Electric vehicle transportation path planning method, system and terminal based on charging constraint

Abstract

The application provides a charging-constraint-based electric vehicle transportation path planning method, system and terminal, and particularly relates to the technical field of new energy sources, and the scheme comprises the following steps: determining a plurality of initial transportation paths formed by a starting point node to an ending point node and passing through at least one transportation task node based on the starting point, the ending point, the transportation task node and a charging node; determining a charging node that needs to be inserted on the initial transportation path according to a preset charging constraint condition and a charging capacity; and optimizing the initial transportation path of each electric vehicle according to the charging node and a preset traffic constraint condition to obtain a target planning path. The scheme fully considers actual situations such as charging and operation cost of electric vehicles in the transportation process, so that the path planning is more reasonable, all tasks on the target planning path can be successfully completed, the operation cost is effectively reduced, and the transportation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to a method, system and terminal for electric vehicle transportation route planning based on charging constraints. Background Technology

[0002] In recent years, the booming development of the electric vehicle (EV) industry and the growing demand for sustainable transportation have brought about tremendous and profound changes. Due to the environmental friendliness and low operating costs of electric vehicles, many logistics and passenger transport companies have replaced traditional internal combustion engine vehicles with electric vehicles. However, with the continued growth of logistics and passenger transport businesses, these companies face increasingly complex transportation network structures, leading to greater difficulties in deploying their electric vehicle fleets.

[0003] Currently, there are various studies on route planning for electric vehicle fleets in logistics or passenger transport. However, the Electric Vehicle Routing Problem (EVRP) is a typical mixed-integer programming (MIP) problem, involving both integer variables (routes) and continuous variables (charging power). The main technical challenge lies in its polynomial complexity (NP-hard problem), resulting in high solution complexity and difficulty. Therefore, existing route planning methods for transport vehicles have very few solutions that can effectively address large-scale electric vehicle route planning, and they often fail to fully consider charging constraints such as the charging demand of electric vehicles during travel and the maximum load of charging stations, leading to low accuracy and unreasonable route planning results. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method, system and terminal for electric vehicle transportation route planning based on charging constraints, which aims to solve the problems of low accuracy and unreasonable route planning results due to the failure to consider charging demand and charging constraints during the driving process in the prior art.

[0005] To achieve the above objectives, the first aspect of the present invention provides a method for electric vehicle transportation route planning based on charging constraints, comprising the following steps:

[0006] Obtain the starting point node, ending point node, and transportation task node of the electric vehicle;

[0007] Based on the transportation task nodes corresponding to the electric vehicle, determine several initial transportation paths for the electric vehicle from the starting point node to the ending point node, passing through at least one of the transportation task nodes.

[0008] The amount of electricity consumed by the electric vehicle to travel the initial transportation route and the remaining electricity of the electric vehicle at the current node are obtained to determine the amount of electricity required for the electric vehicle to travel on the initial transportation route.

[0009] Obtain all charging nodes corresponding to each charging station at the location of the transportation task, and determine the charging node to be inserted on the initial transportation path based on preset charging constraints, the charging capacity, and the charging node.

[0010] Based on the charging nodes to be inserted and the preset traffic flow constraints, the initial transportation path of each electric vehicle is optimized to obtain the target planned path.

[0011] Optionally, based on the transportation task node corresponding to the electric vehicle, several initial transportation paths are determined for the electric vehicle from the starting node to the ending node, passing through at least one of the transportation task nodes, including:

[0012] Construct a transportation network based on the set of road nodes where the transportation task is located;

[0013] Based on the transportation network, a directed acyclic graph is constructed using the starting node, the ending node, the transportation task node, and the charging node;

[0014] Based on the directed acyclic graph, several initial transportation paths are determined for each electric vehicle, consisting of the starting point node, the ending point node, and at least one transportation task node.

[0015] Optionally, based on the charging node to be inserted and preset traffic flow constraints, the initial transportation path of each electric vehicle is optimized to obtain a target planned path, including:

[0016] The service revenue for each initial transportation path is obtained based on the service reward of each transportation task node on each initial transportation path;

[0017] Based on the service revenue and the traffic flow constraints, each of the transportation task nodes is traversed to obtain a service planning path consisting of several of the transportation task nodes.

[0018] Based on the charging node to be inserted, the service planning path for each electric vehicle is optimized to obtain the target planning path.

[0019] Optionally, after obtaining the target planning path, the following may also be included:

[0020] Based on the charging power required for travel between any two transport task nodes on the target planning path, and based on the charging power and the charging unit price, the charging cost between the two transport task nodes is obtained.

[0021] A pre-defined auxiliary decision variable is used to quantify the degree of violation of charging power constraints. Based on the charging cost and the auxiliary decision variable, the transportation task nodes that need to be deleted on the target planning path are determined.

[0022] Update the target planned path based on the transportation task nodes that need to be deleted.

[0023] Optionally, based on the charging cost and the auxiliary decision variables, the transportation task nodes that need to be deleted on the target planning path are determined, including:

[0024] Solve for the values ​​of the auxiliary decision variables at each of the transportation task nodes. If the value of the auxiliary decision variable is greater than zero, it is determined that there is a transportation task node that violates the SOC box constraint before the corresponding transportation task node.

[0025] When there is a transportation task node that violates the SOC box constraint, calculate the power consumption required to travel between the corresponding transportation task node and the two nodes adjacent to the corresponding transportation task node, and the power consumption required to travel only between the two nodes adjacent to the corresponding transportation task node, to obtain the additional power consumption required to pass through the corresponding transportation task node.

[0026] Based on the service rewards of the corresponding transportation task nodes and the additional electricity required to pass through the corresponding transportation task nodes, the quantified revenue values ​​of all transportation task nodes before the corresponding transportation task node are calculated, and the transportation task nodes that need to be deleted on the target planning path are determined.

