Intelligent charging pile parking space scheduling method and system

Abstract

The present invention discloses an intelligent charging pile parking space scheduling method and system, which belongs to the technical field of charging piles. The key points of the technical solution include: obtaining the location and road condition information of a vehicle to be charged when a charging request is issued; obtaining the equipment information of the charging pile within a preset range according to the location, and the preset range is determined according to the remaining power of the vehicle to be charged; planning the path between the location and the charging pile according to the road condition information; determining the target charging pile according to the path and the equipment information; matching the vehicle to be charged with the parking space corresponding to the target charging pile, and completing the charging pile parking space scheduling. The present invention selects a suitable charging pile for the user to charge according to the time required for the user to complete charging and the cost required to complete charging, thereby improving the user's charging experience.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and more particularly to an intelligent charging pile parking space scheduling method and system. Background Art

[0002] At present, due to the imperfection of the electric vehicle dispatching system, there are still many problems that need to be solved in the new energy vehicle charging industry.

[0003] The Chinese patent application with publication number CN111127940A provides a method for intelligent scheduling and control of charging parking spaces, which alleviates the phenomenon that new energy vehicles only park without charging, or still occupy charging parking spaces after charging, causing other new energy vehicles that need charging to be unable to charge in time, and improves the utilization rate of charging piles.

[0004] However, the above invention only involves the scheduling of charging piles after the user arrives at the charging station, so the prior art has shortcomings. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an intelligent charging pile parking space scheduling method and system, so that users can initiate charging requests outside the charging station, and can select and match suitable charging piles for users based on the user's location when the charging request is issued, current road conditions, charging pile status and vehicle status, thereby completing the scheduling of charging pile parking spaces.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a smart charging pile parking space scheduling method, comprising:

[0008] Obtaining the location and road condition information of the vehicle to be charged when issuing a charging request;

[0009] Acquire device information of charging piles within a preset range according to the location, wherein the preset range is determined according to the remaining power of the vehicle to be charged;

[0010] Planning a path between the location and the charging pile according to the road condition information;

[0011] Determine a target charging pile according to the path and the device information;

[0012] Matching the vehicle to be charged with the parking space corresponding to the target charging pile to complete the charging pile parking space scheduling;

[0013] The planning of a path between the location and the charging pile according to the road condition information includes:

[0014] Setting a turning cost parameter and a congestion cost coefficient according to the road condition information;

[0015] An evaluation function is constructed based on the turning cost parameter, the congestion cost coefficient and the dynamic weight coefficient, the path is composed of multiple nodes, and the evaluation function ,in represents any node among the plurality of nodes, For Node The estimated cost to the charging station, ,in For the location to the node The actual cost, is the congestion cost coefficient, dynamic weight coefficient ,in For Node The distance to the charging station, is the distance between the location and the charging pile, Indicates the ratio of the size of the rectangular environment formed by the location and the charging pile to the size of the local environment that the vehicle to be charged can reach;

[0016] Planning a path between the location and the charging pile according to the evaluation function;

[0017] The path between the location and the charging pile is updated through a collinearity test.

[0018] As a further improvement of the present invention, the step of determining a target charging pile according to the path and the device information includes:

[0019] According to the path and the device information, an objective function is established to minimize the time required to complete charging and the cost required to complete charging;

[0020] The objective function is solved by genetic algorithm to obtain the Pareto optimal solution set;

[0021] Determining the weight of the objective function by tomographic analysis;

[0022] The weight is combined with the elimination and selection expression reality method, and the optimal solution is selected from the Pareto optimization solution set as the target charging pile.

[0023] As a further improvement of the present invention, the determining the weight of the objective function by tomographic analysis comprises:

[0024] constructing a comparison matrix of the objective function based on a ratio scale;

[0025] Whether the comparison matrix is ​​reasonable is determined by a consistency test. If so, the weight of the objective function is obtained according to the eigenvector corresponding to the maximum eigenvalue in the comparison matrix.

