Mobile charging pile path planning method and system for electric vehicle

Abstract

The invention discloses a mobile charging pile path planning method and system for an electric vehicle, and relates to the technical field of charging piles, and the method comprises the steps: obtaining a travel plan and a real-time state of a target vehicle, and recognizing a weak energy compensation section; real-time states (including tasks, electric quantity and positions) of all the mobile charging piles in the section are collected; the schedulable electric piles are screened in combination with the vehicle and electric pile states, an alternative set is formed, and schedulability evaluation is carried out; and according to the evaluation result, an optimal service path is planned and issued for the optimal scheduling electric pile according to a preset path strategy, and the optimal service path is guided to execute an energy complementing task. Therefore, the technical effects of improving the scheduling level, improving the energy complementing efficiency and optimizing the energy complementing feeling are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and in particular to a mobile charging pile path planning method and system for electric vehicles. Background Art

[0002] With the widespread adoption of electric vehicles, recharging has become a significant factor impacting the user experience. Currently, electric vehicles rely primarily on fixed charging stations for recharging, but the limited distribution of these stations makes it difficult to meet the recharging needs of electric vehicles in various travel scenarios. This is particularly problematic in some special areas.

[0003] In the existing technology, in the energy replenishment planning for electric vehicles, the identification of weak energy replenishment sections is not accurate enough, and there is a lack of comprehensive and effective information consideration for the scheduling of mobile charging piles. It fails to fully combine the various real-time status information of mobile charging piles for reasonable scheduling, resulting in slow task response, unreasonable scheduling and other problems, which cannot meet the personalized needs of users. Summary of the Invention

[0004] The present invention provides a mobile charging pile path planning method and system for electric vehicles to solve the technical problems in the prior art of unreasonable scheduling, which affects the energy charging efficiency and energy charging experience, and achieve the technical effects of improving the scheduling level, enhancing the energy charging efficiency, and optimizing the energy charging experience.

[0005] In a first aspect, the present invention provides a mobile charging pile path planning method for an electric vehicle, wherein the mobile charging pile path planning method for an electric vehicle comprises: The travel plan information of the target vehicle is obtained, and combined with the real-time vehicle status information of the target vehicle, the weak energy replenishment section is identified.

[0006] The real-time charging pile status information of each mobile charging pile in the weak energy replenishment section is obtained, wherein the real-time charging pile status information at least includes a task status, a power status, and a location status.

[0007] Based on the real-time charging pile status information and the real-time vehicle status information, schedulable mobile charging piles are screened and output as a candidate charging pile set, and the candidate charging pile set is traversed to perform schedulability evaluation.

[0008] According to the schedulability evaluation results, a path planning decision is executed for the optimally scheduled mobile charging pile based on a preset path planning strategy to obtain the optimal service path, and the optimal service path is sent to the optimally scheduled mobile charging pile to guide the execution of the charging task.

[0009] In a feasible implementation, the weak energy replenishment section includes at least one of the following situations: The distance between two adjacent high-efficiency charging points is greater than the preset high-efficiency charging range of the target vehicle.

[0010] There is an inefficient charging point between two high-efficiency charging points, and the distance between the two high-efficiency charging points is greater than the maximum drivable mileage of the target vehicle. The inefficient charging points include those with low power levels, poor equipment stability, and long waiting times.

[0011] The travel plan information contains sections where energy replenishment needs are known but the energy replenishment resource configuration cannot meet the coverage requirements.

[0012] In a feasible implementation, traversing the candidate charging pile set and performing a dispatchability evaluation includes: In combination with the real-time status information of the vehicle, a set of schedulability evaluation indicators is calculated and obtained, and corresponding schedulability evaluation rules are defined, wherein the schedulability evaluation rules include task status rules, power status rules, and energy replenishment timeliness rules.

[0013] Based on the schedulability evaluation rules and the real-time charging pile status information, a schedulability evaluation is performed. If the task status is no task and the power status meets the power status rules, the schedulability is marked as high. If the task status is no task, the power status does not meet the power status rules, and the energy replenishment time calculated based on the power status and the location status meets the energy replenishment timeliness rules, the schedulability is marked as medium. Otherwise, the schedulability is marked as low.

[0014] In one feasible implementation, the preset path planning strategy includes: If the optimally scheduled mobile charging pile is highly schedulable, a path planning starting state is determined, wherein the starting state includes the real-time location of the target vehicle, the real-time location of the optimally scheduled mobile charging pile, the starting time, the remaining available time, and the planned service point.

[0015] Path planning constraints are configured based on the path planning starting state, and service paths are generated in combination with a path planning algorithm.

[0016] In a feasible implementation, the preset path planning strategy further includes: If the optimally scheduled mobile charging pile is not highly schedulable, a self-recharging site of the optimally scheduled mobile charging pile is determined, and a self-recharging path is generated based on the self-recharging site.

[0017] The charging completion time is calculated according to the target charging power of the optimally scheduled mobile charging pile and the corresponding charging characteristic curve.

[0018] The end point of the self-recharging path and the completion time of the recharging are used as the starting state of the path planning, and the service path is generated and optimized in combination with the path planning algorithm.

[0019] In one feasible implementation, path planning algorithms are combined to generate and optimize paths, including: The main optimization objectives are set, including at least minimizing energy consumption in transit, optimizing service response time, and maximizing self-recharging time.

[0020] Additional optimization goals are set, including at least ensuring that the power state falls within the high-efficiency range and minimizing the excess power of the target vehicle.

[0021] The optimization constraints are set, including at least that the target energy replenishment power satisfies the power state rule, the service radius of the optimally scheduled mobile charging pile does not exceed a preset range, and the driving path length of the target vehicle satisfies the current remaining mileage of the target vehicle.

[0022] The main optimization objective, the additional optimization objective and the optimization constraints are comprehensively considered to perform multi-objective path optimization.

[0023] In a feasible implementation, the method further includes: If the optimally scheduled mobile charging pile is not highly dispatchable, the waiting time is calculated based on the optimal service path and the self-recharging path, combined with the real-time status information of the vehicle. The waiting time is a vector value, which is negative when the car is waiting for the pile and positive when the pile is waiting for the car.

[0024] A waiting optimization process is performed according to the waiting time.

[0025] In a feasible implementation, performing waiting optimization processing according to the waiting time includes: When the waiting time is a positive value and exceeds a first preset time threshold, the target power of the optimally scheduled mobile charging pile is correspondingly increased.

[0026] When the waiting time is negative and lower than a second preset time threshold, the latest reachable charging point is searched within the service neighborhood of the planned service point in combination with the real-time status information of the vehicle, and the search is iterated until the waiting time becomes positive.

[0027] If the waiting time after iteration is not a positive value, the service point with the waiting time closest to zero in the service neighborhood is preferably updated as the planned service point, and the optimal service path is replanned.

[0028] In a feasible implementation, based on the real-time charging pile status information and the real-time vehicle status information, the dispatchable mobile charging piles are screened and output as a candidate charging pile set, including: Configure the minimum power constraint and perform a corresponding filter.

