Intelligent charging system and method based on mobile charging robot

Abstract

The invention provides an intelligent charging system and method based on mobile charging robots. The system comprises a cloud scheduling center, a mobile charging robot cluster and an intelligent charging station network. The method comprises a user request stage, a task scheduling and path planning stage, a robot navigation and obstacle avoidance stage, a charging preparation and execution stage, a charging completion and settlement stage, and a task completion report and subsequent scheduling stage. By constructing a three-in-one framework of a cloud dispatching center, a mobile charging robot cluster and an intelligent charging station network, the problems of high construction cost, low utilization rate, occupied parking spaces and the like of fixed charging piles can be solved, so that on-demand response, dynamic dispatching and autonomous energy complementation of charging services are realized, and valley electricity storage and charging and green electricity consumption are supported.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle charging, in particular to an intelligent charging system based on a mobile charging robot, which is suitable for flexible charging services of electric vehicles in public parking lots, residential areas, commercial centers and other scenarios, and is especially suitable for valley electricity storage and charging, green electricity consumption and dynamic power demand response scenarios. BACKGROUND

[0002] With the continuous improvement of the penetration rate of electric vehicles, the traditional fixed charging pile faces many bottlenecks: High construction cost: each charging position needs to be independently configured with a direct current fast charging pile, involving high-voltage cable laying, power distribution capacity increase, long construction period and other problems; Low utilization rate: the "one pile one car" mode leads to high idle rate of equipment and serious waste of resources; Serious occupation of parking spaces: phenomena such as fuel cars occupying spaces and electric cars not leaving after charging are common, affecting the normal charging order; Rigid power dispatching: most charging piles do not have energy storage capability and cannot participate in peak load shifting or consumption of fluctuating green energy such as wind power and photovoltaic power; Poor scalability: new charging demand needs to be re-wired, making it difficult to dynamically respond to peak load.

[0003] Although AGV type charging robot solutions have been proposed in the prior art, they generally have problems such as unreliable automatic charging docking, single energy storage structure, weak obstacle avoidance capability, and low dispatching efficiency, and have not yet achieved stable, efficient and low-cost commercial operation.

[0004] Therefore, there is an urgent need for a new type of charging system that integrates energy storage, mobility, intelligent dispatching, and automatic docking charging to achieve the intelligentization, flexibility and economy of charging services. SUMMARY

[0005] In view of the various deficiencies of the prior art, the present application provides an intelligent charging system based on a mobile charging robot and a method thereof, which realizes on-demand response, dynamic dispatching and autonomous energy replenishment of charging services by constructing a "cloud dispatching center + mobile charging robot cluster + intelligent charging station network" three-in-one architecture, solves the problems of high construction cost, low utilization rate and occupation of fixed charging piles, and supports valley electricity storage and charging and green electricity consumption.

[0006] The present application achieves the above-mentioned purposes through the following technical solutions: An intelligent charging system based on a mobile charging robot, comprising: a cloud dispatching center, a mobile charging robot cluster, and an intelligent charging station network; Each mobile charging robot in the mobile charging robot cluster is provided with a three-level energy storage system composed of a main battery, a buffer battery and a driving battery, and is provided with a charging interface and an automatic docking power supply mechanism for providing charging services for electric vehicles. The intelligent charging station network is distributed in a parking lot to provide centralized power supply services for the mobile charging robots, and each intelligent charging station is provided with a plurality of charging slots, and the charging slots are provided with charging interfaces and guiding devices for docking with the mobile charging robots. The cloud scheduling center is deployed in the cloud or a local server to receive charging requests and parking space position information uploaded by users by scanning unique two-dimensional codes sprayed on the ground of parking spaces, and the parking space position information is provided by an RFID electronic tag embedded in each parking space to assist positioning; the cloud scheduling center further schedules the mobile charging robots to go to the parking space where the target vehicle is located to perform a charging task according to the RFID positioning information, the real-time state of the mobile charging robots and a scheduling algorithm, and plans an optimal path; The mobile charging robot cluster has an autonomous navigation function, and after receiving the task instruction of the cloud scheduling center, can autonomously move to the target parking space according to the planned path, perform a charging operation, and return to an idle state after the charging is completed, and report a task completion state and a remaining power to the cloud scheduling center. The cloud scheduling center is also used to manage the resources of the intelligent charging station network, including monitoring the occupancy of the charging slots, publishing idle slot information, and scheduling the mobile charging robots to perform power supply operations according to the power level of the mobile charging robots and subsequent task requirements.

[0007] According to the intelligent charging system based on the mobile charging robot provided by the application, the main battery is a 60-80kWh lithium iron phosphate battery, which is used to provide charging energy for electric vehicles; the buffer battery is a 5-15kWh super capacitor array, which is used to absorb regenerative braking energy and smooth the charging and discharging peak; and the driving battery is a 3-10kWh ternary lithium battery, which is independently used to power the mobile and control system of the mobile charging robot.

[0008] According to the intelligent charging system based on the mobile charging robot provided by the application, each charging slot in the intelligent charging station is further configured as: The communication module is used for real-time communication with the cloud scheduling center to report the use and idle states of the charging slots and position information; At least three infrared light sources are arranged, which are a central infrared light source, a left infrared light source and a right infrared light source, wherein the central infrared light source is a 360-degree omnidirectional infrared light source arranged on the top of the charging parking position, the left infrared light source and the right infrared light source are directional light sources arranged on the left and right sides below the top of the charging parking position and emit specified coded light, and the flashing of each light source contains preset information for guiding the mobile charging robot to position the charging parking position. Through the central infrared light source, the mobile charging robot can quickly identify and determine the position of the charging parking position. Through the left infrared light source and the right infrared light source, the mobile charging robot can determine whether it deviates from the central axis of the charging path in real time during the approach to the charging parking position, and adjust accordingly to ensure accurate docking. A guide groove is arranged in front of each charging parking position to guide the limit slider at the bottom of the mobile charging robot to accurately dock. Each charging parking position has a unique information code, and the charging parking position continuously reports its occupancy state to the cloud scheduling center, and the cloud scheduling center sends the information code and position of the idle charging parking position to the mobile charging robot in need of power compensation according to the information. After receiving the instruction from the cloud scheduling center, the mobile charging robot plans a path to the vicinity of the specified charging parking position, first confirms the basic position of the charging parking position through the central infrared light source, then fine-tunes the position through the left infrared light source and the right infrared light source, until the limit slider at the bottom accurately enters the guide groove, then moves forward along the guide groove, inserts the charging gun into the charging port of the charging parking position, and completes the docking to start the charging operation.

[0009] According to the intelligent charging system based on the mobile charging robot provided by the application, the mobile charging robot adopts an omnidirectional wheel structure and supports at least four motion modes of forward movement, backward movement, lateral movement and in-place rotation to adapt to the movement demand in narrow space or complex path in a parking lot. The positioning system of the mobile charging robot adopts a fusion positioning mode of RFID parking space tags and inertial navigation odometry, wherein the cumulative error generated by the inertial navigation odometry in the dead reckoning process is corrected by reading the RFID tag information buried in the parking space, and the absolute position information of the RFID tag and the relative displacement data of the inertial navigation odometry are combined to realize the centimeter-level positioning accuracy of the mobile charging robot in the parking lot environment. The communication module of the mobile charging robot integrates 4G / 5G, Wi-Fi and Bluetooth communication functions, supports real-time data interaction between the mobile charging robot and the cloud scheduling center, and at least includes position information reporting, task instruction receiving, state feedback and path planning data transmission.

