A method and device for predicting charging demand of a highway service area

Abstract

The application provides a highway service area charging demand prediction method and device, wherein the method comprises the following steps: acquiring the positions of a plurality of service areas in a target road section of a highway and the positions and congestion lengths of a plurality of congestion points; determining the driving-in traffic volume of each service area in a target period of a working day according to the position of each service area, the position of the congestion point closest to the service area and the congestion length; determining a plurality of charging parameters of various vehicle types driving into the service area in the target period of the working day based on the historical vehicle passing data of each service area in the target period of the working day; and determining the charging demand prediction amount of each service area in the target period of the working day based on the driving-in traffic volume of each service area in the target period of the working day and the plurality of charging parameters of various vehicle types driving into the service area. The application improves the prediction accuracy of the charging demand of the service area.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a method and apparatus for predicting charging demand in highway service areas. Background Technology

[0002] Currently, the layout of charging facilities in highway service areas is mostly based on historical data analysis of charging demand, ignoring the spatiotemporal changes in traffic flow during the spread and dissipation of traffic congestion. This results in a large discrepancy between charging demand forecasts and actual traffic flow distribution, low accuracy in charging demand forecasts, and an imbalance between supply and demand of charging resources during peak hours.

[0003] Moreover, most charging demand forecasting methods use a single time scale for analysis, lacking differentiated analysis of different scenarios such as weekdays, weekends, and holidays, leading to problems such as unbalanced resource allocation and low operational efficiency in practical applications.

[0004] Furthermore, during the dissipation of frequent traffic congestion on highways, there is a short-term surge in charging demand due to the pulse-like release of traffic flow. Traditional fixed charging stations, due to their static layout, cannot dynamically respond to the chain-like demand fluctuations in service areas caused by the dissipation of congestion. Moreover, mobile charging resources lack intelligent scheduling methods, making it difficult to coordinate with fixed facilities to form a flexible supply network. Summary of the Invention

[0005] In view of this, this application provides a method and apparatus for predicting charging demand in highway service areas to solve the above-mentioned technical problems.

[0006] In a first aspect, embodiments of this application provide a method for predicting charging demand in highway service areas, including:

[0007] Obtain the locations of multiple service areas, as well as the locations and congestion lengths of multiple congestion points within the target section of the highway;

[0008] Based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion, determine the inbound traffic volume of each service area during the target time period on weekdays;

[0009] Based on historical vehicle traffic data for each service area during the target time period on weekdays, multiple charging parameters for various vehicle types entering the service area during the target time period on weekdays are determined.

[0010] Based on the inbound traffic volume of each service area during the target period on weekdays and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand of each service area during the target period on weekdays is determined.

[0011] In one possible implementation, the inbound traffic volume for each service area during a target time period on a weekday is determined based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion; including:

[0012] According to the Service areas The location is used to determine the nearest congestion point, among which... , The number of service areas for the target road segment;

[0013] Based on the location of the congestion point and the length of the congestion, the upstream congestion area, the congestion area, and the downstream congestion area are determined;

[0014] When the Service areas Located in the upstream area of ​​the congestion, during the target time period on weekdays Internal pathway Traffic volume at each service area for:

[0015]

[0016] in, To be within the target time period on weekdays Internal pathway Free flow traffic in each service area;

[0017] When the If a service area is located in the congested area, then during the target time period on weekdays... Internal pathway Traffic volume at each service area for:

[0018]

[0019] in, To alleviate congestion of traffic flow from upstream areas, For congested traffic; The attenuation coefficient is... For the first Distance between each service area and the nearest congestion point;

[0020] When the If a service area is located downstream of the congestion area, then during the target time period on weekdays... Internal pathway Traffic volume at each service area for:

[0021]

[0022] During target hours on weekdays Drive into the first Traffic volume at each service area for:

[0023]

[0024] in, For the target time period The scaling factor.

