Charging pile fault-oriented vehicle charging dynamic scheduling method and system

Abstract

The invention belongs to the related technical field of vehicle charging scheduling, and provides a vehicle charging dynamic scheduling method and system for a charging pile fault in order to solve the problem that a charging device cannot respond in time after a fault occurs, and the method comprises the following steps: monitoring the operation state of each charging pile in the execution of an initial charging scheduling scheme in real time; identifying a target vehicle set influenced by the charging pile fault; calculating an emergency priority score of each target vehicle according to the residual electric quantity and the planned departure time of the target vehicle; constructing a dynamic resource profile of the available charging pile based on the residual available power of the available charging pile, and screening a feasible candidate pile set; and according to the vehicle priority queue, reallocating available charging piles to each target vehicle in sequence by taking the minimum cost as a target, and generating a rescheduling scheme. According to the invention, charging of the vehicle at the valley point of the electricity price can be guaranteed to the greatest extent, and the reliability, economy and adaptive capability of charging are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the technical field of vehicle charging scheduling, and in particular relates to a dynamic vehicle charging scheduling method and system for charging pile failures. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The widespread adoption of pure electric buses has made utilizing off-peak electricity prices at night a key means of reducing operating costs. Several optimization schemes for vehicle charging scheduling already exist. However, these existing technologies share a significant drawback: they are mostly based on the ideal assumption of continuous and stable operation of charging facilities. Their scheduling schemes are typically static or semi-static, lacking the ability to handle unforeseen circumstances once deployed.

[0004] In actual operation scenarios, charging piles may be temporarily damaged due to hardware aging, network interruption, component failure, etc. When a charging pile suddenly fails, it will lead to a series of chain problems: (1) Vehicles charging at the pile are forced to stop. If they cannot be transferred in time, they may not be able to be fully charged before departure, affecting the normal operation of the vehicles; (2) Vehicles waiting in line for the pile are disrupted, and they are very likely to miss the off-peak hours and consume high-priced electricity, increasing costs; (3) The resources of the faulty pile are idle, while other available piles may not be fully utilized, resulting in a decrease in the overall system efficiency.

[0005] Therefore, there is an urgent need for a dynamic charging adjustment scheme that can quickly respond to charging equipment failures in order to ensure the economy and reliability of charging pure electric vehicles. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a dynamic scheduling method and system for vehicle charging in response to charging pile failures. It effectively solves the problem of poor robustness of static scheduling methods when equipment fails, and can maximize the guarantee that vehicles can complete charging during off-peak electricity prices, significantly improving the reliability, economy and adaptability of the charging system.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for dynamic scheduling of vehicle charging in response to charging pile failures, comprising: Based on the vehicle status and taking into account the historical fault risk coefficient of the charging pile, an initial charging scheduling scheme is generated. During the execution of the initial charging scheduling plan, the operating status of each charging pile is monitored in real time. When a charging pile malfunction is detected, the set of target vehicles affected by the charging pile malfunction is identified. Calculate the emergency priority score for each target vehicle based on its remaining battery power and planned departure time. A dynamic resource profile of available charging piles is constructed based on the remaining available power of available charging piles, and a set of feasible candidate charging piles is selected based on the dynamic resource profile of available charging piles. Based on the emergency priority score, a vehicle priority queue is generated. Then, according to the vehicle priority queue, available charging piles are redistributed to each target vehicle in turn with the goal of minimizing cost, thus generating a rescheduling scheme.

[0008] Secondly, the present invention provides a vehicle charging dynamic scheduling system for charging pile faults, comprising: The offline scheduling module is configured to generate an initial charging scheduling scheme based on vehicle status and taking into account the historical fault risk coefficient of the charging pile. The online identification module is configured to: monitor the operating status of each charging pile in real time during the execution of the initial charging scheduling plan; and identify the set of target vehicles affected by the charging pile failure when a charging pile failure is detected. The online calculation module is configured to calculate the emergency priority score of each target vehicle based on the remaining battery power and the planned departure time of the target vehicle. The online filtering module is configured to: construct a dynamic resource profile of available charging piles based on the remaining available power of available charging piles, and filter a set of feasible candidate charging piles based on the dynamic resource profile of available charging piles; The online scheduling module is configured to: generate a vehicle priority queue based on the emergency priority score, and then, according to the vehicle priority queue, reassign available charging piles to each target vehicle in sequence with the goal of minimizing cost, thereby generating a rescheduling scheme.