[0027] Optional traffic flow constraints include:

[0028] The electric vehicles that enter each of the transportation task nodes all drive out from the corresponding transportation task node, and all the electric vehicles start from the starting node and finally return to the ending node;

[0029] Each of the aforementioned transportation task nodes is served by the electric vehicle at most once;

[0030] The electric vehicle arrives at each of the transport task nodes no later than the service time of each of the transport task nodes.

[0031] Optional charging constraints include:

[0032] The maximum actual charging power of the electric vehicle does not exceed the power threshold and the charging power threshold, and the power of the electric vehicle is within the range of the power threshold.

[0033] The total power of several electric vehicles charging simultaneously at the same charging node does not exceed the charging power threshold of the corresponding charging node.

[0034] A second aspect of the present invention provides an electric vehicle transportation route planning system based on charging constraints, the system comprising:

[0035] The node acquisition module is used to acquire the starting point node, ending point node, and transportation task node of the electric vehicle.

[0036] An initial transportation route construction module is used to determine, based on the transportation task nodes corresponding to the electric vehicle, several initial transportation routes consisting of the electric vehicle from the starting point node to the ending point node and passing through at least one of the transportation task nodes.

[0037] The charging power calculation module is used to obtain the power consumption required for the electric vehicle to travel the initial transportation path, and the remaining power of the electric vehicle at the current node, and to determine the charging power required for the electric vehicle to travel on the initial transportation path.

[0038] The charging node insertion determination module is used to obtain all charging nodes corresponding to each charging station at the location of the transportation task, and determine the charging node to be inserted on the initial transportation path based on preset charging constraints, the charging capacity and the charging node.

[0039] The target planning path construction module is used to optimize the initial transportation path of each electric vehicle based on the charging node to be inserted and the preset traffic flow constraints, so as to obtain the target planning path.

[0040] A third aspect of the present invention provides a smart terminal, the smart terminal including a memory, a processor, and a charging-constrained electric vehicle transportation route planning program stored in the memory and executable on the processor, wherein the charging-constrained electric vehicle transportation route planning program, when executed by the processor, performs any of the steps of the above-described charging-constrained electric vehicle transportation route planning method.

[0041] A fourth aspect of the present invention provides a computer-readable storage medium storing a charging-constrained electric vehicle transportation route planning program, wherein the charging-constrained electric vehicle transportation route planning program, when executed by a processor, implements the steps of any of the above-described charging-constrained electric vehicle transportation route planning methods.

[0042] Compared with existing technologies, the beneficial effects of this solution are as follows:

[0043] The method of this invention determines several initial transportation paths based on a starting point, an ending point, and transportation task nodes, extending from the starting point node to the ending point node and passing through at least one of the transportation task nodes. Then, based on all available charging nodes, preset charging constraints, and the charging capacity, it determines the charging nodes to be inserted on the initial transportation paths. Finally, based on the charging nodes to be inserted and preset traffic flow constraints, it optimizes the initial transportation path for each electric vehicle to obtain the target planned path. It is evident that this scheme fully considers the actual needs of charging electric vehicles and operating costs during transportation, making the path planning for electric vehicles more realistic and reasonable. This not only ensures the smooth completion of all tasks on the target planned path but also effectively reduces operating costs and improves transportation efficiency. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of the electric vehicle transportation route planning method based on charging constraints of the present invention.

[0046] Figure 2 This is a schematic diagram of the electric vehicle transportation route planning system based on charging constraints according to the present invention.

[0047] Figure 3 This is a schematic diagram of the intelligent terminal of the present invention. Detailed Implementation

[0048] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0049] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0050] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0051] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0052] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0055] Typically, logistics or passenger transport companies need to operate fleets of electric vehicles to complete a range of geographically dispersed transport tasks, such as urban logistics and transportation. At the same time, due to the limited battery capacity of electric vehicles, the issue of charging during the journey also needs to be considered.

[0056] Mathematically, the route planning problem for a fleet of electric vehicles used in logistics or passenger transport is called the Electric Vehicle Routing Problem (EVRP). Its goal is to optimize service rewards and charging costs while satisfying the State of Charge (SOC) constraints by correctly selecting transport routes and scheduling electric vehicles. In fact, EVRP is an important variant of the Vehicle Routing Problem (VRP), one of the most widely studied problems in operations research. The main technical challenge of EVRP lies in its polynomial complexity and nondeterministic nature (NP-hard), making it unsolvable in polynomial time. This is because it is a typical Mixed Integer Programming (MIP) problem, involving both integer variables (routes) and continuous variables (charging power).

[0057] Despite the diverse research on this problem, due to its NP-hard nature, only a few methods can effectively solve large-scale electric vehicle route planning. Meanwhile, the charging capacity constraints of charging stations have received little attention. In reality, the maximum charging capacity of a facility is predetermined by the capacity of the transformer or power line. Undoubtedly, this constraint is coupled with the charging schedule of electric vehicles, further complicating the computation. Furthermore, considering practical needs, it must be recognized that orders or customers frequently change due to demand fluctuations, posing a significant challenge to the computational efficiency of the algorithm. To address the problems of unreasonable route planning for individual electric vehicles and low timeliness of route planning adjustments in existing technologies, this application proposes an electric vehicle transportation route planning method based on charging constraints, considering both the electric vehicle's battery level and the charging station's capacity.