[0025] In one possible implementation, the plurality of charging parameters include: vehicle type ratio, electrification penetration rate, charging rate, and average charging amount; the vehicle type includes: small cars, buses, and trucks;

[0026] Based on historical vehicle traffic data and historical charging orders for each service area during target time periods on weekdays, multiple charging parameters for various vehicle types entering the service area during target time periods on weekdays are determined; including:

[0027] Based on historical vehicle traffic data and historical charging orders for each service area during the target time period on weekdays, statistics are compiled on vehicles entering the nth service area during the target time period on historical weekdays. Total number of vehicles , No. Number of vehicles of each type , No. Number of electric vehicles among various vehicle types , No. Number of electric vehicles charging in this type of vehicle , No. Number of charging orders for this type of vehicle ;No. The charging amount of the jth order for this vehicle model ;

[0028] Calculate the first Vehicle type percentage :

[0029]

[0030] Calculate the first Electrification penetration rate of various vehicle models :

[0031]

[0032] Calculate the first Charging rate of this type of vehicle :

[0033]

[0034] Calculate the first Average charging amount of this type of vehicle :

[0035] .

[0036] In one possible implementation, the predicted charging demand for each service area during the target weekday period is determined based on the inbound traffic volume of each service area during the target weekday period and multiple charging parameters of various vehicle types entering the service area, including:

[0037] No. Service areas During target hours on weekdays Charging demand forecast for:

[0038] .

[0039] In one possible implementation, the method further includes:

[0040] The nth service area The sum of the predicted charging demand for all time periods on weekdays is used to determine the nth service area. Weekday charging needs ;

[0041] According to the Service areas Calculate the historical orders of charging piles. Type of vehicle in the Service areas Average charging time on weekdays ;

[0042] No. Service areas Weekday charging power for:

[0043]

[0044] in, Reliability coefficient; For the nth service area Maximum charging power;

[0045] Based on the Service areas Weekday charging power Determine the first Service areas The charging mode;

[0046] Based on the Service areas The charging mode is determined. Service areas The types, power, and number of charging stations deployed on weekdays.

[0047] In one possible implementation, the method further includes:

[0048] Based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion, determine the inbound traffic volume of each service area during the target weekend period;

[0049] Based on historical vehicle traffic data for each service area during the target weekend period, multiple charging parameters for various vehicle types entering the service area during the target weekend period are determined.

[0050] Based on the inbound traffic volume of each service area during the target weekend period and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand of each service area during the target weekend period is determined.

[0051] In one possible implementation, the method further includes:

[0052] Determine the nth service area Excess charging demand during target weekend hours :

[0053]

[0054] in, For the first Service areas Forecast of charging demand during the target weekend period;

[0055] Obtain the start time of the target time period. Location of the mobile energy replenishment vehicle;

[0056] Define the first decision variable , The value can be 0 or 1, indicating the target time period on the weekend. Inner Can the mobile energy replenishment vehicle be dispatched to the first...? Service areas If so, then ,otherwise ; ;

[0057] Define the second decision variable , Indicates the first The mobile energy replenishment vehicle was allocated to the first [unit] during the target time period. Service areas To replenish energy;

[0058] Build target time periods for weekends First objective function :

[0059]

[0060] Build target time periods for weekends The second objective function :

[0061]

[0062] First objective function Second objective function The constraints include:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] For the first The service area and the Travel time between service areas:

[0070]

[0071] in, For the first The service area and the The distance between service areas; For the first The service area and the Traffic flow speed between service areas; The maximum battery capacity for each mobile energy replenishment vehicle; It is a rounding function. It is a positive integer;

[0072] Solving the first objective function Second objective function We obtain the first and second decision variables, and thus the scheduling scheme for mobile energy replenishment vehicles during the target weekend period.

[0073] In one possible implementation, the method further includes:

[0074] Based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion, determine the inbound traffic volume of each service area during the target period of the holiday.

[0075] Based on the historical vehicle traffic data of each service area during the target holiday period, multiple charging parameters for various vehicle types entering the service area during the target holiday period are determined.

[0076] Based on the inbound traffic volume of each service area during the target holiday period and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand of each service area during the target holiday period is determined.

[0077] Secondly, embodiments of this application provide a charging demand prediction device for highway service areas, comprising:

[0078] The acquisition unit is used to acquire the locations of multiple service areas, as well as the locations and congestion lengths of multiple congestion points within the target section of the highway.

[0079] The first processing unit is used to determine the inbound traffic volume of each service area during the target time period on weekdays based on the location of each service area, the location of the nearest congestion point to the service area, and the length of congestion.

