the main problem performing the space-time flow distribution in the whole traffic network is represented as follows: (4) (5) (6) (7) (8) (9) (10) Constraint (4) is the relationship between the space-time path and the travel demand, constraint (5) ensures the non-negativity of the flow; constraint (6) maps the three-dimensional space-time path flow to the two-dimensional space path flow; constraint (7)-(8) gives the queuing time and travel time functions; constraint (9)-(10) obtains the flow of each charging station and each road section; wherein, is the time period OD pair walks the space path flow; represents the extended path in the time period and the corresponding relationship of the space path ; and are the free-flow travel time and the inherent service time of the charging station; and are the road capacity and the service capacity of the charging station; and respectively indicate whether the path of the OD pair will reach the fast-charging station and the road ; is the feasible path set of the OD pair in the spatial dimension; is the discrete time period set; is the road section set; is the fast-charging station set.

[0015] Compared with the prior art, the present application has at least the following beneficial technical effects: ​The application provides a highway charging load simulation method and system based on multi-period traffic equilibrium. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0017] Figure 1 It is a whole flow chart of the method of the present application.

[0018] Figure 2 It is an 8-node ring road network structure diagram.

[0019] Figure 3 It is a space-time traffic network schematic diagram.

[0020] Figure 4 It is a charging load curve of each charging station per hour.

[0021] Figure 5 It is a total charging amount distribution diagram of each charging station throughout the day.

[0022] Figure 6 It is a charging load curve of each charging station during the holiday.

[0023] Figure 7 It is a total charging amount distribution diagram of each charging station throughout the day during the holiday.

[0024] Figure 8 It is a charging load curve of each charging station when the charging power is changed.

[0025] Figure 9 The total charging amount distribution diagram of each charging station throughout the day for the charging power change.

[0026] Figure 10 The structural block diagram of the system of the present application. DETAILED DESCRIPTION

[0027] In the following, only certain exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and the description are considered to be exemplary in nature rather than limiting.

[0028] In the description of the present application, it is to be understood that the terms "including" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0029] It should also be understood that the terms used in the present application specification are only for the purpose of describing particular embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0030] It should be further understood that the term "and / or" used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations thereof.

[0031] Various structural schematic diagrams according to the disclosed embodiments of the present application are shown in the drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for the purpose of clarity and certain details can be omitted. The shapes of various regions, layers shown in the diagrams and their relative sizes, positional relationships are only exemplary, and in actuality, they can be deviated due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes, relative positions can be additionally designed by those skilled in the art according to actual needs.

[0032] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0033] Embodiment 1 As Figure 1 shown, the highway charging load simulation method based on multi-period traffic equilibrium provided by the present application includes the following steps: Step one: Obtain data, including highway network topology parameters, charging station distribution parameters, user trip origin-destination demand data, and electric vehicle battery parameters; Step two: Based on the characteristics of long distance and large span of driving time of highway sections, combined with the obtained data, a multi-period time-space network model of highway is established, the spatial nodes of the original traffic network are expanded along the time dimension, and a time-space topology structure containing time-space driving arcs and virtual stopping arcs is constructed; Step three: Based on the established multi-period time-space network model of highway, a multi-period traffic distribution model of highway containing main problem and sub-problem is constructed based on traffic equilibrium principle, the main problem is to minimize the total travel cost of all users in the time-space topology structure to distribute time-space flow, and the sub-problem is to solve the optimal time-space path of single vehicle considering range anxiety in the time-space topology structure under given traffic state; Step four: The multi-period traffic distribution model of highway is solved by using iterative algorithm, the path flow in the converged time-space topology structure is mapped to charging demand, and the time-space load distribution of each charging station of highway is obtained.

[0034] In step two of the embodiment, a multi-period time-space network model of highway is established based on the characteristics of long distance and large span of driving time of highway sections, combined with the obtained data, including: The single-period traffic network directed graph considering spatial characteristics is expanded to time-space network graph in time dimension , wherein the node set represents all nodes in the traffic network, the charging station set S is a subset of the node set , the road set connects the nodes in the node set , the expanded node set is the expanded node set, and the expanded road set is the expanded road set. Specifically, in the time-space network, the node set is expanded to the expanded node set , wherein is the time period, and is the maximum time period in the research range; the charging station set S is a subset of the node set , and is expanded to the expanded charging station set , the charging station in the time period, wherein is the original charging station node; the original traffic road of the origin and destination is expanded to the expanded road of each time period, and the cross-period virtual stopping road of the same node is established, at this time, the vehicle can only pass through in one direction along the time period.

