Mobile charging pile path planning method and system for electric vehicles

Abstract

The application discloses a mobile charging pile path planning method and system for electric vehicles, and relates to the technical field of charging piles. The method comprises the following steps: obtaining a target vehicle travel plan and a real-time state, and identifying a weak energy supplement section; collecting the real-time state (including tasks, power and positions) of each mobile charging pile in the section; combining the vehicle and charging pile states to screen schedulable charging piles, form an alternative set and perform schedulability evaluation; according to the evaluation result, planning an optimal service path for the best schedulable charging pile according to a preset path strategy and issuing the optimal service path to guide the charging pile to perform an energy supplement task. The technical effects of improving the scheduling level, improving the energy supplement efficiency and optimizing the energy supplement experience are achieved.

Description

Technical Field

[0001] This invention relates to the field of charging pile technology, and in particular to a method and system for mobile charging pile route planning for electric vehicles. Background Technology

[0002] With the widespread use of electric vehicles, charging has become a significant factor affecting user experience. Currently, electric vehicles mainly rely on fixed charging stations for charging, but the distribution of these stations is limited and cannot meet the charging needs of electric vehicles in different travel scenarios, especially in certain special areas where the charging problem is more prominent.

[0003] In existing technologies, the identification of weak charging areas in the planning of electric vehicle charging is not accurate enough, and the scheduling of mobile charging piles lacks comprehensive and effective information consideration. It fails to fully combine various real-time status information of mobile charging piles for reasonable scheduling, resulting in problems such as slow task response and unreasonable scheduling, which fails to meet the personalized needs of users. Summary of the Invention

[0004] This invention provides a method and system for mobile charging pile route planning for electric vehicles, in order to solve the technical problems of unreasonable scheduling, which affect charging efficiency and charging experience in the prior art, and achieve the technical effects of improving scheduling level, improving charging efficiency, and optimizing charging experience.

[0005] In a first aspect, the present invention provides a mobile charging station route planning method for electric vehicles, wherein the mobile charging station route planning method for electric vehicles includes: Obtain the travel plan information of the target vehicle and combine it with the real-time status information of the target vehicle to identify weak power replenishment sections.

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

[0007] Based on the real-time charging pile status information and the real-time vehicle status information, schedulable mobile charging piles are selected and output as a candidate charging pile set. The candidate charging pile set is then traversed to evaluate its schedulability.

[0008] Based on the schedulability evaluation results, a path planning decision is made for the best-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 best-scheduled mobile charging pile to guide the execution of the charging task.

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

[0010] There is an inefficient charging point between two efficient charging points, and the distance between the two efficient charging points is greater than the maximum driving range of the target vehicle. The inefficient charging points include charging points with low power levels, poor equipment stability, and long queuing times.

[0011] The travel plan information includes sections where there is a known need for additional energy, but the available energy resources cannot meet the coverage requirements.

[0012] In one feasible implementation, the set of candidate charging stations is traversed to perform a schedulability evaluation, including: Based on the real-time vehicle status information, a set of schedulability evaluation indicators is calculated and obtained, and corresponding schedulability evaluation rules are defined. The schedulability evaluation rules include task status rules, battery status rules, and recharge 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 satisfies the power status rules, it is marked as high schedulability. If the task status is "no task," the power status does not satisfy the power status rules, but the replenishment time calculated based on the power status and location status satisfies the replenishment timeliness rules, it is marked as medium schedulability. Otherwise, it is marked as low schedulability.

[0014] In one feasible implementation, the preset path planning strategies include: If the optimally scheduled mobile charging station has high schedulability, then the initial state of the route planning is determined, wherein the initial state includes the real-time location of the target vehicle, the real-time location of the optimally scheduled mobile charging station, the start time, the remaining available time, and the planned service point.

[0015] Configure path planning constraints based on the initial state of the path planning, and generate service paths in conjunction with the path planning algorithm.

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

[0017] The charging time is calculated based on the target charging capacity and corresponding charging characteristic curve of the optimally scheduled mobile charging pile.

[0018] The endpoint of the self-replenishing path and the replenishment completion time are used as the starting state of the path planning, and the service path is generated and optimized by combining the path planning algorithm.

[0019] In one feasible implementation, path generation and optimization are combined with path planning algorithms, including: Set primary optimization objectives, including at least minimizing energy consumption in transit, optimizing service response time, and maximizing self-replenishment time.

[0020] Set additional optimization objectives, including at least ensuring that the battery status is in the high-efficiency range and minimizing the excess remaining battery power of the target vehicle.

[0021] The optimization constraints include at least the target charging capacity satisfying the charging status rules, the service radius of the optimally scheduled mobile charging pile not exceeding a preset range, and the target vehicle's travel path length satisfying the target vehicle's current remaining mileage.

[0022] Multi-objective path optimization is performed by combining the main optimization objective, the additional optimization objective, and the optimization constraints.

[0023] One feasible implementation also includes: If the optimal scheduling mobile charging pile is not highly schedulable, then the waiting time is calculated based on the optimal service path and the self-replenishment path, combined with the real-time vehicle status information. The waiting time is a vector value, and the waiting time is negative when the vehicle is waiting for the charging pile, and positive when the charging pile is waiting for the vehicle.

[0024] Perform wait optimization processing based on the stated wait time.

[0025] In one feasible implementation, waiting optimization processing is performed based on the waiting time, including: When the waiting time is positive and exceeds the first preset time threshold, the target power of the optimally scheduled mobile charging pile is increased accordingly.

[0026] When the waiting time is negative and lower than the second preset time threshold, the latest available charging point is searched within the service neighborhood of the planned service point, based on the real-time vehicle status information, and the process is iterated until the waiting time turns positive.