[0010] According to the application, an intelligent charging system based on a mobile charging robot is provided, and the charging interface and the automatic docking power supply mechanism comprise: The electric vehicle charging interface is equipped with a charging plug conforming to the national standard DC fast charging interface standard, used for physical connection with the electric vehicle charging port and transmission of DC electric energy; The automatic docking charging mechanism for self-power supply of the mobile charging robot comprises: The hard connection straight insertion type docking structure adopts a rigid straight insertion design without a complex mechanical arm, and the physical connection is completed by driving the mobile charging robot by itself; The limiting guide device is arranged at the bottom of the mobile charging robot, cooperates with the guide groove in front of the charging detent of the intelligent charging station, and realizes accurate positioning in the horizontal and vertical directions; The mobile charging robot travels to the vicinity of the target charging detent according to the path planned by the cloud scheduling center, turns on the infrared receiver array at the bottom, receives the coded spectrum light signals of at least three infrared light sources on the charging detent, completes the final positioning stage by decoding the light source information, moves forward along the guide groove, uses the mechanical constraint of the limiting slide block and the guide groove to automatically align the charging plug with the charging slot of the charging detent, and inserts the charging plug into the charging slot by the power of the mobile charging robot to establish physical connection and start power supply.

[0011] According to the application, an intelligent charging system based on a mobile charging robot is provided, and each group of infrared light sources of the intelligent charging station modulates the light signals according to a preset coding rule, wherein: The central infrared light source emits a wide spectrum light signal containing a unique charging detent identification code, used for preliminary orientation identification of the mobile charging robot; the left and right infrared light sources emit narrow spectrum light signals containing position correction information, and the coding spectrum dynamically changes with the angle of the mobile charging robot deviating from the central axis; The bottom of the mobile charging robot is configured with multiple groups of infrared receiver arrays, comprising: The central receiving array is used for receiving the signal of the central infrared light source and analyzing the charging detent identification code from the signal; The left and right receiving units are arranged on the left and right sides of the mobile charging robot respectively, and are used for receiving the directional light source signals in the corresponding directions; Each receiving head is internally provided with a photoelectric conversion module and a digital signal processor, and the light signals are subjected to: Photoelectric intensity threshold detection to filter out environmental light interference; Spectrum analysis and decoding to extract the orientation correction parameters carried in the light source coding; According to the intelligent charging system based on the mobile charging robot provided by the application, in the preliminary positioning stage: the mobile charging robot identifies the signal of the central infrared light source through the central receiving array to determine the rough orientation of the target charging position; in combination with the charging position coordinate information issued by the cloud scheduling center, an initial docking path is planned; In the accurate positioning stage: when the mobile charging robot enters the preset docking area of the charging position, the left receiving unit and the right receiving unit are started; the code spectrum signals of the left infrared light source and the right infrared light source are analyzed in real time, and the following are calculated: The cross detection method is adopted to read the signal intensity IR of the left infrared light source at the right receiving unit and the signal intensity IL of the right infrared light source at the left receiving unit in real time; The intensity difference ΔI = |IL-IR| and the total intensity Isum = IL+IR are calculated to estimate the distance; Suppose that the infrared light source is a Lambertian radiator, and the radiation intensity decays with the angle according to the cosine law. In the case of close distance and symmetrical layout, the two-dimensional lookup table or the analytical function model is established through calibration experiments or simulation, and ΔI + the geometric model is used to inversely deduce the lateral offset Δx and the angle Δθ or the lightweight regression model is used for real-time solving; The control system looks up the table according to the current measurement value or infers through the analytical function model to obtain the estimated Δx and Δθ; The Δx and Δθ are input into the PID controller to generate left and right wheel differential instructions, and the lateral and angle errors are gradually reduced; When the mobile charging robot meets the following conditions, the charging plug insertion action is started: The lateral offset Δx is less than or equal to 2 cm, and the angle offset Δθ is less than or equal to 1°; The signal intensity of the central infrared light source is continuously stable; The cloud scheduling center feedbacks that the charging position state is "idle and can be docked"; The mobile charging robot uniformly advances along the guide groove, and through the mechanical constraint of the limiting slide block and the guide groove, the charging plug is vertically inserted into the charging slot at a set speed.

[0012] According to the intelligent charging system based on the mobile charging robot provided by the application, the mobile charging robot has a multi-modal sensor fusion obstacle avoidance system, which comprises: At least 8 groups of ultrasonic sensors are arranged around the mobile charging robot to cover a 360° detection range in the horizontal direction, for real-time sensing of the contour of a near-distance static / dynamic obstacle; The flexible touch edge structure is wrapped around the robot shell, and a pressure-sensitive resistance array is built in. When the touch edge at any position is pressed beyond a preset threshold, an emergency stop signal is triggered and the power output is cut off; A local obstacle avoidance control module is set up, which constructs a dynamic velocity window based on the robot's current velocity v, angular velocity ω, and acceleration constraints; combined with obstacle distance data fed back in real time by ultrasonic sensors, multiple sets of candidate trajectories are sampled within the velocity window; the optimal trajectory is selected through an evaluation function to generate a real-time obstacle avoidance path; A multi-sensor data fusion module is set up to fuse obstacle distance information from ultrasonic sensors with robot odometry data to construct a local occupancy grid map; dynamic obstacle movement trends are marked in the grid map to optimize the DWA trajectory evaluation weights.

[0013] A smart charging method based on a mobile charging robot, the method employing the aforementioned smart charging system based on a mobile charging robot, the method comprising the following steps: User request stage: After parking their electric vehicle in the parking space, users can scan the unique QR code sprayed on the ground of the parking space through their smart terminal to enter the dedicated charging App or mini-program, submit a charging request, and automatically upload the location information of their parking space to the cloud dispatch center. Task scheduling and path planning stage: After receiving a charging request, the cloud scheduling center combines the current location, power level, and task queue status of the mobile charging robot, and uses an optimized scheduling algorithm to select the optimal mobile charging robot from the mobile charging robot cluster, and plans the optimal path for the robot to the target parking space. Robot navigation and obstacle avoidance phase: The selected mobile charging robot starts up and navigates autonomously through the mobility and navigation system, heading to the parking space of the target vehicle along the planned path, using sensors to detect and avoid obstacles in real time along the way; Charging preparation and execution phase: After the mobile charging robot arrives at the target parking space, the user takes the charging gun from the robot, connects it to the vehicle's charging port, and confirms the start of charging in the charging app or mini-program. The mobile charging robot then begins charging the electric vehicle. Charging completion and settlement stage: After charging is completed, the user unplugs the charging gun and puts it back in its place, and clicks the charging end confirmation in the charging App or mini program to complete the payment settlement. Task completion report and subsequent scheduling phase: The mobile charging robot reports the task completion status and current remaining power to the cloud scheduling center. Based on the mobile charging robot's power level, subsequent task requirements, and the availability of smart charging stations, the cloud scheduling center schedules the robot to continue performing other charging tasks or return to the nearest smart charging station for power replenishment.

