Electric vehicle team scheduling method, device and equipment and storage medium

Abstract

The invention discloses an electric vehicle team scheduling method, device and equipment and a storage medium, and belongs to the technical field of vehicle scheduling. The method comprises the following steps: acquiring an initial battery replacement scheme of each vehicle in an electric vehicle team, wherein the initial battery replacement scheme is composed of a plurality of alternative scheme branches; obtaining a plurality of scheduling optimization targets according to the initial power conversion scheme, and determining a target function value of each scheduling optimization target; pruning a plurality of alternative scheme branches in the initial power conversion scheme based on the target function value to obtain a target power conversion scheme; according to the method, the vehicles in the electric vehicle team are scheduled through the target battery replacement scheme, mutual influence among multiple battery replacement schemes is considered, pruning is performed on the multiple battery replacement schemes by determining the target function value of each scheduling optimization target, so that the battery replacement scheme suitable for each vehicle is obtained, and the scheduling intelligence and the scheduling effect are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle dispatching, and in particular to an electric vehicle fleet dispatching method, device, equipment and storage medium. Background Art

[0002] Electric vehicle fleets generally travel back and forth between two places to carry out long-distance logistics transportation tasks. When carrying out long-distance transportation, the power of the vehicles in the fleet is not enough to support the completion of their transportation tasks, so the vehicles need to be scheduled for battery replacement.

[0003] When scheduling electric vehicle fleets, strategies are often limited to a single battery swap. If a vehicle requires multiple battery swaps during a mission, current methods can only break these multiple swaps down into multiple, single-swap events. This ignores the interplay between various scheduling strategies, making it impossible to arrive at a suitable scheduling solution. This results in less intelligent scheduling and poor results. Summary of the Invention

[0004] The main purpose of the present invention is to provide an electric vehicle fleet scheduling method, device, equipment and storage medium, aiming to solve the technical problems of the existing vehicle scheduling technology that is not intelligent enough and has poor effect.

[0005] In a first aspect, an embodiment of the present application provides an electric vehicle fleet scheduling method, the electric vehicle fleet scheduling method comprising:

[0006] Obtaining an initial battery replacement plan for each vehicle in the electric vehicle fleet, wherein the initial battery replacement plan is composed of multiple alternative plan branches;

[0007] Obtaining multiple scheduling optimization objectives according to the initial battery swapping plan, and determining the objective function value of each scheduling optimization objective;

[0008] Pruning multiple alternative solution branches in the initial battery swap solution based on the objective function value to obtain a target battery swap solution;

[0009] Each vehicle in the electric vehicle fleet is dispatched using the target battery replacement plan.

[0010] This embodiment proposes a method for scheduling an electric vehicle fleet, which obtains an initial battery swapping plan for each vehicle in the electric vehicle fleet, where the initial battery swapping plan consists of multiple alternative plan branches; obtains multiple scheduling optimization objectives based on the initial battery swapping plan, and determines the objective function value of each scheduling optimization objective; prunes the multiple alternative plan branches in the initial battery swapping plan based on the objective function value to obtain a target battery swapping plan; schedules each vehicle in the electric vehicle fleet according to the target battery swapping plan, taking into account the mutual influence between multiple battery swapping plans, and prunes multiple battery swapping plans by determining the objective function value of each scheduling optimization objective, thereby obtaining a suitable battery swapping plan for each vehicle, thereby improving scheduling intelligence and scheduling effect.

[0011] In some embodiments, the pruning, according to the sorting list, the multiple candidate scheme branches in the initial battery swap scheme to obtain a target battery swap scheme comprises:

[0012] obtaining a target function value of each candidate scheme branch based on the target function value;

[0013] sorting each candidate scheme branch according to the size of the target function value of each candidate scheme branch to obtain a sorting list of candidate scheme branches;

[0014] pruning, according to the sorting list, the multiple candidate scheme branches in the initial battery swap scheme to obtain a target battery swap scheme.

[0015] In the technical scheme of the embodiments of the present application, the target function value of each candidate scheme branch is obtained, and each candidate scheme branch is pruned according to the size of the target function value of each candidate scheme branch, so that unsuitable candidate schemes are eliminated, and an optimal target battery swap scheme is obtained, thereby improving the scheduling effect of each vehicle in the electric vehicle fleet.

[0016] In some embodiments, the pruning, according to the sorting list, the multiple candidate scheme branches in the initial battery swap scheme to obtain a target battery swap scheme comprises:

[0017] obtaining a priority of each candidate scheme branch according to the sorting list;

[0018] pruning, based on the priority, the multiple candidate scheme branches in the initial battery swap scheme to eliminate candidate schemes whose priority does not meet a preset priority, and obtaining a target battery swap scheme.

[0019] In the technical scheme of the embodiments of the present application, each candidate scheme branch is sorted, and the priority of each candidate scheme branch is obtained, so that candidate schemes that do not meet a preset priority are eliminated, and an optimal battery swap scheme combination is obtained, thereby improving the scheduling effect of each vehicle in the electric vehicle fleet.

[0020] In some embodiments, the obtaining, according to the initial battery swap scheme, multiple scheduling optimization targets and determining a target function value of each scheduling optimization target comprises:

[0021] obtaining, according to the initial battery swap scheme, a scheduling optimization target corresponding to each candidate scheme branch;

[0022] obtaining a battery swap constraint condition of each candidate scheme branch in the initial battery swap scheme;

[0023] solving the scheduling optimization target based on the battery swap constraint condition to obtain a target function value corresponding to each scheduling optimization target.

[0024] In the technical solution of the embodiment of the application, the scheduling optimization target corresponding to each alternative scheme branch is determined, and the battery swap constraint condition is obtained, so that the scheduling optimization target is solved, the objective function value of the scheduling optimization target corresponding to each alternative scheme is obtained, the alternative scheme branch is pruned through the objective function value, and therefore the calculation efficiency can be improved.

[0025] In some embodiments, the obtaining of the battery swap constraint condition of each alternative scheme branch in the initial battery swap scheme comprises:

[0026] obtaining the specified battery swap station of the scheduling of each alternative scheme branch in the initial battery swap scheme;

[0027] obtaining the battery swap station state information of the specified battery swap station;

[0028] obtaining the vehicle current state information of each vehicle in the electric vehicle fleet;

[0029] obtaining the battery swap constraint condition of each alternative scheme branch based on the battery swap station state information and the vehicle current state information.

[0030] In the technical solution of the embodiment of the application, the battery swap station information corresponding to each alternative scheme branch in the initial battery swap scheme can be determined, and the vehicle information can be obtained, and the battery swap constraint condition of the alternative scheme branch can be quickly determined through the state variables of the battery swap station and the vehicle.

[0031] In some embodiments, the obtaining of the battery swap constraint condition of each alternative scheme branch based on the battery swap station state information and the vehicle current state information comprises:

[0032] obtaining at least one of the vehicle current position, the vehicle current power, the vehicle current power consumption, the vehicle current speed, and the arrival time of the vehicle to the specified battery swap station according to the vehicle current state information;

[0033] obtaining at least one of the inventory information, the queuing number, the battery swap number, and the full-power battery number of the specified battery swap station according to the battery swap station state information;

[0034] obtaining the battery swap constraint condition of each alternative scheme branch according to at least one of the vehicle current position, the vehicle current power, the vehicle current power consumption, the vehicle current speed, and the arrival time of the vehicle to the specified battery swap station, and at least one of the inventory information, the queuing number, the battery swap number, and the full-power battery number of the specified battery swap station.

[0035] In the technical solution of the embodiment of the present application, the battery swap constraint conditions of each alternative solution branch can be flexibly selected through one or more state variables in the vehicle's current state information and one or more state variables in the battery swap station state information, thereby improving the intelligence of battery swap scheduling.

