Mobile charging service method and system based on laser navigation

Abstract

The invention provides a mobile charging service method and system based on laser navigation, and relates to the technical field of electric vehicle charging. After a charging demand of a user is obtained through a mobile terminal, an optimal scheduling instruction is generated by integrating the electric quantity of an energy storage pile, a vehicle position and a battery inventory state, and a mobile charging vehicle is controlled to complete automatic battery replacement; and the laser radar SLAM technology and real-time path planning are utilized to realize accurate navigation to the user position for charging, and finally, the old battery pack is automatically recycled and returned to the energy storage pile for energy complementation, so that efficient and intelligent mobile charging service is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to a mobile charging service method and system based on laser navigation. Background Art

[0002] As the number of electric vehicles continues to increase, the number and distribution of traditional fixed charging piles cannot meet the market's urgent demand for fast and flexible charging. Especially in scenarios with many cars and few charging piles or peak power consumption, electric vehicles frequently queue up to wait for charging, resulting in poor user experience and affecting the normal operation of transportation venues. Therefore, how to improve the energy interaction efficiency between mobile charging vehicles and energy storage piles and reduce the waiting time for electric vehicles to charge has become the focus of the industry.

[0003] In the prior art, although there are charging service solutions based on mobile charging vehicles, most of them rely on the power of the mobile charging vehicle itself or a temporarily installed power generation device. The energy replenishment method is limited, and the efficiency and safety need to be improved. In addition, traditional positioning and navigation methods usually use GPS or inertial navigation technology, which is easily affected by the indoor environment or wall obstacles, making it difficult to ensure positioning accuracy, further limiting the stable operation of mobile charging vehicles in complex scenarios. Therefore, there is an urgent need for a mobile charging service method that combines high-precision positioning, fast battery pack replacement, and efficient energy scheduling to meet the charging needs of electric vehicles in multiple scenarios. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a mobile charging service method and system based on laser navigation, thereby realizing efficient and intelligent mobile charging service.

[0005] To achieve the above object, the present invention provides the following solutions: A mobile charging service method based on laser navigation, comprising: Collect charging requirements submitted by target users through mobile terminals; Generate an optimal dispatch instruction based on a preset optimization algorithm according to the charging demand, the remaining power of the energy storage pile, the real-time location of the mobile charging vehicle and the battery pack inventory status; the optimal dispatch instruction includes the target mobile charging vehicle identifier, the matching battery pack type and the service path; According to the optimal dispatching instruction, the mobile charging vehicle and the energy storage pile are controlled to automatically replace the battery pack; An environmental map is constructed based on a multi-line laser radar and SLAM algorithm, and in combination with real-time obstacle detection and path planning algorithms, the mobile charging vehicle with the replaced battery pack is navigated to the location of the target user according to the optimal scheduling instructions and the environmental map to charge the target user's vehicle; After charging is completed, use the mobile charging vehicle to recycle the used battery pack and control the mobile charging vehicle to return to the energy storage pile for cyclic energy replenishment.

[0006] Preferably, the charging demand includes the location information of the vehicle of the target user and the required battery capacity.

[0007] Preferably, based on the charging demand, the remaining power of the energy storage pile, the real-time position of the mobile charging vehicle, and the battery pack inventory status, generate an optimal scheduling instruction based on a preset priority algorithm, including: Receive the data set of the charging demand of the target user ; where , is the nth demand, and each demand includes the position coordinates , the required battery capacity , and the demand submission time ; Obtain the set of remaining power of the energy storage pile and the battery pack inventory status ; where , , is the power of the mth energy storage pile, represents the number of available fully charged battery packs of the th energy storage pile; Obtain the set of real-time positions of the mobile charging vehicles and the current power ; where , , and are the abscissa and ordinate of the current position of the kth mobile charging vehicle respectively; is the current remaining power of the th mobile charging vehicle; Comprehensively consider the user's urgency, the energy consumption cost of the mobile charging vehicle, and the load balance of the energy storage pile to construct a scheduling objective function F ; Construct constraint conditions; the constraint conditions include inventory constraints and single-service constraints; Based on the constraint conditions, use the mixed integer linear programming algorithm to solve the objective function F , and generate an optimal scheduling instruction set.