[0014] According to the intelligent charging method based on a mobile charging robot provided by the present invention, an improved Hungarian algorithm is used to achieve dynamic matching of multiple robots and multiple tasks in the task scheduling and path planning stage, wherein the cost function is defined as: wherein, is the robot i to the task j Euclidean distance of the parking space; is the current power of the robot, is the full power; is the task j waiting time; is the task priority coefficient; , , , is a dynamic weight factor, which is automatically adjusted by the cloud scheduling center according to the real-time scene; In the robot navigation and obstacle avoidance stage, a weighted grid map is constructed based on the parking lot BIM model, wherein the obstacle region is marked as a high-cost area, and the charging parking space is marked as a target point; An A* algorithm is used to generate an initial path, and the cost function is:

[0015] wherein, is the distance from the node n to the target point, is the expected power consumption of the remaining section of the path, , is a weight coefficient; In combination with the obstacle information fed back by the ultrasonic sensor in real time, a dynamic obstacle avoidance window is set on the global path; when an obstacle is detected, a local detour path is generated in the window by using a DWA algorithm, and the transition is smoothed back to the global path.

[0016] As can be seen, compared with the prior art, the intelligent charging system and method based on the mobile charging robot proposed by the present application have the following beneficial effects: 1. The traditional charging mode needs to install charging piles in each parking space, which not only involves a large amount of charging pile equipment procurement cost, but also includes complex line laying, power capacity increase and other infrastructure reconstruction costs, and the overall construction cost is high. The intelligent charging system of the present application only needs to construct a small number of centralized charging stations to supply power to the mobile charging robot, which greatly reduces the number of charging piles used and reduces the equipment procurement cost. At the same time, the construction of centralized charging stations is relatively unified and standardized, which is conducive to simplifying the line layout and power facility planning, further reducing the complexity and cost investment in the construction process, greatly reducing the construction cost of charging facilities, and providing a more economical solution for the large-scale development of the charging industry, especially suitable for areas where land resources are scarce or power infrastructure reconstruction is difficult.

[0017] 2、The mobile charging robot in the application has a "one-to-many" service capability, and one robot can flexibly provide charging services for multiple parking spaces. Through the intelligent scheduling system, the robot can adjust the service route in real time according to the charging demand of the parking space, realize the optimal allocation of resources, break the limitation of traditional charging pile fixed service, and greatly improve the utilization rate of charging resources. During the peak period of charging demand, the robot can quickly respond to the charging request of multiple parking spaces, avoid the queuing phenomenon caused by insufficient charging piles, effectively shorten the user's charging waiting time, improve the overall charging service efficiency, and provide users with more convenient and efficient charging experience.

[0018] 4、In the traditional charging mode, electric vehicles often occupy the charging parking space for a long time due to various reasons after charging is completed, which causes other vehicles that need to be charged to be unable to use the parking space in time, affecting the normal operation of the charging facility and the parking order. The mobile charging robot of the application can automatically pull out the gun and leave the parking space after charging is completed, avoiding the situation of long-term occupation of the electric vehicle, so that the charging parking space can be released in time, improving the turnover rate of the parking space and optimizing the parking order of the parking lot, providing fair and reasonable charging opportunities for more users, especially suitable for urban areas and commercial parking lots where parking space resources are scarce.

[0019] 5、The mobile charging robot of the application has unique energy management advantages as a mobile energy storage unit. During the low power period or green power surplus period, the robot can concentrate on charging and store the excess power. During the power peak period or green power shortage period, the robot can provide charging services for electric vehicles, realizing the peak load shifting of power. Therefore, the application not only helps to balance the power grid load, improve the stability and operation efficiency of the power grid, but also promotes the consumption of renewable energy, reduces the dependence on traditional fossil energy, promotes the optimization of energy structure and sustainable development, and meets the strategic requirements of national energy saving and green development.

[0020] 6、The application adopts an innovative three-level battery system design, in which the main battery focuses on external power supply to provide stable and continuous power support for electric vehicles; the buffer capacitor can effectively suppress power fluctuations to ensure voltage and current stability during charging and avoid damage to electric vehicle batteries and charging equipment caused by power surges; and the driving battery provides power guarantee for the movement of the robot to ensure that the robot can flexibly and reliably travel in the parking lot. This three-level battery system has clear division of labor and works cooperatively, improving the reliability and stability of the system from multiple aspects, reducing charging interruptions and service abnormalities caused by battery failures or power fluctuations, and providing users with safer and more reliable charging services.

[0021] 7、The automatic docking structure of the application adopts a straight insertion type hard connection + guide groove design, abandoning the traditional complex mechanical arm docking method. The straight insertion type hard connection structure is simple, reducing the number and complexity of mechanical components and reducing the probability of failure. The guide groove design can provide accurate guidance for the docking of the robot, ensuring that the charging gun can be accurately and quickly inserted into the charging interface of the electric vehicle, greatly improving the success rate of power supply. This simple and reliable automatic docking structure not only reduces the manufacturing cost and maintenance difficulty of the equipment, but also improves the convenience and efficiency of the charging operation, providing users with a smoother charging experience.

[0022] 8、The intelligent charging system of the application has extremely high deployment flexibility and scalability. In the initial construction stage, the location and scale of the centralized charging station and the number of mobile charging robots can be flexibly determined according to actual needs and site conditions. As the business develops and the charging demand increases, only the mobile charging robots need to be added to easily improve the service capacity of the system without the need for large-scale modification of the existing power infrastructure. This flexible deployment and expansion method enables the application to quickly adapt to different scales and scenarios of charging demand, whether it is a small parking lot, a large commercial complex or a city public charging network, efficient and convenient deployment and application can be achieved, providing strong support for the diversified development of the charging industry.

[0023] The application will be further described in detail below in combination with the drawings and specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a schematic diagram of an embodiment of an intelligent charging system based on a mobile charging robot.

[0025] Figure 2 is a schematic diagram of the structure of a mobile charging robot in an embodiment of an intelligent charging system based on a mobile charging robot.

[0026] Figure 3 is a schematic diagram of a charging parking structure of an intelligent charging station in an embodiment of an intelligent charging system based on a mobile charging robot.

[0027] Figure 4 is a flowchart of an embodiment of an intelligent charging method based on a mobile charging robot. DETAILED DESCRIPTION

[0028] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0029] Reference to "an embodiment" herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive or alternative embodiments. It is explicitly stated that the described embodiments can be combined with other embodiments.

[0030] An embodiment of an intelligent charging system based on a mobile charging robot Referring to Figures 1 to 3 The embodiment provides an intelligent charging system based on a mobile charging robot, which comprises: a cloud scheduling center, a mobile charging robot cluster and an intelligent charging station network; Each mobile charging robot in the mobile charging robot cluster is provided with a three-level energy storage system composed of a main battery, a buffer battery and a driving battery, and is configured with a charging interface and an automatic docking power supply mechanism, which are used to provide charging services for electric vehicles; The intelligent charging station network is composed of a plurality of intelligent charging stations distributed in a parking lot, and provides centralized power supply services for the mobile charging robots. Each intelligent charging station is equipped with a plurality of charging slots, and each charging slot is provided with a charging interface and a guide device for docking with the mobile charging robot; The cloud scheduling center is deployed in the cloud or a local server, and is used to receive charging requests and parking space position information uploaded by users by scanning a unique two-dimensional code sprayed on the ground of a parking space. The parking space position information is provided by an RFID electronic tag embedded in each parking space for auxiliary positioning. An RFID tag is installed on each parking space, and the unique number (position information) of the parking space is stored in the tag. When a vehicle is parked in the parking space, the vehicle owner can bind the license plate and the parking space through an APP or the like (or the vehicle is recognized through a camera and the parking space is bound). When the charging robot passes near the parking space (for example, within 1-2 meters), it can read the RFID tag of the parking space, thereby confirming the position of the electric vehicle that needs to be charged when parking.

