A method and system for constructing and solving an electric traffic flow queuing-battery model

Abstract

The application provides a construction and solution method and system of an electric traffic flow queuing-battery model, relates to the technical field of intelligent traffic, and comprises the following steps: acquiring traffic network basic parameters, electric vehicle performance parameters and commuting flow data; performing traffic network framework modeling according to the traffic network basic parameters to obtain a traffic network structure; performing electric quantity evolution modeling according to the traffic network structure and the electric vehicle performance parameters to obtain an electric vehicle network model based on a road section; performing traffic flow parameter modeling according to the electric vehicle network model to obtain road section supply-demand parameters; performing network flow distribution modeling according to the road section supply-demand parameters and the commuting flow data, calculating flow transfer between upstream and downstream road sections through a node flow function, and constructing a queuing-battery model; and performing numerical solution according to the queuing-battery model to obtain complete state results of the electric traffic system. The application takes into account the calculation efficiency and accuracy requirements of large-scale electric traffic system modeling.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and more specifically, to a method and system for constructing and solving an electric traffic flow queuing-battery model. Background Technology

[0002] With the rapid popularization of electric vehicles and the accelerating trend of road traffic electrification, the planning and operation of electric transportation systems face challenges such as range anxiety, insufficient charging infrastructure, and increased energy consumption due to congestion. There is an urgent need to develop traffic flow models that can accurately track energy evolution while maintaining computational efficiency. Existing technologies mainly revolve around two approaches: individual modeling and aggregate modeling. Individual models characterize energy status by refining individual vehicle behavior, but suffer from high computational complexity and poor stability, making them unsuitable for large-scale systems. Aggregate models, while improving computational efficiency through group abstraction (e.g., single-commodity spatiotemporal battery network flow models achieve complexity independent of fleet size through event flow modeling), cannot characterize the nonlinear impact of endogenous congestion propagation on energy consumption, resulting in insufficient accuracy in dynamic traffic environments. Both methods struggle to balance computational efficiency and accuracy requirements, hindering the reliability of optimization decisions for electric transportation systems.

[0003] Based on the shortcomings of the existing technologies, there is an urgent need for a method and system for constructing and solving an electric traffic flow queuing-battery model. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for constructing and solving an electric traffic flow queuing-battery model, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a method for constructing and solving an electric traffic flow queuing-battery model, including:

[0006] Acquire basic parameters of the transportation network, performance parameters of electric vehicles, and commuter traffic data;

[0007] Based on the basic parameters of the traffic network, a traffic network framework model is performed to obtain the traffic network structure.

[0008] Based on the traffic network structure and the electric vehicle performance parameters, a power evolution model is performed. By establishing a dynamic evolution relationship between power status and time, and combining the correlation mechanism between vehicle speed and power consumption, a road segment-based electric vehicle network model is obtained.

[0009] Traffic flow parameters are modeled based on the electric vehicle network model. By defining the mathematical relationship between road segment capacity and congestion status, the influence of traffic flow density on vehicle speed is analyzed to obtain road segment supply and demand parameters.

[0010] Based on the supply and demand parameters of the road segment and the commuting traffic data, network traffic allocation modeling is performed. The traffic transfer between upstream and downstream road segments is calculated through the node flow function. Based on the path ratio and the power level ratio, traffic for different customer categories is allocated to construct the queue-battery model.

[0011] The complete state result of the electric transportation system is obtained by numerically solving the queuing-battery model.

[0012] Secondly, this application also provides a system for constructing and solving an electric traffic flow queuing-battery model, including:

[0013] The acquisition module is used to acquire basic parameters of the transportation network, performance parameters of electric vehicles, and commuter traffic data.

[0014] The first modeling module is used to model the traffic network framework based on the basic parameters of the traffic network to obtain the traffic network structure.

[0015] The second modeling module is used to model the power evolution based on the traffic network structure and the electric vehicle performance parameters. By establishing a dynamic evolution relationship between power status and time, and combining the correlation mechanism between vehicle speed and power consumption, a road segment-based electric vehicle network model is obtained.

[0016] The third modeling module is used to model traffic flow parameters based on the electric vehicle network model. By defining the mathematical relationship between road segment capacity and congestion status, it analyzes the influence of traffic flow density on vehicle speed and obtains road segment supply and demand parameters.

[0017] The fourth modeling module is used to model network traffic allocation based on the supply and demand parameters of the road segment and the commuting traffic data. It calculates the traffic transfer between upstream and downstream road segments through the node flow function, and allocates traffic for different customer categories based on the path ratio and the power level ratio to construct a queue-battery model.