[0009] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0010] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0011] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0012] The above one or more technical solutions have the following beneficial effects: In this invention, an initial robust scheduling scheme considering charging pile failure risks is first generated offline. During the online phase, the charging pile status is monitored in real time. When a failure is detected, affected vehicles are quickly identified and their emergency priorities are calculated. Based on a dynamic resource profile, vehicles are reassigned to available charging piles according to priority, generating a rescheduling scheme. This invention effectively solves the problem of poor robustness of static scheduling methods in the event of equipment failure through hierarchical control and a fast rescheduling algorithm. It can maximize the guarantee that vehicles can complete charging during off-peak electricity prices, significantly improving the reliability, economy, and adaptability of the charging system.

[0013] In this invention, to address the real-time requirements of rescheduling problems, an emergency priority score is calculated for each affected vehicle, and these vehicles are sorted in descending order. Simultaneously, faulty charging stations are excluded from available resources. Feasible candidate charging stations are screened based on the dynamic resource profile of each station. A cost function composed of factors such as time, cost, and resource utilization is evaluated, and the feasible station with the lowest cost is selected for allocation. The resource occupancy status of this station is updated in real time. This algorithm has low computational complexity, enabling rapid generation and verification of new solutions, meeting the real-time requirements of on-site scheduling, and ensuring the practical value of the technology.

[0014] In this invention, a dynamic resource profile is constructed for each charging pile, enabling refined and global management of the charging pile's power and time resources. This avoids resource idleness and vehicle queuing congestion caused by faults, improves the overall utilization efficiency of the entire charging station's charging facilities, and allows it to serve more vehicles or cope with more complex operating conditions under the same resources.

[0015] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0017] Figure 1 This is a schematic diagram of the overall architecture of the dynamic scheduling system in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the dynamic rescheduling method in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the vehicle emergency priority quantification calculation in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the rescheduling scheme implemented using dynamic resource profiles in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of the fault risk self-learning process in Embodiment 1 of the present invention. Detailed Implementation

[0018] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0020] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0021] Example 1 This embodiment discloses a dynamic vehicle charging scheduling method for charging pile faults, including: Based on the vehicle status and taking into account the historical fault risk coefficient of the charging pile, an initial charging scheduling scheme is generated. During the execution of the initial charging scheduling plan, the operating status of each charging pile is monitored in real time. When a charging pile malfunction is detected, the target vehicle set affected by the charging pile malfunction is identified. The target vehicle set includes vehicles that are charging at the malfunctioning pile and vehicles waiting in line for that pile. Calculate the emergency priority score for each target vehicle based on its remaining battery power and planned departure time. A dynamic resource profile of available charging piles is constructed based on the remaining available power of available charging piles, and a set of feasible candidate charging piles is selected based on the dynamic resource profile of available charging piles. A vehicle priority queue is generated based on the emergency priority score. Available charging piles are then redistributed to each target vehicle in order of minimizing cost, and a rescheduling scheme is generated.

[0022] The core of this embodiment lies in establishing a hierarchical closed-loop intelligent decision-making framework that includes offline robust planning and online real-time rescheduling to cope with uncertainties such as temporary damage to charging piles and ensure that the charging ratio during off-peak hours at night can still be maximized under disturbances.

[0023] like Figure 2As shown, this embodiment begins with continuous monitoring of the current scheduling scheme. Once a charging pile failure event is detected, a rescheduling process is immediately triggered. First, two sets of vehicles affected by the failure are identified: vehicles currently charging and vehicles waiting in the queue. Then, the emergency priority score of each affected vehicle is calculated and sorted in descending order, while faulty charging piles are excluded from available resources. The core of the rescheduling process is to find the optimal allocation for each vehicle in the queue: feasible candidate charging piles are screened based on the dynamic resource profile of each charging pile, and a cost function composed of factors such as time, cost, and resource utilization is evaluated. The feasible pile with the lowest cost is selected for allocation, and the resource occupancy of that pile is updated in real time. After all vehicles have been reassigned, the scheme needs to be verified by global constraints (such as total power limits). If it fails, a conflict resolution mechanism is initiated for secondary optimization until verification is successful before the new scheme is issued and executed. The system then returns to the monitoring state, forming an intelligent closed-loop management system to deal with sudden failures.