[0035] In step three of the embodiment, the main problem in the freeway multi-period traffic assignment model is to conduct the space-time flow distribution in the entire traffic network, and the objective function (1) is to minimize the total travel cost of all EV users under the user equilibrium condition; formula (2) calculates the total travel cost, including the freeway link travel time cost, service area charging station queuing waiting time cost and charging fee cost; formula (3) is the user equilibrium condition; (1) (2) (3) Wherein, is the minimum travel cost of OD pair ; is the travel demand of OD pair ; is the total travel cost of OD pair on the extended space-time path , including travel time, queuing time and charging fee; is the conversion coefficient of “time-cost”; and respectively represent the traffic flow on road d and the queuing number of charging station k in period t; and are the corresponding travel time and queuing time respectively; the indication and are used to represent the association of path and charging station / road; and are the fast charging price and fast charging quantity respectively; is the traffic flow of OD pair on the extended space-time path ; is the set of feasible extended paths of OD pair ; is the set of OD pairs.

[0036] In step three of the embodiment, the main problem is to conduct the space-time flow distribution in the entire traffic network, and the constraint condition is represented as follows: (4) (5) (6) (7) (8) (9) (10) Constraint (4) is the relationship between the space-time path and the travel demand, constraint (5) ensures the non-negativity of the flow, constraint (6) maps the three-dimensional space-time path flow to the two-dimensional space path flow, constraints (7)-(8) give the queuing time and travel time functions, and constraints (9)-(10) obtain the flow of each charging station and each road section. wherein, is the time period is the OD pair walks the space path flow; represents the extended path in the time period and the space path correspondence; and are the free-flow travel time and the inherent service time of the charging station; and are the road capacity and the service capacity of the charging station; and are the indicators of whether the path of the OD pair will reach the fast-charging station and the road ; is the set of feasible paths of the OD pair in the spatial dimension; is the set of discrete time periods; is the set of road sections; is the set of fast-charging stations.

[0037] In step three of the embodiment, the sub-problem considers the optimal path selection of users with range anxiety under the current traffic conditions given by the main problem, considering the long distance of the highway road section, and the mathematical model is as follows: (11) (12) (13) (14) (15) (16) (17) Formula (11) is an objective function for minimizing travel time, charging queuing time and fast charging cost; formula (12)-formula (17) are constraint conditions, wherein constraint condition (12) is an electric vehicle charging constraint; constraint condition (13) is a maximum charging power constraint; constraint condition (14) is a relaxation constraint of whether passing through a road section; constraint condition (15) is a user charging decision constraint; constraint condition (16) is an initial state of charge constraint; and constraint condition (17) is a user range anxiety constraint; wherein indicates an extended arc in a time period whether corresponding to a road arc ; represents an OD pair whether to select an arc ; is an auxiliary variable of whether to fast charge at a fast charging station , time ; indicates a location , time SoC; is a spatial distance of a road arc (u, v); is a unit distance energy consumption; is a relaxation variable, and is allowed to be non-zero only when the road arc is not selected; is a maximum fast charging power; is an initial SOC of an OD pair , and is set to ; represents a lower limit of SoC set due to range anxiety; is a set of road arcs of an extended network; is a set of all nodes with fast charging capability.

[0038] In step four of the embodiment, an iterative algorithm is used to solve the multi-period highway traffic assignment model, including: After obtaining the optimal flow distribution, the extended space-time path flow and the corresponding charging amount are used to calculate the charging amount of each charging station, as shown in formula (18), so as to determine the space-time distribution of the highway charging load; (18) wherein, represents an OD pair path in a time period an indication variable of whether to fast charge at a fast charging station ; is a charging station in a time period Total fast charging load.

[0039] Example 2 like Figure 1 As shown, the highway charging load simulation method based on multi-time period traffic equilibrium provided by the present invention includes the following steps: Step 1: Obtain highway network topology parameters, charging station distribution parameters, user travel origin and destination demand data, and electric vehicle battery parameters.