[0026] As a further improvement of the present invention, the weight is combined with the elimination and selection expression reality method, and the optimal solution is selected from the Pareto optimization solution set as the target charging pile, including:

[0027] Based on the weights and the Pareto optimal solution set, a weight standard configuration matrix is ​​calculated;

[0028] Calculating the advantage matrix and the disadvantage matrix according to the weight specification configuration matrix;

[0029] Calculating a comprehensive advantage determination matrix according to the advantage matrix and the disadvantage matrix;

[0030] Calculate the net advantage value of each solution in the Pareto optimal solution set according to the comprehensive advantage judgment matrix;

[0031] The optimal solution is determined according to the net advantage value to obtain the target charging pile.

[0032] As a further improvement of the present invention, the device information includes the charging power of the charging pile, and determining the target charging pile according to the path and the device information includes:

[0033] Determine the time for the vehicle to be charged to start charging according to the travel time of the route and the current time;

[0034] According to the time when the vehicle to be charged starts charging, combined with the initial charge state of the vehicle to be charged when charging starts and the charging power, determine the time for the vehicle to be charged to charge in the corresponding parking space;

[0035] The time required to complete charging is calculated according to the driving time of the path and the time the vehicle to be charged is charged in the corresponding parking space, and the target charging pile is determined according to the time required to complete charging.

[0036] As a further improvement of the present invention, the method of determining the duration for charging the vehicle to be charged in the corresponding parking space according to the time when the vehicle to be charged starts charging, combined with the initial charge state and the charging power when the vehicle to be charged starts charging, includes:

[0037] Calculating the estimated charging time of the vehicle to be charged according to the initial state of charge and the charging power;

[0038] Determining a charging interval according to the charging start time and the estimated charging duration;

[0039] Determining a final state of charge of the vehicle to be charged when charging is completed according to the location of the charging interval;

[0040] The duration of the charging is calculated according to the final state of charge.

[0041] As a further improvement of the present invention, the method of determining the final state of charge of the vehicle to be charged when charging is completed according to the location of the charging interval includes:

[0042] If the charging interval is between the off-peak power consumption period and the normal power consumption period, the final state of charge is the optimal state of charge;

[0043] If the charging interval is in the peak power consumption period, the final state of charge is the lower limit of the state of charge;

[0044] If the charging interval spans two power usage time periods, the final state of charge is determined according to the proportion of the charging interval in different time periods.

[0045] The present invention provides an intelligent charging pile parking space scheduling system, comprising:

[0046] Acquisition module: obtains the location and road condition information of the vehicle to be charged when it sends a charging request, and obtains the device information of the charging pile within a preset range according to the location, and the preset range is determined according to the remaining power of the vehicle to be charged;

[0047] A calculation module: planning a path between the location and the charging pile according to the road condition information, and determining a target charging pile according to the path and the device information, wherein the planning of the path between the location and the charging pile according to the road condition information includes:

[0048] Setting a turning cost parameter and a congestion cost coefficient according to the road condition information;

[0049] An evaluation function is constructed based on the turning cost parameter, the congestion cost coefficient and the dynamic weight coefficient, the path is composed of multiple nodes, and the evaluation function ,in represents any node among the plurality of nodes, For Node The estimated cost to the charging station, ,in For the location to the node The actual cost, is the congestion cost coefficient, dynamic weight coefficient ,in For Node The distance to the charging station, is the distance between the location and the charging pile, Indicates the ratio of the size of the rectangular environment formed by the location and the charging pile to the size of the local environment that the vehicle to be charged can reach;

[0050] Planning a path between the location and the charging pile according to the evaluation function;

[0051] By means of a collinearity test, the path between the location and the charging pile is updated;

[0052] Scheduling module: matches the vehicle to be charged with the parking space corresponding to the target charging pile to complete the charging pile parking space scheduling.