[0058] Exemplary methods

[0059] This invention provides a method for electric vehicle transportation route planning based on charging constraints, deployed on electronic devices such as computers and servers. The application scenario is the planning of logistics and passenger transport routes for electric vehicles in logistics or passenger transport companies. It should be noted that, to reduce the complexity of route planning for electric vehicle fleets, this invention selects homogeneous electric vehicles as the research object, meaning each electric vehicle has the same hardware configuration. However, in practical applications, the concept of this invention's charging-constrained electric vehicle transportation route planning method can be used to plan routes for various types of different electric vehicles. Any method that utilizes the concept of this invention to solve electric vehicle route planning problems is within the scope of protection of this invention. Specifically, for example... Figure 1 As shown, the steps of the method in this embodiment include:

[0060] Step S100: Obtain the starting point node, ending point node, and transportation task node of the electric vehicle;

[0061] Specifically, the electric vehicle fleet includes several homogeneous electric vehicles, meaning that each electric vehicle in the fleet has the same hardware configuration, power consumption, and other parameters. The starting point node represents the starting location of the electric vehicle's transportation task, and the ending point node represents the ending location of the electric vehicle's transportation task. The starting point and ending point nodes for each electric vehicle can be the same or different, and the starting point and ending point nodes can be the same location or different locations; this invention does not impose any restrictions. The transportation task node represents the service location where each electric vehicle performs a transportation task, such as the service location required for delivering orders, receiving orders, or picking up and dropping off passengers. Different electric vehicles can perform the same transportation task together or perform different transportation tasks, and the service locations for different transportation tasks can be the same or different; this invention does not impose any restrictions. The charging node represents multiple charging time periods divided according to a time axis from known charging stations that can charge these electric vehicles. For example, if the duration of a single charge is specified as 15 minutes, then a charging station can be divided into a sequence of 96 charging nodes within a day. The maximum allowable charging power of different charging stations can be the same or different; this invention does not impose any restrictions.

[0062] Step S200: Based on the transportation task nodes corresponding to the electric vehicle, determine several initial transportation paths for the electric vehicle from the starting point node to the ending point node and passing through at least one transportation task node.

[0063] Specifically, in the actual route planning process, each electric vehicle will perform at least one transportation task during its journey from the starting node to the ending node. This means it will pass through one or more transportation task nodes. An initial transportation path is formed using the starting node, all the transportation task nodes along the route, and the ending node. Similarly, by rationally planning all the transportation task nodes, the starting and ending nodes of each electric vehicle, and the charging nodes, several initial transportation paths are obtained, and the transportation tasks are performed by different electric vehicles in the electric vehicle fleet.

[0064] Step S300: Obtain the amount of electricity consumed by the electric vehicle to travel the initial transportation route, and the remaining electricity of the electric vehicle at the current node, and determine the charging amount required for the electric vehicle to travel on the initial transportation route.

[0065] Specifically, based on the electricity consumed by the electric vehicle during its initial transportation journey and the remaining electricity at the current node, the amount of electricity needed to be replenished during the entire initial transportation route—that is, the charging electricity—is determined to ensure that the electric vehicle can reach every node on the planned route on time and successfully complete the transportation task. Here, the current node can be the starting node or any transportation task node on the initial transportation route.

[0066] Step S400: Obtain all charging nodes corresponding to each charging station in the location of the transportation task, and determine the charging nodes to be inserted on the initial transportation path based on preset charging constraints, charging capacity and charging nodes.

[0067] Specifically, the system obtains time periods from each charging station at the location of the transportation task, segmented according to the duration of a single charge. Preset charging constraints are used to ensure that the electric vehicle's battery power is always sufficient for driving needs during the transportation task, and does not exceed preset charging power and capacity thresholds for both the electric vehicle and the charging station. In simpler terms, during each electric vehicle's task execution, the service reward earned by completing the transportation task at each node is no less than the charging cost corresponding to the electricity consumed in performing the task. This means that electric vehicles will not travel long distances to complete transportation tasks with low service rewards, thus avoiding losses.

[0068] Based on this, the charging nodes that need to be inserted on the initial transportation route are determined according to the charging capacity, ensuring that the electric vehicle's power always meets the charging threshold and can smoothly reach each transportation task node to complete each transportation task successfully, and the cost of executing each transportation task node will not exceed its service reward.

[0069] Step S500: Based on the charging nodes to be inserted and the preset traffic flow constraints, optimize the initial transportation path of each electric vehicle to obtain the target planned path.

[0070] Specifically, traffic flow constraints are used to ensure that the number of vehicles entering and leaving each road node at the location of the transportation task is equal, thus satisfying the electric vehicle traffic conservation constraint. For each electric vehicle, based on the traffic flow constraints and charging constraints, corresponding charging nodes are inserted into the initial transportation path of each electric vehicle to optimize the initial transportation path and obtain the target planned path. This ensures that the service reward obtained on the target planned path is maximized while the charging cost is minimized, thereby maximizing the benefits of electric vehicles and improving the overall efficiency of the entire electric vehicle fleet.

[0071] In one implementation, step S200 determines several initial transportation paths for the electric vehicle, from the starting point to the ending point and passing through at least one transportation task node, based on the transportation task node corresponding to the electric vehicle. Specifically, this includes:

[0072] Step S210: Construct a transportation network based on the set of road nodes where the transportation task is located;

[0073] Specifically, the set of road nodes where the transportation task is located refers to the set of all intersections of real roads as road nodes, forming a transportation network that maps to real roads.

[0074] Step S220: Based on the transportation network, construct a directed acyclic graph using the origin node, destination node, transportation task node, and charging node;

[0075] Specifically, the target road node refers to the location of the electric vehicle's starting point, ending point, transportation task node, and charging node mapped to the location of the road node. In practice, when the electric vehicle's starting point, ending point, transportation task node, or charging node is not located at a real road intersection (i.e., at an intersection), the intersection of the real road closest to the starting point, ending point, transportation task node, or charging node is mapped as the target road node.

[0076] Step S240: Based on the directed acyclic graph, determine several initial transportation paths for each electric vehicle, from the starting node to the ending node and passing through at least one transportation task node.

[0077] In one implementation, a directed acyclic graph is constructed based on the target road nodes in the set of road nodes where the transportation task is located. The transportation network is represented, in which, Indicates the starting and ending stations {v o ,v d Transportation task nodes and charging nodes The union of {v} o ,v d} represents a fleet of homogeneous electric vehicles The starting point and the ending point, Let G represent the set of edges connecting nodes. Let G be a fully connected graph, and let G be an upper triangular matrix. Since the access order of transport task nodes is limited by the given transport task time, requests with earlier transport task times will be processed before requests with later times.