[0040] This embodiment constructs a ring-shaped highway corridor network consisting of 8 nodes for simulation research, such as... Figure 2 As shown in the diagram, each adjacent node is bidirectionally connected, and the network comprises 16 directed road segments. To refine the simulation of load fluctuations, a 24-hour day is divided into 96 discrete time periods, each with a time step of 15 minutes. Charging infrastructure is deployed at four even-numbered nodes on the ring road (nodes 2, 4, 6, and 8), each equipped with 20 DC fast charging piles, each with a rated power of 180kW. On the demand side, a typical day is designed with 30 origin-destination (OD) pairs, resulting in a total travel demand of 3480 trips. Electric vehicle users follow random departure times and generate charging demand when their battery power is low.

[0041] Step 2: In view of the characteristics of long distances and large travel time spans of highways, a multi-time spatiotemporal network model of highways is established. The spatial nodes of the original traffic network are extended along the time dimension to construct a spatiotemporal topology structure that includes spatiotemporal travel arcs and virtual stop arcs.

[0042] Based on the spatial topology of the transportation network, it is expanded into a spatiotemporal transportation network diagram, such as... Figure 3 As shown in the diagram. Solid lines represent extension roads connecting extension nodes within the same time period, while dashed lines represent extension roads connecting extension nodes at different time periods. The longitude-latitude coordinates in the diagram jointly describe the spatial characteristics of the transportation network, while the time period coordinates describe the temporal characteristics of the transportation network. That is, the transportation network described by the longitude-latitude coordinates at any given time period is the... Figure 2 The original transportation network topology is shown.

[0043] Step 3: Based on the principle of traffic equilibrium, construct a multi-time traffic assignment model for highways that includes a main problem and sub-problems. The main problem is to allocate spatiotemporal traffic with the goal of minimizing the total travel cost for all users. The sub-problems are to solve for the optimal spatiotemporal path for a single vehicle under given traffic conditions, taking into account mileage anxiety.

[0044] In this step, a bi-level optimization model is established. The master problem is located at the system level, aiming to minimize the total cost including travel time, charging station queuing waiting time and charging cost, and update the actual travel time of each road section and the queuing time of each charging station according to the traffic flow of the whole network; especially when the traffic flow of a certain charging station exceeds the service capacity at a certain time period, the model simulates the congestion effect by increasing the queuing time cost. The sub-problem is located at the user level, which searches for the optimal path for each OD pair under the current road network state given by the master problem. The mileage anxiety constraint is explicitly introduced in the sub-problem, which requires the remaining electric quantity of the vehicle on any travel section of the highway to always be higher than the user's set safety threshold (such as 20%), forcing the user to plan the charging stop point in advance according to the electric quantity state in long-distance travel.

[0045] Step four: use an iterative algorithm to solve the multi-period highway traffic assignment model in step three, map the converged space-time path flow to charging demand, and obtain the space-time load distribution of each charging station on the highway.

[0046] This embodiment uses column generation algorithm to solve the master and sub-problems until the traffic flow distribution converges. The simulation results are shown in Figure 4 and Figure 5 The load curve of each charging station presents a significant "double peak" characteristic throughout the day, i.e. the morning peak from 8-11 am and the afternoon to evening peak from 16-19 pm, which is highly consistent with the tidal rules of highway traffic during the day. At the same time, the load of different stations is significantly different, for example, the total charging quantity of charging station 8 is the highest and that of charging station 2 is the lowest throughout the day, reflecting the spatial imbalance of load, which is also consistent with the characteristics of actual highway network.

[0047] To analyze the impact of increased travel during holidays on highway charging load, the demand side is expanded from 30 OD pairs and 3480 vehicle trips to 70 OD pairs and 5680 vehicle trips. The simulation results are shown in Figure 6 and Figure 7 It can be seen that the charging load of each station during the holiday is significantly increased at noon and in the evening, and the total charging quantity of charging station 4 increases more, and the distribution of total charging quantity throughout the day also tilts towards key stations, reflecting that the holiday peak puts higher requirements on the scale and layout of charging facilities.