[0008] Preferably, the expression of the objective function F is: where is the waiting time of the target user , is a binary variable, where 1 represents a mobile charging vehicle Service target users , otherwise it is 0; is the energy storage pile The allocated quantity of battery packs; is the weight of the user's urgency level, is the energy consumption cost of the mobile charging vehicle, is the resource allocation balance degree of the energy storage pile load balance, is the energy storage pile The allocated quantity of battery packs; , where, is the target user The current remaining battery power of the vehicle, is the full battery capacity, is the first dynamic weight coefficient, is the second dynamic weight coefficient, is the current system time, is the maximum tolerable waiting time, is the timestamp when the user submits the demand; , is the mobile charging vehicle to the target user The path distance, is the unit distance energy consumption coefficient of the mobile charging vehicle, is the total number of full battery packs of all energy storage piles, is the inventory balance coefficient, = 0.5; , is the total power capacity of all energy storage piles in the system, is the total number of full battery packs of all energy storage piles in the system.

[0009] Preferably, the expression of the inventory constraint is: ; The expression of the single service constraint is: .

[0010] Preferably, controlling the mobile charging vehicle and the energy storage pile to complete automatic battery pack replacement according to the optimal scheduling instruction includes: Extracting the matching battery pack type in the optimal scheduling instruction to obtain a type identifier; Screening the matching battery packs from the energy storage pile inventory according to the type identifier; Completing the unloading and loading of the matching battery packs under the force feedback control by the robotic arm.

[0011] Preferably, an environmental map is constructed based on a multi-line lidar and a SLAM algorithm, and combined with a real-time obstacle detection and path planning algorithm. According to the optimal scheduling instruction and the environmental map, the mobile charging vehicle with the battery pack replaced is navigated to the location of the target user to charge the vehicle of the target user, including: A multi-line lidar is mounted on the mobile charging vehicle, and the surrounding environment is scanned in real time through the SLAM algorithm to construct a high-precision 2D / 3D environmental map; the environmental map includes static obstacles, temporary marks of dynamic obstacles, and passable areas; According to the service path provided in the optimal scheduling instruction, combined with the environmental map, the Dijkstra algorithm is used to calculate the initial global path; The initial global path is dynamically adjusted through real-time obstacle detection to ensure avoidance of dynamic obstacles; If a path blockage is detected, the Dynamic Window Approach (DWA) is triggered to generate a detour path; According to the updated detour path, the vehicle speed and steering angle of the mobile charging vehicle are adjusted through the PID control algorithm to ensure stable travel along the planned path; When the mobile charging vehicle arrives at the location of the target user, the charging interface position of the user's vehicle is determined through visual recognition technology; According to the charging interface position, the mobile charging vehicle is controlled to perform position fine-tuning so that the mobile charging vehicle is docked with the vehicle charging interface to start the charging process.

[0012] Preferably, according to the service path provided in the optimal scheduling instruction, combined with the environmental map, the Dijkstra algorithm is used to calculate the initial global path, including: The environmental map is converted into a grid map; Each grid cell of the grid map is marked as one of the following states: passable area 0, obstacle area 1, and temporary mark of dynamic obstacle 2; The starting point of the path planning is defined as the current position of the mobile charging vehicle, and the ending point is the position of the target user in the service path provided in the optimal scheduling instruction; The starting point is added to the priority queue, and the cost value is initialized; According to the Dijkstra algorithm, path planning is performed on the initialized priority queue to obtain the initial global path.

[0013] A mobile charging service system based on laser navigation includes: A demand acquisition unit for collecting the charging demand submitted by the target user through a mobile terminal; An instruction generation unit, configured to generate an optimal scheduling instruction based on a preset optimization algorithm according to the charging demand, the remaining power of the energy storage pile, the real-time position of the mobile charging vehicle, and the battery pack inventory status; the optimal scheduling instruction includes a target mobile charging vehicle identifier, a matching battery pack type, and a service path; A battery pack replacement unit, configured to control the mobile charging vehicle and the energy storage pile to complete automatic battery pack replacement according to the optimal scheduling instruction; A path planning unit, configured to construct an environmental map based on a multi-line lidar and a SLAM algorithm, and combine real-time obstacle detection and path planning algorithms to navigate the mobile charging vehicle with the replaced battery pack to the position of the target user according to the optimal scheduling instruction and the environmental map, so as to charge the vehicle of the target user; A vehicle return unit, configured to use the mobile charging vehicle to recycle the used battery pack after charging is completed, and control the mobile charging vehicle to return to the energy storage pile for cyclic energy replenishment.