[0078] For transportation task nodes Its characteristic is a tuple {l i ,t i,r i The location, arrival time, and service reward for the specified transportation task are defined. To complete this transportation task, the electric vehicle must... i Previously arrived at l i For charging nodes It also has a similar tuple {l i ,t i ,q i} is used to characterize it, representing the charging station l i The charging action in the time period t i Initially, the electricity price is q, and it lasts for a fixed period of time ΔT. i Without loss of generality, this invention assumes that the charging time is the same each time and ΔT = 1 to simplify the model.

[0079] It is easy to understand that, based on this transportation network model, the charging service of the same charging station at different times corresponds to different charging nodes, that is, one charging station corresponds to a series of charging nodes on the time axis. In this embodiment, in order to simplify the model, the duration of a single charging session is set to a fixed value, such as 15 minutes, so ΔT = 15 minutes. As another preferred implementation, the charging duration can also be flexibly arranged according to the charging needs of electric vehicles and the power supply of charging stations. For example, if an electric vehicle only needs to charge for 10 minutes to reach the last transportation task node and return to the destination node while completing the last transportation task assigned for the day, then the electric vehicle only needs to charge for 10 minutes, and there is no need to charge for 15 minutes. It can simply reach the destination node and then replenish the power. Or, for example, during the peak charging period of electric vehicles, the existing number of charging stations may not be able to meet the charging needs of each electric vehicle that needs to be charged for 15 minutes each time. This will cause some electric vehicles to be unable to replenish the power in time. Therefore, the charging duration can be set to 10 minutes during the peak charging period and 15 minutes or longer during the off-peak charging period. Meanwhile, in this model, the charging power of electric vehicles at charging stations is not considered an attribute of the charging node, but rather a decision variable determined later.

[0080] In this embodiment, target road nodes are selected from the set of road nodes where the transportation task is located as the starting point node, ending point node, transportation task node, and charging node of the electric vehicle. A directed acyclic graph is then constructed based on these selected nodes to determine several initial transportation paths. It is evident that the initial transportation paths constructed in this embodiment are realistic, feasible, and operable.

[0081] set up Let be a binary decision variable, representing electric vehicles. Has it passed through the edge? Orient from node i∈v to node j∈v. Also, let... Let be a continuous decision variable, representing the charging power of electric vehicle k at node i. Undoubtedly, the charging power... This only has meaning when i is a charging node, i.e. Otherwise, simply set

[0082] The goal of path planning is to increase service rewards and reduce charging costs; therefore, the objective function can be written as:

[0083]

[0084] Where, r i q represents the service reward for node i; i This represents the unit price of charging at node i; This represents the charging power of electric vehicle k at node i; Let be a binary decision variable, representing electric vehicles. Has it passed through the edge? Orienting from node i∈v to node j∈v Indicates passing through the edge Orienting from node i∈v to node j∈v Indicates not passing through an edge Orient from node i∈v to node j∈v; node i and node j include the transportation task node and the charging node.

[0085] Traffic flow constraints specifically include:

[0086] Electric vehicles entering each transportation task node all exit from the corresponding transportation task node, and all electric vehicles start from the starting node and return to the ending node; each transportation task node is served by an electric vehicle at most once; the time when an electric vehicle arrives at each transportation task node is no later than the service time of each transportation task node.

[0087] Specifically, since EVRP is built on a graphical model, it should adhere to the following constraints:

[0088]

[0089] in, Indicates electric vehicles Has it passed through the edge? Orient from node j∈V to node i∈V; b j Let be the difference between the number of vehicles entering and the number of vehicles leaving node j; Represents node v o The difference between the number of vehicles entering and the number of vehicles leaving; Represents node v dThe difference between the number of vehicles entering and the number of vehicles leaving, the constraint condition corresponding to formula (2) is for each electric vehicle, its

[0090] Formula (2) shows that all electric vehicles are subject to the electric vehicle flow conservation constraint, that is, electric vehicles entering the transportation service node must leave from the same transportation service node, and all electric vehicles start from the starting node and finally return to the destination node.

[0091]

[0092] Formula (3) requires that transportation task nodes cannot be visited multiple times, and each customer can only be served by the logistics fleet once at most. It should be noted that Formula (3) applies to charging nodes. Invalid, because a single charging station can charge multiple electric vehicles simultaneously.

[0093]

[0094] Among them, t i T represents the time of departure from node i. ij Let t be the known travel time from node i to node j. i +T ij t represents the time to reach node j. j This represents the specified service time for node j. Formula (4) specifies that the arrival time of an electric vehicle at a transportation service node should not be later than the specified service time of each transportation service node, ensuring time compatibility. If an electric vehicle is charged at the same charging station for two consecutive time periods, only T needs to be set. ij =0.

[0095] Charging constraints, specifically including:

[0096] The maximum actual charging power of an electric vehicle does not exceed the power threshold and the charging power threshold, and the power of the electric vehicle is within the power threshold range; the total power of several electric vehicles charging at the same charging node at the same time does not exceed the charging power threshold of the corresponding charging node.

[0097] Specifically, as an electric vehicle is driven and charged, its battery's remaining charge, SOC (State of Charge), also known as the state of charge, changes over time. Let SOC be the state of electric vehicle k when it reaches node j. Therefore, its dynamic SOC can be represented as follows.

[0098]

[0099] in, Let $\frac{ ... Let represent the charging power of electric vehicle k at node i. Since this embodiment specifies that the charging time for one charge is ΔT = 1, therefore, It also represents the charging power of electric vehicle k at node i, d ij This represents the amount of electricity consumed from node i to node j. Similarly, if i and j represent two charging nodes within the same charging station, then this value is 0. Clearly, the State of Charge (SOC) must be maintained within a certain range.

[0100]

[0101] in, s This represents the lower limit of the SOC threshold for electric vehicles. This represents the upper limit of the SOC threshold for electric vehicles.