[0024] The following is a detailed description of a vehicle charging dynamic scheduling method for charging pile faults proposed in this embodiment.

[0025] like Figure 1 As shown, the dynamic scheduling system in this embodiment adopts a layered and decoupled architecture design, consisting of three layers: Data acquisition layer: Real-time data acquisition of vehicle status (location, battery SOC), charging pile status (working / faulty, power, occupancy) and time-of-use electricity price information is collected through vehicle terminals, charging pile intelligent controllers and grid interfaces.

[0026] The intelligent decision-making layer is the core layer, with an offline scheduling engine and an online rescheduling engine built in. The former performs global optimization based on predictive information to generate robust plans, while the latter performs dynamic adjustments and rapid responses based on real-time data streams.

[0027] Execution control layer: It sends decision commands to physical charging facilities and vehicles through the charging pile control interface, and provides status display and interactive functions to users or managers through the human-machine interface.

[0028] This system achieves intelligent and adaptive management of the entire vehicle charging scheduling process through three-layer collaboration, forming a complete system solution that integrates perception, decision-making, and execution.

[0029] I. Offline Robust Initial Scheduling (Daily Plan).

[0030] Before the start of the nighttime charging cycle (e.g., after all vehicles have returned to the depot by evening), the system uses predicted vehicle information ( , Assuming all charging stations are functioning normally, an initial charging scheduling scheme is generated. ;in, Indicates vehicle The initial remaining battery power when entering the station and starting to wait for charging; Indicates vehicle The latest time when charging must be completed is determined by the scheduled departure time of the following day.

[0031] In this stage, a robust optimization method is used to construct a mixed integer linear programming (MILP) model, so that the initial scheme itself has a certain degree of anti-interference capability.

[0032] The objective function is constructed to minimize the total expected cost, which consists of two parts: the actual electricity cost and the preventative penalty cost for potential failure risks.

[0033]

[0034] in, It is an auxiliary variable, when the vehicle Assigned to charging stations (i.e., at any time) have )hour, Otherwise, it is 0; This is the risk penalty weighting coefficient, used to adjust the trade-off between economic efficiency and the robustness of the solution; For time slices Electricity price; A collection of all pure electric buses that require charging. The total number of vehicles is ; This refers to the collection of all charging stations within the station. The total number of charging piles is ; This is a set of time segments discretized from the entire scheduling period (e.g., the entire night). The length of each time slice is (For example, 15 minutes). It is a continuous decision variable, representing the time... charging pile For vehicles The actual charging power. For charging piles The failure risk coefficient is a value calculated based on data such as the number of historical failures, mean time between failures, and recent maintenance records for the pile, and its range is within [range missing]. Between these values, a higher value indicates a greater estimated risk for that pile. This penalty causes the algorithm to prioritize piles with good historical records and low risk coefficients during initial planning. Low-cost charging stations.

[0035] Constraints: Charging demand constraint: Each vehicle must be fully charged to the required amount of electricity.

[0036]

[0037]

[0038] in, For vehicles Total energy required for this charge; For vehicles The earliest time that charging can begin is usually equal to its entry time; For vehicles The latest time when charging must be completed is determined by the scheduled departure time of the following day. It is a continuous decision variable, representing the time... charging pile For vehicles The actual charging power. For charging efficiency, energy conversion losses from the charging station to the battery should be considered. . For vehicles Total battery capacity; Indicates vehicle The initial remaining battery power when entering the station and starting to wait for charging; For vehicles Target battery level.

[0039] Power constraints: The actual charging power cannot exceed the capacity limits of the vehicle and the charging station, and charging can only be performed when allocated.

[0040]

[0041] in, For vehicles The maximum charging power that the battery can accept; For charging piles The maximum output power that can be provided.

[0042] Time window constraint: Charging operations must be carried out within the available time window for the vehicle.

[0043]

[0044] in, For vehicles The earliest time that charging can begin is usually equal to its entry time; For vehicles The latest time when charging must be completed is determined by the planned departure time the next day.