[0018] The output module is used to obtain the complete state results of the electric transportation system by numerically solving the queue-battery model.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention constructs a modeling framework that combines multiple types of customer flow queuing networks with partial differential equations of electric vehicle power state, and realizes the coupled calculation of congestion propagation and power consumption based on node flow functions. Under the premise of ensuring that the computational complexity is independent of the fleet size, it effectively characterizes the nonlinear impact of endogenous congestion propagation on the evolution of electric vehicle power consumption, thus taking into account the computational efficiency and accuracy requirements of large-scale electric transportation system modeling. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the construction and solution method of an electric traffic flow queuing-battery model as described in an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the structure of a system for constructing and solving an electric traffic flow queuing-battery model as described in an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of a traffic grid network;

[0025] Figure 4 A schematic diagram illustrating the relationship between vehicle speed and power consumption rate;

[0026] Figure 5 A schematic diagram illustrating the relationship between vehicle speed and the number of vehicles;

[0027] Figure 6 A diagram illustrating the relationship between traffic flow and the number of vehicles;

[0028] Figure 7 This is a schematic diagram of linear nodes;

[0029] Figure 8 This is a schematic diagram of the traffic splitting node;

[0030] Figure 9 This is a schematic diagram of the merging node;

[0031] Figure 10 This is a schematic diagram of a common node;

[0032] Figure 11 This is a schematic diagram of a diversion-merging road network;

[0033] Figure 12 This is a schematic diagram of the simulation results for road segment 1;

[0034] Figure 13 This is a schematic diagram of the simulation results for road segment 2;

[0035] Figure 14 This is a schematic diagram of the simulation results for road segment 3;

[0036] Figure 15 This is a schematic diagram of the simulation results for road segment 4;

[0037] Figure 16This is a schematic diagram of the simulation results for road segment 5;

[0038] Figure 17 This is a schematic diagram of the simulation results for the entire road network;

[0039] Figure 18 A comparison chart of the queuing-battery model and the agent simulation model under different OD requirements.

[0040] The diagram is labeled as follows: 901, Acquisition Module; 902, First Modeling Module; 903, Second Modeling Module; 904, Third Modeling Module; 905, Fourth Modeling Module; 906, Output Module. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0042] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0043] Example 1:

[0044] This embodiment provides a method for constructing and solving an electric traffic flow queuing-battery model.

[0045] See Figure 1 The figure shows that the method includes steps S100 to S600.

[0046] Step S100: Obtain basic traffic network parameters, electric vehicle performance parameters, and commuter traffic data;

[0047] Understandably, in step S100, basic parameters of the traffic network are obtained through the monitoring system and historical database of the traffic infrastructure, including the geometric attributes of road segments (such as length and number of lanes), node topological relationships (such as intersection type and connection direction), and distribution of origin and destination points; performance parameters, such as battery capacity, energy efficiency curves, and typical driving range, are extracted from the technical specifications and measured data provided by electric vehicle manufacturers; commuter traffic data includes vehicle arrival rates, vehicle type classification ratios, and initial battery distribution at different times. The integration of these multi-source data provides a realistic input basis for the model.

[0048] Step S200: Model the traffic network framework based on the basic parameters of the traffic network to obtain the traffic network structure;

[0049] It should be noted that step S200, based on the acquired basic parameters of the traffic network, constructs a traffic network framework by defining the spatial mapping relationship between road segments and nodes, abstracting the complex road network structure into a mathematical topological model. The distinction of node types (such as ordinary intersections, diverging or merging points) can naturally reflect the convergence and dispersion characteristics of traffic flow in actual roads, laying a structural foundation for subsequent traffic allocation.

[0050] Step S300: Based on the traffic network structure and electric vehicle performance parameters, perform power evolution modeling. By establishing the dynamic evolution relationship of power status over time and combining the correlation mechanism between vehicle speed and power consumption, obtain a road segment-based electric vehicle network model.

[0051] Understandably, step S300 combines the traffic network structure and electric vehicle performance parameters to establish the dynamic evolution relationship of the battery status over time. By introducing the correlation mechanism between vehicle speed and battery consumption, it portrays the nonlinear phenomenon of accelerated battery consumption when vehicle speed decreases due to congestion, enabling the model to accurately reflect the two-way coupling effect between battery consumption and traffic conditions in actual traffic.

[0052] Step S400: Model traffic flow parameters based on the electric vehicle network model. By defining the mathematical relationship between road segment capacity and congestion status, analyze the influence of traffic flow density on vehicle speed to obtain road segment supply and demand parameters.

[0053] It should be noted that step S400 is based on an electric vehicle network model. By defining the mathematical relationship between road segment capacity and congestion status, it analyzes the influence of traffic flow density on vehicle speed, derives the demand and supply capacity of road segments under different congestion levels, and thus quantifies the constraints of dynamic changes in traffic flow on system performance.

[0054] Step S500: Based on the supply and demand parameters of the road segment and commuter traffic data, network traffic allocation model is performed. The traffic transfer between upstream and downstream road segments is calculated through the node flow function. Based on the path ratio and the power level ratio, traffic for different customer categories is allocated to construct the queue-battery model.

[0055] Understandably, step S500 uses road segment supply and demand parameters and commuter traffic data to calculate the traffic transfer between upstream and downstream road segments through node flow functions, and allocates traffic to different customer categories based on path selection ratio and power level ratio, thereby realizing the collaborative modeling of congestion propagation and power consumption, and finally constructing a queuing-battery model that can simultaneously track traffic flow and power status.

[0056] Step S600: Numerical solution is performed based on the queuing-battery model to obtain the complete state result of the electric transportation system.

[0057] It should be noted that step S600 performs numerical solution on the constructed queuing-battery model and obtains the complete state results of the electric transportation system through iterative calculation, including key indicators such as traffic flow distribution, average power level and total system energy consumption of each road segment, providing quantitative decision support for traffic planning and management.

[0058] Further, step S200 includes steps S210 to S230.

[0059] Step S210: Model the network topology based on the basic parameters of the traffic network. By defining the spatial distribution relationship of the road segment set, node set, starting point set, and ending point set, the basic network topology framework is obtained.