[0048] To analyze the impact of changing the charging power of charging piles on highway charging load, station 6 and station 8 are configured as 150kW×20 guns, and station 2 and station 4 are still 180kW×20 guns. The simulation results are shown in Figure 8 and Figure 9As shown in the figure, the daily charging amount of stations 6 and 8 is reduced, while the daily charging amount of stations 2 and 4 is increased, especially the largest increase is at station 4, which indicates that under the condition of unchanged total demand, the higher power station attracts part of the vehicles to charge, resulting in the transfer and redistribution of charging load in space and time.

[0049] Embodiment 3 As Figure 10 shown, the application provides a highway charging load simulation system based on multi-period traffic equilibrium, comprising: a data acquisition unit: acquiring data, including highway road network topology parameters, charging station distribution parameters, user trip origin-destination demand data, and electric vehicle battery parameters; a first model establishment unit: in view of the characteristics of long distance and large travel time span of highway sections, combined with the acquired data, a highway multi-period space-time network model is established, the spatial nodes of the original traffic network are expanded along the time dimension, and a space-time topology structure containing space-time travel arcs and virtual stopping arcs is constructed; a second model establishment unit: based on the established highway multi-period space-time network model, a highway multi-period traffic distribution model containing main problems and sub-problems is constructed based on the traffic equilibrium principle, the main problem is to minimize the total travel cost of all users in the space-time topology structure to distribute space-time flow, and the sub-problem is to solve the optimal space-time path of a single vehicle considering range anxiety in the space-time topology structure under a given traffic state; a model solving unit: an iterative algorithm is used to solve the highway multi-period traffic distribution model, the path flow in the converged space-time topology structure is mapped to the charging demand, and the space-time load distribution of each charging station on the highway is obtained.

[0050] In the first model establishment unit of this embodiment, in view of the characteristics of long distance and large travel time span of highway sections, combined with the acquired data, a highway multi-period space-time network model is established, comprising: a single-period traffic network directed graph considering spatial characteristics is expanded to a space-time network graph along the time dimension , wherein the node set represents all nodes in the traffic network, the charging station set S is a subset of the node set , is a road set connecting the nodes in , and is an expanded node set , and is an expanded road set; Specifically, in the space-time network, the expanded node is expanded to the expanded node , whereinTo study the maximum period in the range; charging station set S as a subset of node set is extended to the extended charging station set , the charging station of the period, where is the original charging station node; the starting and ending points are The original traffic road is expanded to the extended road of each period , while the cross-period virtual parking road of the same node is established At this time, the vehicle can only pass in one direction along the time period growth.

[0051] In the second model establishment unit of the embodiment, the main problem in the multi-period highway traffic assignment model is to perform space-time flow distribution in the entire traffic network, and the objective function (1) is to minimize the total travel cost of all EV users under the user equilibrium condition; Equation (2) calculates the total travel cost, including highway link travel time cost, service area charging station queuing waiting time cost and charging fee cost; Equation (3) is the user equilibrium condition; (1) (2) (3) where, is the minimum travel cost of OD pair ; is the travel demand of OD pair ; is the total travel cost of OD pair on the extended space-time path , including travel time, queuing time and charging fee; is the conversion coefficient of "time-cost"; and respectively represent the traffic flow on road d in period t and the queuing number of charging station k; and are the corresponding travel time and queuing time respectively; the indication and are used to represent the association of the path and the charging station / road; and are the fast charging price and the fast charging amount respectively; is the traffic flow of OD pair on the extended space-time path ; is the set of feasible extended paths of OD pair ; is the set of OD pairs.

[0052] In the second model establishing unit of the embodiment, the main problem is to perform space-time traffic distribution in the whole traffic network, and constraint conditions are expressed as follows: (4) (5) (6) (7) (8) (9) (10) Constraint condition (4) is the relationship between space-time paths and travel demand, constraint condition (5) guarantees that the traffic is non-negative; constraint condition (6) maps three-dimensional space-time path traffic to two-dimensional space path traffic; constraint conditions (7)-(8) give functions of queuing time and travel time; constraint conditions (9)-(10) obtain the traffic of each charging station and each road section; wherein, is a time period is an OD pair is the traffic of a space path ; represents an extended path in a time period and a space path ; and are free-flow travel time and inherent service time of a charging station; and are road capacity and service capacity of a charging station; and are indication quantities of whether a path of an OD pair will reach a fast-charging station and a road ; is a feasible path set of an OD pair in a space dimension; is a discrete time period set; is a road section set; is a fast-charging station set.