[0031] The cloud scheduling center further schedules the mobile charging robot to go to the parking space where the target vehicle is located to perform a charging task according to the RFID positioning information, the real-time state of the mobile charging robot and a scheduling algorithm, and plans an optimal path. The mobile charging robot cluster has an autonomous navigation function. After receiving a task instruction from the cloud dispatch center, the mobile charging robot cluster can autonomously move to a target parking space according to a planned path, perform a charging operation, and return to an idle state after the charging is completed, and report a task completion state and a remaining power to the cloud dispatch center; The cloud dispatch center is also used to manage resources of the intelligent charging station network, including monitoring of an occupancy state of a charging card position, publishing of idle card position information, and dispatching of the mobile charging robot for power replenishment according to a power level of the mobile charging robot and a subsequent task demand.

[0032] In the embodiment, the main battery (energy storage battery) is a 60-80 kWh lithium iron phosphate battery, which is used to provide charging power for an electric vehicle; the buffer battery (power type energy storage) is a 5-15 kWh super capacitor array, which is used to absorb regenerative braking energy and smooth charging and discharging peaks; and the drive battery (drive dedicated battery) is a 3-10 kWh ternary lithium battery, which is used to independently supply power to a robot moving and control system. It can be seen that the three-level energy storage structure of the embodiment realizes energy management layering, safety improvement, life extension, and system robustness enhancement.

[0033] In the embodiment, each charging card position in the intelligent charging station is further configured as: equipped with a communication module for real-time communication with the cloud dispatch center to report a use idle state and position information of the charging card position; provided with at least three infrared light sources, wherein a central infrared light source is a 360-degree omnidirectional infrared light source arranged on the top of the charging card position, and left and right infrared light sources are directional light sources arranged on the left and right sides below the top of the charging card position and emitting specified coded light, and the flashing of each light source contains preset information for guiding the mobile charging robot to position the charging card position; Through the central infrared light source, the mobile charging robot can quickly identify and determine the position of the charging card position; Using the left and right infrared light sources, the mobile charging robot can determine whether it deviates from the central axis of the return charging path in real time during the approach to the charging card position, and adjust accordingly to ensure accurate docking; A slightly sunken guide groove is arranged in front of each charging card position for guiding a limiting slider at the bottom of the mobile charging robot to accurately dock; Each charging card position has a unique information code, and the charging card position continuously reports its occupancy state to the cloud dispatch center, and the cloud dispatch center sends the information code and position of the idle charging card position to the mobile charging robot that needs power replenishment according to the information; After receiving the instruction from the cloud dispatch center, the mobile charging robot plans a path to the vicinity of the specified charging bay, first confirms the basic position of the charging bay through the central infrared light source, then fine-tunes the position through the left and right infrared light sources, until the bottom limit slider accurately enters the guide slot, then moves forward along the guide slot, inserts the charging gun into the charging port of the charging bay, completes the connection, and starts the charging operation.

[0034] The mobile charging robot adopts an omni-directional wheel structure, supporting at least four motion modes of forward movement, backward movement, lateral movement, and in-place rotation, to adapt to the movement requirements in narrow spaces or complex paths in the parking lot; The positioning system of the mobile charging robot adopts an RFID parking space tag and inertial navigation odometer fusion positioning method, wherein: by reading the RFID tag information buried in the parking space, the accumulated error generated by the inertial navigation odometer in the dead reckoning process is corrected; combining the absolute position information of the RFID tag and the relative displacement data of the inertial navigation odometer, the mobile charging robot achieves centimeter-level positioning accuracy in the parking lot environment; The communication module of the mobile charging robot integrates 4G / 5G, Wi-Fi, and Bluetooth communication functions, supporting real-time data interaction between the mobile charging robot and the cloud dispatch center, including at least position information reporting, task instruction receiving, state feedback, and path planning data transmission.

[0035] As can be seen, the movement and navigation system of the mobile charging robot of the embodiment includes: a mobile chassis: adopting an omni-directional wheel structure, supporting forward movement, backward movement, lateral movement, and in-place rotation, adapting to narrow parking environments; a positioning system: adopting RFID parking space tag + inertial navigation odometer (IMU + encoder) fusion positioning, correcting the dead reckoning error by reading the RFID tag of the parking space, achieving centimeter-level positioning accuracy; a communication module: supporting 4G / 5G, Wi-Fi, and Bluetooth, ensuring real-time communication with the cloud dispatch center.

[0036] In the embodiment, the charging interface and the automatic docking power supply mechanism include: Electric vehicle charging interface: equipped with a charging plug that meets the national standard DC fast charging interface standard (GB / T 20234.3), used for physical connection with the electric vehicle charging port and transmission of DC power; Automatic docking charging mechanism for robot self-power supply: Hard connection straight insertion type docking structure: adopting a rigid straight insertion design without complex mechanical arms, completing physical connection through the robot's own power drive; Limiting guide device: a limiting slider is arranged at the bottom of the robot, cooperating with the guide slot in front of the charging bay of the intelligent charging station, to realize accurate positioning in the horizontal and vertical directions; Wherein, the mobile charging robot travels to the vicinity of the target charging card position according to the path planned by the cloud scheduling center; the infrared receiving head array at the bottom is turned on to receive the coded spectrum light signals of at least three infrared light sources on the charging card position, and the final positioning stage is completed by decoding the light source information; the robot moves forward along the guide groove, and the mechanical constraint of the limit slider and the guide groove makes the charging plug of the robot automatically align with the charging slot of the charging card position; the robot inserts the charging plug into the charging slot by its own power to establish physical connection and start power compensation.