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

[0028] In one feasible implementation, based on the real-time charging pile status information and the real-time vehicle status information, schedulable mobile charging piles are filtered, and a candidate charging pile set is output, including: Configure a minimum power limit and perform a corresponding filter.

[0029] Iterate through the filtering results once, extract the mobile charging piles that are currently without a task, and store them in the candidate charging pile set.

[0030] Iterate through the filtered results once to obtain the remaining task time and the maximum path time within the service range for each mobile charging station, and sum them to obtain the remaining occupied time.

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

[0032] Secondly, the present invention also provides a mobile charging station route planning system for electric vehicles, wherein the mobile charging station route planning system for electric vehicles includes: The section identification module is used to obtain the travel plan information of the target vehicle and, in combination with the real-time status information of the target vehicle, identify the weak power replenishment section.

[0033] The charging pile status acquisition module is used to acquire real-time charging pile status information of each mobile charging pile in the weak power replenishment section, wherein the real-time charging pile status information includes at least 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 a candidate charging pile set, and traverse the candidate charging pile set to perform schedulability evaluation.

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

[0036] This invention discloses a method and system for mobile charging pile path planning for electric vehicles, comprising: acquiring travel plan information of a target vehicle and identifying weak charging sections on the vehicle's travel path by combining the target vehicle's real-time status information; acquiring real-time charging pile status information of each mobile charging pile within the weak charging section, including but not limited to task status, power status, and location status; filtering mobile charging piles that meet scheduling conditions based on the real-time charging pile status information and the target vehicle's real-time status information to form a candidate charging pile set; traversing the candidate charging pile set and evaluating its schedulability based on scheduling capability and availability to select the best-scheduled mobile charging pile; performing path planning decisions on the best-scheduled mobile charging pile based on the schedulability evaluation results and in conjunction with a preset path planning strategy to obtain the optimal service path; and distributing the optimal service path to the best-scheduled mobile charging pile to guide it in completing the charging task. The method and system for mobile charging pile path planning for electric vehicles disclosed in this 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 the charging experience. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the mobile charging station route planning method for electric vehicles according to the present invention.

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

[0039] Figure labeling: Section identification module 11, charging pile status acquisition module 12, screening and schedulability evaluation module 13, route planning and distribution module 14. Detailed Implementation

[0040] The above technical solutions will now be described in detail with reference to the accompanying drawings and specific embodiments to provide a better understanding of them. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments used only to explain the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Furthermore, it should be noted that, for ease of description, only the parts related to the present invention are shown in the drawings, not all of them.

[0041] Example 1, as Figure 1 This is a flowchart illustrating the mobile charging station route planning method for electric vehicles according to the present invention, wherein the mobile charging station route planning method for electric vehicles includes: S100: Obtain the travel plan information of the target vehicle and, in conjunction with the real-time status information of the target vehicle, identify the weak energy replenishment section.

[0042] Specifically, travel plan information refers to the user's pre-set vehicle route plan, including detailed information such as the origin, destination, and possible stops along the way. Real-time vehicle status information covers real-time parameters such as the vehicle's current remaining battery power, driving range, speed, and acceleration, reflecting the vehicle's immediate operating status.

[0043] Specifically, weak energy replenishment sections are road sections identified in the travel plan based on vehicle status and energy replenishment resource allocation that have the risk of energy replenishment or low efficiency. These are manifested in situations such as excessively large distances between two adjacent high-efficiency energy replenishment points, or the existence of inefficient energy replenishment points and the distance between these points exceeding the maximum mileage of the target vehicle.

[0044] Specifically, the system first acquires user-input travel plan information through the vehicle's intelligent connected system, such as the preset route trajectory (e.g., a sequence of waypoints provided by the navigation system), estimated travel time and segment duration, planned stops and their duration, the availability of energy replenishment facilities along the route (e.g., charging piles, battery swapping stations), and user preference settings (e.g., avoiding peak hours, prioritizing fast charging stations). Simultaneously, the system collects and uploads real-time status data such as the vehicle's remaining battery power (assumed to be 30%), driving range (200km), and current speed (60km / h) through the vehicle's battery management system and onboard computer. Then, based on the route trajectory in the travel plan and the current battery status, the system predicts the vehicle's energy consumption trend over a future period and matches it with information on energy replenishment facilities along the route.

[0045] For example, a weak energy compensation section can be identified if any of the following conditions are detected: There are no available charging facilities for several consecutive kilometers along the planned route; the battery level is expected to be below the threshold (e.g., 20%) when reaching this section, and there are no feasible charging options; charging facilities exist but are unavailable (e.g., busy, faulty, or already booked); current driving behavior is causing abnormal energy consumption, which may not be able to support the passage through this section; this section has a high-energy-consumption environment (e.g., long slope, high speed, low temperature), but the charging capacity is insufficient, etc.

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

[0047] Through the above process, risky areas for recharging in travel plans can be accurately identified, providing crucial information for the rational scheduling of mobile charging stations. This reduces the risk of vehicles breaking down due to untimely or inefficient recharging, improves users' sense of security and satisfaction with recharging services, and ensures the smoothness and efficiency of the entire travel process.

[0048] In some embodiments, the weak energy compensation section includes at least one of the following: The following are considered as separate sections: sections where the distance between two adjacent high-efficiency charging points is greater than the preset high-efficiency charging range of the target vehicle; sections where there are inefficient charging points between two high-efficiency charging points, and the distance between the two high-efficiency charging points is greater than the maximum mileage of the target vehicle, wherein the inefficient charging points include charging points with low power levels, poor equipment stability, and long queuing times; and sections where the travel plan information shows known charging needs but the charging resource allocation cannot meet the coverage requirements.