[0053] The foregoing merely illustrates the principles of the application and application of its leading features. This application is not limited to the exact details shown above and described herein, and obvious modifications will occur to those skilled in the art upon reading the foregoing description. Therefore, the scope of the application is not to be determined by the specific examples shown above, but only by the claims below. Any reference signs in the claims should not be construed as limiting the scope of the claims.

[0054] Furthermore, it should be appreciated that although the present specification describes particular embodiments, each of which contain only a single independent technology, the specification makes no implication that the application is limited to such. Rather, the specification using such terms as "in one embodiment" or "in an embodiment" is to establish as many independent embodiments as can be explicitly or implicitly disclosed. The mere inclusion of such terms, however, does not limit those embodiments but rather, the broadest possible interpretation of the specification is intended. The specification is not intended to be limited to the embodiments described herein, but rather the claims should be accorded the full scope consistent with the claims for which the support is recognized in the art. No language in the specification should be construed as indicating any non-claimed element as essential. The foregoing merely illustrates the principles of the application and application of its leading features. This application is not limited to the exact details shown above and described herein, and obvious modifications will occur to those skilled in the art upon reading the foregoing description. Therefore, the scope of the application is not to be determined by the specific examples shown above, but only by the claims below. Any reference signs in the claims should not be construed as limiting the scope of the claims.

[0054] Furthermore, it should be appreciated that although the present specification describes particular embodiments, each of which contain only a single independent technology, the specification makes no implication that the application is limited to such. Rather, the specification using such terms as "in one embodiment" or "in an embodiment" is to establish as many independent embodiments as can be explicitly or implicitly disclosed. The mere inclusion of such terms, however, does not limit those embodiments but rather, the broadest possible interpretation of the specification is intended. The specification is not intended to be limited to the embodiments described herein, but rather the claims should be accorded the full scope consistent with the claims for which the support is recognized in the art. No language in the specification should be construed as indicating any non-claimed element as essential.

Claims

1. A method for simulating highway charging load based on multi-time period traffic equilibrium, characterized in that, Includes the following steps: Step 1: Obtain data, including highway network topology parameters, charging station distribution parameters, user travel origin and destination demand data, and electric vehicle battery parameters; Step 2: Considering the characteristics of long distances and large travel time spans on highways, and based on the acquired data, establish a multi-time-spatial-temporal network model for highways. Extend the spatial nodes of the original traffic network along the time dimension to construct a spatiotemporal topology that includes spatiotemporal travel arcs and virtual stop arcs. Step 3: Based on the established multi-period spatiotemporal network model of the highway, construct a multi-period traffic assignment model of the highway that includes a main problem and sub-problems based on the principle of traffic equilibrium. The main problem is to allocate spatiotemporal traffic with the goal of minimizing the total travel cost of all users in the spatiotemporal topology. The sub-problems are to solve the optimal spatiotemporal path for a single vehicle in the spatiotemporal topology considering mileage anxiety under a given traffic state. Step 4: Use an iterative algorithm to solve the multi-period traffic assignment model of the highway, map the path traffic in the converged spatiotemporal topology to charging demand, and obtain the spatiotemporal load distribution of each charging station on the highway.

2. The method for simulating highway charging load based on multi-time period traffic equilibrium according to claim 1, characterized in that, In step two, considering the characteristics of long highway segments and large travel time spans, a multi-time-spatial network model of the highway is established based on the acquired data, including: Directed graph of single-time traffic network considering spatial characteristics Extending to the time dimension to form a spatiotemporal network diagram , where the node set Let S represent all nodes in the transportation network, and let S be the set of nodes. A subset, For connection The road set of the middle node It is an expanded node set. It is an extended road set; Specifically, in spatiotemporal networks, Expand into an extended node ,in For a period of time, The maximum time period within the research scope; the charging station set S is the node set. A subset, similarly extended to an extended charging station set. , No. Charging stations during certain hours ,in The original charging station node; the start and end points are respectively The original traffic roads were expanded into extended roads for different time periods. Simultaneously, establish virtual stopping routes for the same node across time periods. At this time, vehicles can only pass in one direction along the direction of time increase.