[0036] In some embodiments, obtaining the battery swap constraint conditions of each alternative solution branch based on the battery swap station status information and the vehicle current status information includes:

[0037] Obtaining the arrival time of the vehicle at the designated battery swap station based on the current state information of the vehicle;

[0038] Determining the demand for battery-swapping vehicles in the electric vehicle fleet based on the arrival time;

[0039] Obtain at least one of inventory information, queue quantity, battery swap quantity, and fully charged battery quantity of the designated battery swap station according to the battery swap station status information;

[0040] The battery swap constraint conditions of each alternative plan branch are obtained through at least one of the battery swap vehicle demand, the inventory information of the designated battery swap station, the number of queues, the number of battery swaps, and the number of fully charged batteries.

[0041] In the technical solution of the embodiment of the present application, the demand for battery swapping vehicles in the electric vehicle fleet can be quickly determined based on the current status information of the vehicle, so that the battery swapping constraints of each alternative solution branch can be flexibly selected through the demand for battery swapping vehicles and one or more state variables in the battery swapping station status information, thereby improving the intelligence of battery swapping scheduling.

[0042] In some embodiments, solving the scheduling optimization objective based on the battery swap constraint to obtain the objective function value corresponding to each scheduling optimization objective includes:

[0043] Inputting the battery swap constraint condition into a preset scheduling model, wherein the preset scheduling model represents the corresponding relationship between the battery swap constraint condition and the battery swap optimization target and the objective function value;

[0044] The battery swap optimization target is solved through the preset scheduling model to obtain the objective function value corresponding to each scheduling optimization target.

[0045] In the technical solution of the embodiment of the present application, by modeling the battery swapping constraints and the battery swapping optimization objectives, a preset scheduling model is obtained to characterize the correspondence between the battery swapping constraints and the battery swapping optimization objectives and the objective function values. The battery swapping optimization objectives are solved by the preset scheduling model, and the objective function values ​​corresponding to each scheduling optimization objective are quickly obtained, thereby improving the scheduling efficiency.

[0046] In some embodiments, solving the scheduling optimization objective based on the battery swap constraint to obtain the objective function value corresponding to each scheduling optimization objective includes:

[0047] Based on the battery swap constraint condition, at least one of the vehicle's current location, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station is obtained, as well as at least one of the vehicle's inventory information, the number of queues, the number of battery swaps, and the number of fully charged batteries at the designated battery swap station;

[0048] The scheduling optimization objective is solved through at least one of the vehicle's current position, the vehicle's current power, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station, as well as at least one of the inventory information, queue number, battery swap number, and fully charged battery number of the designated battery swap station to obtain the objective function value corresponding to each scheduling optimization objective.

[0049] In the technical solution of the embodiment of the present application, the scheduling optimization objective can be solved by one or more state variables in the battery replacement constraint conditions, so as to obtain the objective function values ​​corresponding to different scheduling optimization objectives and improve the solution effect.

[0050] In some embodiments, the method further comprises:

[0051] Get the preset culling strategy;

[0052] The elimination strategy is used to eliminate redundancy in the initial battery replacement plan to obtain an updated initial battery replacement plan, and the updated initial battery replacement plan replaces the initial battery replacement plan.

[0053] In the technical solution of the embodiment of the present application, a preset elimination strategy is set in advance to eliminate redundancy in the initial battery replacement plan, narrow the scope of the battery replacement plan, and improve the efficiency of subsequent pruning of various alternative plan branches.

[0054] In some embodiments, obtaining an initial battery replacement plan for each vehicle in the electric vehicle fleet includes:

[0055] Obtaining the initial status information of each vehicle in the electric vehicle fleet;

[0056] Obtaining the initial status information of the battery swap stations along the route to be traveled by the electric vehicle fleet;

[0057] Generating multiple alternative solutions for each vehicle based on the vehicle initial state information and the battery swap station initial state information;

[0058] Multiple alternative plans for each vehicle are branched and expanded to obtain the initial battery replacement plan for each vehicle.

[0059] In the technical solution of the embodiment of the present application, all battery replacement alternatives for each vehicle are generated through the initial state information of each vehicle and the initial state information of the battery replacement station, and the battery replacement alternatives are expanded to obtain the initial battery replacement plan for each vehicle, thereby improving the versatility of battery replacement scheduling.

[0060] In a second aspect, an embodiment of the present invention further provides an electric vehicle fleet dispatching device, the electric vehicle fleet dispatching device comprising:

[0061] An acquisition module is used to obtain an initial battery replacement plan for each vehicle in the electric vehicle fleet, wherein the initial battery replacement plan is composed of multiple alternative plan branches;

[0062] A determination module, configured to obtain a plurality of scheduling optimization objectives according to the initial battery swapping plan, and determine an objective function value of each scheduling optimization objective;

[0063] a pruning module, configured to prune multiple alternative solution branches in the initial battery swap solution based on the objective function value to obtain a target battery swap solution;

[0064] A scheduling module is used to schedule each vehicle in the electric vehicle fleet according to the target battery replacement plan.

[0065] In a third aspect, an embodiment of the present invention further proposes an electric fleet dispatching device, which includes: a memory, a processor, and an electric fleet dispatching program stored on the memory and executable on the processor, wherein the electric fleet dispatching program is configured to implement the electric fleet dispatching method described above.

[0066] In a fourth aspect, an embodiment of the present invention further proposes a storage medium on which an electric vehicle fleet scheduling program is stored. When the electric vehicle fleet scheduling program is executed by a processor, the electric vehicle fleet scheduling method as described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0068] Figure 1 It is a structural diagram of an electric vehicle fleet dispatching device in a hardware operating environment involved in an embodiment of the present invention;

[0069] Figure 2 A schematic diagram of the locations of electric vehicles, battery swap stations, and warehouses in an embodiment of an electric vehicle fleet dispatching method proposed in an embodiment of the present invention;

[0070] Figure 3 A flow chart of an embodiment of an electric vehicle fleet dispatching method according to an embodiment of the present invention;

[0071] Figure 4 A schematic diagram of the relationship between warehouses, alternative battery swap stations, and dispatching battery swap stations in an embodiment of the electric vehicle fleet dispatching method proposed in an embodiment of the present invention;

[0072] Figure 5 This is another flowchart of an embodiment of the electric vehicle fleet scheduling method proposed in an embodiment of the present invention;

[0073] Figure 6 A schematic diagram of a multi-branch tree formed by alternative solution branches in an embodiment of the electric vehicle fleet scheduling method proposed in an embodiment of the present invention;

[0074] Figure 7 This is another flowchart of an embodiment of the electric vehicle fleet scheduling method proposed in an embodiment of the present invention;

[0075] Figure 8 This is a structural block diagram of the first embodiment of the electric vehicle fleet dispatching device of the present invention.

[0076] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0077] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0078] The following embodiments of the technical solution of the present invention will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are therefore only examples and are not intended to limit the scope of protection of the present invention.

[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which the present invention belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions.

[0080] In the description of the embodiments of the present invention, technical terms such as "first" and "second" are used solely to distinguish between different objects and should not be understood to indicate or imply relative importance or to implicitly specify the quantity, specific order, or primary and secondary relationship of the technical features indicated. In the description of the embodiments of the present invention, "plurality" means more than two, unless otherwise specifically defined.

[0081] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0082] In the description of the embodiments of the present invention, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exists simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0083] In the description of the embodiments of the present invention, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0084] In the description of the embodiments of the present invention, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the embodiments of the present invention.

[0085] In the description of the embodiments of the present invention, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and can refer to internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on specific circumstances.