[0049] Specifically, efficient charging range refers to the maximum distance a vehicle can travel while maintaining efficient charging (e.g., charging power above a certain threshold), and is related to factors such as vehicle battery capacity, charging efficiency, and energy consumption. Inefficient charging points refer to charging points characterized by low power levels (e.g., charging power below 30kW), poor equipment stability (e.g., frequent malfunctions or maintenance), and long waiting times (e.g., average waiting time exceeding 15 minutes), which can lead to prolonged charging time or increased uncertainty. Charging resource allocation refers to the number, type, power, and distribution of available charging points within a specific area or road segment, reflecting the charging capacity and service level of that area.

[0050] Specifically, energy replenishment points can include infrastructure with energy replenishment capabilities such as charging piles and battery swapping stations.

[0051] Specifically, the weak energy compensation section includes at least one of the following situations: Weak charging zone with excessively large distance between high-efficiency charging points: The distance between two adjacent high-efficiency charging points is greater than the preset high-efficiency charging range threshold for the target vehicle. High-efficiency charging points refer to charging facilities with high charging power (e.g., fast charging power ≥ 60kW), high availability (high equipment online rate, stable operation), and short waiting time (queue time less than the set threshold, e.g., 5 minutes). The high-efficiency charging range threshold can be dynamically adjusted based on the target vehicle's battery performance, current load, ambient temperature, etc. For example, if an electric vehicle's current high-efficiency driving range is 120km, then if two high-efficiency charging stations are 140km apart along its travel route, this section can be identified as a weak charging zone.

[0052] Inefficient charging point interspersed weak charging sections: There are one or more charging points between two efficient charging points, but these charging points are inefficient charging points; at the same time, the distance between the two efficient charging points is greater than the maximum driving range of the target vehicle (such as theoretical full charge range); inefficient charging points may include, but are not limited to, any of the following: low charging power (such as AC slow charging ≤7kW); poor equipment stability (frequent failures, disconnections, maintenance); long queuing time (such as average waiting time reported by users >15 minutes); stations marked as "not recommended" or "low rating" in user history preferences.

[0053] Weakly equipped charging sections with insufficient charging resources: The travel plan information clearly indicates the expected charging needs (such as planned mid-trip charging, charging required for long-distance travel, etc.), but within this time and space section, the charging resources cannot meet the coverage requirements, including but not limited to: the target charging point is unavailable (such as being booked, out of service, or under maintenance), the number of available charging points is insufficient to meet the concentrated charging needs of multiple high-energy-consuming vehicles on the current route, there are "window periods" or "blank areas" of charging resources (such as remote mountainous areas, no service at night), and the charging point is not on the target route or is too far away, resulting in excessive detour costs, etc.

[0054] Specifically, the steps to identify weak power replenishment sections by analyzing travel plan information and real-time vehicle status information are as follows: Calculate the distance between adjacent high-efficiency charging points: Obtain the location information of high-efficiency charging points (such as charging piles with charging power greater than 50kW) from the charging point database, and calculate the road distance between two adjacent high-efficiency charging points. For example, if a vehicle's high-efficiency charging range is 150km, and 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 driving range (e.g., the vehicle's full-charge range is 300km, and the distance between the two efficient charging points is 320km), then this section is a weak charging section. For example, if the charging power of an inefficient charging point in the middle is only 20kW, the vehicle may have insufficient remaining power when it arrives at this point, and the charging time will be long, failing to effectively guarantee the vehicle's range.

[0056] Check known charging demand coverage: Based on the user's travel plan information (such as a long-distance self-driving tour in a mountainous area), analyze the charging resource allocation in that area. If there is known charging demand in a certain segment of the planned route (such as the user plans to charge in this segment), but there are no available charging points or the charging points have insufficient service capacity (such as only a few inefficient charging piles, which are often in a queue), then it is determined to be a weak charging segment.

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

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

[0059] Specifically, mobile charging stations refer to charging equipment with independent power systems or that can be towed by vehicles, capable of providing temporary or emergency energy replenishment services for electric vehicles in different geographical locations. Mobile charging stations include, but are not limited to, mobile charging vehicles, portable charging robots, and unmanned energy replenishment platforms. Real-time charging station status information refers to the dynamic operational data collected after identifying weak energy replenishment areas for the dispatchable mobile charging stations within those areas. This status information includes at least the following three aspects: Task status: indicates whether the current mobile charging station is idle, performing a task, in standby, or faulty; Power status: indicates the remaining available power, maximum discharge power, and number of service cycles that the current mobile charging station can support; Location status: indicates the geographical coordinates, moving speed, estimated arrival time, and other location information of the current mobile charging station.

[0060] Specifically, after identifying that the target vehicle is about to enter a weak charging zone, based on the vehicle's current GPS location and path planning, weak charging zones with charging risks in the future driving path are determined; available mobile charging pile resources in or around the current charging zone are retrieved from the dispatch 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, whether it is dispatchable); obtaining its current remaining power, maximum output power, service capacity, and other power status; obtaining its current location, direction of movement, average speed, and other location status; finally, the above status information is spatially mapped with the weak charging zone to form a "segment-resource" mapping model, providing a decision-making basis for subsequent charging scheduling.

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

[0062] S300: Based on the real-time charging pile status information and the real-time vehicle status information, filter schedulable mobile charging piles, output a candidate charging pile set, and traverse the candidate charging pile set to perform a schedulability evaluation.

[0063] Specifically, a schedulable mobile charging station refers to a mobile charging device that, under current spatiotemporal conditions, can effectively recharge a target vehicle before or during its journey into a low-energy-recharge zone. This schedulability depends not only on the charging station's own status but also on the target vehicle's real-time status, including its remaining battery power, expected travel route, energy consumption rate, and tolerable waiting time. The candidate charging station set is a preliminary selection of schedulable mobile charging stations that meet the criteria from a large pool of available charging stations, serving as the foundational data for further evaluation and selection.