[0029] Traverse the screening results once, extract the mobile charging piles that are currently not tasked and store them in the candidate charging pile set.

[0030] Traverse the screening results once, obtain the remaining task time and the maximum path time of the service range of each mobile charging pile, and sum them to obtain the remaining occupied time.

[0031] Combined with the real-time status information of the vehicle, the remaining transit time of the target vehicle is calculated, and the mobile charging piles whose remaining occupancy time is less than the remaining transit time are extracted, determined as the schedulable mobile charging piles, and stored in the candidate charging pile set.

[0032] In a second aspect, the present invention further provides a mobile charging pile path planning system for electric vehicles, wherein the mobile charging pile path planning system for electric vehicles comprises: The section identification module is used to obtain the travel plan information of the target vehicle and identify the weak energy replenishment section in combination with the real-time vehicle status information of the target vehicle.

[0033] The charging pile status acquisition module is used to obtain the real-time charging pile status information of each mobile charging pile in the weak energy replenishment section, wherein the real-time charging pile status information at least includes task status, power status, and location status.

[0034] The screening and schedulability evaluation module is used to screen schedulable mobile charging piles based on the real-time charging pile status information and the real-time vehicle status information, output them as a set of candidate charging piles, and traverse the set of candidate charging piles to perform schedulability evaluation.

[0035] The path planning and distribution module is used to perform path planning decisions on the optimally scheduled mobile charging pile based on the schedulability evaluation results and the preset path planning strategy, obtain the optimal service path, and distribute the optimal service path to the optimally scheduled mobile charging pile to guide the execution of the charging task.

[0036] The present invention discloses a mobile charging pile path planning method and system for electric vehicles, comprising: obtaining travel plan information of a target vehicle, and identifying a weak charging section on the vehicle's driving path in combination with the real-time status information of the target vehicle; obtaining real-time charging pile status information of each mobile charging pile in the weak charging section, the status information including but not limited to task status, power status and position status; based on the real-time charging pile status information and the real-time status information of the target vehicle, screening mobile charging piles that meet the scheduling conditions to form a set of alternative charging piles; traversing the set of alternative charging piles, performing schedulability evaluation based on scheduling capability and availability, and screening out the best-scheduled mobile charging pile; according to the schedulability evaluation result and in combination with a preset path planning strategy, executing path planning decision on the best-scheduled mobile charging pile to obtain the optimal service path; sending the optimal service path to the best-scheduled mobile charging pile to guide the mobile charging pile to complete the charging task. The mobile charging pile path planning method and system for electric vehicles disclosed in the present invention solve the technical problems of unreasonable scheduling, affecting charging efficiency and charging experience, and achieve the technical effects of improving scheduling level, enhancing charging efficiency, and optimizing charging experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The figure is a flow chart of a mobile charging pile path planning method for electric vehicles according to the present invention.

[0038] Figure 2 The figure is a schematic structural diagram of a mobile charging pile path planning system for electric vehicles according to the present invention.

[0039] Explanation of reference numerals: section identification module 11 , charging pile status acquisition module 12 , screening and schedulability evaluation module 13 , route planning and issuing module 14 . DETAILED DESCRIPTION

[0040] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0041] Example 1, as Figure 1 The figure is a flow chart of a mobile charging pile path planning method for electric vehicles according to the present invention, wherein the mobile charging pile path planning method for electric vehicles includes: S100: Acquire the travel plan information of the target vehicle, and identify the weak energy replenishment section in combination with the real-time vehicle status information of the target vehicle.

[0042] Specifically, travel plan information refers to the user's pre-set vehicle route plan, including detailed information such as the starting point, end point, and possible locations along the way. Real-time vehicle status information includes real-time parameters such as the vehicle's current remaining battery life, range, speed, and acceleration, reflecting the vehicle's immediate operating status.

[0043] Specifically, the weak charging section is a road section with charging risks or low efficiency determined in the travel plan based on the vehicle status and charging resource configuration. Specifically, the distance between two adjacent high-efficiency charging points is too large, or there is an inefficient charging point and the distance of the interval exceeds the maximum drivable mileage of the target vehicle.

[0044] Specifically, the vehicle's intelligent connected system first obtains user-entered travel plan information, such as the preset route trajectory (e.g., the sequence of waypoints provided by the navigation system), estimated driving time and road section duration, planned stops and their duration, the availability of energy recharging facilities in the area (e.g., charging stations, battery swap stations), and user preferences (e.g., avoiding peak hours, prioritizing fast charging stations, etc.). Simultaneously, the vehicle's battery management system and onboard computer collect and upload real-time status data, including the vehicle's remaining battery charge (assuming 30%), range (200km), and current speed (60km / h). Then, based on the planned route trajectory and current battery status, the system predicts the vehicle's energy consumption trend over the next period of time and compares it with information on recharging facilities along the route.

[0045] For example, if any of the following situations is detected, it can be determined as a weak energy replenishment section: There are no available recharging facilities for several consecutive kilometers on the planned route; the battery level is expected to be below a threshold (such as 20%) when arriving at the section, and there are no feasible recharging options; recharging facilities exist but are unavailable (such as busy, faulty, or reserved); the current driving behavior causes abnormal energy consumption and may not be able to support crossing the section; the section has a high-energy consumption environment (such as long slopes, high speeds, and low temperatures), but the recharging capacity is insufficient, etc.

[0046] Optionally, energy accessibility models, state-path joint prediction models (such as LSTM+path graph), graph-based energy replenishment node reachability analysis, or reinforcement learning strategies can be used to evaluate the energy risk of future paths and identify weak energy replenishment sections.

[0047] Through this process, we can accurately identify areas of recharging risk in travel plans, providing a key basis for the rational scheduling of subsequent mobile charging stations. This reduces the risk of vehicle breakdowns due to untimely or inefficient recharging, improves users' sense of travel safety and satisfaction with recharging services, and ensures a smooth and efficient travel experience.

[0048] In some embodiments, the weak energy compensation section includes at least one of the following situations: The distance between two adjacent high-efficiency charging points is greater than the preset high-efficiency charging range of the target vehicle; the section where there is an inefficient charging point between two high-efficiency charging points, and the distance between the two high-efficiency charging points is greater than the maximum drivable mileage of the target vehicle, wherein the inefficient charging points include charging points with low power levels, poor equipment stability, and long waiting times in queues; the section where there is a known charging demand in the travel plan information but the charging resource configuration cannot meet the coverage requirements.

[0049] Specifically, efficient recharging mileage refers to the maximum mileage a vehicle can travel while maintaining efficient charging (e.g., charging power above a certain threshold), and is related to factors such as the vehicle's battery capacity, charging efficiency, and energy consumption. Inefficient recharging points refer to recharging points with characteristics such as low power levels (e.g., charging power below 30kW), poor equipment stability (e.g., frequent failures or maintenance), and long waiting times (e.g., average waiting times exceeding 15 minutes), which can result in longer recharging times or increased uncertainty. Recharging resource allocation refers to the number, type, power, and distribution of available recharging points within a specific area or road section, and is used to reflect the recharging capacity and service level of the area.