[0080] The second processing unit is used to determine multiple charging parameters for various vehicle types entering the service area during the target time period on weekdays based on the historical vehicle traffic data of each service area during the target time period on weekdays.

[0081] The prediction unit is used to determine the predicted charging demand of each service area during the target period on a weekday, based on the inbound traffic volume of each service area during the target period on a weekday and multiple charging parameters of various vehicle types entering the service area.

[0082] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method of embodiments of this application.

[0083] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the methods of embodiments of this application.

[0084] Fifthly, embodiments of this application provide a computer program product, including a computer program / instructions, which, when executed by a processor, implement the method of embodiments of this application.

[0085] This embodiment considers the spread and dissipation of recurring congestion on the highway where the service area is located, captures the dynamic distribution pattern of highway traffic flow, and then predicts the charging demand of the service area under different congestion conditions and different time and space locations, thereby improving the prediction accuracy of the charging demand of the service area. Attached Figure Description

[0086] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0087] Figure 1 A flowchart illustrating the method for predicting charging demand in highway service areas provided in this application embodiment;

[0088] Figure 2 A cumulative flow-time relationship diagram for recurring congestion provided in an embodiment of this application;

[0089] Figure 3 A functional structure diagram of a charging demand prediction device for highway service areas provided in an embodiment of this application;

[0090] Figure 4 A functional structure diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0091] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0092] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0093] The technical solutions provided in the embodiments of this application will be described below.

[0094] like Figure 1 As shown in the figure, this application provides a method for predicting charging demand in highway service areas, including:

[0095] Step 101: Obtain the locations of multiple service areas, multiple congestion points, and congestion lengths within the target section of the highway;

[0096] For example, each day can be evenly divided into multiple time periods, each lasting one hour: [0,1],[1,2], [23,24], where the target time period is any of these time periods.

[0097] Step 102: Determine the inbound traffic volume for each service area during the target time period on weekdays based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion.

[0098] Step 103: Based on the historical vehicle traffic data of each service area during the target time period on weekdays, determine multiple charging parameters for various vehicle types entering the service area during the target time period on weekdays;

[0099] Step 104: Based on the inbound traffic volume of each service area during the target time period on weekdays and multiple charging parameters of various vehicle types entering the service area, determine the predicted charging demand of each service area during the target time period on weekdays.

[0100] Traditional service area charging facility layouts fail to consider the spatiotemporal distribution characteristics of traffic flow during the propagation and dissipation of traffic congestion, leading to regional supply-demand mismatches of charging resources during peak hours (e.g., upstream idleness coexisting with downstream overload). This embodiment, by considering a model of the recurring congestion propagation and dissipation on the highway where the service area is located, can accurately capture the dynamic distribution patterns of highway traffic flow, thereby predicting the charging demand of service areas under different congestion conditions and at different spatiotemporal locations. This improves the accuracy of service area charging demand prediction, enabling a more rational highway charging network layout plan and optimizing the static configuration of charging piles.

[0101] Frequent traffic congestion occurs when actual traffic demand gradually increases and exceeds the road's design capacity. Its characteristics include relatively fixed times and locations of congestion, exhibiting a predictable pattern. The occurrence process of frequent traffic congestion can be analyzed using a cumulative flow-time graph. Figure 2 As shown, Previously, the cumulative number of vehicles arriving upstream was the same as the cumulative number of vehicles leaving downstream, indicating that the road was clear. to During this period, as the traffic flow from upstream gradually increased, the traffic flow exceeded the road's designed capacity, resulting in the cumulative traffic flow upstream gradually exceeding the cumulative traffic flow downstream, leading to frequent congestion. to During this period, the difference between the cumulative number of vehicles arriving upstream and the cumulative number of vehicles leaving downstream gradually decreases, representing the dissipation of congestion. Afterwards, the cumulative number of vehicles arriving upstream and the cumulative number of vehicles leaving downstream become the same again, indicating that the road has returned to a smooth flow. This is the entire process from the occurrence to the dissipation of recurring congestion.

[0102] This embodiment uses the LWR (Lighthill-Whitham-Richards) traffic flow model to describe congestion propagation. This model is based on fluid dynamics and uses partial differential equations to describe traffic flow, making it suitable for continuous flow analysis such as highways.