[0045] Exclusive constraint of charging piles: One charging pile can only charge one vehicle at the same time.

[0046]

[0047] Among them, is a binary decision variable. When , it means that vehicle is assigned to charging pile for charging at time ; otherwise it is 0. is the set of time segments after discretizing the entire scheduling period (such as the entire night). , and the length of each time slice is (for example, 15 minutes).

[0048] Vehicle charging continuity constraint: Once a vehicle starts charging, it should be completed on one pile as much as possible to avoid frequent switching.

[0049] <0000​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​In the event of a fault (such as loss of heartbeat signal or reporting of fault code), the online rescheduling process is immediately triggered. This process employs a model predictive control (MPC) framework combined with a fast heuristic algorithm to achieve rapid response within minutes or even seconds.

[0057] Step 1: Fault diagnosis and identification of affected vehicles.

[0058] The charging station was determined through monitoring. Failed, and its status Set to 0.

[0059] Subsequently, the set of all vehicles affected by this fault was identified from the current scheduling scheme. .

[0060] .

[0061] .

[0062] .

[0063] Step 2: Multifactor emergency priority assessment.

[0064] To handle the affected vehicles fairly and efficiently, it is necessary to provide each vehicle with [specific details / resources]. Calculate a comprehensive emergency priority score .

[0065] like Figure 3 As shown, the priority score is constructed as a multivariable function based on the real-time vehicle status, comprehensively considering the current state of the vehicle and operational needs to ensure that the most urgent vehicles are prioritized for reallocation. Key parameters include the vehicle's current remaining battery power and the remaining leeway before the scheduled departure time. By assigning higher weights to low battery levels and short time leeways, vehicles with low battery levels and nearing departure receive significantly higher urgency scores, thus being prioritized for charging resource allocation during rescheduling. This calculation model can be visualized as a two-dimensional decision plane, where the priority score increases as battery power decreases and time leeway decreases, thereby transforming the abstract scheduling strategy into a quantifiable ranking criterion, ensuring that the operational needs of the most critical vehicles are prioritized when resources are scarce.

[0066] The priority score calculation formula is as follows:

[0067] in, For vehicles The current real-time battery level, for The vehicles in the middle are equal to ;for The vehicle in the image shows its real-time battery level when charging is interrupted. The current moment; These are adjustable weighting coefficients, used to quantify the degree of power scarcity, the urgency of time, and the relative size of demand. For vehicles The total battery capacity. For vehicles Total energy required for this charge; For vehicles The latest time when charging must be completed is determined by the scheduled departure time of the following day.

[0068] After the calculation is complete, the set The vehicles in the middle are in accordance with The vehicles are sorted from highest to lowest to form a queue of vehicles awaiting rescheduling.

[0069] like Figure 4 As shown, this embodiment employs dynamic resource profiling technology to ensure the implementation of the rescheduling scheme. Dynamic resource profiling is used to accurately depict the real-time available power capacity of each charging station over future time scales. When a charging station is reallocated to an affected vehicle, the vehicle's charging demand, including the required charging power, is considered. The system matches charging time with the dynamic resource profiles of each candidate charging station, selecting those stations with continuous and sufficient available capacity within the demand period as a feasible candidate set. Upon successful matching, the system updates the resource profile of the selected station and deducts the corresponding available resources. This mechanism avoids resource allocation conflicts and achieves accurate and efficient matching of vehicle demand with the actual available capacity of charging stations over time.

[0070] Step 3: Feasible pile selection based on dynamic resource profile.

[0071] Specifically, in order to perform precise redistribution, the system assigns each available charging station... (Right now and Maintain a dynamic resource profile. ; This refers to the set C of all charging stations, excluding faulty charging stations. This dynamic resource profile diagram It's about future time. The function represents the time at time... charging pile The remaining available power capacity. For charging piles In time The status is a binary variable, where 1 indicates availability and 0 indicates failure or being occupied.

[0072] Its initial value is .in, For charging piles The maximum output power that can be provided.

[0073] Whenever a vehicle is assigned to a pile During the period During charging, all items within that time period... Perform the update: ,in The planned power to be allocated. The moment when a vehicle is assigned to a charging station to begin charging; This indicates the estimated time when the vehicle is expected to finish charging at this charging station. This time interval represents the entire period during which the vehicle plans to occupy charging station j.