[0086] When scheduling battery swaps for electric vehicle fleets, existing models often only provide a single-schedule battery swap strategy. If a vehicle needs to swap multiple batteries during a mission, these multiple swaps are broken down into multiple single-schedule events, ignoring the interactions between these events and limiting model accuracy. The battery swap problem for electric vehicle fleets is a multi-segment scheduling problem. Each battery swap scheduling strategy for a vehicle is influenced by its preceding scheduling event and affects subsequent battery swap scheduling strategies. Therefore, each battery swap scheduling is subject to the interdependence of these scheduling events. Furthermore, when scheduling multiple scheduling options, it is impossible to quickly determine the most appropriate one, resulting in low scheduling efficiency.

[0087] In order to solve the above-mentioned defects, the invention of this application is as follows:

[0088] A battery swapping scheme screening and scheduling scheme for electric vehicle fleets is proposed. This method aims to perform decision pruning on each battery swapping scheme, thereby improving the efficiency of determining the appropriate battery swapping scheduling scheme.

[0089] In order to achieve this goal, this embodiment first determines all alternative battery replacement schemes for each vehicle, and calculates the objective function value through the optimization objectives of different alternative battery replacement schemes, thereby pruning the branches of each alternative battery replacement scheme through the objective function value, and continuously screening the alternative schemes to select the most suitable battery replacement scheme and improve the scheduling effect.

[0090] Based on the above considerations, in order to solve the problem that vehicle scheduling is not intelligent enough and the effect is poor, after in-depth research, an electric vehicle fleet scheduling method, device, equipment and storage medium are proposed, which are suitable for various scenarios of battery replacement during the performance of transportation tasks by electric vehicle fleets of different types and numbers, such as the specific application scenario of battery replacement for each electric vehicle in an electric vehicle fleet performing long-distance transportation tasks on high-speed trunk lines or for each electric vehicle in an electric vehicle fleet performing short-distance transportation tasks.

[0091] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the structure of an electric vehicle fleet dispatching device in a hardware operating environment according to an embodiment of the present invention.

[0092] like Figure 1As shown, the electric vehicle fleet dispatching device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 103, a network interface 104, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 103 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 103 may optionally include a standard wired interface and a wireless interface. The network interface 104 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 105 may be a high-speed random access memory (Random Access Memory, RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 105 may optionally be a storage device independent of the aforementioned processor 101.

[0093] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation on the electric fleet dispatching equipment, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0094] like Figure 1 As shown, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module, and an electric vehicle fleet scheduling program.

[0095] exist Figure 1 In the electric vehicle fleet dispatching device shown, the network interface 104 is mainly used for data communication with the network server; the user interface 103 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the electric vehicle fleet dispatching device of the present invention can be set in the electric vehicle fleet dispatching device, and the electric vehicle fleet dispatching device calls the electric vehicle fleet dispatching program stored in the memory 105 through the processor 101, and executes the electric vehicle fleet dispatching method provided by the embodiment of the present invention.

[0096] The executor of the electric vehicle fleet scheduling method provided in the embodiment of the present application may be an electric vehicle fleet scheduling device. The electric vehicle fleet scheduling device includes a preset scheduling model, and a corresponding battery swap scheduling plan can be generated through the preset scheduling model, thereby scheduling the vehicles through the generated battery swap scheduling plan. The electric vehicle fleet scheduling device is also deployed with a pruning module, which prunes the battery swap scheduling plan through the pruning module to remove inappropriate scheduling plans, thereby obtaining the most appropriate battery swap scheduling plan and scheduling the vehicles for battery swap.

[0097] like Figure 2As shown, Figure 2 This diagram shows the locations of electric vehicles, battery swap stations, and warehouses. Along a main route, there are several warehouses, battery swap stations, and several electric vehicles. Each battery swap station has several charging bays and batteries for battery swapping. Each electric vehicle is assigned several missions by the fleet. A mission requires a vehicle to depart from warehouse A at a certain time and arrive at warehouse B. During the mission, the electric vehicle needs to recharge its battery at a battery swap station to ensure successful completion of the mission. Vehicle scheduling ensures that each vehicle arrives at the designated battery swap station for battery swapping.

[0098] In order to illustrate the technical solution of the present application, specific embodiments are provided below.

[0099] In some embodiments of the present invention, reference Figure 3 , an embodiment of the present invention proposes an electric vehicle fleet scheduling method, which may include:

[0100] Step S10: Obtain an initial battery replacement plan for each vehicle in the electric vehicle fleet. The initial battery replacement plan consists of multiple alternative plan branches.

[0101] It should be noted that the electric vehicle fleet includes different types of vehicles. Each vehicle carries a different battery capacity and consumes different amounts of electricity according to its own needs. Therefore, the initial battery replacement plan for each vehicle can be specified in advance based on the status of each vehicle.

[0102] The initial battery replacement plan is statically generated in advance based on the initial status information of the vehicle and the battery replacement station, and can also be dynamically generated based on the current driving status of the vehicle and the current status of the battery replacement station. This embodiment does not impose any restrictions on this.

[0103] In a specific implementation, since there are multiple battery swap stations along the route to be traveled by the vehicle, multiple alternative plans can be generated based on the vehicle's status information, thereby obtaining the initial battery swap plan for each vehicle through a combination of multiple alternative plan branches.

[0104] In some embodiments, obtaining an initial battery replacement plan for each vehicle in an electric vehicle fleet includes the following steps:

[0105] A1: Obtain the initial status information of each vehicle in the electric vehicle fleet;

[0106] It should be noted that the vehicle initial status information may include vehicle mission information, vehicle location, power level, vehicle model, current SOC, vehicle speed, power consumption, etc.

[0107] The initial state information of the vehicle may be state information before the vehicle sets off, or state information while the vehicle is in motion.

[0108] A2: Obtain the initial status information of the battery swap stations along the route to be driven by the electric vehicle fleet;

[0109] It should be noted that the transportation tasks of the electric vehicle fleet can be obtained in advance to determine the route to be traveled, and all battery swap stations along the route can be obtained to obtain the initial status information of all battery swap stations. The initial status information of the battery swap station may include inventory information within the battery swap station, the number of charging bays, charging power, battery swap operation duration, time-of-use electricity price, current SOC of each battery, current demand, number of vehicles in queue, number of vehicles currently swapping batteries, etc. As time goes by, the status information within the battery swap station changes continuously, and the initial status information of the battery swap station can be updated by continuously obtaining updated battery swap station status information.

[0110] A3: Generate multiple alternative plans for each vehicle based on the vehicle initial state information and the battery swap station initial state information;

[0111] It can be understood that all battery replacement schemes for each vehicle, that is, multiple alternative schemes, can be generated through at least one variable in the vehicle's initial state information and at least one variable in the battery replacement station's initial state information.

[0112] The decision variable is which battery swap plan each vehicle chooses, that is, which battery swap stations the vehicle goes to for battery swap during the mission. Each battery swap plan is a vector consisting of 0 or 1 elements, with a length equal to the number of battery swap stations the vehicle passes through, such as Figure 4 As shown, Figure 4 The diagram shows the relationship between the warehouse, alternative battery swap stations, and dispatching battery swap stations. The vehicle passes through 4 battery swap stations from the starting warehouse to the terminal warehouse, and a total of k alternative plans are generated, which are starting warehouse 1, battery swap station 2, and terminal warehouse 1 from left to right. After generating different alternative plans, the battery swap stations included in the alternative plans are dispatching battery swap stations, and the battery swap stations not included in the alternative plans are alternative battery swap stations. The corresponding vectors are expressed as: [0,1,1,0], [1,0,1,0], ..., [0,1,0,1]. It can be assumed that the decision variable is x = {x1,x2,x3,x n} represents the alternatives of each of the n vehicles, where xi∈{1,...,k i} represents the alternative solution space of vehicle i.

[0113] A4: Expand multiple alternative plans for each vehicle to obtain the initial battery replacement plan for each vehicle.