[0050] Specifically, the charging points may include charging piles, battery swap stations and other infrastructure with energy supply capabilities.

[0051] Specifically, the weak energy replenishment section includes at least one of the following situations: A weak charging segment occurs when the distance between two adjacent high-efficiency charging points exceeds the preset high-efficiency charging range threshold for the target vehicle. High-efficiency charging points are defined as charging facilities with high charging power (e.g., fast charging power ≥ 60kW), high availability (high equipment online rate and stable operation), and short waiting times (queue time below a set threshold, such as 5 minutes). The high-efficiency charging range threshold can be dynamically adjusted based on the target vehicle's battery performance, current load, ambient temperature, and other factors. For example, if an electric vehicle's current high-efficiency driving range is 120km, then if the distance between two high-efficiency charging stations on its travel route is 140km, this segment can be considered a weak charging segment.

[0052] Weak charging section with interspersed inefficient charging points: There are one or more charging points between two efficient charging points, but these charging points are inefficient; at the same time, the distance between the two efficient charging points is greater than the maximum drivable mileage of the target vehicle (such as theoretical full-charge range); inefficient charging points may include but are not limited to any of the following situations: low charging power (such as AC slow charging ≤7kW); poor equipment stability (frequent failures, disconnections, maintenance); long waiting time in queues (such as average waiting time >15 minutes as reported by users); stations marked as "not recommended" or "low-rated" in the user's historical preferences.

[0053] Weak energy replenishment section with missing energy replenishment resource configuration: The travel plan information clearly shows the expected energy replenishment demand (such as planned mid-way stop for energy replenishment, energy replenishment required for long-distance travel, etc.), but within this time and space segment, the energy replenishment resource configuration cannot meet the coverage requirements, including but not limited to: the target energy replenishment point is unavailable (such as reserved, faulty, maintenance), the number of available energy replenishment points is insufficient to meet the concentrated energy replenishment needs of multiple high-energy consumption vehicles on the current route, there is an energy replenishment resource "window period" or "blank area" (such as remote mountainous areas, no service at night), the energy replenishment point is not on the target path or deviates too far, resulting in excessively high detour costs, etc.

[0054] Specifically, by analyzing travel plan information and real-time vehicle status information, the steps to identify weak charging sections are as follows: Calculate the distance between adjacent high-efficiency charging points: The system obtains the location information of high-efficiency charging points (such as charging stations with a charging power greater than 50kW) from the charging point database and calculates the road distance between two adjacent high-efficiency charging points. For example, if a vehicle's high-efficiency charging range is 150km, but the distance between two adjacent high-efficiency charging points is 180km, this section is identified as a weak charging section.

[0055] Assessing the impact of inefficient charging points: If an inefficient charging point exists between two efficient charging points, and the distance between them is greater than the target vehicle's maximum range (e.g., a fully charged vehicle has a range of 300km and the distance between the two efficient charging points is 320km), then this section is considered a weak charging section. For example, if the charging power of the intermediate inefficient charging point is only 20kW, the vehicle may have insufficient remaining power when it arrives at this point, and the charging time may be long, making it impossible to effectively guarantee the vehicle's range.

[0056] Checking coverage of known charging needs: Based on the user's travel plan (e.g., a long-distance self-driving tour in a mountainous area), the system analyzes the charging resource allocation in that area. If a section of the planned route has a known charging need (e.g., the user plans to recharge once), but there are no available charging points within that section or the charging point service capacity is insufficient (e.g., there are only a few inefficient charging stations that are often in queues), the section is considered to be a weak charging section.

[0057] Through the above process, different types of weak energy replenishment sections can be comprehensively and accurately identified, providing clear target areas for subsequent mobile charging pile scheduling.

[0058] S200: Acquire real-time charging pile status information of each mobile charging pile in the weak energy replenishment section, wherein the real-time charging pile status information at least includes task status, power status, and location status.

[0059] Specifically, a mobile charging pile refers to a charging device with an independent power system or that can be towed by a vehicle and can provide temporary or emergency energy supply services for electric vehicles in different geographical locations. Mobile charging piles include but are not limited to: mobile charging vehicles, portable charging robots, unmanned charging platforms, etc. Real-time charging pile status information refers to the dynamic operation data collected for the dispatchable mobile charging piles in the weak charging section after the weak charging section is identified. The status information includes at least the following three aspects: Task status: indicates whether the current mobile charging pile is idle, executing a task, on standby, faulty, etc.; Power status: indicates the remaining available power, maximum discharge power, number of supported services, etc. of the current mobile charging pile itself; Location status: indicates the geographical coordinates, moving speed, estimated arrival time and other location information of the current mobile charging pile.

[0060] Specifically, after identifying that the target vehicle is about to enter a weak charging section, based on the vehicle's current GPS position and path planning, the weak charging section with charging risks in the future driving path is determined; the dispatchable mobile charging pile resources currently in the section or its surrounding area are retrieved from the scheduling platform or vehicle-road cooperative system; real-time status information is collected, including querying the task status of each mobile charging pile (whether it is idle or dispatchable); obtaining its current remaining power, maximum output power, service capacity and other power status; obtaining its current position, moving direction, average speed and other position status; finally, the above status information is spatially mapped with the weak charging section to form a "section-resource" mapping model to provide a decision basis for subsequent charging scheduling.

[0061] Through the above process, the mobile energy replenishment resource situation in the section can be fully understood before the target vehicle enters the weak energy replenishment section, which helps to improve the real-time and accuracy of energy replenishment scheduling.

[0062] S300: Based on the real-time charging pile status information and the real-time vehicle status information, screening schedulable mobile charging piles, outputting them as a candidate charging pile set, and traversing the candidate charging pile set to perform schedulability evaluation.

[0063] Specifically, a dispatchable mobile charging station refers to a mobile charging device that, under current spatial and temporal conditions, can effectively recharge the target vehicle before or during the target vehicle's weak recharging zone. This dispatchability depends not only on the status of the charging station itself but also on the target vehicle's real-time status, including factors such as remaining battery life, projected driving path, energy consumption rate, and tolerated waiting time. The candidate charging station set is a collection of preliminarily qualified dispatchable mobile charging stations selected from a large pool of mobile charging stations, serving as the foundational data for subsequent evaluation and selection.

[0064] Specifically, dispatchability evaluation is a process of quantitatively assessing the comprehensive service capabilities of each mobile charging pile in the alternative charging pile set based on preset rules and indicators to determine the extent to which it is suitable for providing services to the current target vehicle.

[0065] Through this process, we can accurately screen dispatchable mobile charging piles with potential service capabilities and conduct a detailed dispatchability evaluation. This not only improves screening efficiency and ensures that the mobile charging piles included in the candidate set have basic charging service capabilities, but also effectively distinguishes the service potential of each mobile charging pile through comprehensive evaluation, providing a scientific basis for subsequent selection of the optimal dispatch target.