[0014] The present invention discloses the following technical effects: The present invention provides a mobile charging service method and system based on laser navigation. The charging demand of a user is obtained through a mobile terminal; based on the demand, the power of the energy storage pile, the vehicle position, and the battery inventory, an optimal scheduling instruction is generated, including a target vehicle, a battery type, and a path; the target vehicle and the energy storage pile are controlled to automatically replace the battery pack; a map is constructed by using a lidar and a SLAM algorithm, and combined with real-time obstacle detection and path planning, it is navigated to the user's position for charging; after charging is completed, the used battery pack is recycled and returned to the energy storage pile for energy replenishment. The present invention is outstanding in improving the scheduling efficiency of the mobile charging vehicle, shortening the user waiting time, and improving the positioning and navigation accuracy, and can provide safe, fast, efficient, and flexible mobile charging services for electric vehicles with low labor input. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of the method provided by the embodiment of the present invention; Figure 2 It is a technical roadmap of path planning provided by the embodiment of the present invention; Figure 3 It is a schematic diagram of the path calculation process provided by the embodiment of the present invention; Figure 4 It is a schematic diagram of the system structure provided by the embodiment of the present invention. Detailed implementation manners

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] The object of the present invention is to provide a mobile charging service method and system based on laser navigation, which can provide safe, fast, efficient and flexible mobile charging services for electric vehicles with relatively low labor input.

[0019] To make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0020] Figure 1 The flowchart of the method provided for the embodiment of the present invention is as Figure 1 shown. The present invention provides a mobile charging service method based on laser navigation, including: Step 100: Collect the charging requirements submitted by the target user through the mobile terminal; Step 200: Generate an optimal scheduling instruction based on a preset optimization algorithm according to the charging requirements, the remaining power of the energy storage pile, the real-time position of the mobile charging vehicle, and the battery pack inventory status; the optimal scheduling instruction includes the target mobile charging vehicle identifier, the matching battery pack type, and the service path; Step 300: Control the mobile charging vehicle and the energy storage pile to complete the automatic replacement of the battery pack according to the optimal scheduling instruction; Step 400: Construct an environmental map based on the multi-line lidar and the SLAM algorithm, and combine the real-time obstacle detection and path planning algorithm. Navigate the mobile charging vehicle with the replaced battery pack to the position of the target user according to the optimal scheduling instruction and the environmental map to charge the vehicle of the target user; Step 500: Recover the used battery pack by using the mobile charging vehicle after charging is completed, and control the mobile charging vehicle to return to the energy storage pile for cyclic energy replenishment.

[0021] Preferably, the charging requirements include the position information of the vehicle of the target user and the required battery capacity.

[0022] Specifically, the user inputs the charging requirements in the mobile terminal APP or mini-program. The system automatically obtains the current position coordinates (longitude x, latitude y) of the vehicle through GPS positioning. At the same time, the user manually inputs or selects the required battery capacity C (unit: kWh). The mobile terminal transmits the data packet containing the location information (x, y) and the capacity requirement C to the cloud scheduling center through the 4G / 5G network. The data packet is encapsulated in JSON format and contains verification information such as the timestamp t and the user ID. After receiving the data, the cloud server decrypts and verifies the data, and stores the valid requirements in the scheduling queue of the real-time database. In this embodiment, the remaining battery power SOC (State of Charge) of the vehicle is automatically read through the in-vehicle OBD interface, and the actual required charging amount ΔC = C×(1 - SOC) is calculated in combination with the total battery capacity of the vehicle. At the same time, the vehicle position accuracy is improved from ±5 meters of GPS to ±0.5 meters by using Bluetooth beacons or vision-assisted positioning technology. When a GPS signal blind area such as an underground parking lot is detected, it automatically switches to obtaining three-dimensional coordinates (x, y, z) based on pre-deployed UWB ultra-wideband positioning base stations to ensure the integrity and reliability of the position information. All the collected data will enter the subsequent scheduling process only after double verification.

[0023] Preferably, according to the charging requirements, the remaining power of the energy storage pile, the real-time position of the mobile charging vehicle, and the battery pack inventory status, an optimal scheduling instruction is generated based on a preset priority algorithm, including: Receiving the data set of the charging requirements of the target user ; where is the nth requirement, and each requirement includes the position coordinates , the required battery capacity , and the requirement submission time ; ; Obtaining the set of the remaining power of the energy storage pile and the battery pack inventory status ; where is the power of the mth energy storage pile, and represents the number of available fully charged battery packs of the th energy storage pile; Obtaining the set of the real-time positions of the mobile charging vehicles and the current power ; where , are the abscissa and ordinate of the current position of the kth mobile charging vehicle respectively; is the current remaining power of the th mobile charging vehicle; and ; is the th Construct a scheduling objective function by comprehensively considering the user's urgency, the energy consumption cost of the mobile charging vehicle, and the load balance of the energy storage piles F ; Construct the constraint conditions; the constraint conditions include inventory constraints and single-service constraints; Based on the constraint conditions, use the mixed integer linear programming algorithm to solve the objective function F , and generate an optimal scheduling instruction set.