[0064] Specifically, dispatchability evaluation is a process of quantitatively assessing the comprehensive service capability of each mobile charging station in the candidate charging station pool based on preset rules and indicators, in order to determine its suitability for providing services to the current target vehicle.

[0065] Through the above process, dispatchable mobile charging stations with potential service capabilities can be accurately screened, and their dispatchability can be evaluated in detail. On the one hand, this improves screening efficiency and ensures that the mobile charging stations entering the candidate set have basic energy replenishment service capabilities. On the other hand, through comprehensive evaluation, the service potential of each mobile charging station can be effectively distinguished, providing a scientific basis for selecting the best dispatch target in the future.

[0066] In some embodiments, based on the real-time charging pile status information and the real-time vehicle status information, schedulable mobile charging piles are filtered, and a candidate charging pile set is output, including: Configure a minimum power constraint and perform a corresponding filter; iterate through the filter results once, extract mobile charging piles with no current tasks and store them in the candidate charging pile set; iterate through the filter results once, obtain the remaining task time and the maximum path time of the service range for each mobile charging pile, and sum them to obtain the remaining occupancy time; combine the real-time vehicle status information to calculate the remaining transit time of the target vehicle, and extract mobile charging piles whose remaining occupancy time is less than the remaining transit time, determine them as the schedulable mobile charging piles, and store them in the candidate charging pile set.

[0067] Specifically, the initial screening refers to the process of screening all mobile charging stations based on a minimum power constraint. The purpose is to quickly eliminate charging stations with severely insufficient power that cannot provide effective charging services to vehicles. The minimum power constraint refers to a preset power threshold used for the initial screening of mobile charging stations, ensuring that the charging station has sufficient discharge capacity during the charging task. This threshold can be dynamically set according to vehicle type, charging power requirements, or task distance.

[0068] Specifically, the remaining occupancy time refers to the total time required for a mobile charging station to complete its current task and reach the service range boundary, reflecting the earliest time that the charging station can theoretically become idle to serve other tasks. The remaining transit time refers to the time required for the target vehicle to pass through the weak charging section, calculated based on the vehicle's real-time location, speed, and section length, representing the available time window for the vehicle within that section.

[0069] Specifically, first, a minimum battery level threshold is set, such as 20% SOC, requiring the mobile charging station to have at least this level of battery power before proceeding to the next filtering step. Then, the filtered results are iterated through, checking the task status of each charging station. If the task status is "no task" (idle), it is added to the candidate charging station set. For charging stations with tasks, the remaining task time (e.g., currently charging another vehicle, estimated 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 obtained, and these two are added together to obtain a remaining occupancy time of 25 minutes.

[0070] Furthermore, based on the target vehicle's real-time location (5km from the starting point of the weak charging section), speed (60km / h), and section length (30km), the remaining transit time for the vehicle through this section is calculated to be 25 minutes ((30-5) / 60×60=25 minutes). If the remaining occupancy time of a mobile charging pile is less than the vehicle's remaining transit time, it is determined that it is likely to complete its current task and reach the charging point before the target vehicle passes through the weak charging section. Therefore, the charging pile can be scheduled, and charging piles that meet the above conditions are added to the candidate charging pile set.

[0071] For example, the status of mobile charging piles A, B, and C is obtained as follows: Pile A: 80% battery, no task → directly included in the candidate charging pile set; Pile B: 75% battery, 10 minutes remaining task time, maximum path time within the service range 15 minutes → remaining occupancy time is 25 minutes, less than the vehicle's remaining transit time of 30 minutes → included in the candidate charging pile set; Pile C: 65% battery, 20 minutes remaining task time, 20 minutes service path time → remaining occupancy time is 40 minutes, greater than the vehicle's transit time → removed.

[0072] The above screening process ensures that the mobile charging stations entering the candidate charging station set have basic power supply guarantees, availability, and time compatibility. This not only improves the accuracy and efficiency of the screening but also enables the finally selected dispatchable mobile charging stations to better meet the charging needs of the target vehicles, reducing delays caused by vehicles waiting for charging stations to become available in areas with low charging capacity.

[0073] In some embodiments, the set of candidate charging stations is traversed to perform a dispatchability evaluation, including: Based on the real-time vehicle status information, a set of schedulability evaluation indicators is calculated and obtained, and corresponding schedulability evaluation rules are defined. These rules include task status rules, battery status rules, and refueling timeliness rules. Based on these rules and the real-time charging pile status information, a schedulability evaluation is performed. If the task status is "no task" and the battery status meets the battery status rules, it is marked as high schedulability. If the task status is "no task," the battery status does not meet the battery status rules, but the refueling time calculated based on the battery status and location status meets the refueling timeliness rules, it is marked as medium schedulability. Otherwise, it is marked as low schedulability.

[0074] Specifically, schedulability evaluation is the process of assessing the schedulability level of each mobile charging pile in the candidate charging pile set for the target vehicle's charging task, based on the current vehicle status and the charging pile status. This assessment is used for subsequent scheduling priority ranking or task allocation. The schedulability evaluation index set is a set of indicators jointly calculated based on the vehicle status and charging pile status. It supports the design of multi-dimensional evaluation rules, including quantitative values ​​of key factors such as task status, battery status, and charging timeliness.

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

[0076] In other words, the energy replenishment time calculated based on the power status and location status refers to the time consumed by a mobile charging station with insufficient remaining power to replenish itself until the power status rules are met. When the energy replenishment time is within the acceptable time window for the vehicle (meeting the energy replenishment timeliness rules), the mobile charging station can be considered to have a certain degree of schedulability (medium schedulability).