[0037] In the embodiment, each group of infrared light sources of the intelligent charging station modulates the light signals according to a preset coding rule, wherein: The central infrared light source emits a wide spectrum light signal containing the unique identification code of the charging card position, which is used for the robot to preliminarily identify the position; the left and right infrared light sources emit narrow spectrum light signals containing position correction information, and the coding spectrum dynamically changes with the angle of the robot deviating from the central axis; The bottom of the mobile charging robot is configured with multiple groups of infrared receiving head arrays, including: The central receiving array is used to receive the signal of the central infrared light source and parse the charging card position identification code from the signal; The left and right receiving units are respectively arranged on the left and right sides of the mobile charging robot and are used to receive the directional light source signals in the corresponding directions; Each receiving head is internally provided with a photoelectric conversion module and a digital signal processor to perform the following operations on the light signal: Photoelectric intensity threshold detection to filter out environmental light interference; Spectrum analysis and decoding to extract the position correction parameters carried in the light source coding; In the preliminary positioning stage, the mobile charging robot identifies the signal of the central infrared light source through the central receiving array to determine the rough position of the target charging card position; combined with the charging card position coordinate information issued by the cloud scheduling center, the initial docking path is planned; In the precise positioning stage, when the mobile charging robot enters the preset docking area of the charging card position (such as within 1 meter from the card position), the left and right receiving units are started; the coded spectrum signals of the left and right infrared light sources are analyzed in real time, and the following calculations are performed: The cross detection method is adopted to read the signal intensity IR of the left infrared light source at the right receiving unit and the signal intensity IL of the right infrared light source at the left receiving unit in real time; The intensity difference ΔI = |IL-IR| and the total intensity Isum = IL+IR are calculated to estimate the distance; Assuming the infrared light source is a Lambertian radiator, its radiation intensity decays with angle according to the cosine law. Under the condition of close distance and symmetrical layout, a two-dimensional lookup table or an analytical function model is established through calibration experiments or simulation. The lateral offset Δx and angle Δθ are inversely calculated using the ΔI+ geometric model or a lightweight regression model for real-time calculation. The control system looks up the table or infers through the analytical function model based on the current measurement to obtain the estimated Δx and Δθ; The Δx and Δθ are input into the PID controller to generate left and right wheel differential instructions, gradually reducing the lateral and angular errors. When the mobile charging robot meets the following conditions, the charging plug insertion action is started: The lateral offset Δx is less than or equal to 2 cm and the angular offset Δθ is less than or equal to 1°. The central infrared light source signal strength is stable (e.g., signal strength fluctuation range <10%); The cloud dispatch center feedback charging card state is "idle and can be docked"; The robot moves at a constant speed along the guide groove, and through the mechanical constraint of the limit slider and the guide groove, the charging plug is inserted vertically into the charging slot at a speed of ≤0.5 m / s.

[0038] In this embodiment, the mobile charging robot has a multi-modal sensor fusion obstacle avoidance system, which includes: At least 8 groups of ultrasonic sensors are arranged around the mobile charging robot, covering a horizontal detection range of 360°. The detection distance range is 0.1-2 m, with an accuracy of ±2 cm, which is used to detect the contour of static / dynamic obstacles in the near distance in real time. The flexible touch edge structure is wrapped around the robot shell, and a pressure-sensitive resistor array is built in. When the touch edge at any position is pressed beyond a preset threshold (e.g., 5N), an emergency stop signal is triggered and the power output is cut off. Local obstacle avoidance control module: based on the current speed v, angular velocity ω and acceleration constraint of the robot, a dynamic speed window is constructed. Combined with the real-time feedback of the ultrasonic sensor obstacle distance data, multiple candidate trajectories are sampled within the speed window. The optimal trajectory is selected through the evaluation function (including obstacle distance, target direction, and speed weight), and a real-time obstacle avoidance path is generated. Multi-sensor data fusion module: fuse the obstacle distance information of the ultrasonic sensor with the robot odometer data to construct a local occupancy grid map. In the grid map, mark the dynamic obstacle motion trend (such as the walking direction of pedestrians), and optimize the DWA trajectory evaluation weight. In addition, safety strategy hierarchical control is also implemented: Speed hierarchical mechanism: Normal cruising mode: max speed ≤ 0.8 m / s; obstacle detection mode: when the ultrasonic sensor detects an obstacle within 50 cm, automatically switch to medium speed mode (≤ 0.5 m / s); vehicle approach mode: when the robot is ≤ 1.5 m away from the target parking space, force into low speed mode (≤ 0.3 m / s); Emergency stop protection strategy: When the collision sensor is triggered, the following actions are immediately performed: Cut off power supply to all drive motors; start mechanical braking device (such as electromagnetic brake); report abnormal state to cloud dispatch center through communication module; after emergency stop, manual reset or receive remote unlocking instruction from cloud dispatch center to resume operation; Obstacle classification response: For static obstacles (such as walls, pillars): keep a safe distance (≥ 30 cm) and detour; for dynamic obstacles (such as pedestrians, vehicles): predict the motion trajectory and use pause-wait or change direction avoidance strategy; Ultrasonic sensor anti-interference design: Use 40 kHz ± 1 kHz frequency modulation signal to avoid interference from same frequency environmental noise; use temperature compensation algorithm to correct the influence of sound speed variation on distance measurement (error compensation range -20℃ ~ +60℃).

[0039] An embodiment of an intelligent charging method based on a mobile charging robot As shown in Figure 4 The embodiment provides an intelligent charging method based on a mobile charging robot, which uses the intelligent charging system based on a mobile charging robot described above. The method includes the following steps: User request stage: after the user parks the electric vehicle in the parking space, scans the unique two-dimensional code sprayed on the ground of the parking space through the intelligent terminal, enters the dedicated charging App or mini program, submits the charging request and automatically uploads the location information of the parking space to the cloud dispatch center; Task scheduling and path planning stage: after the cloud dispatch center receives the charging request, combines the current position, power, and task queue of the mobile charging robot, uses an optimization scheduling algorithm to select the optimal mobile charging robot from the mobile charging robot cluster, and plans the optimal path for the robot to go to the target parking space; Robot navigation and obstacle avoidance stage: the selected mobile charging robot starts, performs autonomous navigation through the mobile and navigation system, goes to the target vehicle parking space according to the planned path, and detects and avoids obstacles in real time on the way; Charging preparation and execution stage: after the mobile charging robot arrives at the target parking space, the user takes out the charging gun from the robot, connects it to the vehicle charging port, and confirms the start of charging in the charging App or mini program, and the mobile charging robot starts charging the electric vehicle. Charging completion and settlement phase: after charging is completed, the user pulls out the charging gun and returns it to its original position, and clicks on the charging end confirmation in the charging App or mini program to complete the fee settlement; Task completion report and subsequent scheduling phase: the mobile charging robot reports the task completion status and the current remaining power to the cloud scheduling center, and the cloud scheduling center schedules the robot to continue to perform other charging tasks or return to the nearest intelligent charging station for power replenishment according to the power level of the mobile charging robot, subsequent task demand, and the idle condition of the intelligent charging station.

[0040] In the task scheduling and path planning phase, an improved Hungarian algorithm is used to realize dynamic matching of multiple robots and multiple tasks, and the cost function is defined as: wherein, is the Euclidean distance from the robot i to the parking space where the task j is located; is the current power of the robot, is the full power; is the waiting time of the task j ; is the task priority coefficient (such as higher weight for emergency charging tasks); , , , is a dynamic weight factor, which is automatically adjusted by the cloud scheduling center according to the real-time scene (such as peak period / low period); In the robot navigation and obstacle avoidance phase, a weighted grid map is constructed based on the parking lot BIM model, wherein the obstacle region is marked as a high-cost area and the charging parking space is marked as a target point; An A* algorithm is used to generate an initial path, and the cost function is:

[0041] wherein, is the distance from the node n to the target point, is the expected power consumption of the remaining section of the path, , is the weight coefficient; Combined with the real-time feedback of the obstacle information of the ultrasonic sensor, a dynamic obstacle avoidance window (radius 2m) is set on the global path; when an obstacle is detected, a local detour path is generated within the window using the DWA algorithm, and the transition back to the global path is smoothed; In this embodiment, a valley electricity period power replenishment strategy is also implemented: Access to time-of-use electricity price API, when detecting that the electricity price is lower than the preset threshold (such as 0.3 yuan / kWh): Screen idle robots with less than 80% battery level; generate a power-up queue based on the "lowest battery level first" principle; plan the shortest path to the nearest intelligent charging station and start power-up; When the proportion of renewable energy generation (such as photovoltaic / wind power) exceeds 30% of the total power supply of the power grid: Automatically activate the green power charging protocol; prioritize scheduling robots to charging stations that support green power access; display the "green power charging" logo on the robot-human interaction interface; In this embodiment, historical data-driven prediction is performed: Build an LSTM neural network model, input parameters include: Charging request frequency of historical period (such as weekdays / weekends); Parking lot surrounding event information (such as concert / sports event start time); Weather data (such as rainfall and charging demand correlation); Output a hotspot area prediction map within the next 2 hours, with a resolution of 5m x 5m; Pre-deploy execution strategy: When the predicted demand probability of a certain area exceeds 60%: Schedule the nearest 3 idle robots to the area to stay ahead; The robot enters "standby power saving mode" (power consumption reduced by 40%); Continuously update the prediction results through the cloud scheduling center and dynamically adjust the robot's position; All modules communicate with the cloud scheduling center in real time through the MQTT protocol, with a data update frequency of ≥1Hz; When there is a conflict between task allocation, path planning or charging scheduling strategy, execute in order of priority: safety strategy> emergency charging task> green power strategy> regular task.

[0042] After the mobile charging robot completes charging service for an electric vehicle, it reports the task completion status and the current remaining power to the cloud scheduling center; the cloud scheduling center determines whether the robot performs the next charging task or returns to the nearest intelligent charging station for self power-up based on the remaining power, the urgency of the next charging request, and the valley electricity period arrangement.

[0043] When the cloud scheduling center determines that the robot's remaining power is less than the first preset threshold (such as 30%), it is scheduled to return to the intelligent charging station for power-up first; when the power is between the first threshold and the second preset threshold (such as 60%), and the next task is close, it is allowed to continue to perform the task and then power up.

[0044] Wherein, the intelligent charging station is equipped with multiple charging card positions; each charging card position is equipped with a communication module, responsible for communicating with the cloud dispatch center about its idle state and position information; three infrared light sources are equipped, the top one is a 360-degree omnidirectional light source, and two light sources are arranged below it, emitting two light beams on the left and right sides; a certain code is applied to each light source, that is, the light source flashes each time containing a certain information.

[0045] Through the omnidirectional light source at the top, the robot can quickly know the orientation of the charging seat; the two light beams below, one left and one right, are used to let the robot know whether it has deviated from the central axis of the return charging path, so as to guide it to the return charging path in time; there is a slightly sunken guide groove in front of each charging card position; each charging card position has its own unique information code; the charging card position continuously reports its occupancy, the cloud dispatch center sends the idle charging card position information code and position information to the mobile charging robot that needs to charge, the mobile charging robot travels to the specified charging card position through path planning; the mobile charging robot first receives the top omnidirectional light source signal to confirm the basic orientation of the charging card position, and adjusts its position through the left and right light source signals, and finally aligns itself, accurately docking the bottom limit slider into the guide groove set in front of the charging card position; the mobile charging robot moves forward along the guide groove, inserts the charging gun into the charging port of the charging card position, and after the docking is completed, the charging operation starts.

[0046] The charging request received by the cloud dispatch center contains the following fields: Parking area number (such as B2 layer); Parking space type identification (ordinary / disabled / charging dedicated parking space); Parking space unique number; User scan timestamp; The cloud dispatch center accurately locates and plans the path according to the multi-field position information and the parking lot topology structure.

[0047] The cloud dispatch center predicts the charging demand hot area of the future period based on historical charging data, and deploys robots to the area in advance to shorten the response time.

[0048] In practical application, taking a large commercial complex underground parking lot as an example: There are 600 parking spaces in the parking lot, 12 intelligent charging stations are set (one for every 50 parking spaces), and 30 mobile charging robots are deployed; After the user parks, he scans the ground QR code, and the App automatically reports the B2-88 charging request; The cloud dispatch system selects the nearest and sufficient robot R7, plans the path and issues instructions; The robot R7 is navigated to the target parking space through inertial navigation + RFID positioning, and pedestrians and temporary obstacles are avoided on the way; After arriving, the user takes out the charging gun carried by the mobile robot, connects to the vehicle charging port, and starts charging after confirming in the App; After charging is completed, the user pulls the gun back to its original position, clicks the charging end confirmation in the charging App, and completes the settlement; the system sends it to the C area charging station to recharge; The robot R7 slides into the charging station through the guide groove, automatically completes the physical connection and communication handshake, and starts charging; The whole process does not need manual intervention, and the user can check the progress and complete the payment through the App.

[0049] In summary, the intelligent charging system based on the mobile charging robot only needs to build a small number of centralized charging stations for the mobile charging robot to recharge, which greatly reduces the number of charging piles and the cost of equipment procurement. At the same time, the construction of centralized charging stations is relatively unified and standardized, which is conducive to simplifying the layout of lines and the planning of power facilities, further reducing the complexity and cost investment in the construction process, greatly reducing the construction cost of charging facilities, and providing a more economical solution for the large-scale development of the charging industry. It is especially suitable for areas where land resources are scarce or power infrastructure reconstruction is difficult.

[0050] Further, the mobile charging robot in the present application has a "one-to-many" service capability, and one robot can flexibly provide charging services for multiple parking spaces. Through the intelligent scheduling system, the robot can adjust the service route in real time according to the charging demand of the parking space, realize the optimal allocation of resources, break the limitation of traditional fixed service of charging piles, and greatly improve the utilization rate of charging resources. During the peak period of charging demand, the robot can quickly respond to the charging requests of multiple parking spaces, avoid the queuing phenomenon caused by insufficient charging piles, effectively shorten the waiting time of users, improve the overall charging service efficiency, and provide users with a more convenient and efficient charging experience.

[0051] Further, the mobile charging robot of the present application can automatically pull out the gun and leave the parking space after charging is completed, avoiding the situation of long-term occupation of the charging vehicle, so that the charging parking space can be released in time, improving the turnover rate of the parking space, optimizing the parking order of the parking lot, and providing more users with fair and reasonable charging opportunities. It is especially suitable for urban areas and commercial parking lots where parking space resources are scarce.

[0052] Further, the mobile charging robot of the present application serves as a mobile energy storage unit, with unique energy management advantages. During periods of low electricity demand or surplus green electricity, the robot can concentrate on charging and store excess electricity. During periods of high electricity demand or insufficient green electricity supply, the robot can provide charging services for electric vehicles, achieving peak load shifting of electricity. Therefore, the present application not only helps to balance the load of the power grid, improve the stability and efficiency of the power grid, but also promotes the consumption of renewable energy, reduces the dependence on traditional fossil energy, promotes the optimization and sustainable development of energy structure, and meets the strategic requirements of national energy conservation and green development.