[0060] Step S220: Model the node connection relationship based on the basic network topology framework. By establishing the mapping relationship between the upstream road segment set and the downstream road segment set at the nodes, and defining the connection rules of the diverging nodes, merging nodes and ordinary nodes, the node connection network is obtained.

[0061] Step S230: Model the path category mapping based on the node connection network. By establishing the correspondence between customer categories and paths, and defining the set of customer categories that are allowed to pass on each road segment, the traffic network structure is obtained.

[0062] Specifically, for convenience, this embodiment focuses on one-way traffic networks, but this does not affect the applicability of the method to two-way traffic networks. Figure 3 Taking the grid network shown as an example, the sets of unidirectional road segments and nodes (intersections of multiple road segments) are respectively composed of... and Indicate. Let. and They represent the starting point ( Figure 3The set of the top left and bottom right nodes and the endpoint ( Figure 3 (Lower left and upper right corner nodes). If the road segment Located at node Upstream of the node It is a section of road The end node is denoted as If the road section Located at node Downstream, then the node It is a section of road The starting node is denoted as .node The set of upstream road segments is represented as The set of its downstream road segments is denoted as If for any ,have So, the road section It is the starting section (e.g.) Figure 3 (the dotted line segment in the middle); if for any have So, the road section It is the end section (e.g.) Figure 3 (The dashed line segment in the text). Use respectively... and This represents the set of starting and ending road segments. This embodiment uses Represents the set of general road segments, where ;use Denotes a general set of nodes, where .for Its length is used In a transportation system, vehicles can be classified into different customer categories based on their attributes (such as route and destination). The set of customer categories in the entire network is denoted as [the set of customer categories]. We use Indicates customer Passed through the section of road Passing through the section of road The set of customer categories is represented as ,Right now Therefore, a one-way traffic network can be represented as... .

[0063] Further, step S300 includes steps S310 to S330.

[0064] Step S310: Perform power status classification modeling based on the traffic network structure. By modeling the density distribution of vehicles with different power levels on the road segment, establish the correspondence between power level and vehicle distribution, and obtain the vehicle density distribution of the road segment based on power level.

[0065] Step S320: Dynamically model the power consumption based on the electric vehicle performance parameters. By establishing a nonlinear functional relationship between vehicle speed and power consumption rate, and considering the impact mechanism of vehicle speed change on power consumption under traffic congestion, dynamic power consumption parameters are obtained.

[0066] Step S330: Model the conservation relationship based on the vehicle density distribution and dynamic power consumption parameters of the road segment. By establishing a partial differential equation for the change of power state over time and combining it with the flow conservation condition at the node, the electric vehicle network model is obtained.

[0067] Specifically, for transportation networks ,use express Time and Section The battery level is Vehicle density, using and They represent Time and Section The battery level is Inflow and outflow. (Using...) This represents the set of electric vehicle battery levels. For road segments... Customer categories ,use Indicates the battery level as The average vehicle density of cars, using and These represent the battery level as follows: The inflow and outflow of vehicles. Therefore, for road segments... We have: , and Similarly, using and express Time and Section The battery level is The total number of vehicles and the number of customer categories. Then there are... and .

[0068] Electric vehicles Time and Section Power consumption rate With the vehicle speed at that moment There exists a relationship as shown in equation (1).

[0069]

[0070]

[0071] According to equation (1b), electric vehicles in Power consumption rate at any time With the speed at the time This is relevant, therefore a graph showing the relationship between battery consumption rate and vehicle speed can be plotted, such as... Figure 4 As shown. From Figure 4 It can be seen that the relationship between vehicle speed and energy consumption rate is not a simple linear one. When the vehicle speed is extremely low, the energy consumption rate is also low, due to severe traffic congestion causing the vehicle to almost come to a standstill. When the speed increases to 5 km / h, the energy consumption rate reaches a local peak, reflecting the high energy consumption caused by frequent starts and stops in congested conditions. As the speed increases to 27 km / h, the energy consumption rate drops to its lowest point because the air resistance is low and the electric motor efficiency is in its optimal range, resulting in minimal energy consumption. However, when the speed increases to a free-flow speed of 60 km / h, the energy consumption rate reaches its maximum. At this point, air resistance increases significantly, and the electric motor generates more heat and power loss under high load, thus requiring more energy to maintain this speed.

[0072] Based on road segment Based on the conservation of flow, the traffic network power system, which includes the electric vehicle's charge, can be derived, as shown below:

[0073]

[0074]

[0075] in, Represented as Time and Section The speed of the car on the road, This represents the derivative with respect to time. By deriving equations (2a) and (2b), we can obtain equations (2c) and (2d).

[0076]

[0077]

[0078] The first term of equations (2c) and (2d) represents the partial derivative with respect to the electric charge; the second term of equations (2c) and (2d) represents the partial derivative with respect to time.

[0079] In addition, according to the nodes The flow is conserved at the point, so we get:

[0080]

[0081]

[0082] Then, equations (2) and (3) together constitute a general road segment-based electric vehicle network model.

[0083] Further, step S400 includes steps S410 to S430.

[0084] Step S410: Model road capacity based on electric vehicle network model. By establishing a unimodal function relationship between traffic flow and density and determining the critical density point corresponding to the maximum capacity, the basic road map model is obtained.