[0066] In some embodiments, based on the real-time charging pile status information and the real-time vehicle status information, screening the dispatchable mobile charging piles and outputting them as a set of candidate charging piles includes: A minimum power constraint is configured and a corresponding screening is performed; the screening results are traversed once, and mobile charging piles that are currently without tasks are extracted and stored in the set of alternative charging piles; the screening results are traversed once, and the remaining task time and the maximum path time of the service range of each mobile charging pile are obtained, and the remaining occupancy time is summed to obtain the remaining occupancy time; in combination with the real-time status information of the vehicle, the remaining transit time of the target vehicle is calculated, and the mobile charging piles whose remaining occupancy time is less than the remaining transit time are extracted, determined as the schedulable mobile charging piles, and stored in the set of alternative charging piles.

[0067] Specifically, primary screening involves preliminary screening of all mobile charging piles based on a minimum power constraint. The goal is to quickly eliminate those with severely insufficient power and unable to effectively recharge vehicles. The minimum power constraint refers to a preset power threshold used for preliminary screening of mobile charging piles to ensure they have sufficient discharge capacity during the recharging mission. This threshold can be dynamically set based on vehicle type, recharging power requirements, or mission distance.

[0068] Specifically, the remaining occupancy time refers to the total time it takes a mobile charging station to complete its current task and reach the service area boundary, reflecting the earliest time the charging station can theoretically be free for other tasks. The remaining transit time refers to the time it takes for the target vehicle to pass through the weak charging section. It is calculated based on the vehicle's real-time location, driving speed, and section length, and represents the time window within which the vehicle can be used within the section.

[0069] Specifically, a minimum charge threshold, such as 20% SOC, is set. The current charge level of a mobile charging station must reach this threshold before it can proceed to the next screening step. The screening results are then iterated through, and the task status of each charging station is checked. If the task status is "no task" (idle), it is stored in the candidate charging station set. For charging stations with tasks, the remaining task time (e.g., if it is currently charging another vehicle and is expected to complete in 15 minutes) and the maximum path time within the service area (e.g., 10 minutes from the current location to the service boundary) are calculated and added together to obtain a remaining occupied time of 25 minutes.

[0070] Furthermore, based on the target vehicle's real-time location (5 km from the start of the weak charging section), its speed (60 km / h), and the section length (30 km), the remaining transit time for the vehicle to pass through this section is calculated to be 25 minutes ((30 - 5) / 60 × 60 = 25 minutes). If the remaining occupancy time of a mobile charging station is less than the vehicle's remaining transit time, it is determined that it is likely to complete its current mission and reach the charging point before the target vehicle passes through the weak charging section. This charging station is then considered available for dispatch, and charging stations that meet these conditions are added to the pool of candidate charging stations.

[0071] Exemplarily, the status of mobile charging piles A, B, and C are obtained as follows: Pile A: power level 80%, no task → directly included in the set of alternative charging piles; Pile B: power level 75%, remaining task time 10 minutes, maximum service range path time 15 minutes → remaining occupancy time is 25 minutes, which is less than the remaining vehicle transit time of 30 minutes → included in the set of alternative charging piles; Pile C: power level 65%, remaining task time 20 minutes, service path time 20 minutes → remaining occupancy time 40 minutes, which is greater than the vehicle transit time → eliminated.

[0072] The above screening process ensures that the mobile charging piles included in the candidate charging pile pool have basic power guarantees, availability, and time compatibility. This not only improves the accuracy and efficiency of the screening, but also ensures that the final dispatchable mobile charging piles can better meet the target vehicle's charging needs, reducing delays caused by vehicles waiting for charging piles to become available in weak charging areas.

[0073] In some embodiments, traversing the candidate charging pile set and performing dispatchability evaluation includes: In combination with the real-time status information of the vehicle, a set of schedulability evaluation indicators is calculated and obtained, and corresponding schedulability evaluation rules are defined, wherein the schedulability evaluation rules include task status rules, power status rules, and energy replenishment timeliness rules; based on the schedulability evaluation rules and the real-time charging pile status information, a schedulability evaluation is performed, wherein, if the task status is no task and the power status meets the power status rules, it is marked as high schedulability; if the task status is no task and the power status does not meet the power status rules, and the energy replenishment time consumption calculated based on the power status and the position status meets the energy replenishment timeliness rules, it is marked as medium schedulability; otherwise, it is marked as low schedulability.

[0074] Specifically, dispatchability evaluation involves assessing the dispatchability of each mobile charging station in the candidate charging station set for the target vehicle's recharging task, combining the vehicle's current state with the charging station's state. This is used for subsequent scheduling priority sorting or task allocation. The dispatchability evaluation index set, calculated jointly based on the vehicle and charging station states, supports the design of multi-dimensional evaluation rules, including quantified values ​​for key factors such as task status, battery status, and recharging timeliness.

[0075] Specifically, the schedulability evaluation rules include but are not limited to: task status rules, which determine whether the mobile charging pile is currently in a "no task" state. If it is "no task", it has the ability to respond immediately; power status rules: which determine whether the current remaining power of the mobile charging pile is higher than a preset threshold (such as 70%, determined based on the power required for replenishment of the target vehicle), to ensure that it has the ability to complete an effective energy replenishment; energy replenishment timeliness rules: in the case of insufficient power, determine whether the time it takes to move from the current position to the self-energy replenishment point for the mobile charging pile to self-recharge is within the time window acceptable to the vehicle, to ensure that it can still respond quickly even if the power is insufficient.

[0076] In other words, the recharging time calculated based on the power status and location status refers to the time consumed by a mobile charging pile with insufficient remaining power to recharge itself until the power status rules are met. When the recharging time is within the acceptable time window for the vehicle (meeting the recharging time limit rules), it can be considered that the mobile charging pile has a certain degree of dispatchability (medium dispatchability).

[0077] Specifically, the schedulability level can be divided into three categories: high schedulability: having the ability to respond and complete tasks immediately; medium schedulability: having some capability defects but still meeting the scheduling requirements; low schedulability: not having the ability to meet the current energy replenishment task.

[0078] The dispatchability evaluation process described above enables precise quantitative assessment of the service capacity and potential of each mobile charging station, providing a scientific and detailed basis for selecting the optimal dispatch target. This hierarchical assessment approach helps optimize dispatching decisions, ensuring that highly dispatchable charging stations are prioritized, thereby improving charging efficiency, reducing vehicle wait times, and enhancing the user experience. It also enables more rational allocation and utilization of mobile charging station resources, avoiding resource waste and service delays caused by blind dispatch, and enhancing the reliability and operational efficiency of the entire charging system.

[0079] S400: According to the schedulability evaluation result, a path planning decision is executed for the optimally scheduled mobile charging pile based on a preset path planning strategy to obtain an optimal service path, and the optimal service path is sent to the optimally scheduled mobile charging pile to guide the execution of the charging task.