[0102] At the same time, there are individual charging power limitations for each electric vehicle.

[0103]

[0104] Where, constant This represents the upper limit of the charging power that charging node i can provide to a single electric vehicle.

[0105] And the total charging power constraint of the entire site,

[0106]

[0107] The above charging constraints must be met during the charging process, where the constant is... This represents the maximum output power of charging node i.

[0108] In one implementation, step S500 optimizes the initial transport path of each electric vehicle based on the charging node to be inserted and preset traffic flow constraints to obtain the target planned path, specifically including:

[0109] Step S510: Obtain the service revenue for each initial transportation path based on the service reward of the transportation task nodes on each initial transportation path;

[0110] Step S520: Based on service revenue and traffic flow constraints, traverse each transportation task node to obtain a service planning path consisting of several transportation task nodes;

[0111] Step S530: Optimize the service planning path for each electric vehicle based on the charging node to be inserted to obtain the target planning path.

[0112] Since formula (1) describes a typical mixed integer programming problem, it includes integer variables. and continuous variables This is a nondeterministic problem with polynomial complexity, involving large computational loads and long processing times, making it a very challenging problem for large-scale applications. Furthermore, the constraints in formulas (3)-(8) also couple the decisions between different electric vehicles, further increasing computational complexity. To simplify the solution complexity and ensure the planned path is realistic, this embodiment decouples the electric vehicle transportation task allocation and charging problem into two sub-problems, considering the transportation task allocation and charging problem separately. This simplifies the model described by formula (1), thereby reducing the computational complexity of solving the electric vehicle transportation path planning problem considering charging constraints and improving solution efficiency.

[0113] If we only consider the task reward and traffic flow constraints (including formulas (2)-(4)) and ignore the electric vehicle's battery level and charging constraints (including formulas (5)-(8)), then the transportation task allocation sub-problem is the standard vehicle routing problem (VRP) on the acyclic graph G, that is:

[0114]

[0115] It is worth noting that the charging cost item has been omitted here. Because if the SOC constraint is not considered, no electric vehicles will enter the charging node, which actually reduces overall revenue. For the same reason, routing only needs to be performed on the subgraph with transport task nodes and warehouses. Clearly, the path planning subproblem is a pure linear integer programming (LIP) problem, not a massively multiplication (MIP) problem, and only integer variables are determined. Convert (9) to the equivalent LP form.

[0116] Since all electric vehicles are assumed to be homogeneous, therefore, 3 index variables are used. It can be replaced with a 2-indexed variable x ij ∈{0,1}, without affecting the solution. By substitution, the objective function in formula (9) becomes:

[0117]

[0118] Similarly, the formulas (2)-(4) corresponding to the traffic flow constraints are modified as follows:

[0119]

[0120]

[0121]

[0122]

[0123] The constraint condition corresponding to formula (11) applies to the entire homogeneous electric vehicle fleet, where all electric vehicles at node v o The difference between the number of vehicles entering and the number of vehicles leaving is At node v d The difference between the number of vehicles entering and the number of vehicles leaving is

[0124] Clearly, the objective function (10) in x ij In equation (11), the index k is removed compared to equation (2), meaning the variable applies not to each individual vehicle but to the entire fleet. Furthermore, constraints (11) and (12) satisfy the unimodality condition. The unimodality condition, also known as the total unimodularity, is a term in integer programming. Integer programming problems satisfying the unimodality condition can be linearly relaxed to solve as linear programming problems, while the optimal solution remains an integer solution. Therefore, integer programming problems satisfying the unimodality condition are a class of easily solvable problems.

[0125] At the same time, it can be proven that constraint (13) does not violate the monotonical condition. Therefore, this embodiment can accurately represent the integer variable x. ij Relaxed to a continuous variable x in {0,1} ij ∈[0,1], thus transforming the LIP problem into a linear programming (LP) problem. According to formula (10), the planned path consisting of the starting node, the ending node, and a series of transportation task nodes can be obtained.

[0126] Similarly, if we only consider the electric vehicle's battery capacity and charging constraints (including formulas (5)-(8)) and ignore the task reward and traffic flow constraints (including formulas (2)-(4)), then the subproblem shown below is a pure linear integer programming problem (LIP) for inserting charging nodes, that is:

[0127]

[0128] in, This means that the total charging power of all electric vehicles charging at a charging station at the same time does not exceed the upper limit of the charging station's output power; This represents a path consisting of all charging nodes along a planned route.

[0129] Consider the path of electric vehicle k, which is a sequence of transportation task nodes. The process should be traversed sequentially by electric vehicle k. Clearly, electric vehicles consume electricity during operation and need to connect to charging stations between transport task nodes to recharge their batteries. To increase the chances of accessing charging stations, as many billing nodes as possible should be inserted between any two consecutive nodes in the path, for example, i m and i m+1 This operation is equivalent to finding a line from i. m Starting from point i, passing through the maximum number of billing nodes, and finally returning to point i. m+1 The route. By considering all consecutive transport task nodes of electric vehicle k, i m and i m+1 By repeatedly solving (14), a path can be obtained. It only includes all inserted charging nodes.

[0130] Then, in chronological order and Combined, this generates a complete path for electric vehicle k that connects the transportation task nodes and the charging nodes. Obtain the target planning path.

[0131] In one implementation, after obtaining the target planned path, the method further includes step S600: updating the target planned path, specifically:

[0132] Step S610: Based on the charging power required for travel between any two transportation task nodes on the target planned path, and based on the charging power and the charging unit price, obtain the charging cost between the two transportation task nodes.

[0133] Step S620: Preset auxiliary decision variables to quantify the degree of violation of charging power constraints, and determine the transportation task nodes that need to be deleted on the target planning path based on charging cost and auxiliary decision variables;

[0134] Step S630: Update the target planning path according to the transportation task nodes that need to be deleted.