[0053] The present invention provides users with a variety of charging pile parking space scheduling solutions according to the time required for users to complete charging and the cost required to complete charging, so that users can match appropriate charging pile parking spaces for charging according to their own conditions, thereby improving the user's charging experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the steps of the scheduling method of the present invention;

[0055] Figure 2 A schematic diagram of the steps of path planning of the present invention;

[0056] Figure 3 A schematic diagram of a process for determining a target charging pile according to the present invention;

[0057] Figure 4 It is a schematic diagram of the comprehensive optimal charging pile and driving route determined under multi-objective conditions;

[0058] Figure 5 A schematic diagram of the target charging pile and driving route determined based on the shortest total time consumption;

[0059] Figure 6 A schematic diagram of the target charging pile and driving route determined based on the minimum total cost. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations of the technical solution of the present invention.

[0061] The term "and / or" in the following text is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0062] like Figure 1 As shown, the embodiment of the present application provides a smart charging pile parking space scheduling method, including:

[0063] Obtaining the location and road condition information of the vehicle to be charged when issuing a charging request;

[0064] Obtain device information of charging piles within a preset range according to the location, where the preset range is determined according to the remaining power of the vehicle to be charged;

[0065] Plan the path between the location and the charging station based on traffic information;

[0066] Determine the target charging pile based on the path and equipment information;

[0067] Match the vehicle to be charged with the parking space corresponding to the target charging pile to complete the charging pile parking space scheduling.

[0068] The method provided in this embodiment enables the user to initiate a charging request outside the charging station, and can select a suitable charging pile for the user based on the user's location when the charging request is issued, current road conditions, charging pile status and vehicle status, and match and pre-set target charging pile with a parking space to complete the scheduling of charging pile parking spaces.

[0069] Further, such as Figure 2 As shown, this embodiment provides a method for planning a path between a location and a charging pile according to road condition information, including:

[0070] Set turning cost parameters and congestion cost coefficients according to traffic information;

[0071] An evaluation function is constructed based on the turning cost parameter, the congestion cost coefficient and the dynamic weight coefficient;

[0072] Plan the path between the location and the charging pile according to the evaluation function;

[0073] Through the collinearity test, the path between the location and the charging pile is updated.

[0074] Specifically, the location of the vehicle to be charged can be set as the starting point, the location of the charging pile can be set as the end point, each section between the starting point and the end point can be recorded as a node, and a to-be-processed set and a processed set can be created, the starting point can be placed in the to-be-processed set, and then the following steps can be repeated:

[0075] 1. Traverse the set to be processed, calculate the cost function of each node in the set to be processed, take the node with the smallest cost function as the current node, and place the current node in the processed set.

[0076] 2. Check the 8 nodes adjacent to the current node;

[0077] If the neighboring node is in the processed set or the neighboring node is unreachable, the node is ignored;

[0078] If the adjacent node is not in the pending set, put this node in the pending set and make the current node the parent node of this node;

[0079] If the adjacent node is in the set to be processed, calculate the cost function of the current node and this node. If the cost function value calculated at this time is smaller than the previous calculation result, update the cost function value of this node and modify its parent node to be the current node.

[0080] 3. Stop repeating until the end point is added to the pending set.

[0081] Finally, starting from the end point, move along the parent node to the starting point to form a path between the location of the vehicle to be charged and the charging pile. If there are multiple charging piles in the preset range, the above steps can be repeated multiple times to form a path between the location of the vehicle to be charged and each charging pile.

[0082] The evaluation function is constructed based on the turning cost parameter, congestion cost coefficient and dynamic weight coefficient. ,in is the actual cost from the starting point to the current node, is the estimated cost from the current node to the end point.

[0083] In the actual operation of the vehicle, the path planned by the traditional evaluation function may be tortuous, causing the vehicle to turn too many times between the starting and ending points. Therefore, the turning cost is introduced to improve the evaluation function.

[0084] The coordinates of the current node are recorded as , the coordinates of the end point are ;

[0085] like , indicating that there is no turn between the current node and the end point. ;

[0086] like , indicating that there is a turn between the current node and the end point. , is the turning cost parameter, which represents the straight-line distance between the current node and the end point.