[0080] Specifically, a path planning strategy refers to a set of pre-defined rules and methods used to determine the optimal route for a mobile charging station from its current location to its target charging location. This strategy takes into account factors such as path length, time, and energy consumption. The optimal service path is the optimal route determined for a mobile charging station, satisfying various constraints and integrating various optimization objectives. This approach aims to achieve efficient charging service.

[0081] Specifically, path planning strategies include two categories: Direct service path planning: Applicable to highly dispatchable charging stations, directly planning the service path from the current location to the target vehicle; Post-recharge service path planning: Applicable to medium / low dispatchable charging stations, first planning the path to the self-recharge station, and then planning the service path to the target vehicle from the location after the recharge is completed.

[0082] Specifically, based on the dispatchability evaluation results, the mobile charging pile with the highest dispatchability is selected from the candidate charging pile set as the optimal mobile charging pile. For example, if the candidate charging pile set includes charging pile A (high dispatchability), charging pile B (medium dispatchability), and charging pile C (low dispatchability), charging pile A is selected as the optimal mobile charging pile. Then, based on the preset path planning strategy, a path planning decision is made for charging pile A.

[0083] Through this process, the optimal service path can be determined for the optimally scheduled mobile charging station, ensuring that it can provide charging services to vehicles efficiently and promptly. This not only improves the efficiency and success rate of charging tasks and reduces vehicle waiting time, but also optimizes the energy utilization efficiency of the mobile charging station, extends its range, and enhances the operating efficiency and service quality of the entire charging system.

[0084] In some embodiments, the preset path planning strategy includes: If the optimally scheduled mobile charging pile has high schedulability, a path planning starting state is determined, wherein the starting state includes the real-time location of the target vehicle, the real-time location of the optimally scheduled mobile charging pile, the starting time, the remaining available time, and the planned service point; path planning constraints are configured based on the path planning starting state, and a service path is generated in combination with a path planning algorithm.

[0085] Specifically, one of the preset strategies is applicable to charging piles with a high dispatchability level. It aims to quickly generate the optimal service path and guide the execution of the recharging task without the need for recharging in the middle of the journey, including: determining the starting state of path planning; configuring path planning constraints; executing the path planning algorithm; and generating and issuing the optimal service path.

[0086] Specifically, when the optimally scheduled mobile charging station is assessed as highly dispatchable, the starting state for route planning is determined, including the real-time location of the mobile charging station (e.g., coordinates (longitude 116.3°, latitude 39.9°)), the location of the target vehicle (coordinates (longitude 116.35°, latitude 39.95°)), the starting time (current time), and the planned service point (the location where the target vehicle is expected to stop or be available for recharging). Then, route planning constraints are configured, such as requiring that charging station A's service radius not exceed 50km, requiring the vehicle's remaining range to reach the recharging point, and reserving a safety redundancy time window. These constraints guide the optimization objectives and constraints during route generation.

[0087] Furthermore, combining the aforementioned path planning starting state and constraints with a path planning algorithm (such as Dijkstra or A*), multiple possible paths from charging station A's current location to the target charging location are calculated. These paths are then evaluated and selected based on pre-set optimization objectives (such as minimizing in-transit energy consumption and optimizing service response time). The optimal service path output by the path planning algorithm is then issued as a dispatch instruction to the optimally scheduled mobile charging station, guiding it to execute subsequent charging tasks.

[0088] Through this process, the optimal service path can be determined for the optimally scheduled mobile charging station, ensuring efficient and timely recharging services for vehicles. This improves the efficiency and success rate of recharging tasks, reduces vehicle waiting time, and helps reduce the operating costs of mobile charging stations and improve the user's recharging experience.

[0089] In some embodiments, the preset path planning strategy further includes: If the optimally scheduled mobile charging pile is not highly dispatchable, a self-recharging site for the optimally scheduled mobile charging pile is determined, and a self-recharging path is generated based on the self-recharging site. The recharging completion time is calculated based on the target recharging power of the optimally scheduled mobile charging pile and the corresponding recharging characteristic curve. The end point of the self-recharging path and the recharging completion time are used as the starting states of the path planning, and the service path is generated and optimized in combination with the path planning algorithm.

[0090] Specifically, the energy replenishment characteristic curve is a curve that describes the relationship between the change in power and time of the mobile charging pile during the energy replenishment process. It reflects the change pattern of its charging efficiency, power and other characteristics over time, and can be used to predict the time required for energy replenishment.

[0091] Specifically, for the optimally dispatched mobile charging piles with non-high dispatchability (i.e., mobile charging piles that require recharging before service), first, the optimal self-recharging site is selected based on the current power level, geographical location, and distribution of self-recharging sites, such as using the shortest distance or shortest time strategy to screen sites; then, a path planning algorithm is used to generate a path from the current location to the self-recharging site; then, based on the target recharging power of mobile charging pile B (e.g., recharging to 70% SOC to meet subsequent task requirements) and its recharging characteristic curve, the time required to complete recharging is calculated.

[0092] Furthermore, the endpoint of the self-charging path (i.e., the location of the self-charging station) and the self-charging completion time are used as the new starting state for path planning. A service path from the self-charging station to the target charging location is generated and optimized using the path planning algorithm to ensure that mobile charging station B can reach the target vehicle location in the shortest possible time after charging is completed, thus completing the charging task.

[0093] The above process effectively handles situations where mobile charging piles are not highly dispatchable, ensuring they can provide timely and efficient recharging services to vehicles after completing self-recharging. This not only improves the utilization rate of mobile charging piles and expands their service range, but also optimizes the flexibility and reliability of the entire recharging service.

[0094] In some implementations, path generation and optimization are performed in conjunction with a path planning algorithm, including: The main optimization objectives are set, including at least minimizing energy consumption in transit, optimizing service response time, and maximizing self-recharging time; additional optimization objectives are set, including at least ensuring that the power state belongs to the high-efficiency range and minimizing the excess of the remaining power of the target vehicle; optimization constraints are set, including at least ensuring that the target recharging power satisfies the power state rule, the service radius of the optimally scheduled mobile charging pile does not exceed a preset range, and the driving path length of the target vehicle satisfies the current remaining mileage of the target vehicle; and multi-objective path optimization is performed by combining the main optimization objectives, the additional optimization objectives, and the optimization constraints.

[0095] Optionally, during the execution of the path planning algorithm to generate the service path, a multi-objective path optimization mechanism can be further introduced to improve the path's energy efficiency, service responsiveness, and system resource utilization. The path optimization mechanism includes: setting the primary optimization objective, introducing additional optimization objectives, configuring optimization constraints, and executing the multi-objective path optimization algorithm.

[0096] Specifically, first, the main optimization goal is set. The main optimization goal is the core evaluation indicator used to define the optimality of the path, which includes at least: minimum on-route energy consumption, that is, the mobile charging pile has the lowest total energy consumption when executing the service path; optimal service response time, that is, the total time from the start of the path to the completion of energy replenishment of the target vehicle is the shortest; and maximization of self-energy replenishment time, that is, without affecting the service task, leaving a maximum energy replenishment time window for the mobile charging pile itself.