[0114] In specific implementation, the multiple alternative plan spaces of each vehicle can be branched and expanded, for example, in the form of a multi-branch tree. Each layer of the tree exhaustively enumerates the values ​​of the decision variables corresponding to each plan, thereby summarizing the initial battery replacement plan for each vehicle.

[0115] All battery swap alternatives for each vehicle are generated through the initial state information of each vehicle and the initial state information of the battery swap station, and the battery swap alternatives are expanded to obtain the initial battery swap plan for each vehicle, thereby improving the versatility of battery swap scheduling.

[0116] Step S20: Obtain multiple scheduling optimization objectives based on the initial battery replacement plan, and determine the objective function value of each scheduling optimization objective.

[0117] It should be noted that the scheduling optimization objectives set in advance for each battery replacement plan can be determined based on the initial battery replacement plan, such as minimizing the total driving distance, minimizing the total number of battery replacements, maximizing the battery life, minimizing the total queuing time, minimizing the battery replacement cost, maximizing the total transportation revenue, etc. This embodiment does not impose any restrictions on this.

[0118] In specific implementations, the objective function values ​​of each scheduling optimization objective can be determined based on preset optimization objectives. For example, corresponding weight values ​​can be set for each alternative battery swapping solution branch, and the weighted sum of each alternative battery swapping solution branch can be calculated to obtain the objective function value of the scheduling optimization objective corresponding to each alternative battery swapping solution branch. It is also possible to set some constraints and solve each scheduling optimization objective based on the constraints to obtain the objective function value corresponding to the alternative solution branch. Multiple objective function values ​​can be determined based on specific needs.

[0119] The objective function value is a quantitative representation of each scheduling optimization objective under the corresponding battery swapping alternative. It can be a numerical value, weight, priority, etc., used to measure the degree to which the optimization objective is achieved under the battery swapping alternative. These objective function values ​​will be used in the subsequent pruning module to evaluate and select the optimal battery swapping scheduling solution.

[0120] Step S30: Prune multiple alternative solution branches in the initial battery replacement solution based on the objective function value to obtain a target battery replacement solution.

[0121] It should be noted that among the initial battery swapping solutions, some may perform poorly on certain scheduling optimization objectives. These solutions may consume a large amount of computing resources in subsequent calculations and scheduling, and may not ultimately be selected as the optimal solution. Therefore, by pruning multiple alternative solution branches based on the objective function value, screening and removing these solutions can greatly improve computational efficiency and reduce unnecessary computational overhead.

[0122] By pruning multiple alternative plans in the initial battery replacement plan, a suitable target battery replacement plan can be quickly obtained.

[0123] By pruning the initial battery swap plan, alternative plan branches that do not meet the preset conditions can be removed, thereby reducing the amount of calculation and improving scheduling efficiency.

[0124] Step S40: Dispatch each vehicle in the electric vehicle fleet through the target battery replacement plan.

[0125] In specific implementation, scheduling each vehicle in the electric vehicle fleet according to the target battery swap plan can ensure that the electric vehicle fleet can perform battery swap operations according to the optimal battery swap plan during the execution of the mission, thereby meeting the vehicle's operating needs and improving the overall operating efficiency of the electric vehicle fleet.

[0126] During the dispatch process, the vehicle's status and the battery swap station's status can be monitored in real time, and the target battery swap plan can be dynamically adjusted based on actual conditions. For example, when a vehicle's battery level falls below a preset threshold, a battery swap operation can be triggered, and the vehicle can be dispatched to the target battery swap station for a swap. Furthermore, battery swap times can be planned in advance based on the vehicle's mission information and driving route to ensure that the battery swap can be completed promptly and efficiently when it is needed.

[0127] This embodiment obtains an initial battery swapping plan for each vehicle in the electric vehicle fleet, where the initial battery swapping plan consists of multiple alternative plan branches; multiple scheduling optimization objectives are obtained based on the initial battery swapping plan, and the objective function value of each scheduling optimization objective is determined; multiple alternative plan branches in the initial battery swapping plan are pruned based on the objective function value to obtain a target battery swapping plan; each vehicle in the electric vehicle fleet is scheduled according to the target battery swapping plan, taking into account the mutual influence between multiple battery swapping plans, and the multiple battery swapping plans are pruned by determining the objective function value of each scheduling optimization objective, thereby obtaining a suitable battery swapping plan for each vehicle, thereby improving scheduling intelligence and scheduling effect.

[0128] In some embodiments, the accuracy of the battery swap solution can be improved by pruning multiple alternative solutions. Figure 5 , step S30 includes:

[0129] Step S301: Obtain the objective function value of each alternative solution branch based on the objective function value.

[0130] It should be noted that the objective function value includes the objective function values ​​corresponding to multiple alternative solutions, so the objective function value of each alternative solution branch can be obtained based on the objective function value.

[0131] In specific implementation, the objective function value can be analyzed to understand the performance of various alternative solutions on different scheduling optimization objectives, providing a basis for subsequent pruning operations.

[0132] Step S302: sorting the alternative solution branches according to the magnitude of the objective function value of each alternative solution branch to obtain a sorted list of the alternative solution branches.

[0133] It's understandable that alternative branches can be ranked based on the magnitude of the objective function to determine the order of merit of each solution for different optimization objectives. This ranking provides a clearer understanding of the performance of each alternative branch and provides a basis for decision-making in subsequent pruning operations.

[0134] For example, the alternative solution branches may be sorted from large to small according to the objective function value, thereby obtaining a sorted list of the alternative solution branches from large to small according to the objective function value.

[0135] Step S303: Prune multiple alternative solution branches in the initial battery replacement solution according to the sorted list to obtain the target battery replacement solution.

[0136] In a specific implementation, multiple alternative solution branches in the initial battery replacement solution can be pruned through a sorted list to obtain a target battery replacement solution.

[0137] It can be understood that the number of target battery swap plans can be one or more, and can be adjusted according to the specific number of battery swap stations and the initial battery swap plan.

[0138] In a specific implementation, pruning rules can be used to prune multiple alternative solution branches, such as using a branch and bound method with a pruning mechanism for pruning. The initial battery replacement plan can also be determined by the branch and bound method.

[0139] Pruning rules can be set according to actual needs. For example, pruning can be performed based on factors such as the threshold of the objective function value, the priority relationship between solutions, and the conflict relationship between solutions.

[0140] For example, a preset threshold corresponding to the objective function value can be set to prune multiple alternative branches based on the preset threshold. The preset threshold can be a standard set according to actual needs to evaluate the quality of alternative branches. For example, if the goal is to minimize queue time, the objective function value of the total queue time of each alternative branch can be compared with the preset threshold to filter out alternative branches with total queue time below the preset threshold. This can quickly eliminate some poorly performing solutions and reduce the subsequent computational effort.

[0141] In the technical solution of the embodiment of the present application, by obtaining the objective function value of each alternative solution branch, the alternative solution branch is pruned according to the size of the objective function value of each alternative solution branch, and inappropriate alternative solutions are eliminated, so as to obtain the optimal target battery replacement solution and improve the scheduling effect of each vehicle in the electric vehicle fleet.

[0142] In some embodiments, the priority of each alternative solution branch may be set according to the magnitude of the objective function value, thereby pruning the alternative solution branches. Step S303 includes:

[0143] Obtain the priority of each alternative branch according to the sorted list;

[0144] Based on the priority, multiple alternative branches in the initial battery replacement plan are pruned, and the alternative plans whose priorities do not meet the preset priorities are eliminated to obtain the target battery replacement plan.

[0145] It should be noted that, based on the sorted list, each candidate branch can be prioritized. Priority can be set based on the magnitude of the objective function value. For example, a candidate branch with a higher objective function value can be assigned a higher priority. This ensures that the best performing solutions are retained during pruning, improving scheduling optimization.