[0085] Step S420: Model the speed-density relationship based on the basic road map model, use an exponential function to describe the law of speed changing with density, and use measured data from multiple observation points to fit the model parameters to obtain the quantitative relationship between vehicle speed and density.

[0086] Step S430: Model the supply and demand function based on the quantitative relationship between vehicle speed and density. Define the demand function to reflect the maximum outflow capacity of the road segment under smooth conditions, define the supply function to reflect the maximum receiving capacity of the road segment under congested conditions, and divide different traffic states based on the critical density point to obtain the road segment supply and demand parameters.

[0087] Specifically, assuming road segments in the transportation network Corresponding basic road map Among them, the road section density , It is a section of road The blocking density. Furthermore... It is about The single-peak function, the traffic capacity of the road segment. At key density Where it is obtained, that is, .

[0088] Different basic diagrams can be obtained based on different velocity models. Preferably, this embodiment uses an exponential model, given by equation (4a). This model requires three standard points. To calibrate its parameters and . and It is a section of road The number of vehicles on board and The number of vehicles is and The average driving speed at that time.

[0089]

[0090]

[0091]

[0092] Once the velocity model is given, the basic road map can be given by equation (4d):

[0093]

[0094] Assuming a road segment is 1km long and the free-flow speed is 60km / h, It is 143 veh / km. Three standard points are taken as follows: We can plot velocity curves and basic graphs, such as... Figure 5 and Figure 6 As shown. Figure 5 It shows that the average driving speed gradually decreases as the number of vehicles increases. Figure 6 The basic graph shows that it is a unimodal function of the number of vehicles.

[0095] For the road section Road segment demand can be defined by the average density of road segments as follows:

[0096]

[0097] Similarly, the average density of road segments can be used to define road segment supply as:

[0098]

[0099] Next, this embodiment is... Figures 7 to 10 The four typical nodes shown provide efficient flux functions. First, the following definitions are made:

[0100] In general nodes Place, record for Time and Section to section of road Traffic; Record for Time and Section Customer Category to section of road Traffic; Record for Time and Section The battery level is Customer categories to section of road The traffic, of which On the road section Above, record for Always on the road The next destination is... The number of vehicles; record for Always on the road The next destination is... Customer categories are The number of vehicles; record for Time and Section The next destination is... The power level is Customer categories are The number of vehicles.

[0101] for Figure 7 Linear nodes with upstream segment 1 and downstream segment 2 are:

[0102]

[0103]

[0104]

[0105] in, yes Customer categories on upstream section 1 at any time The proportion of vehicles wanting to go to downstream section 2. ; yes The power level on upstream segment 1 at that time was Customer categories are The proportion of vehicles wanting to go to downstream section 2. .

[0106] for Figure 8 The following are traffic splitting nodes with upstream road segment 1 and downstream road segments 2 and 3:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] in, and yes The proportion of vehicles in upstream segment 1 that want to go to downstream segments 2 and 3 at any given time. ; yes Customer categories on upstream section 1 at any time The proportion of vehicles wanting to go to downstream section 3. ; yes The power level on upstream segment 1 at that time was Customer categories are The proportion of vehicles wanting to go to downstream section 3 .

[0114] for Figure 9 The merging nodes with upstream road segments 1 and 2 and downstream road segment 3 are:

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] in, yes Customer categories on upstream section 2 of the time The proportion of vehicles wanting to go to downstream section 3. ; yes The power level on upstream section 2 at that time was Customer categories are The proportion of vehicles wanting to go to downstream section 3 .

[0122] for Figure 10 It has upstream section and The common nodes of the downstream road section are:

[0123]

[0124]

[0125]

[0126] in, yes upstream section of time The vehicles in the middle want to go to the downstream section of the road. proportion, ; yes upstream section of time Customer Category The vehicle wanted to go to the downstream section of the road. Path ratio, ; yes upstream section of time The battery level is Customer categories are The vehicle wanted to go to the downstream section of the road. proportion, .

[0127] Further, step S500 includes steps S510 to S530.

[0128] Step S510: Model the inflow flow of the starting segment based on commuter traffic data. Calculate the actual arrival rate by considering the cumulative impact of historically unentered vehicles, and determine the actual inflow flow of the starting segment in conjunction with the segment's supply capacity, thus obtaining the input parameters for the starting segment flow.

[0129] Step S520: Model the node flow allocation based on the road segment supply and demand parameters and the initial road segment flow input parameters. Design the flow functions of diversion nodes, merging nodes and ordinary nodes, and calculate the flow transfer relationship between upstream and downstream road segments based on the principle of minimum supply and demand to obtain the flow allocation results between nodes.

[0130] Step S530: Model customer category traffic allocation based on the traffic allocation results between nodes, determine the traffic allocation for different customer categories based on the path selection ratio, and subdivide the power status by combining the power level ratio to construct the queue-battery model.

[0131] Specifically, based on the flow function (8), the general node can be calculated using the following equation (9). upstream section Outflow and downstream sections Inflow:

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138] Starting section The inflow rate can be calculated using equation (10):

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] yes Start time of the road segment Electric vehicle arrival rate. Customer category is Electric vehicle arrival rate. Customer category is The power level is The arrival rate of electric vehicles. All three of them are known boundary conditions. and These are the corresponding actual arrival rates, calculated using equations 10(d), 10(e), and 10(f). It is an infinitesimal quantity, which in the discrete version is equal to the time step. Here, it is assumed that the storage capacity of the starting point is infinite. Equations 10(d), 10(e), and 10(f) show that the actual arrival rate consists of two parts: the arrival rate at the current moment (the first term on the right side of the equation) and the arrival rate generated by vehicles that have not entered the road segment in the past (the second term on the right side of the equation).