[0097] Then, additional optimization goals are set to improve the comprehensive performance and service quality of the path plan, including at least: the power state belongs to the high-efficiency range, that is, after the mobile charging pile completes the service, its power remains in the set high-efficiency working range (for example, 20% to 80%); the remaining power of the target vehicle is minimized to avoid the target vehicle battery being too much below the minimum SOC (for example, below 10%) due to untimely replenishment, thereby reducing battery health risks and the owner's anxiety level.

[0098] Next, configure the optimization constraints. The optimization constraints are used to define the feasibility boundaries of the path. They include at least: the target charging power meets the power status rules, that is, the expected charging target needs to be achieved to ensure that the next charging point can be reached; the service radius does not exceed the preset range, that is, the service path length of the mobile charging pile must not exceed its maximum service radius (such as 50km); the driving path length of the target vehicle does not exceed its current remaining mileage, ensuring that the target vehicle will not fail due to exhaustion before charging.

[0099] Furthermore, based on the aforementioned primary optimization objective, additional optimization objectives, and optimization constraints, a multi-objective path optimization algorithm is invoked to solve and iteratively optimize the preliminary path. This involves evaluating the comprehensive performance metrics of multiple candidate paths in each generation. Through crossover, mutation, and selection, the algorithm gradually approaches an optimal solution set that excels across multiple objective dimensions. Based on the requirements and priorities of the actual application scenario, an optimal solution is selected as the final path planning solution. Examples of multi-objective path optimization algorithms include, but are not limited to, weighted linear combination methods, multi-objective genetic algorithms, and NSGA-II (non-dominated sorting genetic algorithm).

[0100] By integrating the primary optimization objective, additional optimization objectives, and optimization constraints, multi-objective path optimization can effectively improve the charging efficiency and service quality of mobile charging stations. This process fully considers various key factors and practical constraints, ensuring that the planned path not only quickly responds to vehicle charging needs, but also minimizes energy consumption, improves the self-charging efficiency of the mobile charging station, and ensures the safety and reliability of the charging process. The optimized path planning solution helps improve the user's charging experience and enhances the overall performance and stability of the charging system.

[0101] In some embodiments, it further includes: If the optimally scheduled mobile charging pile is not highly dispatchable, the waiting time is calculated based on the optimal service path and the self-recharging path, combined with the real-time status information of the vehicle, wherein the waiting time is a vector value, and is negative when the car is waiting for the pile and positive when the pile is waiting for the car; waiting optimization processing is performed according to the waiting time.

[0102] Specifically, when the optimally scheduled mobile charging station is not highly schedulable (for example, due to a high current task density, limited self-recharging capabilities, or a high cost for path reconstruction), the estimated waiting time is calculated based on the optimal service path and the self-recharging path, combined with the target vehicle's real-time status information (including current location, SOC, power consumption rate, etc.). The waiting time is a vector value that represents the time synchronization between the two service parties in the scheduling path. It is used to characterize the temporal characteristics of "car waiting for the charging station" or "charging station waiting for the car" in the scheduling path: when the waiting time is a positive value, it indicates that the mobile charging station needs to wait at the service point for the target vehicle to arrive, which is called "charging station waiting for the car"; when the waiting time is a negative value, it indicates that the target vehicle needs to wait at the service point for the mobile charging station to arrive, which is called "car waiting for the charging station"; a waiting time of zero indicates that both parties arrive synchronously, without waiting.

[0103] Specifically, the estimated arrival time of the vehicle and mobile charging station is calculated based on the optimal service route and the timing of the self-charging route, combined with the vehicle's real-time status information (such as current speed and remaining range). For example, if the vehicle is expected to arrive at the charging point at 2:30 PM, and the mobile charging station is expected to complete self-charging and arrive at the charging point at 2:45 PM, the waiting time is +15 minutes (a positive number indicates the station is waiting for the vehicle); if the mobile charging station is expected to arrive at 2:15 PM, the waiting time is -15 minutes (a negative number indicates the vehicle is waiting for the station).

[0104] In some implementations, performing wait optimization processing according to the wait time includes: When the waiting time is a positive value and exceeds a first preset time threshold, the target power of the optimally scheduled mobile charging pile is increased accordingly; when the waiting time is a negative value and is lower than a second preset time threshold, the latest reachable charging point is searched within the service neighborhood of the planned service point in combination with the real-time status information of the vehicle, and the process is iterated until the waiting time becomes positive; if the waiting time is not a positive value after iteration, the service point with the waiting time closest to zero within the service neighborhood is preferably updated as the planned service point, and the optimal service path is replanned.

[0105] Specifically, when the waiting time is positive and exceeds a first preset time threshold, such as 10 minutes, the mobile charging station is considered to have been idle for a long time. To improve its energy efficiency, its target charging capacity can be dynamically increased, extending its self-charging time without affecting service scheduling. For example, the original charging target can be increased from 70% to 80% to fully utilize the waiting time window.

[0106] Specifically, when the waiting time is a negative value and its absolute value exceeds a second preset time threshold (for example, -2 minutes), the real-time position, current SOC, power consumption rate and other status information of the target vehicle can be combined to iteratively search for alternative service points within the service neighborhood of the originally planned service point (for example, within a radius of 1.5 kilometers), and recalculate the waiting time until the waiting time becomes a positive value, indicating that the two parties can achieve synchronization or a dispatching state of the pile waiting for the vehicle.

[0107] Furthermore, if all feasible service points cannot make the waiting time become positive after iteration, the service point with the waiting time closest to zero is selected and updated as the new planned service point; then, the optimal service path is replanned based on the new planned service point to match the new service point location and time requirements.

[0108] The above-mentioned waiting time-driven dynamic optimization of the scheduling path reduces ineffective waiting by adjusting the charging target or service point location and dynamically coordinating the arrival sequence of both service parties; it extends the self-charging time in the scenario where the vehicle is waiting for the charging pile, effectively improving the subsequent service capability of the mobile charging pile; in the scenario where the vehicle is waiting for the charging pile, the dynamic replacement and path reconstruction mechanism of the service point is provided to enhance the adaptability of the scheduling strategy, avoid the target vehicle from waiting for a long time in the weak charging section, and improve its travel experience and energy guarantee continuity.

[0109] In summary, the mobile charging pile path planning method for electric vehicles provided by the present invention has the following technical effects: By obtaining the travel plan information of the target vehicle and combining it with the real-time status information of the target vehicle, the weak charging section on the vehicle's driving path is identified; the real-time charging pile status information of each mobile charging pile in the weak charging section is obtained, and the status information includes but is not limited to the task status, power status and location status; based on the real-time charging pile status information and the real-time status information of the target vehicle, the mobile charging piles that meet the scheduling conditions are screened to form a set of alternative charging piles; the set of alternative charging piles is traversed, and the schedulability evaluation is performed based on the scheduling capability and availability to screen out the best scheduling mobile charging pile; according to the schedulability evaluation results and combined with the preset path planning strategy, the path planning decision is executed for the best scheduling mobile charging pile to obtain the optimal service path; the optimal service path is sent to the best scheduling mobile charging pile to guide the mobile charging pile to complete the charging task, thereby achieving the technical effect of improving the scheduling level, improving the charging efficiency, and optimizing the charging experience.