[0146] After setting the priority, we can prune the multiple alternative branches in the initial battery swap solution based on this priority. Specifically, we can eliminate alternatives whose priorities do not meet the preset priority to obtain the target battery swap solution. The preset priority can be adjusted according to actual needs.

[0147] For example, when using the branch and bound method to solve integer programming problems, we can get the following Figure 6 The multitree shown, Figure 6 A schematic diagram of a multi-branch tree formed by alternative solution branches. In this example, there are three vehicles, of which vehicle 1 has four alternative battery swap solutions, vehicle 2 has three alternative battery swap solutions, and vehicle 3 has two alternative battery swap solutions. The nodes in each layer of the diagram represent the selected solution combinations. For example, S132 represents vehicle 1 selecting solution 1, vehicle 2 selecting solution 3, and vehicle 3 selecting solution 2. Each node can calculate the current objective function value based on the selected solution, and explore possible solution combinations in a depth-first search manner. For example, if solution combination S111 is searched first, its corresponding objective function value l can be obtained accordingly. 111 , and record the current optimal value l優=l111, continue the depth-first search, if the target value l corresponding to S112 112 <l优,则更新l优=l 112 , find the objective function value l corresponding to the combination of vehicle 1 and 2 S12 12 If l3 ≥ l is optimal, prune directly, that is, there is no need to branch S12. If l3 ≥ l is optimal corresponding to S3, prune it directly until all nodes are traversed to obtain the solution combination corresponding to the optimal value, that is, the target scheduling strategy.

[0148] In the technical solution of the embodiment of the present application, the priority of each alternative solution branch is obtained by sorting the alternative solution branches, and the alternative solutions that do not meet the preset priority are eliminated to obtain the optimal battery replacement solution combination, thereby improving the scheduling effect of each vehicle in the electric vehicle fleet.

[0149] In some embodiments, the objective function value corresponding to each battery swapping solution can be determined to facilitate pruning of the alternative solution branches. Figure 7 , step S20 includes:

[0150] Step S201: Obtain the scheduling optimization target corresponding to each alternative solution branch based on the initial battery replacement solution.

[0151] It should be noted that since there are multiple alternative branches for each vehicle, the scheduling optimization objectives set can be the same or different. For example, the scheduling optimization objective can be set to minimize the total queuing time, then the scheduling optimization objective of each vehicle is to minimize the queuing time.

[0152] Step S202: Obtain the battery replacement constraints of each alternative solution branch in the initial battery replacement solution.

[0153] In specific implementation, the battery replacement constraints of each alternative plan branch can be obtained through the initial battery replacement plan. The battery replacement constraints are composed of the state variables of the vehicle and / or battery replacement station at each moment. The battery replacement constraints can be set according to demand. Therefore, the battery replacement constraints corresponding to each alternative plan branch can be obtained.

[0154] Step S203: Solve the scheduling optimization objectives based on the battery replacement constraints to obtain the objective function values ​​corresponding to each scheduling optimization objective.

[0155] In specific implementations, the scheduling optimization objectives can be solved based on the battery swapping constraints, for example, using optimization algorithms such as linear programming, integer programming, and dynamic programming. Integer programming methods can include cutting plane methods, branch-and-bound methods, primal-dual algorithms, and heuristic algorithms, to obtain the objective function values ​​corresponding to each scheduling optimization objective. These objective function values ​​can reflect the quality of each alternative solution branch and provide data support for subsequent pruning of alternative solution branches.

[0156] It should be noted that different scheduling optimization objectives can be solved using the same or different solution methods and algorithms. Therefore, in specific implementations, appropriate solution methods and algorithms should be selected based on actual conditions to ensure the accuracy and effectiveness of the solution results.

[0157] In the technical solution of the embodiment of the present application, the scheduling optimization target corresponding to each alternative solution branch is determined, and the battery replacement constraint conditions are obtained to solve the scheduling optimization target, and the objective function value of the scheduling optimization target corresponding to each alternative solution is obtained. The alternative solution branches are pruned according to the objective function value, thereby improving the calculation efficiency.

[0158] In some embodiments, the scheduling optimization objective can be solved by the battery swap constraint condition to quickly obtain the objective function value, and step S203 includes:

[0159] B1: Input the battery swapping constraints into the preset scheduling model, which represents the correspondence between the battery swapping constraints and the battery swapping optimization objectives and objective function values.

[0160] B2: Solve the battery swap optimization objectives through the preset scheduling model to obtain the objective function values ​​corresponding to each scheduling optimization objective.

[0161] In practice, the pre-set scheduling model can be based on historical data, expert experience, or a machine learning algorithm to map battery swap constraints to corresponding objective function values. By inputting the battery swap constraints, the pre-set scheduling model can quickly calculate the objective function values ​​for each alternative solution branch, providing data support for subsequent pruning operations.

[0162] It should be noted that the preset scheduling model represents the correspondence between the battery swapping constraints, battery swapping optimization objectives and objective function values. The battery swapping constraints and battery swapping optimization objectives can be determined in advance, and then the battery swapping constraints and battery swapping optimization objectives can be used to model and obtain the preset scheduling model. By solving the preset scheduling model, the objective function values ​​of each alternative solution branch can be obtained.

[0163] The battery swap optimization target can be solved by using a preset scheduling model using a branch and bound method with a pruning mechanism, or other solving methods, which are not limited in this embodiment.

[0164] In the technical solution of the embodiment of the present application, by modeling the battery swapping constraints and the battery swapping optimization objectives, a preset scheduling model is obtained to characterize the correspondence between the battery swapping constraints and the battery swapping optimization objectives and the objective function values. The battery swapping optimization objectives are solved by the preset scheduling model, and the objective function values ​​corresponding to each scheduling optimization objective are quickly obtained, thereby improving the scheduling efficiency.

[0165] In some embodiments, the scheduling optimization objectives are solved based on the battery swapping constraints to obtain the objective function values ​​corresponding to each scheduling optimization objective, including:

[0166] Based on the battery swap constraint condition, at least one of the vehicle's current location, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station is obtained, as well as at least one of the vehicle's inventory information, the number of queues, the number of battery swaps, and the number of fully charged batteries at the designated battery swap station;

[0167] The scheduling optimization objective is solved through at least one of the vehicle's current position, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station, as well as at least one of the designated battery swap station's inventory information, number of queues, number of battery swaps, and number of fully charged batteries, to obtain the objective function values ​​corresponding to each scheduling optimization objective.

[0168] It should be noted that the battery swap constraint conditions can be obtained based on the vehicle's current status information and the designated battery swap station status information. Therefore, at least one of the vehicle's current position, the vehicle's current power, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station can be obtained based on the battery swap constraint conditions, and at least one of the inventory information, queue number, battery swap number, and fully charged battery number of the designated battery swap station can be obtained based on the designated battery swap station status information.

[0169] After obtaining this information, the scheduling optimization objectives can be solved based on the aforementioned state variables, using optimization algorithms such as linear programming, integer programming, and dynamic programming. These algorithms can then determine the objective function values ​​corresponding to each scheduling optimization objective. These objective function values ​​can reflect the quality of each alternative solution branch and provide data support for subsequent pruning of alternative solution branches.

[0170] Optionally, in order to improve computational efficiency, a pruning mechanism may be added to the solution process to prune branches of alternative solutions that do not meet the constraints, thereby narrowing the search scope and improving computational efficiency.

[0171] Furthermore, in practical applications, scheduling optimization objectives can be flexibly set based on actual needs. For example, multiple factors such as queue length, battery swap efficiency, and battery utilization can be comprehensively considered to arrive at a more reasonable scheduling solution. Furthermore, algorithms such as machine learning can be combined to continuously optimize the scheduling model and improve scheduling effectiveness.