[0146] At the end of the road network In this embodiment, it is assumed that there is infinite space to receive departing vehicles. Therefore, the final road segment... The outflow is equal to the last segment The demand, namely:

[0147]

[0148]

[0149]

[0150] Based on equation (9-11), we can obtain Real-time inflow and outflow volumes for all road segments.

[0151] Further, step S600 includes steps S610 to S630.

[0152] Step S610: Perform spatiotemporal discretization processing based on the queuing-battery model. By setting the time step and the power level step, the continuous model is transformed into a discrete calculation format, and the correspondence between the discrete state variables and the continuous model is established to obtain the discrete calculation framework.

[0153] Step S620: Perform iterative calculations based on the discrete computing framework. Calculate the number of vehicles and traffic flow changes at each time step sequentially based on the preset time recursion algorithm, and consider the impact mechanism of power consumption on state transitions to obtain time series state data.

[0154] Step S630: Perform state integration based on time series state data. By aggregating vehicle distribution and battery status at each road segment time step, obtain the complete state result of the electric transportation system.

[0155] Specifically, equations (2c, 2d) in the queue-battery network model proposed in this embodiment are continuous partial differential equations, which are difficult to solve analytically. Therefore, this embodiment introduces a simple numerical differentiation method—the Euler method—for approximate solution. Our model can also be solved more accurately using other numerical differentiation methods (such as the Runge-Kutta method), although the algorithm complexity will increase.

[0156] The discrete form of equation (2c, 2d) in the queue-battery model is equation (12).

[0157]

[0158]

[0159] In order to apply the Euler method, this embodiment uses a time step. (like (minutes) across the entire timeline Discretize the data, and set the level step size. (like For the entire horizontal axis of electrical quantity Discretize the variables. In the discrete version, our variables... Indicates the first The start of a time step (i.e.) ),variable Indicates the first The start of each energy step (i.e.) Assuming in Time, Road Section The battery level is The number of vehicles is Road section The battery level is Customer categories are The number of vehicles is Then in time, and It can be calculated using equation (12).

[0160] According to equation (12), given the initial time Time Section The battery level is Number of vehicles and customer categories Corresponding number of vehicles Then, it is possible to recursively calculate any time interval. Below and In summary, the time complexity of the queuing-battery network model and algorithm is O(n). That is, only related to the number of road segments. Time division quantity Number of power level classifications The product of these factors is relevant, but not to fleet size or road capacity. Therefore, the model and algorithm are suitable for decision-making in large-scale electric transportation systems.

[0161] Furthermore, in this embodiment, the above-mentioned electric traffic flow queuing-battery model is verified through agent-based simulation. Agent-based simulation (ABS) is currently the most accurate method for battery tracking in electric vehicle traffic flow modeling. However, this model has extremely high computational complexity, especially in complex multi-node networks, with long simulation times and significant randomness in the calculation results, limiting its practical application in rapid and large-scale decision-making scenarios. However, it is commonly used for evaluating the accuracy of mathematical models.

[0162] In this embodiment, an agent-based object-oriented discrete event simulation is used to simulate events such as... Figure 11 The traffic conditions of the diversion-merging road network shown were simulated over 200 minutes, and the simulation parameters were set in a high degree of consistency with the queue-battery model. Figures 12-16 The queuing-battery model and the agent simulation model were used to demonstrate the average vehicle battery level on different road sections. The situation changes over time. Figure 17 This displays the power consumption rate of all vehicles in the road network. Changes over time. It should be noted that the "agent simulation model" in the figure represents the average of 10 simulation results, while the "95% confidence interval" represents the confidence interval range of the simulation results.

[0163] As the results shown in the figures, the calculation results of the electric traffic flow queuing-battery model are highly consistent with those of the agent simulation model. Compared with the average of 10 simulation results, the average relative error (MRE) of the energy consumption rate of all vehicles in the road network calculated by the electric traffic flow queuing-battery model is 3.19%. The average relative errors of the average energy levels of vehicles on road segments 1-5 are 3.01%, 1.56%, 3.92%, 2.37%, and 2.74%, respectively, with an average of 2.72%. Furthermore, in terms of computational efficiency, the average computation time of the queuing-battery model is only 0.385 seconds, far lower than the 1.975 seconds of a single agent simulation model. This represents a computational time saving of 80.51%.

[0164] To further verify the computational efficiency and accuracy of the queuing-battery model, we constructed several experiments under different OD (Operational Demand) levels. We used an agent simulation model to comprehensively evaluate the computational error and efficiency of the queuing-battery model, and the results are as follows: Figure 18 As shown in the figure, experiments demonstrate that although the queuing-battery model is simpler in modeling details than the agent simulation model, its computational accuracy remains highly consistent with the simulation model at various OD (Original Demand) levels (relative errors are all below 5%). More significantly, the computational complexity of the queuing-battery model is independent of the fleet size. Therefore, as OD demand increases, the queuing-battery model's advantage in computation time over the agent simulation model becomes increasingly apparent (time savings increase from approximately 70% to approximately 90%), exhibiting stronger computational scalability and efficiency. This result indicates that the queuing-battery model not only maintains high battery tracking accuracy but also possesses the potential for rapid response in optimizing large-scale electric transportation systems.