[0074] For vehicles awaiting allocation Its feasible candidate pile set The following conditions must be met: 1. Power matching: The piles are usable and power-compatible.

[0075] 2. Time feasibility: There are one or more consecutive time windows. , ] [ , ], so that at any time within this window They all And the length of the window ( ) is sufficient to complete the vehicle The remaining charging needs.

[0076] Step 4: Constrained cost minimization allocation.

[0077] A sequential greedy algorithm is used for allocation, and a suitable charging pile is assigned to each vehicle in turn according to the priority queue generated in step 2.

[0078] For the vehicle with the highest current priority : Step 41: Traverse its candidate stub set .

[0079] Step 42: For each candidate stake Evaluate a cost function :

[0080] in: It is a vehicle At the charging station The expected charging period. It depends on the charging station The charging power curve estimated from the resource profile. It is a vehicle Current location to charging station The physical distance (if the vehicle needs to move). These are weighting coefficients used to measure economic efficiency, transfer costs, and failure risk, respectively. For charging piles The failure risk coefficient is a value calculated based on data such as the number of historical failures, mean time between failures, and recent maintenance records for the pile, and its range is within [range missing]. Between these values, the higher the value, the greater the estimated risk for that pile. For time slices Electricity prices.

[0081] Step 43: From all candidate stakes that satisfy the constraints, select the one that makes... The smallest charging station .

[0082] Step 44: Degradation Strategy: If, after traversing all candidate stubs, no stub can be found that simultaneously satisfies all hard constraints (e.g., it must be filled before the deadline), then the degradation strategy is activated: Strategy A (Power Adjustment): In consultation with the system or driver, temporarily reduce vehicle power. charging power To adapt to a pile with a high load but still has remaining capacity.

[0083] Strategy B (Time Fine-tuning): Interact with the public transport operation dispatch system to request appropriate delays for vehicles. The next day's departure time For example, delaying the charging time by 15-30 minutes in exchange for a longer charging time window.

[0084] Step 5: Resource update, solution verification and closed-loop execution.

[0085] like Figure 5As shown, the fault risk self-learning mechanism in this embodiment is used to dynamically optimize the long-term robustness of the system. This mechanism maintains a dynamic risk coefficient for each charging pile. When a charging pile fails, the duration and severity of the failure are recorded, and its severity level is determined according to preset rules. Based on different levels, different incremental rules are used to adjust the risk coefficient of the failed pile upwards; for example, a larger coefficient penalty is applied to failures with long durations or severe impacts. Simultaneously, for charging piles that have been operating stably for a long time, historical operating data is collected periodically. If no failure occurs within the risk coefficient assessment period, its risk coefficient is periodically adjusted downwards using a decay factor. This self-learning update based on historical performance allows the risk coefficient to dynamically reflect the reliability status of each charging pile, thereby proactively avoiding high-risk resources in future planning and scheduling decisions and improving the overall resilience of the system.

[0086] Specifically, this includes: Step 51: Update resource status.

[0087] Once the vehicle Successfully assigned to a charging station Then upgrade the charging pile. Dynamic resource profile During its planned charging period Every point in time within Perform the operation:

[0088] in, For charging piles Updated dynamic resource profile, For vehicles The maximum charging power that the battery can accept. For charging piles The maximum output power that can be provided.

[0089] This operation ensures data consistency. A pre-check is performed before the update to prevent power overrun issues after the update.

[0090] Step 52: Global solution verification and conflict resolution.

[0091] The new scheme was generated after all affected vehicles were assigned. It needs to undergo global verification.

[0092] Key verification: Check at any future time. Total charging power of the station Is it always satisfied? .in, The total power limit of the entire charging station is determined by the substation capacity; For charging piles Maximum output power that can be provided; For charging piles Dynamic resource profile.

[0093] Conflict resolution mechanism: If the verification finds that the total power exceeds the limit at certain times, a lightweight secondary optimization process is initiated: the charging power of a few lower priority vehicles is temporarily reduced during peak periods, or their charging start time is slightly postponed, in order to eliminate system-level conflicts.

[0094] Step 53: Instruction issuance and closed-loop monitoring.