[0077] Specifically, schedulability levels can be divided into three categories: high schedulability: capable of immediate response and task completion; medium schedulability: has some capability deficiencies but can still meet scheduling requirements; low schedulability: lacks the capability to meet the current energy replenishment task.

[0078] The aforementioned dispatchability evaluation process allows for precise quantification and 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 evaluation method helps optimize dispatch decisions, ensuring priority is given to charging stations with high dispatchability, thereby improving charging efficiency, reducing vehicle waiting time, and enhancing user experience. Simultaneously, it enables a more rational allocation and utilization of mobile charging station resources, avoiding resource waste and service delays caused by indiscriminate dispatching, and enhancing the reliability and operational efficiency of the entire charging system.

[0079] S400: Based on the schedulability evaluation results, perform path planning decisions for the best-scheduled mobile charging piles according to the preset path planning strategy, obtain the optimal service path, and send the optimal service path to the best-scheduled mobile charging piles to guide the execution of the charging task.

[0080] Specifically, path planning strategy refers to a pre-defined set of rules and methods for determining the optimal route for a mobile charging station from its current location to the target charging location. This includes a comprehensive consideration of factors such as path length, time, and energy consumption. The optimal service path is the best route determined for a mobile charging station after satisfying various constraints and integrating various optimization objectives, aiming to achieve efficient charging services.

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

[0082] Specifically, firstly, based on the schedulability evaluation results, the mobile charging pile with the highest schedulability is selected as the optimal mobile charging pile from the pool of candidate charging piles. For example, if the pool of candidate charging piles includes charging pile A (high schedulability), charging pile B (medium schedulability), and charging pile C (low schedulability), then charging pile A is selected as the optimal mobile charging pile. Then, based on a pre-defined path planning strategy, a path planning decision is made for charging pile A.

[0083] Through the above process, the optimal service path for mobile charging stations can be determined, ensuring that they can provide charging services to vehicles efficiently and promptly. This not only improves the execution efficiency and success rate of charging tasks and reduces vehicle waiting time, but also optimizes the energy utilization efficiency of mobile charging stations, extends their driving range, and enhances the overall operational efficiency and service quality of the charging system.

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

[0085] Specifically, one of the preset strategies is applicable to charging piles with a high schedulability level. It aims to quickly generate the optimal service path and guide the execution of the charging task without the need for intermediate charging. This includes: determining the starting state of path planning; configuring path planning constraints; executing the path planning algorithm; and generating and distributing the optimal service path.

[0086] Specifically, when the optimal mobile charging station is assessed as highly schedulable, the initial state of the route planning is determined, including the real-time location of the mobile charging station (e.g., coordinates (116.3° longitude, 39.9° latitude)), the location of the target vehicle (coordinates (116.35° longitude, 39.95° latitude)), the start time (current time), and the planned service point (the location where the target vehicle is expected to stop or can accept charging). Then, route planning constraints are configured, such as the service radius of charging station A not exceeding 50km, the vehicle's remaining range needing to reach the charging point, and a reserved safety margin time window, to guide the optimization objectives and constraints during the route generation process.

[0087] Furthermore, combining the initial state and constraints of the path planning described above, and using path planning algorithms (such as Dijkstra's algorithm, A*, etc.), multiple possible paths from the current location of charging pile A to the target charging location are calculated. These paths are then evaluated and selected based on preset optimization objectives (such as minimizing energy consumption en route and optimizing service response time). The optimal service path output by the path planning algorithm is then sent as a scheduling instruction to the optimally scheduled mobile charging pile to guide it in executing subsequent charging tasks.

[0088] Through the above process, the optimal service path for mobile charging stations can be determined, ensuring that they can provide charging services to vehicles efficiently and promptly. This improves the execution efficiency and success rate of charging tasks, reduces vehicle waiting time, and helps lower the operating costs of mobile charging stations, thereby enhancing the user's charging experience.

[0089] In some embodiments, the preset path planning strategy further includes: If the optimally scheduled mobile charging station is not highly schedulable, then the self-charging station of the optimally scheduled mobile charging station is determined, and a self-charging path is generated based on the self-charging station; the charging completion time is calculated according to the target charging capacity and the corresponding charging characteristic curve of the optimally scheduled mobile charging station; the end point of the self-charging path and the charging completion time 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.

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

[0091] Specifically, for optimal scheduling of mobile charging piles with low schedulability (i.e., mobile charging piles that need to be recharged before service), path planning is performed. First, based on the current power level, geographical location, and distribution of self-recharging stations, the optimal self-recharging station is selected, such as by using the shortest distance or shortest time strategy to select stations. Then, a path planning algorithm is used to generate a path from the current location to the self-recharging station. Next, based on the target recharging power of mobile charging pile B (e.g., it needs to be recharged to 70% SOC to meet the needs of subsequent tasks) and its recharging characteristic curve, the time required to complete the 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 starting state for the new path planning. A service path from the self-charging station to the target charging location is generated using a path planning algorithm and optimized to ensure that mobile charging pile B can reach the target vehicle location in the shortest possible time after charging is completed, thus executing the charging task.

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

[0094] In some implementations, path generation and optimization are combined with path planning algorithms, including: The primary optimization objective is set, including at least minimizing energy consumption during transit, optimizing service response time, and maximizing self-recharge time. Additional optimization objectives are set, including at least ensuring the battery status is within an efficient range and minimizing the target vehicle's remaining battery level exceeding the limit. Optimization constraints are set, including at least ensuring the target recharge capacity meets the battery status rules, that the service radius of the optimally scheduled mobile charging station does not exceed a preset range, and that the target vehicle's travel path length meets the target vehicle's current remaining mileage. Multi-objective path optimization is then performed by combining the primary optimization objective, the additional optimization objectives, and the optimization constraints.