[0024] Furthermore, in this embodiment, the scheduling problem is first transformed into a standard MILP model: Define decision variables including mobile charging vehicle service assignment variables (binary), battery pack allocation variables (integer), and path selection variables (continuous). Normalize the input real-time data, including: converting the user's power demand into a 0-1 standardized value, converting the vehicle position coordinates into grid map indices, and quantifying the energy storage pile power by percentage. At the same time, establish a constraint matrix, and transform the inventory constraint (the battery packs allocated to each energy storage pile do not exceed the inventory) and the single-service constraint (each user is served by only one vehicle) into a system of linear inequalities.

[0025] Then, this embodiment uses the branch and bound framework for solving. First, relax the integer constraints to obtain the initial linear programming solution. If the solution does not meet the integer conditions, branch. Branch preferentially for the vehicle assignment variables (binary), and generate a branch tree through depth-first search. Each node is quickly solved using the dual simplex method, and the upper and lower bounds are calculated in real time during the process: the upper bound comes from the current optimal integer solution, and the lower bound comes from the relaxed solution. When it is detected that the load of the energy storage pile is unbalanced, automatically add cutting plane constraints (such as a cutting plane that prohibits a certain energy storage pile from allocating more than 70% of its capacity), to accelerate convergence. The solver checks for newly arrived urgent demands every 0.5 seconds and dynamically adjusts the branching strategy.

[0026] After obtaining an integer feasible solution, this embodiment performs three verifications: (1) Battery pack type matching verification to ensure that the allocated battery packs are compatible with the vehicle interface; (2) Path reachability verification, check whether there is physical isolation in the path through the fast A* algorithm; (3) Power safety verification to ensure that the remaining power of the mobile charging vehicle can support the round trip. After all pass, convert the solution into executable instructions: including the target vehicle ID, battery pack serial number, path waypoint list (one waypoint every 5 meters), and estimated time of arrival (ETA), and distribute them to the corresponding mobile charging vehicle and energy storage pile through the MQTT protocol. If the verification fails, trigger re-solving and preferentially retain the satisfied constraint conditions.

[0027] Preferably, the expression of the objective function F is: Among them, is the waiting time of the target user , is a binary variable, where 1 indicates that the mobile charging vehicle serves the target user , otherwise it is 0; is the number of battery pack allocations of the energy storage pile ; is the weight of the user's urgency, is the energy consumption cost of the mobile charging vehicle, is the resource allocation balance degree of the energy storage pile load balancing, is the energy storage pile 's number of battery pack allocations; , among which, is the current remaining power of the vehicle of the target user , is the full charge battery capacity, is the first dynamic weight coefficient, is the second dynamic weight coefficient, is the current system time, is the maximum tolerable waiting time, is the timestamp when the user submits the demand; , is the mobile charging vehicle to the target user 's path distance, is the unit distance energy consumption coefficient of the mobile charging vehicle, is the total number of full charge battery packs of all energy storage piles, is the inventory balance coefficient, = 0.5; , is the total power capacity of all energy storage piles in the system, is the total number of full charge battery packs of all energy storage piles in the system.

[0028] Preferably, the expression of the inventory constraint is: ; the expression of the single service constraint is: .

[0029] Preferably, controlling the mobile charging vehicle and the energy storage pile to complete automatic battery pack replacement according to the optimal scheduling instruction includes: Extracting the matching battery pack type in the optimal scheduling instruction to obtain a type identifier; Screening the matching battery packs from the energy storage pile inventory according to the type identifier; Completing the unloading and loading of the matching battery packs under the force feedback control of the robotic arm.

[0030] Specifically, after receiving the scheduling instruction, the mobile charging vehicle in this embodiment first parses the battery pack type identifier therein (such as "VDA-150-3.2"), and sends a query request containing this identifier to the target energy storage pile through wireless communication; the main control system of the energy storage pile retrieves the local database, filters out the fully charged battery packs that meet the requirements (which need to meet the three conditions of type matching, battery power ≥ 95%, and health state SOH ≥ 90% at the same time), and returns the RFID code and three-dimensional coordinates (x, y, z) of the positions where the battery packs are located. After the mobile charging vehicle navigates to the docking position of the energy storage pile, the six-axis collaborative robotic arm carried by it locks the position of the target battery pack through a visual positioning system (2D camera + laser rangefinder). The force-torque sensor equipped at the end effector monitors the six-dimensional force data (Fx, Fy, Fz, Mx, My, Mz) in real time when contacting the battery pack. When the detected Z-axis contact force reaches 5 ± 0.5 N, it triggers electric unlocking and rotates the lock with a preset torque of 40 N·m; after the old battery pack is taken out and temporarily stored on the transfer platform, the robotic arm then grabs a new battery pack from the designated position, and secondarily verifies the type matching by scanning its RFID tag (frequency 13.56 MHz). Finally, the new battery pack is installed into the battery slot of the charging vehicle at a constant speed of 0.1 m / s. During the installation process, the pose is dynamically adjusted to ensure that the interface contact resistance ≤ 5 mΩ. After the installation is completed, the system automatically checks the voltage stability (within the fluctuation range of ±2%) and uploads the replacement completion signal to the scheduling center.