[0135] Specifically, given any two consecutive charging nodes j n and j n+1 The electrical energy d required between n,n+1 If j n and j n+1 If there are transportation task nodes between them, then d n,n+1 This equals the cumulative energy consumption for serving these tasks; otherwise, if there are no intermediate transport task nodes, then d n,n+1 =0. Therefore, the SOC dynamics of electric vehicle k along its planned path are as follows:

[0136]

[0137] in, This indicates that electric vehicle k originates from node j. n The remaining battery power at the start of the trip. This indicates that electric vehicle k is at node j. n The charging power, since this embodiment specifies that the charging time for one charge is ΔT = 1, therefore, It also indicates that electric vehicle k is at node j n The charging capacity, This indicates that electric car k has arrived at node j. n+1 Battery level at that time.

[0138] Undoubtedly, the box constraints of SOC and charging power (6)-(8) should also be satisfied. Meanwhile, the goal here is to reduce charging costs. It is worth noting that when the task time is too tight, formula (14) may not be feasible if the number of charging nodes inserted in the path is insufficient. To address this issue, this embodiment relaxes the box constraints of SOC. And modify formula (14) as follows:

[0139]

[0140] Where α represents the penalty weight, and α > 0. This represents a user-defined auxiliary decision-making variable. Used to quantify the degree of violation of SOC box constraints. If no violation occurs, then... And the relaxation problem (16) is the same as (18); otherwise, This means that the SOC box constraint has been violated. The next step is to propose an iterative adjustment process of "deletion-insertion-rescheduling" to avoid the violation of the SOC box constraint.

[0141] In one implementation, step S620, based on charging cost and auxiliary decision variables, determines the transportation task nodes that need to be deleted on the target planning path, specifically including:

[0142] Step S621: Solve for the values ​​of the auxiliary decision variables at each transportation task node. If the value of the auxiliary decision variable is greater than zero, it is determined that there is a transportation task node that violates the SOC box constraint before the corresponding transportation task node.

[0143] Step S622: When there is a transportation task node that violates the SOC box constraint, calculate the power consumption required to travel between the corresponding transportation task node and the two nodes adjacent to the corresponding transportation task node, and the power consumption required to travel only between the two nodes adjacent to the corresponding transportation task node, and obtain the additional power consumption required to pass through the corresponding transportation task node.

[0144] Step S623: Based on the service reward of the corresponding transportation task node and the additional power consumption required to pass through the corresponding transportation task node, calculate the quantitative revenue value of all transportation task nodes before the corresponding transportation task node, and determine the transportation task nodes that need to be deleted on the target planning path.

[0145] Specifically, since the slack subproblem (16) is always feasible, it can be computed using existing solvers. The value of . Let the transport task node j be . - The target path for electric vehicle k The first appearance in The transportation task node, which means that the SOC box constraint of electric vehicle k is at transportation task node j. - The first violation occurs at point j, meaning there is a transport task node that violates the SOC box constraint before transport task node j. or

[0146] When a transportation task node that violates the SOC box constraint is detected, the power consumption required to travel between the corresponding transportation task node j and the two nodes adjacent to the corresponding transportation task node is calculated, as well as the power consumption required to travel only between the two nodes adjacent to the corresponding transportation task node j, to obtain the additional power consumption Δd required to pass through the corresponding transportation task node. j This is the difference between the energy consumed by electric vehicle k when it travels from transportation task node A through transportation task node j to transportation task node B and the energy consumed by electric vehicle k when it travels directly from transportation task node A to transportation task node B.

[0147] Then, calculate the service reward r for transportation task node j. j And the additional power consumption Δd required to pass through transport task node j j Calculate the quantified revenue value at all transportation task nodes preceding the corresponding transportation task node. The smaller this value, the smaller the service reward obtained from going to the corresponding transportation task node, while the greater the charging cost due to the extra power consumption, making it less worthwhile to go to that transportation task node for service. Based on this principle, the calculation at transportation task node j... - By comparing the quantified revenue values ​​of the previously passed transportation task nodes, the transportation task node with the smallest quantified revenue value is obtained and removed from the target planning path.

[0148] As transportation tasks change over time, a path is planned towards the target based on the real-time transportation tasks and according to formula (14). Additional charging nodes are reinserted and the charging power is rescheduled according to formula (16) to achieve iterative adjustment of the path planning by "deletion-insertion-rescheduling" until no SOC violation occurs in any electric vehicle, so as to achieve both the completion of the transportation task on the planned path and the minimum number of charging times. This not only reduces the charging time and saves costs, but also improves transportation efficiency, thereby improving customer satisfaction.

[0149] It should be noted that the application fields of this invention include, but are not limited to, the logistics and transportation field and the passenger transport field. When applied to the logistics and transportation field, the transportation task nodes include pickup nodes, delivery nodes, and warehouse nodes. When applied to the passenger transport field, the transportation task nodes include passenger boarding nodes and passenger alighting nodes.

[0150] The path planning method of this invention is tested and verified using the Belgian dataset. Specifically, the Belgian dataset contains real information about the Belgian road network and is divided into two datasets: belgium-n50-k10.vrp and belgium-n1000-k20.vrp, with 50 and 1000 customers respectively. In the experiment, a certain number of electric vehicles and charging stations were allocated for each dataset based on the number of customers and their spatial distribution. The solution was performed using the Gurobi solver version 9.5.2 under the computing conditions of an Intel Core i7-8750H CPU, 2.2GHz, and 8GB RAM. For example, for the dataset with 50 customers, 4 charging stations and 10 electric vehicles were allocated, and the charging station locations were randomly selected based on the spatial distribution of the customers. The 4 charging stations are located in Brussels, Hippini, Tilford, and Molde. Similarly, for the dataset with 1000 customers, 10 charging stations and 20 electric vehicles were allocated, and the charging station locations were randomly selected based on the spatial distribution of the customers. Experimental results consistently demonstrate that the method of this invention can adjust the path planning strategy in a timely manner when order demand changes in real time. Using the Gurobi solver version 9.5.2, the computation time for a dataset of 50 customers is 11.84s, and the computation time for a dataset of 1000 customers is 165.76s. It is evident that the method of this invention can not only adapt well to real-time changing order demand, but also has high computational efficiency and broad application prospects.