[0095] Optionally, during the process of generating service paths using the execution path planning algorithm, a multi-objective path optimization mechanism can be further introduced to improve the energy efficiency, service responsiveness, and system resource utilization of the paths. This 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, firstly, a primary optimization objective is set, which is the core evaluation indicator used to define the optimality of the path. This objective includes at least the following: minimum energy consumption during the journey, i.e., the minimum total energy consumption of the mobile charging station when executing the service path; optimal service response time, i.e., the shortest total time from the start of the path to the target vehicle completing the recharge; and maximum self-recharge time, i.e., maximizing the recharge time window for the mobile charging station itself without affecting the service task.

[0097] Then, additional optimization goals are set to improve the overall performance and service quality of the route plan. These goals include at least: the battery status is in the high-efficiency range, that is, after the mobile charging station completes its service, its battery level remains in the set high-efficiency working range (e.g., 20% to 80%); and the remaining battery level of the target vehicle is minimized to avoid the target vehicle's battery falling too far below the minimum SOC (e.g., below 10%) due to untimely charging, thereby reducing battery health risks and the anxiety level of car owners.

[0098] Next, configure and optimize constraints. These constraints are used to limit the feasibility boundary of the path and include at least the following: the target charging power meets the power status rules, that is, the expected charging target needs to be reached 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 does not exceed its maximum service radius (e.g., 50km); and the driving path length of the target vehicle does not exceed its current remaining mileage to ensure that the target vehicle will not fail due to depletion of power before charging.

[0099] Furthermore, based on the aforementioned primary optimization objective, secondary optimization objectives, and optimization constraints, a multi-objective path optimization algorithm is invoked to solve and iteratively optimize the initial path. This involves evaluating the comprehensive performance indicators of multiple candidate paths in each generation, and through crossover, mutation, and selection operations, gradually approximating the optimal solution set that performs well across multiple objective dimensions. Finally, based on the needs and priorities of the actual application scenario, an optimal solution is selected as the final path planning scheme. These 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] Multi-objective path optimization, which integrates the primary optimization objective, secondary optimization objectives, and optimization constraints, 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 responds quickly to vehicle charging needs but also minimizes energy consumption, improves the self-charging efficiency of mobile charging stations, and guarantees the safety and reliability of the charging process. The optimized path planning scheme helps improve the user's charging experience and enhances the overall performance and stability of the charging system.

[0101] In some embodiments, it also includes: If the optimal scheduling mobile charging pile is not highly schedulable, then the waiting time is calculated based on the optimal service path and the self-replenishment path, combined with the real-time vehicle status information. The waiting time is a vector value, and it is negative when the vehicle is waiting for the charging pile and positive when the charging pile is waiting for the vehicle. Waiting optimization processing is then performed based on the waiting time.

[0102] Specifically, when the optimally scheduled mobile charging station lacks high schedulability (e.g., its current task density is high, its self-recharging capability is limited, or the path reconstruction cost is high), the estimated waiting time is calculated based on the optimal service path and the self-recharging path, combined with the real-time status information of the target vehicle (including current location, SOC, power consumption rate, etc.). The waiting time is a vector value representing the time synchronization between the service providers and the target vehicle within the scheduling path, used to characterize the temporal characteristics of "vehicle waiting for charging station" or "charging station waiting for vehicle" within the scheduling path: a positive waiting time indicates that the mobile charging station needs to wait at the service point for the target vehicle to arrive, termed "charging station waiting for vehicle"; a negative waiting time indicates that the target vehicle needs to wait at the service point for the mobile charging station to arrive, termed "vehicle waiting for charging station"; and a zero waiting time indicates that both parties arrive synchronously without waiting.

[0103] Specifically, based on the timing of the optimal service path and the self-charging path, combined with the vehicle's real-time status information (such as current speed and remaining range), the estimated arrival time of the vehicle and the mobile charging station is calculated. For example, if the vehicle is expected to arrive at the charging point at 14:30, and the mobile charging station is expected to complete self-charging and arrive at the charging point at 14:45, then the waiting time is +15 minutes (a positive number indicates the charging station is waiting for the vehicle); if the mobile charging station is expected to arrive at 14:15, then the waiting time is -15 minutes (a negative number indicates the vehicle is waiting for the charging station).

[0104] In some implementations, waiting optimization processing is performed based on the waiting time, including: When the waiting time is positive and exceeds a first preset time threshold, the target charging capacity of the optimally scheduled mobile charging pile is increased accordingly. When the waiting time is negative and below a second preset time threshold, the latest reachable charging point is searched within the service neighborhood of the planned service point, based on the vehicle's real-time status information, and iterated until the waiting time becomes positive. If the waiting time is not positive after iteration, the service point with the waiting time closest to zero within the service neighborhood is preferred as the planned service point, and the optimal service path is replanned.

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

[0106] Specifically, when the waiting time is negative and its absolute value exceeds the second preset time threshold (e.g., -2 minutes), the system can combine the target vehicle's real-time location, current SOC, power consumption rate, and other status information to iteratively search for alternative service points within the service neighborhood of the original planned service point (e.g., within a radius of 1.5 kilometers) and recalculate the waiting time until the waiting time turns positive, indicating that both parties can achieve a synchronous or waiting-for-vehicle scheduling state.