[0031] Preferably, as Figure 2 shown, an environmental map is constructed based on a multi-line lidar and the SLAM algorithm, and combined with real-time obstacle detection and path planning algorithms. According to the optimal scheduling instruction and the environmental map, the mobile charging vehicle with the replaced battery pack is navigated to the position of the target user to charge the vehicle of the target user, including: A multi-line lidar is carried on the mobile charging vehicle, and the surrounding environment is scanned in real time through the SLAM algorithm to construct a high-precision 2D / 3D environmental map; the environmental map includes static obstacles, temporary markings of dynamic obstacles, and passable areas; According to the service path provided in the optimal scheduling instruction, combined with the environmental map, the Dijkstra algorithm is used to calculate the initial global path; The initial global path is dynamically adjusted through real-time obstacle detection to ensure avoidance of dynamic obstacles; If a path blockage is detected, the dynamic window approach DWA is triggered to generate a detour path; According to the updated detour path, the vehicle speed and steering angle of the mobile charging vehicle are adjusted through the PID control algorithm to ensure stable progress along the planned path; When the mobile charging vehicle arrives at the position of the target user, the charging interface position of the user's vehicle is determined through visual recognition technology; Control the mobile charging vehicle to perform fine position adjustment according to the position of the charging interface, so that the mobile charging vehicle is docked with the vehicle charging interface, and the charging process is started.

[0032] Specifically, the navigation and charging docking of the mobile charging vehicle are realized as follows: The 16-line lidar (scanning frequency 10Hz, ranging accuracy ±2cm) carried by the mobile charging vehicle collects environmental point cloud data in real time, and constructs a 3D grid map (resolution 5cm) containing semantic information through a tightly coupled laser-IMU SLAM algorithm (using the Cartographer framework). Static obstacles (such as walls) in the map are marked as red non-passable areas, dynamic obstacles (such as pedestrians) are detected in real time through the DBSCAN clustering algorithm and marked as yellow temporary restricted areas, and the passable areas are displayed in green. The global path issued by the scheduling system is solved by the Dijkstra algorithm to generate a key waypoint sequence (spacing 1m). At the same time, the local processor (NVIDIA Jetson AGX Xavier) runs a real-time obstacle detection thread. When the lidar detects a dynamic obstacle (point cloud clustering diameter > 0.5m) within 5 meters, the DWA local planner is immediately triggered to recalculate the path. This planner evaluates the speed space of the mobile charging vehicle (linear speed 0-1m / s, angular speed 0-0.5rad / s) at a period of 0.1 seconds, and selects a trajectory that simultaneously satisfies obstacle avoidance, minimum energy consumption and kinematic constraints.

[0033] When the vehicle arrives within 3 meters of the target position, switch to the precise positioning mode: The front-view binocular camera (baseline 15cm, resolution 1280×800) identifies the AprilTag marker of the vehicle charging interface based on the YOLOv5 model (recognition accuracy ±1cm), and at the same time the ultrasonic sensor (detection distance 0.1-2.5m) assists in measuring the docking distance. The control system adopts two-level PID regulation (outer position loop + inner speed loop). The outer loop generates a speed command according to the deviation (Δx, Δy, Δθ) fed back by vision, and the inner loop closes the loop control of the hub motor steering through the encoder (resolution 0.1mm) and IMU data, and finally makes the plane positioning error between the charging pile interface and the vehicle interface ≤3mm, and the angle deviation ≤1°. After successful docking, the floating connector of the charging gun (allowing ±5mm position compensation) is automatically plugged under the action of the electric push rod (thrust 50N), and the contact detection circuit (sampling rate 1kHz) monitors the connection impedance in real time. When the impedance stabilizes at 10mΩ ±1% for 200ms, the charging process is started and the status is uploaded to the cloud.