[0172] The model is built through the state variables in the battery swap constraint conditions and the battery swap optimization objectives, as shown in Equation 1:

[0173]

[0174] In the above formula 1, To meet the demand for battery-swappable vehicles, is the number of fully charged batteries (inventory information), Q j t The number of vehicles queuing for battery replacement, is the vehicle's battery charge, is the electricity consumption of battery swap station j.

[0175] By determining the battery swap constraints and battery swap optimization objectives, the above formula (1) is adjusted to obtain the preset scheduling model as shown in formula (2):

[0176]

[0177] In the above formula 2, l() is the objective function, which can be the total cost of battery swapping, the total queuing time, etc., t(x) is the time it takes for the vehicle to arrive at the designated battery swap station according to each alternative plan, and T j (z) is the indicator function, and the constraints include the state variable updates at each moment, the SOC updates of vehicles and battery swap stations, and other business constraints.

[0178] In some embodiments, in order to improve solution efficiency, the decision variables may be used to narrow the scope of the decision space. The electric vehicle fleet scheduling method further includes:

[0179] Get the preset culling strategy;

[0180] The initial battery replacement plan is redundantly eliminated through an elimination strategy to obtain an updated initial battery replacement plan, and the updated initial battery replacement plan replaces the initial battery replacement plan.

[0181] It's important to note that the preset elimination strategies can be flexibly configured to suit different scenarios and needs. For example, if a vehicle's battery level is low, distant battery swap stations can be prioritized to reduce unnecessary search space. Furthermore, a more appropriate elimination strategy can be developed based on factors such as vehicle routes and the distribution of battery swap stations to improve solution efficiency and scheduling effectiveness.

[0182] In actual applications, the preset elimination strategy can be to eliminate the battery swap stations that the vehicle cannot reach in the initial battery swap plan, and can also be to eliminate redundant battery swap stations at the tail end. For example, based on the vehicle's current power information, the battery swap plans corresponding to the battery swap stations that the vehicle cannot reach with its current power are eliminated, and redundant battery swap stations at the tail end are eliminated. For example, if the vehicle can reach the destination directly from the second-to-last battery swap station on the route to be traveled, the battery swap plans that include the last two battery swap stations are eliminated, thereby eliminating the redundancy of the initial battery swap plan, narrowing the scope of the decision space, and improving the solution efficiency.

[0183] By setting a preset elimination strategy in advance, the redundancy of the initial battery replacement plan can be eliminated, the scope of the battery replacement plan can be narrowed, and the efficiency of subsequent pruning of various alternative plan branches can be improved.

[0184] In the technical solution of the embodiment of the present application, the scheduling optimization objective can be solved by one or more state variables in the battery replacement constraint conditions, so as to obtain the objective function values ​​corresponding to different scheduling optimization objectives and improve the solution effect.

[0185] In some embodiments, obtaining the battery swap constraint conditions of each alternative solution branch in the initial battery swap solution includes:

[0186] Obtain the designated battery swap stations for each alternative plan branch in the initial battery swap plan;

[0187] Get the status information of the specified battery swap station;

[0188] Obtain the current status information of each vehicle in the electric vehicle fleet;

[0189] The battery swap constraint conditions of each alternative solution branch are obtained based on the battery swap station status information and the current vehicle status information.

[0190] It should be noted that in order to increase the speed of determining the battery swap constraints, the battery swap stations can be screened, so that only the status information of the reachable battery swap stations needs to be obtained, so as to quickly determine the corresponding battery swap constraints. The designated battery swap stations for each alternative branch scheduling in the initial battery swap plan can be obtained. For example, for vehicle 1, the route to be traveled by the vehicle includes battery swap station A, battery swap station B, battery swap station C, battery swap station D, and battery swap station E. The battery swap station alternatives include battery swap station A, battery swap station C, and battery swap station E. Therefore, the designated battery swap stations for the alternative branch scheduling are battery swap station A, battery swap station C, and battery swap station E.

[0191] The battery swap station status information of the specified battery swap station can be obtained separately, such as one or more status information of the specified battery swap station, the number of battery swap vehicles in queue, the current number of battery swaps, and the number of newly fully charged batteries.

[0192] In practice, after obtaining the station status information for a designated battery swap station, the current status information of each vehicle in the electric vehicle fleet can be further obtained, including vehicle location, power level, power consumption, speed, and other information. Then, based on the station status information and the current vehicle status information, the battery swap constraints for each alternative solution branch can be derived. For example, the time when the vehicle arrives at the battery swap station, the inventory information of the designated battery swap station, and the number of vehicles in the battery swap queue can be used as battery swap constraints.

[0193] By combining the battery swap station status information of the specified battery swap station with the current vehicle status information, the battery swap constraints of each alternative solution branch can be obtained more accurately, providing more accurate data support for the subsequent pruning of alternative solution branches.

[0194] In the technical solution of the embodiment of the present application, the corresponding battery swap station information can be determined through each alternative solution branch in the initial battery swap plan, and the vehicle information can be obtained, and the battery swap constraint conditions of the alternative solution branch can be quickly determined through the state variables of the battery swap station and the vehicle.

[0195] In some embodiments, the battery swap constraint conditions of each alternative solution branch are obtained based on the battery swap station status information and the current vehicle status information, including:

[0196] obtaining at least one of the vehicle's current position, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at a designated battery swap station based on the vehicle's current status information;

[0197] Obtain at least one of inventory information, queue quantity, battery swap quantity, and fully charged battery quantity of a designated battery swap station according to the battery swap station status information;

[0198] The battery swap constraint conditions of each alternative plan branch are obtained based on at least one of the vehicle's current position, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station, as well as at least one of the designated battery swap station's inventory information, number of queues, number of battery swaps, and number of fully charged batteries.

[0199] During implementation, multiple state variables of the vehicle and the battery swap station can be comprehensively considered to determine battery swap constraints. For example, the specific battery swap constraints can be determined based on information such as the vehicle's current location, power level, power consumption, and speed, combined with information such as the battery swap station's inventory, queue numbers, number of battery swaps, and number of fully charged batteries.

[0200] For battery swap stations, status information such as inventory, number of queues, number of battery swaps, and number of fully charged batteries are equally important. For example, if a battery swap station has less inventory or a long queue, it may be necessary to adjust the battery swap strategy and select other battery swap stations with more inventory and shorter queues for battery swaps. In addition, the number of fully charged batteries is also a key factor. If a battery swap station has a small number of fully charged batteries, it may be necessary to give priority to the battery swap demand of the station to ensure the normal operation of each vehicle in the electric fleet. Therefore, different battery swap constraints can also be determined according to the battery swap optimization goal. For example, if the battery swap optimization goal is to minimize the battery swap cost, the battery swap constraints need to include the time-of-use electricity price in the battery swap station, the battery swap speed, etc., and also need to include variables such as the vehicle's driving speed and the distance of the vehicle from the battery swap station.

[0201] By comprehensively considering multiple state variables of vehicles and battery swap stations, we can derive the battery swap constraints for each alternative solution. These constraints serve as an important basis for subsequent optimization of the scheduling model, enabling efficient scheduling and management of the electric vehicle fleet. Furthermore, by continuously collecting and analyzing real-time status information from vehicles and battery swap stations, the scheduling model can be dynamically adjusted and optimized to adapt to different scenarios and changing needs.

[0202] In the technical solution of the embodiment of the present application, the battery swap constraint conditions of each alternative solution branch can be flexibly selected through one or more state variables in the vehicle's current state information and one or more state variables in the battery swap station state information, thereby improving the intelligence of battery swap scheduling.