[0053] Further, the present application adopts an innovative three-level battery system design, in which the main battery focuses on external power supply to provide stable and continuous power support for electric vehicles; the buffer capacitor can effectively smooth power fluctuations to ensure voltage and current stability during charging, avoiding damage to electric vehicle batteries and charging equipment caused by sudden power changes; the drive battery provides power support for the movement of the robot to ensure that the robot can flexibly and reliably travel within the parking lot. This three-level battery system has clear division of labor and works in coordination, improving the reliability and stability of the system from multiple aspects, reducing charging interruptions and service abnormalities caused by battery failures or power fluctuations, and providing safer and more reliable charging services for users.

[0054] Further, the automatic docking structure of the present application adopts a straight insertion hard connection + guide groove design, abandoning the traditional complex mechanical arm docking method. The straight insertion hard connection structure is simple, reducing the number and complexity of mechanical components and reducing the probability of failure; the guide groove design can provide accurate guidance for the docking of the robot, ensuring that the charging gun can be accurately and quickly inserted into the charging interface of the electric vehicle, greatly improving the success rate of power replenishment. This simple and reliable automatic docking structure not only reduces the manufacturing cost and maintenance difficulty of the equipment, but also improves the convenience and efficiency of charging operations, providing users with a smoother charging experience.

[0055] Further, the intelligent charging system of the present application has high deployment flexibility and scalability. In the initial construction phase, the location and scale of centralized charging stations and the number of mobile charging robots can be flexibly determined according to actual needs and site conditions. As the business develops and charging demand increases, the service capacity of the system can be easily improved by simply adding mobile charging robots, without the need for large-scale modification of existing power infrastructure. This flexible deployment and expansion method enables the present application to quickly adapt to different scales and scenarios of charging demand, whether it is a small parking lot, a large commercial complex, or a city public charging network, achieving efficient and convenient deployment and application, providing strong support for the diversified development of the charging industry.

[0056] Any technical features in the above embodiments can be combined, and for the sake of brevity, not every possible combination is described above. However, any combination of the technical features described above is considered to be within the scope of the disclosure.

[0057] The above embodiments are merely preferred embodiments of the present application, and cannot be used to limit the protection scope of the present application. Any non-essential changes and replacements made by those skilled in the art based on the present application shall fall within the protection scope of the present application.

Claims

1. An intelligent charging system based on a mobile charging robot, characterized in that, This includes a cloud dispatch center, a cluster of mobile charging robots, and a network of smart charging stations; Each mobile charging robot in the mobile charging robot cluster has a three-level energy storage system consisting of a main battery, a buffer battery, and a drive battery, and is equipped with a charging interface and an automatic docking and charging mechanism to provide charging services for electric vehicles. The intelligent charging station network is distributed in the parking lot to provide centralized charging services for the mobile charging robot. Each intelligent charging station is equipped with multiple charging slots, and each charging slot is equipped with a charging interface and a guide device that can be connected to the mobile charging robot. The cloud dispatch center is deployed on the cloud or a local server to receive charging requests and parking space location information uploaded by users by scanning a unique QR code painted on the ground of the parking space. The parking space location information is assisted by RFID electronic tags embedded in each parking space. The cloud dispatch center further dispatches the mobile charging robot to the parking space where the target vehicle is located to perform the charging task based on the RFID location information, the real-time status of the mobile charging robot and the dispatch algorithm, and plans the optimal path. The mobile charging robot cluster has autonomous navigation capabilities. After receiving the task instruction from the cloud dispatch center, it can autonomously move to the target parking space according to the planned path, perform the charging operation, and return to the idle state after charging is completed, and report the task completion status and remaining power to the cloud dispatch center. The cloud dispatch center is also used to manage the resources of the smart charging station network, including monitoring the occupancy of charging slots, publishing information on available slots, and scheduling the mobile charging robots to perform recharging operations based on their power levels and subsequent task requirements.

2. The system according to claim 1, characterized in that: The main battery is a 60-80kWh lithium iron phosphate battery, used to provide charging energy for electric vehicles; the buffer battery is a 5-15kWh supercapacitor array, used to absorb regenerative braking energy and smooth out charging and discharging peaks; the drive battery is a 3-10kWh ternary lithium battery, which independently powers the mobile charging robot's movement and control system.

3. The system according to claim 1, characterized in that, Each charging slot in the intelligent charging station is further configured as follows: Equipped with a communication module for real-time communication with the cloud dispatch center, reporting the usage and availability status and location information of the charging slots; There are at least three infrared light sources: a central infrared light source, a left infrared light source, and a right infrared light source. The central infrared light source is a 360-degree omnidirectional infrared light source located at the top of the charging slot. The left and right infrared light sources are directional light sources located on the left and right sides below the top of the charging slot, emitting specified coded light. The flashing of each light source contains preset information to guide the mobile charging robot to locate the charging slot. The mobile charging robot can quickly identify and determine the location of the charging slot using a central infrared light source. Using infrared light sources on the left and right, the mobile charging robot can determine in real time whether it has deviated from the central axis of the return charging path as it approaches the charging position, and make adjustments accordingly to ensure accurate docking. Each charging slot is provided with a guide groove in front of it to guide the limiting slider at the bottom of the mobile charging robot to accurately dock; Each charging slot has a unique information code. The charging slot continuously reports its occupancy status to the cloud dispatch center. Based on this information, the cloud dispatch center sends the information code and location of the vacant charging slot to the mobile charging robot that needs to be recharged. After receiving instructions from the cloud dispatch center, the mobile charging robot plans a path to the vicinity of the designated charging station. First, it confirms the basic orientation of the charging station using the central infrared light source, and then fine-tunes the position using the left and right infrared light sources until the bottom limit slider accurately enters the guide groove. Subsequently, it moves forward along the guide groove, inserts the charging gun into the charging port of the charging station, and begins the charging operation after completing the docking.

4. The system according to claim 1, characterized in that: The mobile charging robot adopts an omnidirectional wheel structure and supports at least four movement modes: forward, backward, lateral movement, and rotation in place, in order to adapt to the movement needs in narrow spaces or complex paths within parking lots. The positioning system of the mobile charging robot adopts a fusion positioning method of RFID parking space tags and inertial navigation odometry. Specifically, by reading the information of RFID tags embedded in the parking space, the cumulative error generated by the inertial navigation odometry during dead reckoning is corrected. By combining the absolute position information of the RFID tags with the relative displacement data of the inertial navigation odometry, the mobile charging robot achieves centimeter-level positioning accuracy in the parking lot environment. The communication module of the mobile charging robot integrates 4G / 5G, Wi-Fi and Bluetooth communication functions, and supports real-time data interaction between the mobile charging robot and the cloud dispatch center, including at least location information reporting, task instruction reception, status feedback and path planning data transmission.

5. The system according to claim 3, characterized in that, The charging interface and automatic docking and power replenishment mechanism include: Electric vehicle charging interface: Equipped with a charging plug that conforms to the national standard DC fast charging interface, used to physically connect to the electric vehicle charging port and transmit DC power; An automatic docking and charging mechanism for the mobile charging robot to replenish its own power: Hard-connect direct-insertion docking structure: It adopts a rigid direct-insertion design without complex robotic arms, and completes the physical connection through the power drive of the mobile charging robot itself; Limiting and guiding device: A limiting slider is set at the bottom of the mobile charging robot, which cooperates with the guide groove in front of the charging slot of the intelligent charging station to achieve precise positioning in the horizontal and vertical directions. The mobile charging robot travels along the path planned by the cloud dispatch center to the vicinity of the target charging station, activates the infrared receiver array at its bottom, and receives the coded spectrum light signals from at least three infrared light sources on the charging station. It then completes the final positioning stage by decoding the light source information. The mobile charging robot moves forward along the guide groove, and uses the mechanical constraint between the limiting slider and the guide groove to automatically align its charging plug with the charging slot of the charging station. The mobile charging robot then uses its own power to insert the charging plug into the charging slot, establish a physical connection, and start charging.