[0034] Preferably, as Figure 3 shown, according to the service path provided in the optimal scheduling instruction, combined with the environmental map, use the Dijkstra algorithm to calculate the initial global path, including: Convert the environmental map into a grid map; Mark each grid cell of the grid map as one of the following states: passable area 0, obstacle area 1, and temporary mark for dynamic obstacle 2; Define the starting point of path planning as the current position of the mobile charging vehicle, and the ending point as the position of the target user in the service path provided in the optimal scheduling instruction; Add the starting point to the priority queue and initialize the cost value; Perform path planning on the initialized priority queue according to the Dijkstra algorithm to obtain the initial global path.

[0035] Specifically, the specific implementation process of path planning in this embodiment is as follows: First, convert the 3D point cloud map constructed by SLAM into a 2D grid map (grid resolution 10cm×10cm) through downsampling and projection. Each grid is classified and marked according to real-time environmental information: static obstacles (such as walls) are marked as 1 (non-passable), the unoccupied area in the latest lidar scan is marked as 0 (passable), and dynamic obstacles (such as moving vehicles) are identified and marked as 2 (temporary restricted area, effective duration 5 seconds) through a temporal filtering algorithm. The map processor will establish a life cycle timer for each dynamic obstacle, and it will automatically return to the passable state after the timeout. The current coordinates (x, y) of the mobile charging vehicle are obtained through the laser odometer and mapped to the starting grid coordinates (i, j), and the target user position coordinates are extracted from the scheduling instruction and converted to the ending grid coordinates (m, n).

[0036] When the path planner is initialized, add the starting grid to the minimum heap priority queue (sorted by cost value g), and initialize its g value to 0. During the algorithm iteration, each time the grid with the minimum cost value is taken out of the queue, check its 8-neighborhood grids: if the neighborhood grid is 0 (passable), then calculate the new cost value from the current grid to this neighborhood (horizontal / vertical movement cost 1.0, diagonal movement cost 1.4); if the new cost value is less than the original value of this neighborhood, update the cost value and add this neighborhood to the queue, and record the parent node information at the same time. When the ending grid is taken out, the algorithm terminates, and the path is generated by backtracking the parent node. To improve real-time performance, the system adopts the Jump Point Search (JPS) optimization technique to skip redundant node checks in sparse obstacle areas, increasing the planning speed by 3 times (the measured planning time for a 50m×50m map is <50ms). The generated path will be smoothed (cubic B-spline curve fitting) to ensure compliance with the minimum turning radius (0.8m) constraint of the mobile charging vehicle.

[0037] Specifically, the battery recycling and replenishment process after charging in this embodiment is realized as follows: when the charging pile detects that the charging current drops below 5% of the rated value, the recycling process is automatically triggered. First, a termination charging instruction is sent through the CAN bus, and the electric unlocking mechanism of the charging gun (thrust 30N) completes tripping within 200ms. The robotic arm of the mobile charging vehicle then accurately positions to the on-vehicle battery compartment, grabs the depleted battery pack using the end gripper (clamping force adjustable range 20 - 50N), and at the same time, the force sensor continuously monitors the Z-axis contact force (threshold set to 15 ± 2N) to prevent overload. The recycled battery pack is temporarily stored in the transfer compartment of the charging vehicle (with buffer and shock absorption design). Subsequently, the vehicle autonomously navigates back to the nearest available energy storage pile (preferably choosing an energy storage pile with a load rate < 60%) at a cruising speed of 0.8m / s according to the updated environmental map (obtained by real-time scanning with a lidar) and the return path issued by the scheduling system (planned using the improved A* algorithm). After arrival, the robotic arm exchanges the depleted battery pack with the fully charged battery pack of the energy storage pile (including RFID verification and contact resistance detection). Finally, the charging management system of the energy storage pile automatically starts the gradient replenishment program for the recycled battery pack (constant current first and then constant voltage mode) to complete the entire energy cycle. The state data of the whole process (including battery pack SOH, replenishment progress, etc.) is transmitted back to the cloud monitoring platform in real-time through the 5G module.

[0038] Corresponding to the above method, as Figure 4 shown, this embodiment also provides a mobile charging service system based on laser navigation, including: A demand acquisition unit for collecting the charging demands submitted by target users through a mobile terminal; An instruction generation unit for generating an optimal scheduling instruction based on a preset optimization algorithm according to the charging demand, the remaining power of the energy storage pile, the real-time position of the mobile charging vehicle, and the battery pack inventory status; the optimal scheduling instruction includes the target mobile charging vehicle identifier, the matching battery pack type, and the service path; A battery pack replacement unit for controlling the mobile charging vehicle and the energy storage pile to complete automatic battery pack replacement according to the optimal scheduling instruction; A path planning unit for constructing an environmental map based on a multi-line lidar and the SLAM algorithm, and combining real-time obstacle detection and path planning algorithms to navigate the mobile charging vehicle with the replaced battery pack to the position of the target user according to the optimal scheduling instruction and the environmental map to charge the vehicle of the target user; A vehicle return unit for recycling the used battery pack using the mobile charging vehicle after charging is completed, and controlling the mobile charging vehicle to return to the energy storage pile for cyclic energy replenishment.