[0165] Example 2:

[0166] like Figure 2 As shown, this embodiment provides a system for constructing and solving an electric traffic flow queuing-battery model. The system includes:

[0167] Module 901 is used to acquire basic parameters of the traffic network, performance parameters of electric vehicles, and commuter traffic data.

[0168] The first modeling module 902 is used to model the traffic network framework based on the basic parameters of the traffic network to obtain the traffic network structure.

[0169] The second modeling module 903 is used to model the power evolution based on the traffic network structure and electric vehicle performance parameters. By establishing the dynamic evolution relationship of power status over time and combining the correlation mechanism between vehicle speed and power consumption, a road segment-based electric vehicle network model is obtained.

[0170] The third modeling module 904 is used to model traffic flow parameters based on the electric vehicle network model. By defining the mathematical relationship between road segment capacity and congestion status, it analyzes the influence of traffic flow density on vehicle speed and obtains road segment supply and demand parameters.

[0171] The fourth modeling module 905 is used to model network traffic allocation based on road segment supply and demand parameters and commuter traffic data. It calculates the traffic transfer between upstream and downstream road segments through node flow functions and allocates traffic for different customer categories based on path ratio and power level ratio to build a queue-battery model.

[0172] Output module 906 is used to obtain the complete state results of the electric transportation system by numerically solving the queuing-battery model.

[0173] In one specific embodiment of this application, the first modeling module includes:

[0174] The first modeling unit is used to model the network topology based on the basic parameters of the traffic network. By defining the spatial distribution relationship of the road segment set, node set, starting point set and ending point set, the basic network topology framework is obtained.

[0175] The second modeling unit is used to model the node connection relationship based on the basic network topology framework. By establishing the mapping relationship between the upstream road segment set and the downstream road segment set at the node, and defining the connection rules of the diverging node, merging node and ordinary node, the node connection network is obtained.

[0176] The third modeling unit is used to perform path category mapping modeling based on the node connection network. By establishing the correspondence between customer categories and paths, and defining the set of customer categories that are allowed to pass on each road segment, the traffic network structure is obtained.

[0177] In one specific embodiment of this application, the second modeling module includes:

[0178] The fourth modeling unit is used to classify and model the power status according to the traffic network structure. By modeling the density distribution of vehicles with different power levels on the road segment, the correspondence between power level and vehicle distribution is established, and the vehicle density distribution of the road segment based on power level is obtained.

[0179] The fifth modeling unit is used to dynamically model the power consumption based on the performance parameters of electric vehicles. By establishing a nonlinear functional relationship between vehicle speed and power consumption rate, and considering the impact mechanism of vehicle speed change on power consumption under traffic congestion, dynamic power consumption parameters are obtained.

[0180] The sixth modeling unit is used to model the conservation relationship based on the vehicle density distribution and dynamic power consumption parameters of the road segment. By establishing a partial differential equation of the power state changing with time and combining it with the flow conservation condition at the node, the electric vehicle network model is obtained.

[0181] In one specific embodiment of this application, the third modeling module includes:

[0182] The seventh modeling unit is used to model road capacity based on the electric vehicle network model. By establishing a unimodal function relationship between traffic flow and density and determining the critical density point corresponding to the maximum capacity, the basic road map model is obtained.

[0183] The eighth modeling unit is used to model the speed-density relationship based on the basic road map model. It uses an exponential function to describe the law of speed changing with density and uses measured data from multiple observation points to fit the model parameters to obtain a quantitative relationship between vehicle speed and density.

[0184] The ninth modeling unit is used to model the supply and demand function based on the quantitative relationship between vehicle speed and density. It defines the demand function to reflect the maximum outflow capacity of the road segment under smooth conditions, and the supply function to reflect the maximum receiving capacity of the road segment under congested conditions. It also divides different traffic states based on the critical density point to obtain the supply and demand parameters of the road segment.

[0185] In one specific embodiment of this application, the fourth modeling module includes:

[0186] The tenth modeling unit is used to model the inflow flow of the starting segment based on commuter traffic data. It calculates the actual arrival rate by considering the cumulative impact of historical non-entry vehicles and determines the actual inflow flow of the starting segment in combination with the segment's supply capacity, thus obtaining the input parameters for the starting segment flow.

[0187] The eleventh modeling unit is used to model the flow distribution of nodes based on the supply and demand parameters of the road segment and the flow input parameters of the starting road segment. By designing the flow functions of diversion nodes, merging nodes and ordinary nodes, and calculating the flow transfer relationship between upstream and downstream road segments based on the principle of minimum supply and demand, the flow distribution results between nodes are obtained.

[0188] The twelfth modeling unit is used to model customer category traffic allocation based on the traffic allocation results between nodes. It determines the traffic allocation for different customer categories based on the path selection ratio, and further subdivides the power status by combining the power level ratio, thus constructing a queue-battery model.

[0189] In one specific embodiment of this application, the output module includes:

[0190] The first output unit is used to perform spatiotemporal discretization processing based on the queuing-battery model. By setting the time step and the power level step, the continuous model is transformed into a discrete calculation format, and the correspondence between discrete state variables and the continuous model is established to obtain the discrete calculation framework.