[0103] The commonly used velocity-density relationship is a linear model (Greenshields model), where velocity... for:

[0104]

[0105] in, For free flow velocity; This is the maximum density, which is also the blockage density;

[0106] Corresponding traffic for:

[0107]

[0108] According to the LWR traffic flow model, congestion propagates upstream, forming a shockwave. Shockwave speed... Given by the Rankine-Hugoniot condition:

[0109]

[0110] in, It is the upstream density; It refers to the downstream density (density at congestion points); the density near congestion points decreases exponentially.

[0111] Length of congested area Over time:

[0112]

[0113] in, This refers to the congestion time.

[0114] (1) In the congested upstream area, the flow is free flow. .

[0115] (2) Congested areas, due to the traffic flow from upstream , The congested flow causes the congestion to spread upstream. Under quasi-steady-state conditions, the length of the congestion zone is fixed, but the density distribution changes with distance, with the density near the congestion point decreasing. The calculation formula is as follows:

[0116]

[0117] in, For congestion points The density; The free flow density; The attenuation coefficient is... This indicates a location near a congestion point.

[0118] Traffic flow near congestion points The calculation formula is as follows:

[0119]

[0120] in,

[0121] (3) In the downstream congestion area, as the congestion dissipates, according to the law of conservation, the downstream flow remains constant. .

[0122] In some embodiments, the inbound traffic volume for each service area during a target time period on a weekday is determined based on the location of each service area, the location of the nearest congestion point to the service area, and the length of congestion; including:

[0123] According to the Service areas The location is used to determine the nearest congestion point, among which... , The number of service areas for the target road segment;

[0124] Based on the location of the congestion point and the length of the congestion, the upstream area, the congested area, and the downstream area of ​​congestion are determined;

[0125] When the Service areas If located upstream of congestion, then during the target time period on weekdays... Internal pathway Traffic volume at each service area for:

[0126]

[0127] in, To target time periods on weekdays Internal pathway Free flow traffic in each service area;

[0128] When the If a service area is located in a congested area, then during the target time period on weekdays... Internal pathway Traffic volume at each service area for:

[0129]

[0130] in, To alleviate congestion of traffic flow from upstream areas, For congested traffic; The attenuation coefficient is... For the first Distance between each service area and the nearest congestion point;

[0131] When the If a service area is located downstream of congestion, then during the target time period on weekdays... Internal pathway Traffic volume at each service area for:

[0132]

[0133] During the target time period on weekdays Drive into the first Traffic volume at each service area for:

[0134]

[0135] in, For the target time period The scaling factor.

[0136] In some embodiments, multiple charging parameters include: vehicle type ratio, electrification penetration rate, charging rate, and average charging amount; vehicle type includes: small cars, buses, and trucks;

[0137] Based on historical vehicle traffic data and historical charging orders for each service area during target time periods on weekdays, multiple charging parameters for various vehicle types entering the service area during target time periods on weekdays are determined; including:

[0138] Based on historical vehicle traffic data and historical charging orders for each service area during the target time period on weekdays, statistics are compiled on vehicles entering the nth service area during the target time period on historical weekdays. Total number of vehicles , No. Number of vehicles of each type , No. Number of electric vehicles among various vehicle types , No. Number of electric vehicles charging in this type of vehicle , No. Number of charging orders for this type of vehicle ;No. The charging amount of the jth order for this vehicle model ;

[0139] Calculate the first Vehicle type percentage :

[0140]

[0141] Calculate the first Electrification penetration rate of various vehicle models :

[0142]

[0143] Calculate the first Charging rate of this type of vehicle :

[0144]

[0145] Calculate the first Average charging amount of this type of vehicle :

[0146] .

[0147] In some embodiments, the predicted charging demand for each service area during the target time period on a weekday is determined based on the inbound traffic volume of each service area during a target time period on a weekday and multiple charging parameters of various vehicle types entering the service area, including:

[0148] No. Service areas During the target time period on weekdays Charging demand forecast for:

[0149] .

[0150] In some embodiments, the method further includes:

[0151] The nth service area The sum of the predicted charging demand for all time periods on weekdays is used to determine the nth service area. Weekday charging needs ;

[0152] According to the Service areas Calculate the historical orders of charging piles. Type of vehicle in the Service areas Average charging time on weekdays ;

[0153] No. Service areas Weekday charging power for:

[0154]

[0155] in, Reliability coefficient; For the nth service area Maximum charging power;

[0156] Based on the Service areas Weekday charging power Determine the first Service areas The charging mode;

[0157] Based on the Service areas The charging mode is determined. Service areas The types, power, and number of charging stations deployed on weekdays.