[0039] The beneficial effects of the present invention are as follows: (1)Through the optimal scheduling algorithm, the present invention reasonably distributes fully charged battery packs of the energy storage piles to mobile charging vehicles with suitable geographical locations and the lowest energy consumption costs, and performs priority scheduling according to the urgency of demand, which can effectively reduce the waiting time of users and significantly improve the scheduling efficiency.

[0040] (2)The present invention uses a robotic arm to complete the automatic unloading and loading of battery packs, reducing the safety risks and errors caused by manual intervention; after the mobile charging vehicle uses up the battery pack, it can be recycled to the energy storage pile for recharging, realizing the recycling and efficient utilization of battery resources.

[0041] (3)The present invention uses a multi-line lidar and SLAM algorithm to construct an environmental map, combined with the Dijkstra path planning mechanism, which can identify and avoid dynamic obstacles in real time, ensuring the accurate positioning and stable driving of the mobile charging vehicle in complex scenarios.

[0042] (4)The present invention realizes the accurate positioning of the vehicle charging interface through visual recognition and completes the fine-tuning of the docking position under the PID control algorithm; the overall system function, from demand collection, scheduling instruction generation, battery pack replacement, path planning to vehicle docking and recycling, has a high degree of automation in the whole process, effectively reducing labor costs and error rates.

[0043] (5)While comprehensively considering the urgency of users and the energy consumption costs of mobile charging vehicles, the present invention also takes into account the factors of load balance of energy storage piles and battery inventory status, ensuring the efficient and orderly scheduling of battery packs and power, reducing energy waste and extending the service life of batteries.

[0044] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method part.

[0045] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A mobile charging service method based on laser navigation, characterized in that: include: Collect charging requirements submitted by target users through mobile terminals; Generate an optimal dispatch instruction based on a preset optimization algorithm according to the charging demand, the remaining power of the energy storage pile, the real-time location of the mobile charging vehicle and the battery pack inventory status; the optimal dispatch instruction includes the target mobile charging vehicle identifier, the matching battery pack type and the service path; According to the optimal dispatching instruction, the mobile charging vehicle and the energy storage pile are controlled to automatically replace the battery pack; An environmental map is constructed based on a multi-line laser radar and SLAM algorithm, and in combination with real-time obstacle detection and path planning algorithms, the mobile charging vehicle with the replaced battery pack is navigated to the location of the target user according to the optimal scheduling instructions and the environmental map to charge the target user's vehicle; After charging is completed, the mobile charging vehicle is used to recycle the used battery pack, and the mobile charging vehicle is controlled to return to the energy storage pile for cyclic energy replenishment.

2. The mobile charging service method based on laser navigation according to claim 1, characterized in that: The charging requirement includes the location information of the target user's vehicle and the required battery capacity.

3. The mobile charging service method based on laser navigation according to claim 2 is characterized in that: According to the charging demand, the remaining power of the energy storage pile, the real-time location of the mobile charging vehicle and the battery pack inventory status, the optimal scheduling instruction is generated based on the preset priority algorithm, including: Receive the charging demand dataset of the target user ;in, , For the nth requirement, each requirement contains the location coordinates , the required battery capacity , demand submission time ; Get the remaining power of the energy storage pile and battery pack inventory status ;in, , , is the power of the mth energy storage pile, Indicates The number of fully charged battery packs available for each energy storage pile; Get the real-time location of mobile charging vehicles and current power ;in, , , and are the horizontal and vertical coordinates of the current position of the kth mobile charging vehicle respectively; For the The current remaining power of the mobile charging vehicle; The scheduling objective function F is constructed by comprehensively considering the user's urgency, the energy consumption cost of the mobile charging vehicle and the load balance of the energy storage pile; Constructing constraint conditions; the constraint conditions include inventory constraints and single service constraints; Based on the constraints, a mixed integer linear programming algorithm is used to solve the objective function F and generate an optimal scheduling instruction set.