[0203] In some embodiments, the battery swap constraint conditions of each alternative solution branch are obtained based on the battery swap station status information and the current vehicle status information, including:

[0204] Obtain the arrival time of the vehicle at the designated battery swap station based on the vehicle's current status information;

[0205] Determine the demand for battery-swap vehicles in the electric fleet by arrival time;

[0206] Obtain at least one of inventory information, queue quantity, battery swap quantity, and fully charged battery quantity of a designated battery swap station according to the battery swap station status information;

[0207] The battery swap constraint conditions of each alternative plan branch are obtained through at least one of the battery swap vehicle demand, inventory information of the designated battery swap station, number of queues, number of battery swaps, and number of fully charged batteries.

[0208] In specific implementation, the vehicle's driving speed and current position can be obtained based on the vehicle's current status information. The arrival time of the vehicle at the designated battery swap station can be calculated based on the vehicle's current position, driving speed and the designated battery swap station position. The vehicle's battery swap demand at the designated battery swap station at the time of arrival can be determined by the arrival time. The battery swap demand of all vehicles in the electric fleet, that is, the battery swap vehicle demand, can be obtained by summarizing.

[0209] It can be understood that efficient scheduling and management of electric vehicle fleets can be achieved by using the demand for battery swapping vehicles and the status information of battery swapping stations as battery swapping constraints.

[0210] For example, the demand for battery swapping vehicles, inventory information of designated battery swapping stations, the number of queues, the number of battery swaps, and the number of fully charged batteries are all used as battery swapping constraints. If the battery swapping optimization goal is to minimize the battery swapping time, when the demand for battery swapping vehicles is high and the inventory of battery swapping stations is small, priority can be given to dispatching vehicles with lower battery power to battery swapping stations with larger inventories to ensure the normal operation of the electric vehicle fleet.

[0211] Furthermore, during implementation, other state variables can be flexibly selected as battery swap constraints based on actual needs. For example, if the operating costs of an electric vehicle fleet are taken into account, the battery swap cost can be included as one of the battery swap constraints. By optimizing the battery swap cost, the goal of reducing the operating costs of the electric vehicle fleet can be achieved.

[0212] In the technical solution of the embodiment of the present application, the demand for battery swapping vehicles in the electric vehicle fleet can be quickly determined based on the current status information of the vehicle, so that the battery swapping constraints of each alternative solution branch can be flexibly selected through the demand for battery swapping vehicles and one or more state variables in the battery swapping station status information, thereby improving the intelligence of battery swapping scheduling.

[0213] To achieve the above objectives, the present invention further provides an electric vehicle fleet dispatching device. Figure 8 The following is a structural diagram of an embodiment of an electric vehicle fleet dispatching device provided in an embodiment of the present application. The electric vehicle fleet dispatching device includes:

[0214] The acquisition module 10 is used to obtain the initial battery replacement plan of each vehicle in the electric vehicle fleet. The initial battery replacement plan is composed of multiple alternative plan branches.

[0215] The determination module 20 is used to obtain multiple scheduling optimization objectives based on the initial battery replacement plan and determine the objective function value of each scheduling optimization objective.

[0216] The pruning module 30 is used to prune multiple alternative solution branches in the initial battery replacement solution based on the objective function value to obtain a target battery replacement solution.

[0217] The scheduling module 40 is used to schedule each vehicle in the electric vehicle fleet through a target battery replacement plan.

[0218] This embodiment obtains an initial battery swapping plan for each vehicle in the electric vehicle fleet, where the initial battery swapping plan consists of multiple alternative plan branches; obtains multiple scheduling optimization objectives based on the initial battery swapping plan, and determines the objective function value of each scheduling optimization objective; prunes the multiple alternative plan branches in the initial battery swapping plan based on the objective function value to obtain a target battery swapping plan; schedules each vehicle in the electric vehicle fleet according to the target battery swapping plan, taking into account the mutual influence between multiple battery swapping plans, and prunes multiple battery swapping plans by determining the objective function value of each scheduling optimization objective, thereby obtaining a suitable battery swapping plan for each vehicle, thereby improving scheduling intelligence and scheduling effect.

[0219] In one embodiment, the pruning module 30 is also used to obtain the objective function value of each alternative solution branch based on the objective function value; sort the alternative solution branches according to the size of the objective function value of each alternative solution branch to obtain a sorted list of alternative solution branches; and prune multiple alternative solution branches in the initial battery replacement solution according to the sorted list to obtain a target battery replacement solution.

[0220] In one embodiment, the pruning module 30 is also used to obtain the priority of each alternative solution branch based on the sorted list; prune the multiple alternative solution branches in the initial battery replacement solution based on the priority, eliminate the alternative solutions whose priorities do not meet the preset priority, and obtain the target battery replacement solution.

[0221] In one embodiment, the determination module 20 is also used to obtain the scheduling optimization target corresponding to each alternative plan branch based on the initial battery replacement plan; obtain the battery replacement constraint conditions of each alternative plan branch in the initial battery replacement plan; solve the scheduling optimization target based on the battery replacement constraint conditions to obtain the objective function value corresponding to each scheduling optimization target.

[0222] In one embodiment, the determination module 20 is also used to obtain the designated battery swap stations for scheduling of each alternative plan branch in the initial battery swap plan; obtain the battery swap station status information of the designated battery swap stations; obtain the current status information of each vehicle in the electric vehicle fleet; and obtain the battery swap constraint conditions of each alternative plan branch based on the battery swap station status information and the vehicle current status information.

[0223] In one embodiment, the determination module 20 is further used to obtain at least one of the vehicle's current position, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at a designated battery swap station based on the vehicle's current status information; obtain at least one of the designated battery swap station's inventory information, the number of queues, the number of battery swaps, and the number of fully charged batteries based on the battery swap station status information; and obtain the battery swap constraint conditions of each alternative plan branch based on at least one of the vehicle's current position, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at a designated battery swap station and at least one of the designated battery swap station's inventory information, the number of queues, the number of battery swaps, and the number of fully charged batteries.

[0224] In one embodiment, the determination module 20 is further used to obtain the arrival time of the vehicle at the designated battery swap station based on the current status information of the vehicle; determine the demand for battery swap vehicles in the electric vehicle fleet through the arrival time; obtain at least one of the inventory information, queue number, battery swap number, and fully charged battery number of the designated battery swap station based on the status information of the battery swap station; and obtain the battery swap constraint conditions of each alternative plan branch through at least one of the demand for battery swap vehicles, inventory information, queue number, battery swap number, and fully charged battery number of the designated battery swap station.

[0225] In one embodiment, the determination module 20 is also used to input the battery swapping constraint conditions into a preset scheduling model, which characterizes the correspondence between the battery swapping constraint conditions and the battery swapping optimization objectives and the objective function values; the battery swapping optimization objectives are solved through the preset scheduling model to obtain the objective function values ​​corresponding to each scheduling optimization objective.

[0226] In one embodiment, the determination module 20 is also used to obtain at least one of the vehicle's current position, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station based on the battery swap constraint conditions, as well as at least one of the inventory information, queue number, battery swap number, and fully charged battery number of the designated battery swap station; the scheduling optimization objectives are solved through at least one of the vehicle's current position, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station, as well as the inventory information, queue number, battery swap number, and fully charged battery number of the designated battery swap station to obtain the objective function values ​​corresponding to each scheduling optimization objective.

[0227] In one embodiment, the determination module 20 is also used to obtain a preset elimination strategy; the initial battery replacement plan is redundantly eliminated through the elimination strategy to obtain an updated initial battery replacement plan, and the updated initial battery replacement plan replaces the initial battery replacement plan.

[0228] In one embodiment, the acquisition module 10 is also used to obtain the vehicle initial status information of each vehicle in the electric vehicle fleet; obtain the initial status information of the battery swap station on the route to be traveled by the electric vehicle fleet; generate multiple alternative plans for each vehicle through the vehicle initial status information and the battery swap station initial status information; and branch and expand the multiple alternative plans for each vehicle to obtain the initial battery swap plan for each vehicle.