[0158] The charging modes for electric vehicles include: connecting to a 220V AC charging network, connecting to a 380V DC charging network, connecting to a high-voltage DC charging network, and not charging.

[0159] For example, if there are 24 time periods in a day, then the nth service area Weekday charging needs for:

[0160]

[0161] In some embodiments, the method further includes:

[0162] Based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion, determine the inbound traffic volume for each service area during the target weekend period;

[0163] Based on historical vehicle traffic data for each service area during the target weekend period, multiple charging parameters for various vehicle types entering the service area during the target weekend period are determined.

[0164] Based on the traffic volume entering each service area during the target weekend period and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand for each service area during the target weekend period is determined.

[0165] In some embodiments, the method further includes:

[0166] Determine the nth service area Excess charging demand during target periods on weekends :

[0167]

[0168] in, For the first Service areas Forecasted charging demand during target periods on weekends;

[0169] Obtain the start time of the target time period. Location of the mobile energy replenishment vehicle;

[0170] Define the first decision variable , The value can be 0 or 1, indicating the target time period on the weekend. Inner Can the mobile energy replenishment vehicle be dispatched to the first...? Service areas If so, then ,otherwise ; ;

[0171] Define the second decision variable , Indicates the first The mobile energy replenishment vehicle was allocated to the first [unit] during the target time period. Service areas To replenish energy;

[0172] To maximize the satisfaction of needs, construct target weekend time periods. First objective function :

[0173]

[0174] With the goal of minimizing total waiting time, waiting time can be modeled as the difference between excess demand and replenishment capacity, thus constructing the target weekend period. The second objective function :

[0175]

[0176] First objective function Second objective function The constraints include:

[0177]

[0178] Mobile charging vehicle power constraints:

[0179]

[0180] Energy replenishment should not exceed requirements:

[0181]

[0182] Each mobile energy replenishment vehicle can only be used in one service area at a time:

[0183]

[0184] Charging is prohibited during the journey.

[0185]

[0186] If the target time period Inner The mobile energy vehicle is located at the first Service areas and in arrive Then the middle You must be on the road during these time periods:

[0187]

[0188] For the first The service area and the Travel time between service areas:

[0189]

[0190] in, For the first The service area and the The distance between service areas; For the first The service area and the Traffic flow speed between service areas; The maximum battery capacity for each mobile energy replenishment vehicle; It is a rounding function. It is a positive integer;

[0191] Solving the first objective function Second objective function We obtain the first and second decision variables, and thus the scheduling scheme for mobile energy replenishment vehicles during the target weekend period.

[0192] Traditional fixed charging stations, due to their static layout, cannot dynamically respond to fluctuations in service area demand and are ill-suited to sudden peak demand periods such as holidays. Furthermore, existing mobile charging solutions largely rely on empirical rules for scheduling, lacking a systematic optimization by failing to coordinate with fixed infrastructure to form a flexible supply network. This embodiment, based on weekday charging demand, establishes a multi-objective optimization model for mobile charging vehicle scheduling during weekend peak hours. With the goal of maximizing service capacity and minimizing total waiting time, and combined with real-time traffic data, it can dynamically adjust the deployment location and charging routes of mobile charging vehicles. This enables global optimization of charging resource allocation, improves overall charging service capacity, and reduces user waiting time.

[0193] In some embodiments, the method further includes:

[0194] Based on the location of each service area, the location of the nearest congestion point, and the length of congestion, determine the inbound traffic volume for each service area during the target holiday period;

[0195] Based on the historical vehicle traffic data of each service area during the target holiday period, multiple charging parameters for various vehicle types entering the service area during the target holiday period are determined.

[0196] Based on the traffic volume entering each service area during the target period of the holiday and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand of each service area during the target period of the holiday is determined.