[0095] Once the verification is successful, a specific set of timestamped operation instructions will be generated. .

[0096] For vehicles that need to be transferred For vehicles that were originally charging at a faulty charging station, the instruction is: .

[0097] The instructions are sent to the vehicle terminal and charging pile controller via the communication network, and the system waits for confirmation of key instructions, such as the vehicle arriving at the new charging pile or the new charging pile starting to charge.

[0098] The system then switched to the new monitoring solution. The system executes and updates information such as vehicle SOC and pile status in real time, forming a closed-loop feedback control of "monitoring-decision-execution-re-monitoring".

[0099] Step 54: Parameter self-learning and optimization: Record key performance indicators for each rescheduling event, such as: total rescheduling calculation time, average waiting time of affected vehicles, and percentage of off-peak charging loss due to faults.

[0100] For the faulty pile risk factor The timeframe will be dynamically adjusted upwards based on the severity of the incident and the time required for repair. For example:

[0101] in, It is a learning rate coefficient.

[0102] The fault risk coefficient of the updated charging pile will be used in the calculation of the offline scheduling objective function and cost function.

[0103] Optionally, historical operational data can be collected periodically, and machine learning methods such as regression analysis or reinforcement learning can be used to optimize the weight parameters in the algorithm offline. This enables scheduling strategies to better adapt to the specific operating modes and fault characteristics of each site, thereby achieving continuous performance improvements.

[0104] This embodiment employs a layered strategy combining "offline robust planning" and "online real-time rescheduling," along with a closed-loop monitoring mechanism, enabling the charging dispatch system to cope with sudden equipment failures. Single-point failures no longer cause global dispatch collapse; the system can automatically isolate faults and quickly self-heal, greatly improving the continuity and reliability of bus charging operations and effectively mitigating the risk of operational interruptions due to charging problems.

[0105] One of the core objectives of the dynamic rescheduling algorithm in this embodiment is to maximize charging during off-peak hours. Through precise emergency priority assessment and cost function optimization, even in the event of a fault, affected vehicles can be intelligently guided to other available off-peak resources.

[0106] To address the real-time requirements of rescheduling problems, this embodiment designs a fast heuristic algorithm based on priority sorting and constraint satisfaction checks, replacing the computationally complex online global optimization. This algorithm has low computational complexity, can quickly generate and verify new solutions, meets the real-time requirements of on-site scheduling, and ensures the practical value of the technology.

[0107] The dynamic resource profile model and update mechanism in this embodiment enable refined and global management of the power and time resources of charging piles. This avoids resource idleness and vehicle queuing congestion caused by faults, improves the overall utilization efficiency of the charging facilities at the entire site, and allows more vehicles to be served or more complex operating conditions to be handled with the same resources.

[0108] This embodiment introduces a dynamic update mechanism for the fault risk coefficient and a self-learning mechanism for algorithm parameters, enabling the system to learn from historical operational data and continuously optimize its scheduling strategy. This allows the system not only to solve current problems but also to better predict and adapt to future uncertainties, demonstrating continuous intelligent improvement characteristics.

[0109] Example 2 The purpose of this embodiment is to provide a dynamic vehicle charging scheduling system for charging pile failures, including: The offline scheduling module is configured to generate an initial charging scheduling plan based on vehicle status and taking into account the risk of charging pile failure. The online identification module is configured to: monitor the operating status of each charging pile in real time during the execution of the initial charging scheduling plan; and identify the set of target vehicles affected by the charging pile failure when a charging pile failure is detected. The online calculation module is configured to calculate the emergency priority score of each target vehicle based on the remaining battery power and the planned departure time of the target vehicle. The online filtering module is configured to: construct a dynamic resource profile of available charging piles based on the remaining available power of available charging piles, and filter a set of feasible candidate charging piles based on the dynamic resource profile of available charging piles; The online scheduling module is configured to: generate a vehicle priority queue based on the emergency priority score, and then, according to the vehicle priority queue, reassign available charging piles to each target vehicle in sequence with the goal of minimizing cost, thereby generating a rescheduling scheme.