[0087] At the same time, when congestion occurs, vehicles need to stop and wait or re-plan the route to bypass, which will increase the vehicle's travel time, so the congestion cost coefficient is introduced to improve the evaluation function. This embodiment uses traffic volume as a parameter to determine the degree of congestion of the path. Traffic volume (load) is defined as the number of vehicles passing through a certain passable node within a period of time. If a vehicle stays at a node, the number is increased by 1 after 1s, and the load of the node for 60s is calculated. The degree of traffic congestion is divided into 4 levels according to the traffic volume. When load < 10, there is no congestion, the cost is 0, and the congestion cost coefficient is 0. Set to 1; when 10≤load<15, the node is a lightly congested section, and the congestion cost coefficient Set to 2; when 15≤load<20, the node is a moderately congested section, and the congestion cost coefficient Set to 5; when load ≥ 20, the node is a heavily congested section, and the congestion cost coefficient Set to 20.

[0088] Since in the early stages of the search, The impact is greater, at this time, the traversal nodes are relatively small and the search speed is faster. As the search progresses, Gradually decrease, The influence of is gradually increasing, the number of traversed nodes increases, and the search speed slows down, so the dynamic weight coefficient is introduced Control their proportions and get the final evaluation function ,in .

[0089] in , is the distance from the current node to the end point, is the distance from the starting point to the end point, It represents the ratio of the size of the rectangular environment formed by the start point and the end point to the size of the local environment reachable by the vehicle, where the local environment reachable by the vehicle consists of adjacent nodes that are reachable and not in the processed set.

[0090] Since the final path may contain unnecessary redundant nodes, all nodes in the path can be traversed, and a collinearity test can be performed to determine whether three consecutive nodes are collinear. If they are collinear, it means that the middle node is a redundant node and can be deleted. The method for determining whether the three nodes are collinear can be a vector method, a distance method, a slope method, etc., which is not limited in this embodiment.

[0091] This embodiment optimizes the evaluation function by introducing a turning cost parameter, a congestion cost coefficient and a dynamic weight coefficient, and reduces redundant nodes in the path through a collinearity test, thereby obtaining the optimal path between the vehicle to be charged and the charging pile. If there are multiple charging piles within a preset range, the optimal path between the vehicle to be charged and each charging pile is obtained respectively.

[0092] Further, such as Figure 3 As shown, this embodiment provides a method for determining a target charging pile according to path and device information, including:

[0093] According to the path and equipment information, establish the objective function of minimizing the time required to complete charging and minimizing the cost required to complete charging;

[0094] The objective function is solved by genetic algorithm to obtain the Pareto optimal solution set;

[0095] Determine the weight of the objective function through tomographic analysis;

[0096] The weight is combined with the elimination and selection expression reality method to select the optimal solution in the Pareto optimization solution set as the target charging pile.

[0097] This embodiment establishes an objective function and solves the objective function based on a genetic algorithm, a hierarchical analysis method, and an elimination and selection representation method, and finally determines a target charging pile that takes into account both the shortest total time and the smallest total cost, thereby completing the scheduling of charging pile parking spaces.

[0098] Since multi-objective optimization requires considering two or more optimization objectives at the same time, these objective functions are often related and conflicting, and it is difficult to find a solution that optimizes all objective functions at the same time. Therefore, the result of solving the multi-objective optimization problem through genetic algorithms is actually to find a set containing some compromise solutions, called the Pareto optimal solution set. The process of determining the target charging pile is actually the process of finding the optimal solution in the Pareto optimal solution set. Therefore, further, this implementation provides a method for determining the optimal solution in the Pareto optimal solution set through the hierarchical analysis method and the elimination and selection expression reality method.

[0099] The step of determining the weight of the objective function by tomographic analysis includes:

[0100] Construct a comparison matrix of the objective function based on the ratio scale;

[0101] The consistency test is used to determine whether the comparison matrix is ​​reasonable. If so, the weight of the objective function is obtained based on the eigenvector corresponding to the maximum eigenvalue in the comparison matrix.