[0197] In some embodiments, it also includes:

[0198] Calculate the nth service area Excess charging demand during target periods of holidays :

[0199]

[0200] in, For the first Service areas Forecast of charging demand during target periods of holidays;

[0201] Based on the same solution method used for the first and second decision variables of mobile refueling vehicles during the target weekend period, determine Dispatch scheme for mobile energy replenishment vehicles.

[0202] This embodiment constructs a collaborative scheduling mechanism between static charging facilities and mobile charging vehicles to maximize resource utilization efficiency. This improves charging resource utilization efficiency, alleviates peak-hour congestion, and enhances user experience. Furthermore, it introduces a dynamic scheduling strategy for mobile charging vehicles to alleviate charging resource shortages during peak periods such as holidays, thereby improving the reliability and efficiency of electric vehicle charging services on highways.

[0203] Based on the same inventive concept, this application provides a charging demand prediction device for highway service areas, see reference. Figure 3 As shown, the charging demand prediction device 200 for highway service areas provided in this application embodiment includes at least:

[0204] The acquisition unit 201 is used to acquire the locations of multiple service areas and multiple congestion points and congestion lengths within the target section of the highway.

[0205] The first processing unit 202 is used to determine the inbound traffic volume of each service area during the target time period on weekdays based on the location of each service area, the location of the nearest congestion point to the service area, and the length of congestion.

[0206] The second processing unit 203 is used to determine multiple charging parameters of various vehicle types entering the service area during the target time period on weekdays based on the historical vehicle traffic data of each service area during the target time period on weekdays.

[0207] The prediction unit 204 is used to determine the predicted charging demand of each service area during the target period on a weekday based on the inbound traffic volume of each service area during the target period on a weekday and multiple charging parameters of various vehicle types entering the service area.

[0208] It should be noted that the principle of the charging demand prediction device 200 for highway service areas provided in this application embodiment to solve the technical problem is similar to the method provided in this application embodiment. Therefore, the implementation of the charging demand prediction device 200 for highway service areas provided in this application embodiment can refer to the implementation of the method provided in this application embodiment, and the repeated parts will not be described again.

[0209] Based on the same inventive concept, embodiments of this application also provide an electronic device, such as... Figure 4 As shown, it includes a memory and a processor, wherein the memory stores an executable program, and the processor executes the executable program to implement the steps of the charging demand prediction method for highway service areas provided in the above embodiments.

[0210] The aforementioned processor can be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0211] Since the electronic device described in this application embodiment is an electronic device equipped with a memory for implementing the charging demand prediction method for highway service areas disclosed in this application embodiment, those skilled in the art can understand the structure and variations of the electronic device described in this application embodiment based on the charging demand prediction method for highway service areas described in this application embodiment, and therefore will not be described in detail here.

[0212] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the charging demand prediction method for highway service areas provided in the above embodiments.

[0213] The storage medium in this embodiment may be included in an electronic device; or it may exist independently and not be assembled into an electronic device. The storage medium carries one or more computer programs, which, when executed, implement the steps of the charging demand prediction method for highway service areas provided in the above embodiment.

[0214] It should be understood that the various solutions in this embodiment have the same technical effects as those in the above method embodiments, and will not be repeated here.

[0215] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. Optionally, specific examples in this embodiment can refer to the examples described in any embodiment of this application, which will not be repeated here. Obviously, those skilled in the art should understand that the various modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular hardware and software combination.

[0216] This application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the charging demand prediction method for highway service areas provided in the above embodiments.

[0217] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions targeted in the blocks may occur in a different order than those targeted in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0218] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0219] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Multitasking and parallel processing may be advantageous in certain environments. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