[0151] In summary, this invention fully considers the actual situation of route planning and charging needs of electric vehicles during transportation tasks, and uses the output power of charging stations and dynamic electricity prices as reference factors, making the constructed route planning model closer to actual application scenarios. Furthermore, by optimizing the route planning model, the complex mixed-integer programming problem is cleverly transformed into a linear programming problem, greatly simplifying the solution complexity, reducing computation time, and significantly improving solution efficiency.

[0152] Exemplary System

[0153] like Figure 2 As shown, corresponding to the above-described electric vehicle transportation route planning method based on charging constraints, this embodiment of the invention also provides an electric vehicle transportation route planning system based on charging constraints, the above-described electric vehicle transportation route planning system based on charging constraints includes:

[0154] The node acquisition module is used to acquire the starting point node, ending point node, and transportation task node of the electric vehicle.

[0155] The initial transportation route construction module is used to determine several initial transportation routes for electric vehicles, which are formed by the electric vehicle from the starting point node to the ending point node and passing through at least one transportation task node, based on the transportation task node corresponding to the electric vehicle.

[0156] The charging power calculation module is used to obtain the power consumption required for the electric vehicle to travel the initial transportation route, as well as the remaining power of the electric vehicle at the current node, and to determine the charging power required for the electric vehicle to travel on the initial transportation route.

[0157] The charging node insertion determination module is used to obtain all charging nodes corresponding to each charging station at the location of the transportation task, and determine the charging node to be inserted on the initial transportation path based on preset charging constraints, charging capacity and charging nodes.

[0158] The target planning path construction module is used to optimize the initial transportation path of each electric vehicle based on the charging nodes to be inserted and the preset traffic flow constraints, so as to obtain the target planning path.

[0159] Specifically, in this embodiment, the specific functions of the electric vehicle transportation route planning system based on charging constraints can also be referred to the corresponding description in the electric vehicle transportation route planning method based on charging constraints, and will not be repeated here.

[0160] Based on the above embodiments, the present invention also provides a smart terminal, the principle block diagram of which can be as follows: Figure 3As shown. The aforementioned intelligent terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a charging-constrained electric vehicle transportation route planning program. The internal memory provides an environment for the operation of the operating system and the charging-constrained electric vehicle transportation route planning program stored in the non-volatile storage medium. The network interface of the intelligent terminal is used for communication with external terminals via a network connection. When the charging-constrained electric vehicle transportation route planning program is executed by the processor, it implements the steps of any of the aforementioned charging-constrained electric vehicle transportation route planning methods. The display screen of the intelligent terminal can be a liquid crystal display (LCD) or an e-ink display.

[0161] Those skilled in the art will understand that Figure 3 The block diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the smart terminal to which the present invention is applied. A specific smart terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0162] In one embodiment, a smart terminal is provided, the smart terminal including a memory, a processor, and a charging-constrained electric vehicle transportation route planning program stored in the memory and executable on the processor. When the charging-constrained electric vehicle transportation route planning program is executed by the processor, it implements the steps of any of the charging-constrained electric vehicle transportation route planning methods provided in the embodiments of the present invention.

[0163] This invention also provides a computer-readable storage medium storing a charging-constrained electric vehicle transportation route planning program. When executed by a processor, the charging-constrained electric vehicle transportation route planning program implements the steps of any of the charging-constrained electric vehicle transportation route planning methods provided in this invention.

[0164] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0165] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the above device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0166] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0167] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0168] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of the above modules or units is merely a logical functional division, and in actual implementation, it can be divided in other ways. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0169] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not mean that the essence of the corresponding technical solutions deviates from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for electric vehicle transportation route planning based on charging constraints, characterized in that, Includes the following steps: Obtain the starting point node, ending point node, and transportation task node of the electric vehicle; Based on the transportation task nodes corresponding to the electric vehicle, determine several initial transportation paths for the electric vehicle from the starting point node to the ending point node, passing through at least one of the transportation task nodes. The amount of electricity consumed by the electric vehicle to travel the initial transportation route and the remaining electricity of the electric vehicle at the current node are obtained to determine the amount of electricity required for the electric vehicle to travel on the initial transportation route. Obtain all charging nodes corresponding to each charging station at the location of the transportation task, and determine the charging node to be inserted on the initial transportation path based on preset charging constraints, the charging capacity, and the charging node. Based on the charging nodes to be inserted and the preset traffic flow constraints, the initial transportation path of each electric vehicle is optimized to obtain the target planned path. The step of optimizing the initial transportation path of each electric vehicle based on the charging node to be inserted and preset traffic flow constraints to obtain a target planning path includes: obtaining the service revenue of each initial transportation path based on the service reward of the transportation task nodes on each initial transportation path; traversing each transportation task node based on the service revenue and the traffic flow constraints to obtain a service planning path composed of several transportation task nodes; and optimizing the service planning path of each electric vehicle based on the charging node to be inserted to obtain a target planning path. After obtaining the target planning path, the process further includes: based on the charging power required for travel between any two transport task nodes on the target planning path, and based on the charging power and the charging unit price, obtaining the charging cost between the two transport task nodes; pre-setting auxiliary decision variables to quantify the degree of violation of charging power constraints, and based on the charging cost and the auxiliary decision variables, determining the transport task nodes that need to be deleted on the target planning path; and updating the target planning path based on the transport task nodes that need to be deleted. The auxiliary decision variables are obtained by calculating the relaxation subproblem using a solver. The calculation formula for the relaxation subproblem is as follows: ； in, It is a fleet of homogeneous electric vehicles; It is a path consisting of all charging nodes along a planned route; It is at the node The unit price of charging; It is an electric car At the node The charging power; The total charging power of all electric vehicles charging at a charging station at the same time does not exceed the upper limit of the output power of that charging station; It is the penalty weight; These are the auxiliary decision variables; It is the lower limit of the SOC threshold for electric vehicles; It is an electric car Reaching the node SOC at that time; It is the upper limit of the SOC threshold for electric vehicles; It is a charging node The upper limit of charging power for a single electric vehicle; It is a charging node Maximum output power; It is a mathematical symbol that represents any one of them.