[0107] Furthermore, if none of the feasible service points can make the waiting time 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 aforementioned waiting time-driven dynamic optimization of the scheduling path dynamically coordinates the arrival sequence of both parties by adjusting the location of the charging target or service point, reducing ineffective waiting; in the scenario of waiting for a vehicle while waiting for a charging station, it extends the self-charging time and effectively improves the subsequent service capabilities of mobile charging stations; in the scenario of waiting for a charging station while waiting for a vehicle, the dynamic replacement of service points and path reconstruction mechanism enhances the adaptability of the scheduling strategy, avoids the target vehicle from waiting for a long time in the weak charging section, and improves its travel experience and the continuity of energy guarantee.

[0109] In summary, the mobile charging station route planning method for electric vehicles provided by this invention has the following technical effects: By acquiring the travel plan information of the target vehicle and combining it with the real-time status information of the target vehicle, weak charging sections on the vehicle's travel path are identified. Real-time charging pile status information of each mobile charging pile within the weak charging sections is acquired, including but not limited to task status, battery status, and location status. Based on the real-time charging pile status information and the real-time status information of the target vehicle, mobile charging piles that meet the scheduling conditions are selected to form a candidate charging pile set. The candidate charging pile set is traversed, and its schedulability is evaluated based on scheduling capacity and availability to select the best-scheduled mobile charging pile. Based on the schedulability evaluation results and combined with a preset path planning strategy, path planning decisions are executed on the best-scheduled mobile charging pile to obtain the optimal service path. The optimal service path is then distributed to the best-scheduled mobile charging pile to guide it in completing the charging task, thereby achieving the technical effects of improving scheduling level, enhancing charging efficiency, and optimizing the charging experience.

[0110] Example 2, as Figure 2 This is a schematic diagram of the mobile charging station route planning system for electric vehicles according to the present invention. For example, Figure 1 The flowchart of the mobile charging pile route planning method for electric vehicles of the present invention can be illustrated as follows: Figure 2 The structure shown is implemented.

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

[0112] The charging pile status acquisition module 12 is used to acquire the real-time charging pile status information of each mobile charging pile in the weak power replenishment section, wherein the real-time charging pile status information includes at least 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 a candidate charging pile set, and traverse the candidate charging pile set to perform schedulability evaluation.

[0114] The path planning and distribution module 14 is used to perform path planning decisions on the best-scheduled mobile charging piles based on the schedulability evaluation results and a preset path planning strategy, obtain the optimal service path, and distribute the optimal service path to the best-scheduled mobile charging piles to guide the execution of the charging task.

[0115] In some embodiments, the weak energy compensation segment in the segment identification module 11 includes at least one of the following: The following are considered as separate sections: sections where the distance between two adjacent high-efficiency charging points is greater than the preset high-efficiency charging range of the target vehicle; sections where there are inefficient charging points between two high-efficiency charging points, and the distance between the two high-efficiency charging points is greater than the maximum mileage of the target vehicle, wherein the inefficient charging points include charging points with low power levels, poor equipment stability, and long queuing times; and sections where the travel plan information shows known charging needs but the charging resource allocation 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 by combining 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 recharge timeliness rules.

[0117] The schedulability evaluation unit is used to evaluate schedulability based on the schedulability evaluation rules and the real-time charging pile status information. 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 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 route planning and distribution module 14 includes: The route planning start state determination unit is used to determine the route planning start state if the optimal scheduling mobile charging pile is highly schedulable. The start state includes the real-time location of the target vehicle, the real-time location of the optimal scheduling mobile charging pile, the start time, the remaining available time, and the planned service point.

[0119] The path planning constraint configuration and service path generation unit is used to configure path planning constraints based on the initial state of path planning and generate service paths in conjunction with the path planning algorithm.

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

[0121] The energy replenishment completion time calculation unit is used to calculate the energy replenishment completion time based on the target energy replenishment capacity 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 generate and optimize the service path by taking the end point of the self-replenishing path and the replenishment completion time as the starting state of the path planning, and combining the path planning algorithm.

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

[0124] The additional optimization target setting subunit is used to set additional optimization targets, which include at least the battery status being in the high-efficiency range and minimizing the excess remaining battery power of the target vehicle.

[0125] The optimization constraint setting subunit is used to set optimization constraints, including at least the target charging capacity satisfying the power status rules, the service radius of the optimally scheduled mobile charging pile not exceeding a preset range, and the target vehicle's travel path length satisfying the target vehicle's current remaining mileage.

[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 also includes: The waiting time calculation unit is used to calculate the waiting time based on the optimal service path and the self-supplementing path, combined with the real-time status information of the vehicle, when the schedulability of the optimally scheduled 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] A waiting optimization processing unit is used to perform waiting optimization processing based on the waiting time.

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

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

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

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

[0133] The no-task mobile charging pile extraction unit is used to traverse the filtering results once and extract the currently no-task mobile charging piles and store them in the candidate charging pile set.

[0134] The remaining occupancy time calculation unit is used to iterate through the filtering results once, obtain the remaining task time and the maximum path time of the service range for each mobile charging station, and sum them to obtain the remaining occupancy time.

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

[0136] It should be understood that the focus of the embodiments mentioned in this specification is their difference from other embodiments. The specific embodiments in the foregoing Embodiment 1 are also applicable to the mobile charging pile route planning system for electric vehicles described in Embodiment 2. For the sake of brevity, they will not be elaborated further here.

[0137] It should be understood that the embodiments disclosed in this invention and the above description enable those skilled in the art to implement this invention. However, this 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 foregoing embodiments or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this invention, and should all be included within the protection scope of this invention.