[0102] Specifically, the ratio scale is shown in Table 1 .

[0103] Table 1 Ratio Scale

[0104]

[0105] Through the ratio scale, a comparison matrix between objective functions can be constructed , used to express the relative importance between targets, where , From Table 1, we can get Relative to target The importance of , indicating the target Relative to target The importance of Relative to target importance value, so in measuring the goal Relative to target The importance of needs to be calculated inversely. , is the number of objective functions. Then the consistency index of the comparison matrix is ​​calculated. If the value of the consistency index is less than 0.1, the comparison matrix is ​​considered reasonable. Then the eigenvector corresponding to the maximum eigenvalue in the comparison matrix is ​​calculated and normalized to obtain the weight of the objective function. If the comparison matrix is ​​unreasonable, it is necessary to re-establish the comparison matrix and re-perform the consistency test.

[0106] The steps of combining weight with elimination and selection expression reality method and selecting the optimal solution in the Pareto optimization solution set as the target charging pile include:

[0107] Based on the weights and the Pareto optimal solution set, the weighted standard configuration matrix is ​​calculated;

[0108] The advantage matrix and disadvantage matrix are calculated based on the weight specification configuration matrix;

[0109] The comprehensive advantage judgment matrix is ​​calculated based on the advantage matrix and disadvantage matrix;

[0110] Calculate the net advantage value of each solution in the Pareto optimal solution set according to the comprehensive advantage judgment matrix;

[0111] The optimal solution is determined based on the net advantage value, and the target charging pile is obtained.

[0112] Specifically, we first establish the initial data matrix based on the Pareto optimization solution set. ,in Indicates The first solution The value of the target, , ,in is the number of solutions in the Pareto optimal solution set, is the number of objective functions.

[0113] According to the initial data matrix , calculate the normalized decision matrix ,in According to the normalized decision matrix , and further obtain the weight specification configuration matrix ,in , For the The weight of a target.

[0114] Before calculating the advantage matrix and disadvantage matrix, it is necessary to determine the advantage set and disadvantage set according to the weight specification configuration matrix. Compare any two different rows in Middle No. The value of the row is The value of the row is high, Classified as advantage set , otherwise it is classified as the inferior set ,in .

[0115] According to the advantage set, we get the advantage matrix ,in , represents the relative advantage index. According to the disadvantage set, we get the disadvantage matrix ,in , represents the relative disadvantage index, .

[0116] Multiply the advantage matrix and the disadvantage matrix to obtain the comprehensive advantage judgment matrix ,in , according to the comprehensive advantage judgment matrix , and obtain the net advantage value of each solution , , The larger the The better the solution, the solution with the largest net advantage value is selected as the optimal solution, which is the target charging pile.

[0117] In the prior art, the method for determining the optimal solution based on the comprehensive advantage determination matrix is ​​that if the elements in a column of the comprehensive advantage determination matrix contain 1, the solution corresponding to this column is eliminated. However, this method is prone to the situation where multiple solutions still exist after the solutions are eliminated, and the purpose of obtaining the optimal solution is not achieved. Therefore, this implementation selects the net advantage value of each solution to evaluate the advantage of each solution, and a unique solution can be selected as the user quota scheduling plan.

[0118] This embodiment combines the hierarchical analysis method with the elimination and selection expression reality method, and applies the weights determined by the hierarchical analysis method to the elimination and selection expression reality method, thereby avoiding the problem of subjective parameter determination in the traditional elimination and selection expression reality method, and thus can effectively select the optimal solution from the Pareto optimization solution set, and use the charging pile corresponding to the optimal solution as the target charging pile to complete the parking space scheduling.