[0191] The second output unit is used to perform iterative calculations based on the discrete computing framework. It calculates the number of vehicles and traffic flow changes at each time step in turn based on the preset time recursion algorithm, and considers the impact mechanism of power consumption on state transition to obtain time series state data.

[0192] The third output unit is used to integrate the state based on the time series state data. By aggregating the vehicle distribution and battery status at each road segment time step, it obtains the complete state result of the electric transportation system.

[0193] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for constructing and solving an electric traffic flow queuing-battery model, characterized in that, include: Acquire basic parameters of the transportation network, performance parameters of electric vehicles, and commuter traffic data; Based on the basic parameters of the traffic network, a traffic network framework model is performed to obtain the traffic network structure. Based on the traffic network structure and the electric vehicle performance parameters, a power evolution model is performed. By establishing a dynamic evolution relationship between power status and time, and combining the correlation mechanism between vehicle speed and power consumption, a road segment-based electric vehicle network model is obtained. Traffic flow parameters are modeled based on the electric vehicle network model. By defining the mathematical relationship between road segment capacity and congestion status, the influence of traffic flow density on vehicle speed is analyzed to obtain road segment supply and demand parameters. Based on the supply and demand parameters of the road segment and the commuting traffic data, network traffic allocation modeling is performed. The traffic transfer between upstream and downstream road segments is calculated through the node flow function. Based on the path ratio and the power level ratio, traffic for different customer categories is allocated to construct the queue-battery model. The complete state result of the electric transportation system is obtained by numerically solving the queuing-battery model.

2. The method for constructing and solving the electric traffic flow queuing-battery model according to claim 1, characterized in that, Based on the aforementioned basic parameters of the transportation network, a transportation network framework model is performed to obtain the transportation network structure, including: Based on the basic parameters of the traffic network, network topology modeling is performed. By defining the spatial distribution relationship of road segment set, node set, starting point set, and ending point set, the basic network topology framework is obtained. Based on the aforementioned basic network topology framework, node connection relationships are modeled. By establishing the mapping relationship between the upstream and downstream road segment sets at the nodes, and defining the connection rules for branching nodes, merging nodes, and ordinary nodes, a node connection network is obtained. Based on the node connection network, path category mapping modeling is performed. By establishing the correspondence between customer categories and paths, and defining the set of customer categories allowed to pass on each road segment, the traffic network structure is obtained.

3. The method for constructing and solving the electric traffic flow queuing-battery model according to claim 1, characterized in that, Based on the traffic network structure and the electric vehicle performance parameters, a power evolution model is performed. By establishing a dynamic evolution relationship between power status and time, and combining this with the correlation mechanism between vehicle speed and power consumption, a road segment-based electric vehicle network model is obtained, including: Based on the traffic network structure, a power status classification model is performed. By modeling the density distribution of vehicles with different power levels on road segments, a correspondence between power levels and vehicle distribution is established, resulting in a road segment vehicle density distribution based on power levels. Based on the electric vehicle performance parameters, dynamic modeling of power consumption is performed. By establishing a nonlinear functional relationship between vehicle speed and power consumption rate, and considering the impact mechanism of vehicle speed change on power consumption under traffic congestion, dynamic power consumption parameters are obtained. Based on the vehicle density distribution of the road segment and the dynamic power consumption parameters, a conservation relationship model is constructed. By establishing a partial differential equation for the change of power state over time and combining it with the flow conservation condition at the node, an electric vehicle network model is obtained.

4. The method for constructing and solving the electric traffic flow queuing-battery model according to claim 1, characterized in that, Traffic flow parameters are modeled based on the electric vehicle network model. By defining the mathematical relationship between road segment capacity and congestion status, the influence of traffic flow density on vehicle speed is analyzed to obtain road segment supply and demand parameters, including: Based on the electric vehicle network model, road capacity modeling is performed. By establishing a unimodal function relationship between traffic flow and density, and determining the critical density point corresponding to the maximum capacity, a basic road graph model is obtained. Based on the road basic map model, a speed-density relationship model is constructed. An exponential function is used to describe the law of speed changing with density. The model parameters are fitted using measured data from multiple observation points to obtain a quantitative relationship between vehicle speed and density. Based on the quantitative relationship between vehicle speed and density, supply and demand functions are modeled. The demand function is defined to reflect the maximum outflow capacity of the road segment under smooth conditions, and the supply function is defined to reflect the maximum receiving capacity of the road segment under congested conditions. Different traffic states are divided based on the critical density point to obtain the supply and demand parameters of the road segment.

5. The method for constructing and solving the electric traffic flow queuing-battery model according to claim 1, characterized in that, Based on the supply and demand parameters of the road segments and the commuter traffic data, network traffic allocation modeling is performed. Traffic transfer between upstream and downstream road segments is calculated using node flow functions. Traffic for different customer categories is allocated based on path ratio and battery level ratio, thus constructing a queuing-battery model, including: The inflow flow of the starting segment is modeled based on the commuter flow data. The actual arrival rate is calculated by considering the cumulative impact of historically unentered vehicles, and the actual inflow flow of the starting segment is determined by combining the segment supply capacity, thus obtaining the input parameters of the starting segment flow. Based on the supply and demand parameters of the road segment and the flow input parameters of the starting road segment, the flow allocation model of the nodes is performed. By designing the flow functions of diversion nodes, merging nodes and ordinary nodes, and calculating the flow transfer relationship between upstream and downstream road segments based on the principle of minimum supply and demand, the flow allocation results between nodes are obtained. Based on the traffic allocation results between the nodes, customer category traffic allocation modeling is performed. Based on the path selection ratio, the traffic allocation of different customer categories is determined. Combined with the power level ratio, the power status is subdivided to construct the queue-battery model.