Claims

1. A method for predicting charging demand in highway service areas, characterized in that, include: Obtain the locations of multiple service areas, as well as the locations and congestion lengths of multiple congestion points within the target section of the highway; Based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion, determine the inbound traffic volume of each service area during the target time period on weekdays; Based on historical vehicle traffic data for each service area during the target time period on weekdays, multiple charging parameters for various vehicle types entering the service area during the target time period on weekdays are determined. Based on the inbound traffic volume of each service area during the target period on weekdays and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand of each service area during the target period on weekdays is determined. Based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion, determine the inbound traffic volume of each service area during the target time period on weekdays; include: According to the Service areas The location is used to determine the nearest congestion point, among which... , The number of service areas for the target road segment; Based on the location of the congestion point and the length of the congestion, the upstream congestion area, the congestion area, and the downstream congestion area are determined; When the Service areas Located in the upstream area of ​​the congestion, during the target time period on weekdays Internal pathway Traffic volume at each service area for: in, To be within the target time period on weekdays Internal pathway Free flow traffic in each service area; When the If a service area is located in the congested area, then during the target time period on weekdays... Internal pathway Traffic volume at each service area for: in, To alleviate congestion of traffic flow from upstream areas, For congested traffic; The attenuation coefficient is... For the first Distance between each service area and the nearest congestion point; When the If a service area is located downstream of the congestion area, then during the target time period on weekdays... Internal pathway Traffic volume at each service area for: During target hours on weekdays Drive into the first Traffic volume at each service area for: in, Target time period for weekdays Scale factor; The multiple charging parameters include: vehicle type ratio, electrification penetration rate, charging rate, and average charging amount; the vehicle types include: small cars, buses, and trucks; Based on historical vehicle traffic data and historical charging orders for each service area during the target weekday time period, multiple charging parameters for various vehicle types entering the service area during the target weekday time period are determined; including: Based on historical vehicle traffic data and historical charging orders for each service area during the target time period on weekdays, statistics are compiled on vehicles entering the nth service area during the target time period on historical weekdays. Total number of vehicles , No. Number of vehicles of each type , No. Number of electric vehicles among various vehicle types , No. Number of electric vehicles charging in this type of vehicle , No. Number of charging orders for this type of vehicle ;No. The charging amount of the jth order for this vehicle model ; Calculate the first Vehicle type percentage : Calculate the first Electrification penetration rate of various vehicle models : Calculate the first Charging rate of this type of vehicle : Calculate the first Average charging amount of this type of vehicle : ； Based on the inbound traffic volume of each service area during the target weekday period and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand for each service area during the target weekday period is determined, including: No. Service areas During target hours on weekdays Charging demand forecast for: The method further includes: Based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion, determine the inbound traffic volume of each service area during the target weekend period; Based on historical vehicle traffic data for each service area during the target weekend period, multiple charging parameters for various vehicle types entering the service area during the target weekend period are determined. Based on the inbound traffic volume of each service area during the target weekend period and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand of each service area during the target weekend period is determined. The method further includes: Determine the nth service area Excess charging demand during target weekend hours : in, For the first Service areas Forecast of charging demand during the target weekend period; Obtain the start time of the target time period. Location of the mobile energy replenishment vehicle; Define the first decision variable , The value can be 0 or 1, indicating the target time period on the weekend. Inner Can the mobile energy replenishment vehicle be dispatched to the first...? Service areas If so, then ,otherwise ; ; Define the second decision variable , Indicates the first The mobile energy replenishment vehicle was allocated to the first [unit] during the target time period. Service areas To replenish energy; Build target time periods for weekends First objective function : Build target time periods for weekends The second objective function : First objective function Second objective function The constraints include: For the first The service area and the Travel time between service areas: in, For the first The service area and the The distance between service areas; For the first The service area and the Traffic flow speed between service areas; The maximum battery capacity for each mobile energy replenishment vehicle; It is a rounding function. It is a positive integer; Solving the first objective function Second objective function We obtain the first and second decision variables, and thus the scheduling scheme for mobile energy replenishment vehicles during the target weekend period.

2. The method according to claim 1, characterized in that, The method further includes: The nth service area The sum of the predicted charging demand for all time periods on weekdays is used to determine the nth service area. Weekday charging needs ; According to the Service areas Calculate the historical orders of charging piles. Type of vehicle in the Service areas Average charging time on weekdays ; No. Service areas Weekday charging power for: in, Reliability coefficient; For the nth service area Maximum charging power; Based on the Service areas Weekday charging power Determine the first Service areas The charging mode; Based on the Service areas The charging mode is determined. Service areas The types, power, and number of charging stations deployed on weekdays.

3. The method according to claim 1, characterized in that, The method further includes: Based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion, determine the inbound traffic volume of each service area during the target period of the holiday. Based on the historical vehicle traffic data of each service area during the target holiday period, multiple charging parameters for various vehicle types entering the service area during the target holiday period are determined. Based on the inbound traffic volume of each service area during the target holiday period and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand of each service area during the target holiday period is determined.