3. The method for simulating highway charging load based on multi-time period traffic equilibrium according to claim 2, characterized in that, In step three, the main problem in the multi-period traffic assignment model of the highway is to allocate the spatiotemporal flow in the entire traffic network. Its objective function (1) is to minimize the total travel cost of all EV users under the user equilibrium condition; Equation (2) calculates the total travel cost, including the travel time cost of the highway section, the waiting time cost of the service area charging station, and the charging cost; Equation (3) is the user equilibrium condition. (1) (2) (3) in, For OD The minimum travel cost; Is OD to The travel demand; Is OD to In expanding the spatiotemporal path The total cost of travel includes travel time, waiting time, and charging costs; This is the conversion factor for "time-cost". and These represent the traffic flow on road d during time period t and the queue length at charging station k, respectively. and These correspond to the travel time and queuing time, respectively; [Indicator] and Used to characterize the association between a path and a charging station / road; and These are the fast charging price and fast charging capacity, respectively. For OD In expanding the spatiotemporal path Traffic flow on the road; For OD The set of feasible expansion paths; For OD pairs.

4. The method for simulating highway charging load based on multi-time period traffic equilibrium according to claim 3, characterized in that, In step three, the main problem involves the spatiotemporal flow allocation across the entire transportation network, with the following constraints: (4) (5) (6) (7) (8) (9) (10) Constraint (4) is the relationship between spatiotemporal path and travel demand; constraint (5) ensures that the flow is non-negative; constraint (6) maps the three-dimensional spatiotemporal path flow to the two-dimensional spatial path flow; constraints (7)–(8) give the queuing time and travel time functions; constraints (9)–(10) obtain the flow of each charging station and each road segment. in, Time period Internal OD Take a spatial path Traffic; Indicates extended path In time period Spatial path The correspondence; and This refers to the free-flow driving time and the inherent service time of the charging station. and For road capacity and charging station service capacity; and OD pairs respectively path Will it reach a fast charging station? With roads The indicated quantity; For OD The set of feasible paths in the spatial dimension; It is a set of discrete time periods; For road segment sets; For fast charging stations.

5. The method for simulating highway charging load based on multi-time period traffic equilibrium according to claim 4, characterized in that, In step three, considering the long distances of highway sections, the sub-problem, under the current traffic conditions given by the main problem, performs the optimal path selection for users, taking into account mileage anxiety. Its mathematical model is as follows: (11) (12) (13) (14) (15) (16) (17) Equation (11) is the objective function, which minimizes driving time, charging queuing time, and fast charging cost; Equations (12)-(17) are constraints, where constraint (12) is the electric vehicle charging constraint; constraint (13) is the maximum charging power constraint; constraint (14) is the relaxation constraint for whether the road segment has been passed; constraint (15) is the user charging decision constraint; constraint (16) is the initial charge constraint; and constraint (17) is the user range anxiety constraint. in Indicator arc During the period Does it correspond to a road arc? ; Indicates OD pair Do you want to select an arc? ; Whether at a fast charging station ,time The amount of auxiliary power used for fast charging is controlled by Big-M logic; Indicate location ,time SoC; Let (u,v) be the spatial distance of the road arc. Energy consumption per unit distance; It is a slack variable, only on the road arc. Non-zero values ​​are allowed when not selected; This is the maximum power for fast charging; For OD The initial SOC is set to ; This indicates the lower limit of the SoC set due to range anxiety; To expand the network's arc set; This is a set of all nodes with fast charging capabilities.

6. The method for simulating highway charging load based on multi-time period traffic equilibrium according to claim 5, characterized in that, Step four involves using an iterative algorithm to solve the multi-time-period traffic assignment model for highways, including: After obtaining the optimal traffic distribution, the extended spatiotemporal path flow is utilized. And the corresponding charging amount, calculate the charging amount of each charging station, as shown in Equation (18), so as to determine the spatiotemporal distribution of the charging load of the highway. (18) in, Indicates OD pair During the period path Is it at a fast charging station? Indicator value for fast charging; For fast charging stations During the period Total fast charging load.