6. A system for constructing and solving an electric traffic flow queuing-battery model, characterized in that, include: The acquisition module is used to acquire basic parameters of the transportation network, performance parameters of electric vehicles, and commuter traffic data. The first modeling module is used to model the traffic network framework based on the basic parameters of the traffic network to obtain the traffic network structure. The second modeling module is used to model the power evolution based on the traffic network structure and the electric vehicle performance parameters. By establishing a dynamic evolution relationship between power status and time, and combining the correlation mechanism between vehicle speed and power consumption, a road segment-based electric vehicle network model is obtained. The third modeling module is used to model traffic flow parameters based on the electric vehicle network model. By defining the mathematical relationship between road segment capacity and congestion status, it analyzes the influence of traffic flow density on vehicle speed and obtains road segment supply and demand parameters. The fourth modeling module is used to model network traffic allocation based on the supply and demand parameters of the road segment and the commuting traffic data. It calculates the traffic transfer between upstream and downstream road segments through the node flow function, and allocates traffic for different customer categories based on the path ratio and the power level ratio to construct a queue-battery model. The output module is used to obtain the complete state results of the electric transportation system by numerically solving the queue-battery model.

7. The system for constructing and solving the electric traffic flow queuing-battery model according to claim 6, characterized in that, The first modeling module includes: The first modeling unit is used to model the network topology based on the basic parameters of the traffic network. By defining the spatial distribution relationship of the road segment set, node set, starting point set, and ending point set, a basic network topology framework is obtained. The second modeling unit is used to model the node connection relationship based on the basic network topology framework. By establishing the mapping relationship between the upstream road segment set and the downstream road segment set at the node, and defining the connection rules of the branching node, merging node and ordinary node, the node connection network is obtained. The third modeling unit is used to perform path category mapping modeling based on the node connection network. By establishing the correspondence between customer categories and paths, and defining the set of customer categories allowed to pass on each road segment, the traffic network structure is obtained.

8. The system for constructing and solving the electric traffic flow queuing-battery model according to claim 6, characterized in that, The second modeling module includes: The fourth modeling unit is used to perform power status classification modeling based on the traffic network structure. By modeling the density distribution of vehicles with different power levels on the road segment, the correspondence between power level and vehicle distribution is established, and the vehicle density distribution of the road segment based on power level is obtained. The fifth modeling unit is used to perform dynamic modeling of power consumption based on the electric vehicle performance parameters. By establishing a nonlinear functional relationship between vehicle speed and power consumption rate, and considering the influence mechanism of vehicle speed change on power consumption under traffic congestion, dynamic power consumption parameters are obtained. The sixth modeling unit is used to model the conservation relationship based on the vehicle density distribution of the road segment and the dynamic power consumption parameters. By establishing a partial differential equation of power state changing with time and combining it with the flow conservation condition at the node, the electric vehicle network model is obtained.

9. The system for constructing and solving the electric traffic flow queuing-battery model according to claim 6, characterized in that, The third modeling module includes: The seventh modeling unit is used to model road capacity based on the electric vehicle network model. By establishing a unimodal function relationship between traffic flow and density and determining the critical density point corresponding to the maximum capacity, a basic road map model is obtained. The eighth modeling unit is used to model the speed-density relationship based on the road basic map model. It uses an exponential function to describe the law of speed changing with density and uses measured data from multiple observation points to fit the model parameters to obtain a quantitative relationship between vehicle speed and density. The ninth modeling unit is used to model the supply and demand function based on the quantitative relationship between vehicle speed and density. It defines a demand function to reflect the maximum outflow capacity of the road segment under smooth conditions and a supply function to reflect the maximum receiving capacity of the road segment under congested conditions. It also divides different traffic states based on the critical density point to obtain the supply and demand parameters of the road segment.

10. The system for constructing and solving the electric traffic flow queuing-battery model according to claim 6, characterized in that, The fourth modeling module includes: The tenth modeling unit is used to model the inflow flow of the starting road segment based on the commuter traffic data. It calculates the actual arrival rate by considering the cumulative impact of historical non-entry vehicles and determines the actual inflow flow of the starting road segment in combination with the road segment supply capacity, thereby obtaining the input parameters of the starting road segment flow. The eleventh modeling unit is used to model the node flow allocation based on the road segment supply and demand parameters and the initial road segment flow input parameters. By designing the flow functions of diversion nodes, merging nodes and ordinary nodes, and calculating the flow transfer relationship between upstream and downstream road segments based on the principle of minimum supply and demand, the flow allocation results between nodes are obtained. The twelfth modeling unit is used to model customer category traffic allocation based on the traffic allocation results between the nodes, determine the traffic allocation of different customer categories based on the path selection ratio, and subdivide the power status by combining the power level ratio to construct the queue-battery model.

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