[0119] When the user is in an emergency, charging needs to be completed in the shortest possible time. At this time, there is no need to consider the impact of charging costs. Therefore, this embodiment further provides another method for determining a target charging pile according to path and device information, including:

[0120] Determine the time for the vehicle to be charged to start charging based on the travel time of the route and the current time;

[0121] According to the time when the vehicle to be charged starts charging, combined with the initial charge state of the vehicle to be charged when charging starts and the charging power of the charging pile, the charging time of the vehicle to be charged in the corresponding parking space is determined;

[0122] The time required to complete charging is calculated based on the driving time of the route and the time the vehicle to be charged takes to charge in the corresponding parking space, and the target charging pile is determined based on the time required to complete charging.

[0123] The charging power is obtained from the equipment information of the charging pile, and the time required to complete charging is ,in Indicates the time it takes for the vehicle to be charged to reach the target charging pile according to the planned route. Indicates the time it takes for the vehicle to be charged to charge in the corresponding parking space. If there are many people charging, the effect of the queuing time on the time required to complete charging can be further considered. ,in Indicates the length of time that vehicles waiting to be charged wait in line at the corresponding parking space.

[0124] Specifically, according to the time when the vehicle to be charged starts charging, combined with the initial charge state of the vehicle to be charged when charging starts and the charging power of the charging pile, the charging time of the vehicle to be charged in the corresponding parking space is determined, including:

[0125] Calculate the estimated charging time of the vehicle to be charged based on the initial state of charge and power;

[0126] Determine the charging interval according to the time when charging starts and the estimated duration of charging;

[0127] Determine the final charge state of the vehicle to be charged when charging is completed according to the location of the charging zone;

[0128] The charging time of the vehicle to be charged in the corresponding parking space is calculated according to the final state of charge.

[0129] Since the initial state of charge of the vehicle to be charged is available and the power of the charging pile is determined, the time required for the vehicle to be charged to reach the optimal state of charge can be estimated, and the charging interval can be determined based on the time when charging starts and the estimated duration of charging. If the charging interval is located in the off-peak power consumption period and the normal power consumption period, the final state of charge is the optimal state of charge; if the charging interval is located in the peak power consumption period, the final state of charge is the lower limit of the charge state; if the charging interval spans two power consumption periods, the final state of charge is determined based on the proportion of the charging interval in different time periods.

[0130] The optimal state of charge (OSOC) usually refers to the state of charge that can achieve the best balance between battery performance, life and safety under specific application conditions. It is usually a numerical range. The specific data needs to be determined according to the battery type, usage environment and specific application requirements. The lower boundary of state of charge (LB-SOC) is a parameter set in the battery management system (BMS) to define the minimum power percentage that the battery should not be lower than during discharge.

[0131] Since charging piles are high-power electrical equipment, they will have a certain impact on the power grid when used in a concentrated manner, especially during peak power consumption periods. In order to reduce the impact on the power grid, the State Grid has issued a peak-valley time-of-use electricity price charging strategy to encourage companies with large power loads to choose to use electricity during low power consumption periods. For charging pile operators, in order to reduce electricity costs and reduce the impact on the power grid, they can provide users with different charging options during different power consumption periods.

[0132] The method provided in this embodiment is based on the idea of ​​avoiding peak hours and filling valley hours. During the charging process, vehicles in the valley hours and normal hours will be charged to the optimal state of charge, while vehicles in the peak hours will be charged to the lower limit of the state of charge, which saves users' charging time and cost while reducing the company's electricity cost. If the charging interval spans two electricity consumption periods, the final state of charge is determined according to the proportion of the charging interval in different periods. For example, assuming that the lower limit of the state of charge of the vehicle to be charged is The optimal state of charge is , assuming that charging to the lower limit of the state of charge requires hours, including The final state of charge is .

[0133] Through the method provided in this embodiment, the time required for the charging vehicle to be charged can be accurately calculated during different power consumption periods, and then the target charging pile can be matched for the user according to the time required to complete the charging, thereby completing the charging pile parking space scheduling.

[0134] Furthermore, since charging charging standards in different charging stations are different, this embodiment provides another method for determining a target charging pile according to path and device information, including:

[0135] Calculate the cost of charging the vehicle at the charging station based on the route and equipment information;

[0136] Determine the target charging station based on the cost required to complete charging.