4. The mobile charging service method based on laser navigation according to claim 3 is characterized in that: The expression of the objective function F is: in, For target users The waiting time, It is a binary variable, 1 represents a mobile charging vehicle Target users , otherwise 0; Energy storage pile The number of battery packs allocated; is the weight of the user's urgency, The energy consumption cost of the mobile charging vehicle is The resource allocation balance for energy storage pile load balancing. Energy storage pile The number of battery packs allocated; ,in, For target users The current remaining battery power of the vehicle, is the full battery capacity, is the first dynamic weight coefficient, is the second dynamic weight coefficient, is the current system time, is the maximum tolerable waiting time, The timestamp of the user submitting the request; , For mobile charging vehicles To target users The path distance, is the energy consumption coefficient per unit distance of the mobile charging vehicle, is the total number of fully charged battery packs in all energy storage piles, is the inventory equilibrium coefficient, =0.5; , is the total power capacity of all energy storage piles in the system, The total number of fully charged battery packs of all energy storage piles in the system.

5. The mobile charging service method based on laser navigation according to claim 4 is characterized in that: The expression of the inventory constraint is: ; The expression of the single service constraint is: .

6. The mobile charging service method based on laser navigation according to claim 4 is characterized in that: According to the optimal dispatching instruction, the mobile charging vehicle and the energy storage pile are controlled to automatically replace the battery pack, including: Extracting the matching battery pack type in the optimal scheduling instruction to obtain a type identifier; Filter matching battery packs from the energy storage pile inventory according to the type identifier; The matching battery packs are unloaded and loaded through the robotic arm under force feedback control.

7. The mobile charging service method based on laser navigation according to claim 1, characterized in that: An environmental map is constructed based on a multi-line laser radar and SLAM algorithm, and in combination with real-time obstacle detection and path planning algorithms, the mobile charging vehicle with the replaced battery pack is navigated to the location of the target user according to the optimal scheduling instruction and the environmental map to charge the target user's vehicle, including: The mobile charging vehicle is equipped with a multi-line laser radar, which scans the surrounding environment in real time through the SLAM algorithm to build a high-precision 2D / 3D environmental map; the environmental map includes static obstacles, temporary marks of dynamic obstacles and passable areas; According to the service path provided in the optimal scheduling instruction, combined with the environment map, the initial global path is calculated using the Dijkstra algorithm; Dynamically adjust the initial global path through real-time obstacle detection to ensure dynamic obstacle avoidance; If a path obstruction is detected, the dynamic window method DWA is triggered to generate a detour path; According to the updated detour path, the speed and steering angle of the mobile charging vehicle are adjusted through the PID control algorithm to ensure stable travel along the planned path; When the mobile charging vehicle arrives at the target user's location, it uses visual recognition technology to determine the location of the user's vehicle's charging port; The mobile charging vehicle is controlled to perform position fine-tuning according to the position of the charging interface, so that the mobile charging vehicle is docked with the vehicle charging interface and the charging process is started.

8. The mobile charging service method based on laser navigation according to claim 7, characterized in that: According to the service path provided in the optimal scheduling instruction, combined with the environment map, the Dijkstra algorithm is used to calculate the initial global path, including: Converting the environment map into a raster map; Marking each grid cell of the grid map as one of the following states: traversable area 0, obstacle area 1, and dynamic obstacle temporary mark 2; The starting point of the path planning is defined as the current position of the mobile charging vehicle, and the end point is the position of the target user in the service path provided in the optimal scheduling instruction; Add the starting point to the priority queue and initialize the cost value; The path planning is performed on the initialized priority queue according to the Dijkstra algorithm to obtain the initial global path.

9. A mobile charging service system based on laser navigation, characterized in that: include: A demand collection unit, used to collect charging demands submitted by target users through a mobile terminal; An instruction generation unit, configured to generate an optimal dispatch instruction based on a preset optimization algorithm according to the charging demand, the remaining power of the energy storage pile, the real-time location of the mobile charging vehicle, and the inventory status of the battery pack; the optimal dispatch instruction includes a target mobile charging vehicle identifier, a matching battery pack type, and a service path; A battery pack replacement unit, used to control the mobile charging vehicle and the energy storage pile to automatically replace the battery pack according to the optimal scheduling instruction; A path planning unit is used to construct an environmental map based on a multi-line laser radar and a SLAM algorithm, and to navigate the mobile charging vehicle with a replaced battery pack to the location of a target user according to the optimal scheduling instruction and the environmental map in combination with real-time obstacle detection and a path planning algorithm, so as to charge the target user's vehicle; The vehicle return unit is used to recycle the used battery pack using the mobile charging vehicle after charging is completed, and control the mobile charging vehicle to return to the energy storage pile for cyclic energy replenishment.

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