[0110] Example 2, as Figure 2 This is a schematic diagram of the structure of the mobile charging pile path planning system for electric vehicles of the present invention. For example, Figure 1 The flow chart of the mobile charging pile path planning method for electric vehicles in the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0111] Based on the same concept as the mobile charging pile path planning method for electric vehicles in the above embodiment, the present invention also provides a mobile charging pile path planning system for electric vehicles, including: The section identification module 11 is used to obtain the travel plan information of the target vehicle and identify the weak energy replenishment section in combination with the real-time vehicle status information of the target vehicle.

[0112] The charging pile status acquisition module 12 is used to obtain the real-time charging pile status information of each mobile charging pile in the weak charging section, wherein the real-time charging pile status information at least includes the task status, power status, and location status.

[0113] The screening and schedulability evaluation module 13 is used to screen schedulable mobile charging piles based on the real-time charging pile status information and the real-time vehicle status information, output them as a set of candidate charging piles, and traverse the set of candidate charging piles to perform schedulability evaluation.

[0114] The path planning and sending module 14 is used to perform path planning decisions on the optimally scheduled mobile charging pile based on the schedulability evaluation results and a preset path planning strategy, obtain the optimal service path, and send the optimal service path to the optimally scheduled mobile charging pile to guide the execution of the charging task.

[0115] In some embodiments, the weak energy replenishment section in the section identification module 11 includes at least one of the following situations: The distance between two adjacent high-efficiency charging points is greater than the preset high-efficiency charging range of the target vehicle; the section where there is an inefficient charging point between two high-efficiency charging points, and the distance between the two high-efficiency charging points is greater than the maximum drivable mileage of the target vehicle, wherein the inefficient charging points include charging points with low power levels, poor equipment stability, and long waiting times in queues; the section where there is a known charging demand in the travel plan information but the charging resource configuration cannot meet the coverage requirements.

[0116] In some embodiments, the screening and schedulability evaluation module 13 includes: The schedulability evaluation index set calculation and rule definition unit is used to calculate and obtain the schedulability evaluation index set in combination with the real-time status information of the vehicle, and define the corresponding schedulability evaluation rules, wherein the schedulability evaluation rules include task status rules, power status rules, and energy replenishment timeliness rules.

[0117] A schedulability evaluation unit is used to perform a schedulability evaluation based on the schedulability evaluation rules and the real-time charging pile status information, wherein if the task status is no task and the power status meets the power status rules, it is marked as high schedulability; if the task status is no task, the power status does not meet the power status rules, and the energy replenishment time consumption calculated based on the power status and the location status meets the energy replenishment timeliness rules, it is marked as medium schedulability; otherwise, it is marked as low schedulability.

[0118] In some embodiments, the path planning and issuing module 14 includes: A path planning starting state determination unit is used to determine the path planning starting state if the optimal scheduling mobile charging pile is highly schedulable, wherein the starting state includes the real-time position of the target vehicle, the real-time position of the optimal scheduling mobile charging pile, the starting time, the remaining available time, and the planned service point.

[0119] The path planning constraint condition configuration and service path generation unit is used to configure the path planning constraint conditions based on the path planning starting state and generate the service path in combination with the path planning algorithm.

[0120] In some embodiments, the path planning and issuing module 14 further includes: The self-recharging site determination and path generation unit is configured to determine the self-recharging site of the optimally scheduled mobile charging pile if the optimally scheduled mobile charging pile is not highly schedulable, and generate a self-recharging path based on the self-recharging site.

[0121] The energy replenishment completion time calculation unit is used to calculate the energy replenishment completion time according to the target energy replenishment power of the optimally scheduled mobile charging pile and the corresponding energy replenishment characteristic curve.

[0122] The service path generation and optimization unit is used to use the end point of the self-recharging path and the recharging completion time as the starting state of the path planning, and generate and optimize the service path in combination with the path planning algorithm.

[0123] In some implementations, the service path generation and optimization unit in the path planning and issuing module 14 further includes: The main optimization target setting subunit is used to set the main optimization target, which at least includes minimizing the energy consumption in transit, optimizing the service response time, and maximizing the self-recharging time.

[0124] The additional optimization target setting subunit is used to set additional optimization targets, including at least that the power state belongs to the high-efficiency range and the remaining power of the target vehicle is minimized.

[0125] The optimization constraint setting subunit is used to set optimization constraints, including at least that the target energy replenishment power meets the power status rule, the service radius of the optimal scheduling mobile charging pile does not exceed a preset range, and the driving path length of the target vehicle meets the current remaining mileage of the target vehicle.

[0126] The multi-objective path optimization subunit is used to integrate the main optimization objective, the additional optimization objective and the optimization constraints to perform multi-objective path optimization.

[0127] In some embodiments, it further includes: A waiting time calculation unit is configured to calculate a waiting time based on the optimal service path and the self-replenishing path, combined with the real-time status information of the vehicle, when the dispatchability of the optimal scheduling charging pile is not high. The waiting time is a vector value, which is negative when the vehicle is waiting for the charging pile and positive when the charging pile is waiting for the vehicle.

[0128] The waiting optimization processing unit is used to perform waiting optimization processing according to the waiting time.

[0129] In some implementations, the waiting optimization processing unit further includes: The target power replenishment adjustment unit is configured to correspondingly increase the target power replenishment of the optimally scheduled mobile charging pile when the waiting time is a positive value and exceeds a first preset time threshold.

[0130] The latest refueling point search and iteration unit is used to search for the latest refueling point that can be reached within the service neighborhood of the planned service point in combination with the real-time status information of the vehicle when the waiting time is a negative value and is lower than a second preset time threshold, and iterate until the waiting time becomes a positive value.

[0131] The planned service point updating and path replanning unit is used to update the service point with the waiting time closest to zero in the service neighborhood as the planned service point if the waiting time is not a positive value after iteration, and replan the optimal service path.

[0132] In some embodiments, the screening and schedulability evaluation module 13 further includes: The minimum power constraint configuration and screening unit is used to configure the minimum power constraint and perform a corresponding screening.

[0133] The untaskable mobile charging pile extraction unit is used to traverse the screening results once, extract the mobile charging piles that are currently untaskable and store them in the candidate charging pile set.

[0134] The remaining occupied time calculation unit is used to traverse the screening results once, obtain the remaining task time and the maximum path time of the service range of each mobile charging pile, and sum them to obtain the remaining occupied time.

[0135] The dispatchable mobile charging pile determination unit is used to calculate the remaining transit time of the target vehicle in combination with the real-time status information of the vehicle, and extract the mobile charging pile whose remaining occupancy time is less than the remaining transit time, determine it as the dispatchable mobile charging pile, and store it in the set of alternative charging piles.