[0110] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0111] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0112] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0113] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0114] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0115] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0116] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0117] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0118] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0119] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0120] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for dynamic scheduling of vehicle charging in response to charging pile faults, characterized in that, include: Based on the vehicle status and taking into account the historical fault risk coefficient of the charging pile, an initial charging scheduling scheme is generated. During the execution of the initial charging scheduling plan, the operating status of each charging pile is monitored in real time. When a charging pile malfunction is detected, the set of target vehicles affected by the charging pile malfunction is identified. Calculate the emergency priority score for each target vehicle based on its remaining battery power and planned departure time. A dynamic resource profile of available charging piles is constructed based on the remaining available power of available charging piles, and a set of feasible candidate charging piles is selected based on the dynamic resource profile of available charging piles. A vehicle priority queue is generated based on the emergency priority score. Available charging piles are then redistributed to each target vehicle in order of minimizing cost, and a rescheduling scheme is generated.

2. The vehicle charging dynamic scheduling method for charging pile faults as described in claim 1, characterized in that, Based on the remaining battery power and planned departure time of the target vehicles, the emergency priority score for each target vehicle is calculated as follows: in, For vehicles The urgency priority score; For vehicles The current real-time battery level; The current moment; For vehicles The latest time that charging must be completed; For vehicles Total battery capacity; For vehicles Total energy required for this charge; These are adjustable weighting coefficients, used to quantify the degree of power scarcity, the urgency of time, and the relative size of demand.

3. The vehicle charging dynamic scheduling method for charging pile faults as described in claim 1, characterized in that, A dynamic resource profile is constructed for each available charging station. The dynamic resource profile is a function of future time, representing the remaining available power capacity of the charging station at a future time.

4. The vehicle charging dynamic scheduling method for charging pile faults as described in claim 1, characterized in that, Based on the emergency priority score, a vehicle priority queue is generated. Then, according to the vehicle priority queue, available charging stations are reassigned to each target vehicle in order of minimum cost, generating a rescheduling scheme, specifically: For the vehicle with the highest current priority, iterate through the set of feasible candidate stakeouts and evaluate the cost function for each feasible candidate stakeout; where the cost function is composed of time, cost, and resource utilization. Select the feasible candidate stake with the minimum cost from all feasible candidate stakes that satisfy the constraints.

5. The vehicle charging dynamic scheduling method for charging pile faults as described in claim 4, characterized in that, The constraints that feasible candidate charging stations for each target vehicle must meet include: power matching and time feasibility; where power matching means that the minimum value between the maximum output power that the charging station can provide and the maximum charging power that the vehicle battery can accept is greater than 0; the time feasibility means that there are one or more consecutive time windows. So that at any time within the time window Both have dynamic resource profiles Furthermore, the window is long enough to meet the remaining charging needs of the target vehicle; among which, The earliest time that vehicle i can begin charging. The current moment; For vehicles The latest time that charging must be completed; For charging piles Maximum output power that can be provided; For vehicles The maximum charging power that the battery can accept.

6. The vehicle charging dynamic scheduling method for charging pile faults as described in claim 1, characterized in that, Also includes: Once the target vehicle is reassigned to a charging station, the calculation is performed at each time point within the planned charging time of the charging station: Update the dynamic resource profile of charging piles; among which, For charging piles Updated dynamic resource profile, For vehicles The maximum charging power that the battery can accept. For charging piles The maximum output power that can be provided.

7. A vehicle charging dynamic scheduling system for charging pile faults, characterized in that, include: The offline scheduling module is configured to generate an initial charging scheduling scheme based on vehicle status and taking into account the historical fault risk coefficient of the charging pile. The online identification module is configured to: monitor the operating status of each charging pile in real time during the execution of the initial charging scheduling plan; and identify the set of target vehicles affected by the charging pile failure when a charging pile failure is detected. The online calculation module is configured to calculate the emergency priority score of each target vehicle based on the remaining battery power and the planned departure time of the target vehicle. The online filtering module is configured to: construct a dynamic resource profile of available charging piles based on the remaining available power of available charging piles, and filter a set of feasible candidate charging piles based on the dynamic resource profile of available charging piles; The online scheduling module is configured to: generate a vehicle priority queue based on the emergency priority score, and then, according to the vehicle priority queue, reassign available charging piles to each target vehicle in sequence with the goal of minimizing cost, thereby generating a rescheduling scheme.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-6.

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