2. The electric vehicle transportation route planning method based on charging constraints according to claim 1, characterized in that, The step of determining several initial transportation paths for the electric vehicle, from the starting point node to the ending point node and passing through at least one of the transportation task nodes, based on the transportation task node corresponding to the electric vehicle, includes: Construct a transportation network based on the set of road nodes where the transportation task is located; Based on the transportation network, a directed acyclic graph is constructed using the starting node, the ending node, the transportation task node, and the charging node; Based on the directed acyclic graph, several initial transportation paths are determined for each electric vehicle, consisting of the starting point node, the ending point node, and at least one transportation task node.

3. The electric vehicle transportation route planning method based on charging constraints according to claim 1, characterized in that, The step of determining the transportation task nodes that need to be deleted on the target planning path based on the charging cost and the auxiliary decision variables includes: Solve for the values ​​of the auxiliary decision variables at each of the transportation task nodes. If the value of the auxiliary decision variable is greater than zero, it is determined that there is a transportation task node that violates the SOC box constraint before the corresponding transportation task node. When there is a transportation task node that violates the SOC box constraint, calculate the power consumption required to travel between the corresponding transportation task node and the two nodes adjacent to the corresponding transportation task node, and the power consumption required to travel only between the two nodes adjacent to the corresponding transportation task node, to obtain the additional power consumption required to pass through the corresponding transportation task node. Based on the service rewards of the corresponding transportation task nodes and the additional electricity required to pass through the corresponding transportation task nodes, the quantified revenue values ​​of all transportation task nodes before the corresponding transportation task node are calculated, and the transportation task nodes that need to be deleted on the target planning path are determined.

4. The electric vehicle transportation route planning method based on charging constraints according to claim 1, characterized in that, The traffic flow constraints include: The electric vehicles that enter each of the transportation task nodes all drive out from the corresponding transportation task node, and all the electric vehicles start from the starting node and finally return to the ending node; Each of the aforementioned transportation task nodes is served by the electric vehicle at most once; The electric vehicle arrives at each of the transport task nodes no later than the service time of each of the transport task nodes.

5. The electric vehicle transportation route planning method based on charging constraints according to claim 1, characterized in that, The charging constraints include: The maximum actual charging power of the electric vehicle does not exceed the power threshold and the charging power threshold, and the power of the electric vehicle is within the range of the power threshold. The total power of several electric vehicles charging simultaneously at the same charging node does not exceed the charging power threshold of the corresponding charging node.

6. An electric vehicle transportation route planning system based on charging constraints, characterized in that, The system includes: The node acquisition module is used to acquire the starting point node, ending point node, and transportation task node of the electric vehicle. An initial transportation route construction module is used to determine, based on the transportation task nodes corresponding to the electric vehicle, several initial transportation routes consisting of the electric vehicle from the starting point node to the ending point node and passing through at least one of the transportation task nodes. The charging power calculation module is used to obtain the power consumption required for the electric vehicle to travel the initial transportation path, and the remaining power of the electric vehicle at the current node, and to determine the charging power required for the electric vehicle to travel on the initial transportation path. The charging node insertion determination module is used to obtain all charging nodes corresponding to each charging station at the location of the transportation task, and determine the charging node to be inserted on the initial transportation path based on preset charging constraints, the charging capacity and the charging node. The target planning path construction module is used to optimize the initial transportation path of each electric vehicle based on the charging node to be inserted and the preset traffic flow constraints, so as to obtain the target planning path. The electric vehicle transportation route planning system based on charging constraints is further used to obtain the service revenue of each initial transportation route according to the service reward of each transportation task node on each initial transportation route; based on the service revenue and the traffic flow constraints, traverse each transportation task node to obtain a service planning route composed of several transportation task nodes; and optimize the service planning route of each electric vehicle according to the charging node to be inserted to obtain the target planning route. The electric vehicle transportation route planning system based on charging constraints is further used to obtain the charging cost between any two transportation task nodes on the target planning path based on the charging amount required for travel between them and the charging unit price; preset auxiliary decision variables for quantifying the degree of violation of charging amount constraints; determine the transportation task nodes that need to be deleted on the target planning path based on the charging cost and the auxiliary decision variables; and update the target planning path based on the transportation task nodes that need to be deleted. The auxiliary decision variables are obtained by calculating the relaxation subproblem using a solver. The calculation formula for the relaxation subproblem is as follows: ； in, It is a fleet of homogeneous electric vehicles; It is a path consisting of all charging nodes along a planned route; It is at the node The unit price of charging; It is an electric car At the node The charging power; The total charging power of all electric vehicles charging at a charging station at the same time does not exceed the upper limit of the output power of that charging station; It is the penalty weight; These are the auxiliary decision variables; It is the lower limit of the SOC threshold for electric vehicles; It is an electric car Reaching the node SOC at that time; It is the upper limit of the SOC threshold for electric vehicles; It is a charging node The upper limit of charging power for a single electric vehicle; It is a charging node Maximum output power; It is a mathematical symbol that represents any one of them.

7. A smart terminal, characterized in that, The smart terminal includes a memory, a processor, and a charging-constrained electric vehicle transportation route planning program stored in the memory and executable on the processor. When the charging-constrained electric vehicle transportation route planning program is executed by the processor, it implements the steps of the charging-constrained electric vehicle transportation route planning method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a charging-constrained electric vehicle transportation route planning program, which, when executed by a processor, implements the steps of the charging-constrained electric vehicle transportation route planning method as described in any one of claims 1-5.

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