[0137] The cost of completing the charging ,in The power consumption of the vehicle to be charged from the location where the charging request is initiated to the target charging pile. is the charging price per unit of charging capacity in the last charge, is the current charging amount, is the charging price per unit of charging capacity, This is the service fee for this charge.

[0138] The method provided in this embodiment is suitable for users who have ample time for charging, and can help users select the charging pile with the lowest cost, thereby saving charging costs.

[0139] It can be seen from the above embodiments that the intelligent charging pile parking space scheduling method provided in the embodiments of the present application fully considers the various charging needs of users, provides users with the optimal scheduling scheme and driving path under different objectives, and improves the user experience.

[0140] The present application further provides a smart charging pile parking scheduling system, including:

[0141] Acquisition module: obtains the location and road condition information of the vehicle to be charged when it sends a charging request, and obtains the device information of the charging pile within a preset range according to the location. The preset range is determined according to the remaining power of the vehicle to be charged;

[0142] Calculation module: plans the path between the location and the charging pile according to the road condition information, and determines the target charging pile according to the path and equipment information;

[0143] Scheduling module: matches the vehicle to be charged with the parking space corresponding to the target charging pile to complete the charging pile parking space scheduling.

[0144] The system provided in this embodiment can be installed on a terminal device such as a mobile phone, and can quickly provide users with a real-time scheduling solution. Figure 4 It is the comprehensive optimal charging pile and driving path determined under multi-objective conditions. Figure 5 To determine the target charging pile and driving path based on the shortest total time, Figure 6 In order to determine the target charging pile and driving route with the lowest total cost, users can match the corresponding parking space according to their own needs, make a parking space reservation, and complete the charging pile parking space scheduling.

[0145] The embodiment of the present application provides a smart charging pile parking space scheduling method and system, which takes into account the three situations of optimal charging time, optimal charging cost and optimal comprehensive goal, provides users with multiple scheduling schemes, and improves the cost function, providing users with the optimal driving path, thereby improving the user experience.

[0146] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A smart charging pile parking space scheduling method, characterized in that: include: Obtaining the location and road condition information of the vehicle to be charged when issuing a charging request; Acquire device information of charging piles within a preset range according to the location, wherein the preset range is determined according to the remaining power of the vehicle to be charged; Planning a path between the location and the charging pile according to the road condition information; Determine a target charging pile according to the path and the device information; Matching the vehicle to be charged with the parking space corresponding to the target charging pile to complete the charging pile parking space scheduling; The planning of a path between the location and the charging pile according to the road condition information includes: Setting a turning cost parameter and a congestion cost coefficient according to the road condition information; An evaluation function is constructed based on the turning cost parameter, the congestion cost coefficient and the dynamic weight coefficient, the path is composed of multiple nodes, and the evaluation function ,in represents any node among the plurality of nodes, For Node The estimated cost to the charging station, ,in For the location to the node The actual cost of is the congestion cost coefficient, dynamic weight coefficient ,in For Node The distance to the charging station, is the distance between the location and the charging pile, Indicates the ratio of the size of the rectangular environment formed by the location and the charging pile to the size of the local environment that the vehicle to be charged can reach; Planning a path between the location and the charging pile according to the evaluation function; The path between the location and the charging post is updated through a collinearity test.

2. The method for scheduling parking spaces for intelligent charging piles according to claim 1, characterized in that: The determining a target charging pile according to the path and the device information includes: According to the path and the device information, an objective function is established to minimize the time required to complete charging and the cost required to complete charging; The objective function is solved by genetic algorithm to obtain the Pareto optimal solution set; Determining the weight of the objective function by tomographic analysis; The weight is combined with the elimination and selection expression reality method, and the optimal solution is selected from the Pareto optimization solution set as the target charging pile.