[0136] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the mobile charging pile path planning system for electric vehicles described in embodiment two. For the sake of brevity of the specification, they will not be further elaborated here.

[0137] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A mobile charging pile path planning method for electric vehicles, characterized in that: include: Obtain the target vehicle's travel plan information and, combined with the target vehicle's real-time status information, identify the weak energy replenishment section; Acquire real-time charging pile status information of each mobile charging pile in the weak energy replenishment section, wherein the real-time charging pile status information at least includes task status, power status, and location status; Based on the real-time charging pile status information and the real-time vehicle status information, screening schedulable mobile charging piles, outputting them as a set of candidate charging piles, and traversing the set of candidate charging piles to perform schedulability evaluation; According to the schedulability evaluation results, a path planning decision is executed for the optimally scheduled mobile charging pile based on a preset path planning strategy to obtain the optimal service path, and the optimal service path is sent to the optimally scheduled mobile charging pile to guide the execution of the charging task.

2. The mobile charging pile path planning method for electric vehicles according to claim 1, characterized in that: The weak energy replenishment section includes at least one of the following situations: The distance between two adjacent high-efficiency charging points is greater than the preset high-efficiency charging range of the target vehicle; There is an inefficient charging point between two high-efficiency charging points, and the distance between the two high-efficiency charging points is greater than the maximum drivable range of the target vehicle. The inefficient charging points include those with low power levels, poor equipment stability, and long waiting times in queues. The travel plan information contains sections where energy replenishment needs are known but the energy replenishment resource configuration cannot meet the coverage requirements.

3. The mobile charging pile path planning method for electric vehicles according to claim 2, characterized in that: Traversing the candidate charging pile set and performing dispatchability evaluation, including: In combination with the real-time status information of the vehicle, a schedulability evaluation index set is calculated and obtained, and a schedulability evaluation rule is defined accordingly, wherein the schedulability evaluation rule includes a task status rule, a power status rule, and a refueling timeliness rule; Based on the schedulability evaluation rules and the real-time charging pile status information, a schedulability evaluation is performed, wherein, if the task status is no task and the power status meets the power status rules, it is marked as high schedulability; if the task status is no task, the power status does not meet the power status rules, and the energy replenishment time consumption calculated based on the power status and the location status meets the energy replenishment timeliness rules, it is marked as medium schedulability; otherwise, it is marked as low schedulability.

4. The mobile charging pile path planning method for electric vehicles according to claim 3, characterized in that: The preset path planning strategies include: If the optimally scheduled mobile charging pile is highly dispatchable, determining a path planning starting state, wherein the starting state includes the real-time location of the target vehicle, the real-time location of the optimally scheduled mobile charging pile, the starting time, the remaining available time, and the planned service point; Path planning constraints are configured based on the path planning starting state, and service paths are generated in combination with a path planning algorithm.

5. The mobile charging pile path planning method for electric vehicles according to claim 4, characterized in that: The preset path planning strategies also include: If the optimally scheduled mobile charging pile is not highly schedulable, determining a self-recharging site of the optimally scheduled mobile charging pile, and generating a self-recharging path based on the self-recharging site; Calculating the charging completion time based on the target charging capacity of the optimally scheduled mobile charging pile and the corresponding charging characteristic curve; The end point of the self-recharging path and the completion time of the recharging are used as the starting state of the path planning, and the service path is generated and optimized in combination with the path planning algorithm.

6. The mobile charging pile path planning method for electric vehicles according to claim 5, characterized in that: Combined with the path planning algorithm to generate and optimize the path, including: Setting the main optimization objectives, including at least minimizing energy consumption in transit, optimizing service response time, and maximizing self-recharging time; Setting additional optimization goals, including at least ensuring that the battery state falls within the high-efficiency range and minimizing excess battery power of the target vehicle; Setting optimization constraints, including at least that the target charging power meets the power state rule, the service radius of the optimally scheduled mobile charging pile does not exceed a preset range, and the target vehicle's driving path length meets the target vehicle's current remaining mileage; The main optimization objective, the additional optimization objective and the optimization constraints are comprehensively considered to perform multi-objective path optimization.

7. The mobile charging pile path planning method for electric vehicles according to claim 1, characterized in that: Also includes: If the optimal dispatchable mobile charging pile is not highly dispatchable, the waiting time is calculated based on the optimal service path and the self-recharging path, combined with the real-time status information of the vehicle, wherein the waiting time is a vector value, and is negative when the vehicle is waiting for the pile and positive when the pile is waiting for the vehicle; A waiting optimization process is performed according to the waiting time.

8. The mobile charging pile path planning method for electric vehicles according to claim 7, characterized in that: Performing a waiting optimization process according to the waiting time includes: When the waiting time is a positive value and exceeds a first preset time threshold, the target power of the optimally scheduled mobile charging pile is correspondingly increased; When the waiting time is negative and lower than a second preset time threshold, searching for the latest reachable charging point within the service neighborhood of the planned service point in combination with the real-time vehicle status information, and iterating until the waiting time becomes positive; If the waiting time after iteration is not a positive value, the service point with the waiting time closest to zero in the service neighborhood is preferably updated as the planned service point, and the optimal service path is replanned.

9. The mobile charging pile path planning method for electric vehicles according to claim 1, characterized in that: Based on the real-time charging pile status information and the real-time vehicle status information, the dispatchable mobile charging piles are screened and output as a candidate charging pile set, including: Configure the minimum power constraint and perform a corresponding filter; Traverse the screening results once, extract the mobile charging piles that are currently not tasked and store them in the candidate charging pile set; Traverse the screening results once, obtain the remaining task time and the maximum path time of the service range of each mobile charging pile, and sum them to obtain the remaining occupancy time; Combined with the real-time status information of the vehicle, the remaining transit time of the target vehicle is calculated, and the mobile charging piles whose remaining occupancy time is less than the remaining transit time are extracted, determined as the schedulable mobile charging piles, and stored in the candidate charging pile set.

10. A mobile charging pile path planning system for electric vehicles, characterized in that: A method for planning a mobile charging pile path for an electric vehicle according to any one of claims 1 to 9, comprising: The section identification module is used to obtain the travel plan information of the target vehicle and identify the weak energy replenishment section in combination with the real-time vehicle status information of the target vehicle; A charging pile status acquisition module is used to obtain real-time charging pile status information of each mobile charging pile in the weak energy replenishment section, wherein the real-time charging pile status information at least includes task status, power status, and location status; A screening and schedulability evaluation module is used to screen schedulable mobile charging piles based on the real-time charging pile status information and the real-time vehicle status information, output them as a set of candidate charging piles, and traverse the set of candidate charging piles to perform schedulability evaluation; The path planning and distribution module is used to perform path planning decisions on the optimally scheduled mobile charging pile based on the schedulability evaluation results and the preset path planning strategy, obtain the optimal service path, and distribute the optimal service path to the optimally scheduled mobile charging pile to guide the execution of the charging task.

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