7. A highway charging load simulation system based on multi-time period traffic equilibrium, characterized in that, include: Data acquisition unit: Acquires data, including highway network topology parameters, charging station distribution parameters, user travel origin and destination demand data, and electric vehicle battery parameters; The first model building unit: In view of the characteristics of long distances and long travel time spans of highways, and in combination with the acquired data, a multi-time spatiotemporal network model of highways is established. The spatial nodes of the original traffic network are extended along the time dimension to construct a spatiotemporal topology structure that includes spatiotemporal travel arcs and virtual stop arcs. The second model building unit: Based on the established multi-period spatiotemporal network model of the highway, a multi-period traffic allocation model of the highway containing a main problem and sub-problems is constructed based on the principle of traffic balance. The main problem is to allocate spatiotemporal traffic with the goal of minimizing the total travel cost of all users in the spatiotemporal topology. The sub-problems are to solve the optimal spatiotemporal path of a single vehicle in the spatiotemporal topology considering mileage anxiety under a given traffic condition. Model Solving Unit: An iterative algorithm is used to solve the multi-period traffic assignment model of the highway. The path flow in the spatiotemporal topology obtained after convergence is mapped to the charging demand, and the spatiotemporal load distribution of each charging station on the highway is obtained.

8. The highway charging load simulation system based on multi-time period traffic equilibrium according to claim 7, characterized in that, In the first model building unit, considering the characteristics of long highway segments and large travel time spans, a multi-time-spatial network model of the highway is established based on the acquired data, including: Directed graph of single-time traffic network considering spatial characteristics Extending to the time dimension to form a spatiotemporal network diagram , where the node set Let S represent all nodes in the transportation network, and let S be the set of nodes. A subset, For connection The road set of the middle node It is an expanded node set. It is an extended road set; Specifically, in spatiotemporal networks, Expand into an extended node ,in For a period of time, The maximum time period within the research scope; the charging station set S is the node set. A subset, similarly extended to an extended charging station set. , No. Charging stations during certain hours ,in The original charging station node; the start and end points are respectively The original traffic roads were expanded into extended roads for different time periods. Simultaneously, establish virtual stopping routes for the same node across time periods. At this time, vehicles can only pass in one direction along the direction of time increase.

9. The highway charging load simulation system based on multi-time period traffic equilibrium according to claim 8, characterized in that, In the second model building unit, the main problem in the highway multi-time traffic assignment model is to allocate the spatiotemporal flow in the entire traffic network. Its objective function (1) is to minimize the total travel cost of all EV users under the user equilibrium condition; Equation (2) calculates the total travel cost, including the highway section driving time cost, the service area charging station queuing time cost and the charging cost; Equation (3) is the user equilibrium condition. (1) (2) (3) in, For OD The minimum travel cost; Is OD to The travel demand; Is OD to In expanding the spatiotemporal path The total cost of travel includes travel time, waiting time, and charging costs; This is the conversion factor for "time-cost". and These represent the traffic flow on road d during time period t and the queue length at charging station k, respectively. and These correspond to the travel time and queuing time, respectively; [Indicator] and Used to characterize the association between a path and a charging station / road; and These are the fast charging price and fast charging capacity, respectively. For OD In expanding the spatiotemporal path Traffic flow on the road; For OD The set of feasible expansion paths; For OD pairs.

10. The highway charging load simulation system based on multi-time period traffic equilibrium according to claim 9, characterized in that... (4) (5) (6) (7) (8) (9) (10) Constraint (4) is the relationship between spatiotemporal path and travel demand; constraint (5) ensures that the flow is non-negative; constraint (6) maps the three-dimensional spatiotemporal path flow to the two-dimensional spatial path flow; constraints (7)–(8) give the queuing time and travel time functions; constraints (9)–(10) obtain the flow of each charging station and each road segment. in, Time period Internal OD Take a spatial path Traffic; Indicates extended path In time period Spatial path The correspondence; and This refers to the free-flow driving time and the inherent service time of the charging station. and For road capacity and charging station service capacity; and OD pairs respectively path Will it reach a fast charging station? With roads The indicated quantity; For OD The set of feasible paths in the spatial dimension; It is a set of discrete time periods; For road segment sets; For fast charging stations.

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