3. The intelligent charging pile parking space scheduling method according to claim 2 is characterized in that: Determining the weight of the objective function by tomographic analysis includes: constructing a comparison matrix of the objective function based on a ratio scale; Whether the comparison matrix is ​​reasonable is determined by a consistency test. If so, the weight of the objective function is obtained according to the eigenvector corresponding to the maximum eigenvalue in the comparison matrix.

4. The method for scheduling parking spaces for intelligent charging piles according to claim 2, characterized in that: Combining the weight with the elimination and selection expression reality method, selecting the optimal solution in the Pareto optimization solution set as the target charging pile, including: Based on the weights and the Pareto optimal solution set, a weight standard configuration matrix is ​​calculated; Calculating the advantage matrix and the disadvantage matrix according to the weight specification configuration matrix; Calculating a comprehensive advantage determination matrix according to the advantage matrix and the disadvantage matrix; Calculate the net advantage value of each solution in the Pareto optimal solution set according to the comprehensive advantage judgment matrix; The optimal solution is determined according to the net advantage value to obtain the target charging pile.

5. The method for scheduling parking spaces for intelligent charging piles according to claim 1, characterized in that: The device information includes the charging power of the charging pile, and determining the target charging pile according to the path and the device information includes: Determine the time for the vehicle to be charged to start charging according to the travel time of the route and the current time; According to the time when the vehicle to be charged starts charging, combined with the initial charge state of the vehicle to be charged when charging starts and the charging power, determine the time for the vehicle to be charged to charge in the corresponding parking space; The time required to complete charging is calculated according to the driving time of the path and the time the vehicle to be charged is charged in the corresponding parking space, and the target charging pile is determined according to the time required to complete charging.

6. The method for scheduling parking spaces for intelligent charging piles according to claim 5, characterized in that: The determining, according to the time when the vehicle to be charged starts charging, in combination with the initial state of charge of the vehicle to be charged when charging starts and the charging power, the duration for the vehicle to be charged to charge in the corresponding parking space includes: Calculating the estimated charging time of the vehicle to be charged according to the initial state of charge and the charging power; Determining a charging interval according to the charging start time and the estimated charging duration; Determining a final state of charge of the vehicle to be charged when charging is completed according to the location of the charging interval; The duration of the charging is calculated according to the final state of charge.

7. The method for scheduling parking spaces for intelligent charging piles according to claim 6, characterized in that: The determining, according to the location of the charging interval, the final state of charge of the vehicle to be charged when charging is completed includes: If the charging interval is between the off-peak power consumption period and the normal power consumption period, the final state of charge is the optimal state of charge; If the charging interval is in the peak power consumption period, the final state of charge is the lower limit of the state of charge; If the charging interval spans two power usage time periods, the final state of charge is determined according to the proportion of the charging interval in different time periods.

8. An intelligent charging pile parking space scheduling system, characterized in that: include: Acquisition module: obtains the location and road condition information of the vehicle to be charged when it sends a charging request, and obtains the device information of the charging pile within a preset range according to the location, and the preset range is determined according to the remaining power of the vehicle to be charged; A calculation module: planning a path between the location and the charging pile according to the road condition information, and determining a target charging pile according to the path and the device information, wherein the planning of the path between the location and the charging pile according to the road condition information includes: Setting a turning cost parameter and a congestion cost coefficient according to the road condition information; An evaluation function is constructed based on the turning cost parameter, the congestion cost coefficient and the dynamic weight coefficient, the path is composed of multiple nodes, and the evaluation function ,in represents any node among the plurality of nodes, For Node The estimated cost to the charging station, ,in For the location to the node The actual cost of is the congestion cost coefficient, dynamic weight coefficient ,in For Node The distance to the charging station, is the distance between the location and the charging pile, Indicates the ratio of the size of the rectangular environment formed by the location and the charging pile to the size of the local environment that the vehicle to be charged can reach; Planning a path between the location and the charging pile according to the evaluation function; By means of a collinearity test, the path between the location and the charging pile is updated; Scheduling module: matches the vehicle to be charged with the parking space corresponding to the target charging pile to complete the charging pile parking space scheduling.

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