4. A charging demand prediction device for highway service areas, characterized in that, include: The acquisition unit is used to acquire the locations of multiple service areas, as well as the locations and congestion lengths of multiple congestion points within the target section of the highway. The first processing unit is used to determine the inbound traffic volume of each service area during the target time period on weekdays based on the location of each service area, the location of the nearest congestion point to the service area, and the length of congestion. The second processing unit is used to determine multiple charging parameters for various vehicle types entering the service area during the target time period on weekdays based on the historical vehicle traffic data of each service area during the target time period on weekdays. The first prediction unit is used to determine the predicted charging demand of each service area during the target period on a weekday based on the inbound traffic volume of each service area during the target period on a weekday and multiple charging parameters of various vehicle types entering the service area. The first processing unit is specifically used for: According to the Service areas The location is used to determine the nearest congestion point, among which... , The number of service areas for the target road segment; Based on the location of the congestion point and the length of the congestion, the upstream congestion area, the congestion area, and the downstream congestion area are determined; When the Service areas Located in the upstream area of ​​the congestion, during the target time period on weekdays Internal pathway Traffic volume at each service area for: in, To be within the target time period on weekdays Internal pathway Free flow traffic in each service area; When the If a service area is located in the congested area, then during the target time period on weekdays... Internal pathway Traffic volume at each service area for: in, To alleviate congestion of traffic flow from upstream areas, For congested traffic; The attenuation coefficient is... For the first Distance between each service area and the nearest congestion point; When the If a service area is located downstream of the congestion area, then during the target time period on weekdays... Internal pathway Traffic volume at each service area for: During target hours on weekdays Drive into the first Traffic volume at each service area for: in, Target time period for weekdays Scale factor; The multiple charging parameters include: vehicle type ratio, electrification penetration rate, charging rate, and average charging amount; the vehicle types include: small cars, buses, and trucks; The second processing unit is specifically used for: Based on historical vehicle traffic data and historical charging orders for each service area during the target time period on weekdays, statistics are compiled on vehicles entering the nth service area during the target time period on historical weekdays. Total number of vehicles , No. Number of vehicles of each type , No. Number of electric vehicles among various vehicle types , No. Number of electric vehicles charging in this type of vehicle , No. Number of charging orders for this type of vehicle ;No. The charging amount of the jth order for this vehicle model ; Calculate the first Vehicle type percentage : Calculate the first Electrification penetration rate of various vehicle models : Calculate the first Charging rate of this type of vehicle : Calculate the first Average charging amount of this type of vehicle : ； The first prediction unit is specifically used for: No. Service areas During target hours on weekdays Charging demand forecast for: The device further includes: a second prediction unit, specifically used for: Based on the location of each service area, the location of the nearest congestion point to the service area, and the length of the congestion, determine the inbound traffic volume of each service area during the target weekend period; Based on historical vehicle traffic data for each service area during the target weekend period, multiple charging parameters for various vehicle types entering the service area during the target weekend period are determined. Based on the inbound traffic volume of each service area during the target weekend period and multiple charging parameters of various vehicle types entering the service area, the predicted charging demand of each service area during the target weekend period is determined. The device further includes: a scheduling unit, specifically used for: Determine the nth service area Excess charging demand during target weekend hours : in, For the first Service areas Forecast of charging demand during the target weekend period; Obtain the start time of the target time period. Location of the mobile energy replenishment vehicle; Define the first decision variable , The value can be 0 or 1, indicating the target time period on the weekend. Inner Can the mobile energy replenishment vehicle be dispatched to the first...? Service areas If so, then ,otherwise ; ; Define the second decision variable , Indicates the first The mobile energy replenishment vehicle was allocated to the first [unit] during the target time period. Service areas To replenish energy; Build target time periods for weekends First objective function : Build target time periods for weekends The second objective function : First objective function Second objective function The constraints include: For the first The service area and the Travel time between service areas: in, For the first The service area and the The distance between service areas; For the first The service area and the Traffic flow speed between service areas; The maximum battery capacity for each mobile energy replenishment vehicle; It is a rounding function. It is a positive integer; Solving the first objective function Second objective function We obtain the first and second decision variables, and thus the scheduling scheme for mobile energy replenishment vehicles during the target weekend period.

5. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1-3.

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