6. The system according to claim 5, characterized in that: The infrared light sources in the intelligent charging station modulate the light signal using a preset encoding rule, wherein: The central infrared light source emits a wide-spectrum light signal containing a unique identifier for the charging slot, which is used for the initial orientation identification of the mobile charging robot; the left and right infrared light sources emit narrow-spectrum light signals containing position correction information, and their encoded spectrum dynamically changes with the angle of the mobile charging robot's deviation from the central axis. The mobile charging robot is equipped with multiple infrared receiver arrays on its bottom, including: Central receiving array: used to receive signals from the central infrared light source and decode the charging card slot identification code from the signal; Left receiving unit and right receiving unit: respectively arranged on the left and right sides of the mobile charging robot, used to receive directional light source signals in the corresponding directions; Each receiver head has a built-in photoelectric conversion module and a digital signal processor to process the optical signal. Photoelectric intensity threshold detection to filter out ambient light interference; Spectrum analysis and decoding are used to extract the orientation correction parameters carried in the light source encoding.

7. The system according to claim 6, characterized in that: In the initial positioning stage: the mobile charging robot identifies the signal of the central infrared light source through the central receiving array to determine the approximate location of the target charging station; Based on the charging station coordinates information issued by the cloud dispatch center, an initial docking path is planned; During the precise positioning phase: when the mobile charging robot enters the preset docking area of ​​the charging station, the left and right receiving units are activated; the coded spectrum signals of the left and right infrared light sources are analyzed in real time, and calculations are performed. The cross-detection method is used to read the signal strength IR of the left infrared light source in the right receiving unit and the signal strength IL of the right infrared light source in the left receiving unit in real time. Calculate the intensity difference ΔI=|IL-IR| and the total intensity Isum=IL+IR to estimate the distance; Assuming the infrared source is a Lambertian radiator, and its radiation intensity decays with angle according to a cosine law, an approximation is made under close-range and symmetrical layout. Through calibration experiments or simulations, a two-dimensional lookup table or analytical function model is established. The ΔI+ geometric model is used to back-calculate the lateral offset Δx and angle Δθ, or a lightweight regression model is used for real-time calculation. The control system obtains the estimated Δx and Δθ by looking up a table based on the current measured values ​​or by reasoning through an analytical function model. Inputting Δx and Δθ into the PID controller generates differential commands for the left and right wheels, gradually reducing lateral and angular errors. The mobile charging robot initiates the charging plug insertion action when the following conditions are met: The lateral offset Δx ≤ 2cm and the angular offset Δθ ≤ 1°; The signal strength of the central infrared light source remained consistently stable. The cloud dispatch center reports that the charging slot status is "idle and available for connection"; The mobile charging robot moves forward at a constant speed along the guide groove. Through the mechanical constraint between the limiting slider and the guide groove, it ensures that the charging plug is inserted vertically into the charging slot at a set speed.

8. The system according to any one of claims 1 to 7, characterized in that, The mobile charging robot has a multimodal sensor fusion obstacle avoidance system, which includes: At least eight sets of ultrasonic sensors are arranged around the mobile charging robot, covering a 360° horizontal detection range, for real-time perception of the outlines of near static / dynamic obstacles. The robot's shell is wrapped with a flexible contact edge structure and has a built-in pressure-sensitive resistor array; when the pressure on any contact edge exceeds a preset threshold, an emergency stop signal is immediately triggered and the power output is cut off. A local obstacle avoidance control module is set up, which constructs a dynamic velocity window based on the robot's current velocity v, angular velocity ω, and acceleration constraints; combined with obstacle distance data fed back in real time by ultrasonic sensors, multiple sets of candidate trajectories are sampled within the velocity window; the optimal trajectory is selected through an evaluation function to generate a real-time obstacle avoidance path; A multi-sensor data fusion module is set up to fuse obstacle distance information from ultrasonic sensors with robot odometry data to construct a local occupancy grid map; dynamic obstacle movement trends are marked in the grid map to optimize the DWA trajectory evaluation weights.

9. A smart charging method based on a mobile charging robot, characterized in that, This method employs the intelligent charging system based on a mobile charging robot as described in any one of claims 1 to 8, and includes the following steps: User request stage: After parking their electric vehicle in the parking space, users can scan the unique QR code sprayed on the ground of the parking space through their smart terminal to enter the dedicated charging App or mini-program, submit a charging request, and automatically upload the location information of their parking space to the cloud dispatch center. Task scheduling and path planning stage: After receiving a charging request, the cloud scheduling center combines the current location, power level, and task queue status of the mobile charging robot, and uses an optimized scheduling algorithm to select the optimal mobile charging robot from the mobile charging robot cluster, and plans the optimal path for the robot to the target parking space. Robot navigation and obstacle avoidance phase: The selected mobile charging robot starts up and navigates autonomously through the mobility and navigation system, heading to the parking space of the target vehicle along the planned path, using sensors to detect and avoid obstacles in real time along the way; Charging preparation and execution phase: After the mobile charging robot arrives at the target parking space, the user takes the charging gun from the robot, connects it to the vehicle's charging port, and confirms the start of charging in the charging app or mini-program. The mobile charging robot then begins charging the electric vehicle. Charging completion and settlement stage: After charging is completed, the user unplugs the charging gun and puts it back in its place, and clicks the charging end confirmation in the charging App or mini program to complete the payment settlement. Task completion report and subsequent scheduling phase: The mobile charging robot reports the task completion status and current remaining power to the cloud scheduling center. Based on the mobile charging robot's power level, subsequent task requirements, and the availability of smart charging stations, the cloud scheduling center schedules the robot to continue performing other charging tasks or return to the nearest smart charging station for power replenishment.

10. The method according to claim 9, characterized in that: In the task scheduling and path planning phase, an improved Hungarian algorithm is used to achieve dynamic matching of multiple robots and multiple tasks, where the cost function is defined as: in, For robots i To the mission j Euclidean distance to the parking space; This is the robot's current battery level. Fully charged; For the task j Waiting time; This is the task priority coefficient; , , , It is a dynamic weighting factor that is automatically adjusted by the cloud scheduling center according to the real-time scenario; In the robot navigation and obstacle avoidance phase, a weighted grid map is constructed based on the parking lot BIM model, where obstacle areas are marked as high-cost areas and charging spaces are marked as target points. The initial path is generated using the A* algorithm, with the following cost function: in, For nodes n Distance to the target point The expected power consumption for the remaining segment of the path. , These are the weighting coefficients; By combining obstacle information fed back in real time by ultrasonic sensors, a dynamic obstacle avoidance window is set on the global path; when an obstacle is detected, a local detour path is generated within the window using the DWA algorithm, and then smoothly transitions back to the global path.

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