[0229] In addition, an embodiment of the present invention further provides a storage medium on which an electric vehicle fleet scheduling program is stored. When the electric vehicle fleet scheduling program is executed by a processor, the steps of the electric vehicle fleet scheduling method described above are implemented.

[0230] Since the storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0231] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for dispatching an electric vehicle fleet, characterized in that: The electric vehicle fleet dispatching method comprises: Obtaining an initial battery replacement plan for each vehicle in the electric vehicle fleet, wherein the initial battery replacement plan is composed of multiple alternative plan branches; Obtaining multiple scheduling optimization objectives according to the initial battery swapping plan, and determining the objective function value of each scheduling optimization objective; Pruning multiple alternative solution branches in the initial battery swap solution based on the objective function value to obtain a target battery swap solution; Each vehicle in the electric vehicle fleet is dispatched using the target battery replacement plan.

2. The electric vehicle fleet dispatching method according to claim 1, characterized in that: The pruning of multiple alternative solution branches in the initial battery swap solution based on the objective function value to obtain a target battery swap solution includes: Obtaining objective function values ​​of each alternative solution branch based on the objective function value; Sort each alternative solution branch according to the size of the objective function value of each alternative solution branch to obtain a sorted list of alternative solution branches; According to the sorted list, multiple alternative solution branches in the initial battery replacement solution are pruned to obtain a target battery replacement solution.

3. The electric vehicle fleet dispatching method according to claim 2, wherein: The pruning of multiple alternative solution branches in the initial battery swap solution according to the sorted list to obtain a target battery swap solution includes: Obtaining the priority of each alternative solution branch according to the sorted list; Based on the priority, multiple alternative solution branches in the initial battery replacement solution are pruned, and alternative solutions whose priorities do not meet the preset priority are eliminated to obtain the target battery replacement solution.

4. The electric vehicle fleet dispatching method according to any one of claims 1 to 3, characterized in that: The step of obtaining multiple scheduling optimization objectives based on the initial battery swapping plan and determining the objective function value of each scheduling optimization objective includes: Obtaining the scheduling optimization objectives corresponding to each alternative solution branch according to the initial battery replacement solution; Obtaining the battery swap constraint conditions of each alternative solution branch in the initial battery swap solution; The scheduling optimization objectives are solved based on the battery replacement constraints to obtain the objective function values ​​corresponding to each scheduling optimization objective.

5. The electric vehicle fleet dispatching method according to claim 4, characterized in that: The obtaining of the battery swap constraint conditions of each alternative solution branch in the initial battery swap solution includes: Obtaining designated battery swap stations for scheduling of each alternative solution branch in the initial battery swap solution; Obtaining the battery swap station status information of the designated battery swap station; Obtain the current status information of each vehicle in the electric vehicle fleet; The battery swap constraint conditions of each alternative solution branch are obtained based on the battery swap station status information and the vehicle current status information.

6. The electric vehicle fleet dispatching method according to claim 5, characterized in that: The battery swap constraint conditions of each alternative solution branch are obtained based on the battery swap station status information and the vehicle current status information, including: obtaining at least one of the vehicle's current position, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station based on the vehicle's current state information; Obtain at least one of inventory information, queue quantity, battery swap quantity, and fully charged battery quantity of the designated battery swap station according to the battery swap station status information; The battery swap constraint conditions of each alternative plan branch are obtained based on at least one of the vehicle's current position, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station, as well as at least one of the designated battery swap station's inventory information, number of queues, number of battery swaps, and number of fully charged batteries.

7. The electric vehicle fleet dispatching method according to claim 5, wherein: The battery swap constraint conditions of each alternative solution branch are obtained based on the battery swap station status information and the vehicle current status information, including: Obtaining the arrival time of the vehicle at the designated battery swap station based on the current state information of the vehicle; Determining the demand for battery-swapping vehicles in the electric vehicle fleet based on the arrival time; Obtain at least one of inventory information, queue quantity, battery swap quantity, and fully charged battery quantity of the designated battery swap station according to the battery swap station status information; The battery swap constraint conditions of each alternative plan branch are obtained through at least one of the battery swap vehicle demand, the inventory information of the designated battery swap station, the number of queues, the number of battery swaps, and the number of fully charged batteries.

8. The electric vehicle fleet dispatching method according to claim 4, wherein: Solving the scheduling optimization objective based on the battery swap constraint condition to obtain the objective function value corresponding to each scheduling optimization objective includes: Inputting the battery swap constraint condition into a preset scheduling model, wherein the preset scheduling model represents the corresponding relationship between the battery swap constraint condition and the battery swap optimization target and the objective function value; The battery swap optimization target is solved through the preset scheduling model to obtain the objective function value corresponding to each scheduling optimization target.

9. The electric vehicle fleet dispatching method according to claim 4, wherein: Solving the scheduling optimization objective based on the battery swap constraint condition to obtain the objective function value corresponding to each scheduling optimization objective includes: Based on the battery swap constraint condition, at least one of the vehicle's current location, the vehicle's current power level, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station is obtained, as well as at least one of the vehicle's inventory information, the number of queues, the number of battery swaps, and the number of fully charged batteries at the designated battery swap station; The scheduling optimization objective is solved through at least one of the vehicle's current position, the vehicle's current power, the vehicle's current power consumption, the vehicle's current speed, and the vehicle's arrival time at the designated battery swap station, as well as at least one of the inventory information, queue number, battery swap number, and fully charged battery number of the designated battery swap station to obtain the objective function value corresponding to each scheduling optimization objective.

10. The electric vehicle fleet dispatching method according to any one of claims 1 to 9, characterized in that: The method further comprises: Get the preset culling strategy; The elimination strategy is used to eliminate redundancy in the initial battery replacement plan to obtain an updated initial battery replacement plan, and the updated initial battery replacement plan replaces the initial battery replacement plan.

11. The electric vehicle fleet dispatching method according to any one of claims 1 to 10, characterized in that: The obtaining of the initial battery replacement plan for each vehicle in the electric vehicle fleet includes: Obtaining the initial status information of each vehicle in the electric vehicle fleet; Obtaining the initial status information of the battery swap stations along the route to be traveled by the electric vehicle fleet; Generating multiple alternative solutions for each vehicle based on the vehicle initial state information and the battery swap station initial state information; Multiple alternative plans for each vehicle are branched and expanded to obtain the initial battery replacement plan for each vehicle.

12. An electric vehicle fleet dispatching device, characterized in that: The electric vehicle fleet dispatching device comprises: An acquisition module is used to obtain an initial battery replacement plan for each vehicle in the electric vehicle fleet, wherein the initial battery replacement plan is composed of multiple alternative plan branches; A determination module, configured to obtain a plurality of scheduling optimization objectives according to the initial battery swapping plan, and determine an objective function value of each scheduling optimization objective; a pruning module, configured to prune multiple alternative solution branches in the initial battery swap solution based on the objective function value to obtain a target battery swap solution; A scheduling module is used to schedule each vehicle in the electric vehicle fleet according to the target battery replacement plan.

13. An electric vehicle fleet dispatching device, characterized in that: The electric vehicle fleet dispatching device includes: a memory, a processor, and an electric vehicle fleet dispatching program stored in the memory and executable on the processor, wherein the electric vehicle fleet dispatching program is configured to implement the electric vehicle fleet dispatching method according to any one of claims 1 to 11.

14. A storage medium, characterized in that The storage medium stores an electric vehicle fleet scheduling program, which, when executed by a processor, implements the electric vehicle fleet scheduling method according to any one of claims 1 to 11.

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