Claims

1. A method for planning the route of a mobile charging station for electric vehicles, characterized in that, include: Obtain the travel plan information of the target vehicle and combine it with the real-time status information of the target vehicle to identify weak energy replenishment sections. The real-time charging pile status information of each mobile charging pile in the weak power replenishment section is obtained, wherein the real-time charging pile status information includes at least the task status, power status, and location status. Based on the real-time charging pile status information and the real-time vehicle status information, schedulable mobile charging piles are selected and output as a candidate charging pile set. The candidate charging pile set is then traversed to evaluate its schedulability. Based on the schedulability evaluation results, a path planning decision is made for the best-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 best-scheduled mobile charging pile to guide the execution of the charging task. The weak energy replenishment section includes at least one of the following situations: Sections where the distance between two adjacent high-efficiency refueling points is greater than the preset high-efficiency refueling range of the target vehicle; There is an inefficient charging point between two efficient charging points, and the distance between the two efficient charging points is greater than the maximum driving range of the target vehicle. The inefficient charging points include charging points with low power levels, poor equipment stability, and long queuing times. The travel plan information includes sections where there is a known need for additional energy, but the allocation of additional energy resources cannot meet the coverage requirements. Traverse the set of candidate charging stations and perform a dispatchability evaluation, including: Based on the real-time vehicle status information, a set of schedulability evaluation indicators is calculated and obtained, and corresponding schedulability evaluation rules are defined. The schedulability evaluation rules include task status rules, battery status rules, and recharge timeliness rules. 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 satisfies the power status rules, it is marked as "high schedulability". If the task status is "no task", the power status does not satisfy the power status rules, and the energy replenishment time calculated based on the power status and the location status satisfies the energy replenishment timeliness rules, it is marked as "medium schedulability". Otherwise, it is marked as "low schedulability". If the optimal scheduling mobile charging pile is not highly schedulable, then the waiting time is calculated based on the optimal service path and the self-replenishment path, combined with the real-time vehicle status information. The waiting time is a vector value, and the waiting time is negative when the vehicle is waiting for the charging pile, and positive when the charging pile is waiting for the vehicle. Perform wait optimization processing based on the aforementioned wait time; Perform wait optimization processing based on the said wait time, including: When the waiting time is positive and exceeds the first preset time threshold, the target power of the optimally scheduled mobile charging pile is increased accordingly. When the waiting time is negative and lower than the second preset time threshold, the latest available charging point is searched within the service neighborhood of the planned service point, based on the real-time vehicle status information, and iterated until the waiting time turns positive. If the waiting time after iteration is not positive, the service point with the waiting time closest to zero in the service neighborhood is preferred to be updated as the planned service point, and the optimal service path is replanned.

2. The mobile charging station route planning method for electric vehicles as described in claim 1, characterized in that, The preset route planning strategies include: If the optimally scheduled mobile charging station is highly schedulable, then the initial state of the route planning is determined, wherein the initial state includes the real-time location of the target vehicle, the real-time location of the optimally scheduled mobile charging station, the start time, the remaining available time, and the planned service point. Configure path planning constraints based on the initial state of the path planning, and generate service paths in conjunction with the path planning algorithm.

3. The mobile charging station route planning method for electric vehicles as described in claim 2, characterized in that, The preset route planning strategies also include: If the optimally scheduled mobile charging station is not highly schedulable, then the self-recharging station of the optimally scheduled mobile charging station is determined, and a self-recharging path is generated based on the self-recharging station. The charging time is calculated based on the target charging capacity and corresponding charging characteristic curve of the optimally scheduled mobile charging pile. The endpoint of the self-replenishing path and the replenishment completion time are used as the starting state of the path planning, and the service path is generated and optimized by combining the path planning algorithm.

4. The mobile charging station route planning method for electric vehicles as described in claim 3, characterized in that, Path generation and optimization are performed using path planning algorithms, including: Set primary optimization objectives, including at least minimizing energy consumption in transit, optimizing service response time, and maximizing self-replenishment time. Set additional optimization objectives, including at least the battery status being in the high-efficiency range and minimizing the excess remaining battery power of the target vehicle. The optimization constraints include at least the target charging capacity satisfying the power status rules, the service radius of the optimally scheduled mobile charging pile not exceeding a preset range, and the target vehicle's travel path length satisfying the target vehicle's current remaining mileage. Multi-objective path optimization is performed by combining the main optimization objective, the additional optimization objective, and the optimization constraints.

5. The mobile charging station route planning method for electric vehicles as described in claim 1, characterized in that, Based on the real-time charging pile status information and the real-time vehicle status information, select schedulable mobile charging piles and output a candidate charging pile set, including: Configure a minimum power constraint and perform a corresponding filter; Iterate through the filtering results once, extract the mobile charging piles that are currently without tasks, and store them in the candidate charging pile set; Iterate through the filtered results once to obtain the remaining task time and the maximum path time within the service range of each mobile charging station, and sum them to obtain the remaining occupied time. Based on the real-time vehicle status information, the remaining transit time of the target vehicle is calculated, and mobile charging piles with remaining occupancy time less than the remaining transit time are extracted, identified as the schedulable mobile charging piles, and stored in the candidate charging pile set.

6. A mobile charging station route planning system for electric vehicles, characterized in that, The method for planning the route of a mobile charging station for electric vehicles according to any one of claims 1-5 includes: The section identification module is used to obtain the travel plan information of the target vehicle and, in combination with the real-time status information of the target vehicle, identify the weak power supplementation section. The charging pile status acquisition module is used to acquire the real-time charging pile status information of each mobile charging pile in the weak power replenishment section, wherein the real-time charging pile status information includes at least the task status, power status, and location status. 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 a candidate charging pile set, and traverse the candidate charging pile set to perform schedulability evaluation. The path planning and distribution module is used to perform path planning decisions for the best-scheduled mobile charging piles based on the schedulability evaluation results and a preset path planning strategy, obtain the optimal service path, and distribute the optimal service path to the best-scheduled mobile charging piles to guide the execution of the charging task.

Images