Automatic charging control method and system for autonomous vehicle

Abstract

The application discloses an automatic charging control method and system of an automatic driving vehicle, wherein the method comprises the following steps: when the residual power of the vehicle is lower than a preset power supplement threshold, sending a power supplement request to a cloud intelligent scheduling platform; the cloud intelligent scheduling platform determines a target charging station by combining the vehicle position, the station state information of the candidate charging station and the green energy output information, and guides the vehicle to automatically park; after the vehicle is parked, the identity is verified through the license plate information and the vehicle-mounted Bluetooth authentication information, the charging port cover is opened after the verification is passed, and the mechanical arm is controlled to complete the docking of the charging gun; after the docking is completed and the charging request is received, the vehicle charging path is switched to, the green energy is preferentially used for power supply, and the power supply is supplemented by the energy storage device and the public power grid when the green energy is insufficient; after the charging is completed, the power is automatically cut off, the gun is reset, the charging port cover is closed, the settlement information is generated, and the vehicle is controlled to drive away. The application improves the automation degree, continuity and green energy utilization efficiency of the power supplement process of the automatic driving vehicle.

Description

Technical Field

[0001] This invention relates to the field of automatic charging technology for autonomous vehicles, and particularly to an automatic charging control method, system, computer equipment, and storage medium for autonomous vehicles. Background Technology

[0002] With the development of autonomous driving and new energy vehicle technologies, the application of autonomous electric vehicles in scenarios such as park shuttles, unmanned delivery, and limited-area travel is gradually increasing. Unlike traditional vehicles where recharging is done by human intervention, autonomous vehicles place higher demands on the automation, continuity, and coordination of the recharging process. If, after the vehicle's battery level drops, it still requires manual intervention to locate a charging station, verify the vehicle's identity, open the charging port cover, plug or unplug the charging gun, or switch the power supply mode, then although the autonomous vehicle can drive autonomously, its recharging process will still be difficult to eliminate its dependence on manual operation, making it difficult to adapt to the actual needs of unmanned operation scenarios.

[0003] Existing charging stations typically provide basic charging services primarily for manned vehicles. The processes involved in vehicle refueling—including station selection, vehicle parking, identity verification, charging port opening, charging gun docking, power supply control, and settlement and departure after charging—are often fragmented, lacking a continuous control mechanism tailored to the refueling needs of autonomous vehicles. Meanwhile, with the increasing application of photovoltaic, wind power, and energy storage devices in charging stations, while these stations possess some green energy supply and distribution capabilities, current technologies typically maintain a relatively independent power supply and distribution level, failing to effectively link with the vehicle refueling process. This often results in low automation levels, insufficient refueling continuity, and poor matching between station energy supply and vehicle refueling demands. Summary of the Invention

[0004] The purpose of this application is to propose an automatic charging control method, system, computer equipment, and storage medium for autonomous vehicles, so as to realize continuous control of the charging process of autonomous vehicles and improve the charging efficiency of charging stations and the level of green energy utilization.

[0005] To address the aforementioned technical problems, this application provides an automatic charging control method for autonomous vehicles, employing the following technical solution: Obtain the remaining battery power information of the target vehicle, and when the remaining battery power information is lower than a preset charging threshold, send a charging request to the cloud intelligent dispatching platform; The cloud-based intelligent dispatch platform determines the target charging station based on the location information of the target vehicle, the station status information and green energy output information of the candidate charging stations, and issues a navigation entry command to the target vehicle. The system controls the target vehicle to drive autonomously to the charging station of the target charging station, and after detecting that the target vehicle has entered the station, it collects the vehicle's license plate information and vehicle Bluetooth authentication information for identity verification. After identity verification is passed, a charging port cover opening command is sent to the target vehicle, and after the charging port cover is detected to be open, the robotic arm is controlled to dock the charging gun to the charging port of the target vehicle according to the visual positioning result. After detecting that the charging gun has completed docking and receiving the charging request from the target vehicle, the green energy allocation module is controlled to switch to the vehicle charging path and prioritize the use of green energy to supply power to the target vehicle. When green energy is insufficient, at least one of the energy storage device and the public power grid is controlled to supplement the power supply. Upon detection that charging is complete, the system stops supplying power, controls the robotic arm to remove and reset the charging gun, closes the charging port cover of the target vehicle, generates charging settlement information, and controls the target vehicle to leave the target charging station.

[0006] To address the aforementioned technical problems, this application also provides an automatic charging control system for autonomous vehicles, employing the following technical solution: The acquisition module is used to acquire the remaining battery power information of the target vehicle, and send a power replenishment request to the cloud intelligent dispatch platform when the remaining battery power information is lower than a preset power replenishment threshold. The determination module is used by the cloud intelligent scheduling platform to determine the target charging station based on the location information of the target vehicle, the station status information and green energy output information of the candidate charging stations, and to issue a navigation entry command to the target vehicle. The first control module is used to control the target vehicle to drive autonomously to the charging station of the target charging station, and after detecting that the target vehicle has entered the station, to collect the license plate information and vehicle Bluetooth authentication information of the target vehicle for identity verification. The second control module is used to send a charging port cover opening command to the target vehicle after the identity verification is passed, and after detecting that the charging port cover is open, control the robotic arm to dock the charging gun to the charging port of the target vehicle according to the visual positioning result. The third control module is used to control the green energy allocation module to switch to the vehicle charging path after detecting that the charging gun has completed docking and receiving the charging request from the target vehicle, and to give priority to using green energy to supply power to the target vehicle. When green energy is insufficient, it controls at least one of the energy storage device and the public power grid to supplement the power supply. The fourth control module is used to control the power supply to stop after detecting that charging is complete, control the robotic arm to pull out the charging gun and reset it, close the charging port cover of the target vehicle, generate charging settlement information, and control the target vehicle to leave the target charging station.

[0007] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution: A computer device includes a memory and a processor, the memory storing computer-readable instructions, the processor executing the computer-readable instructions to implement the steps of the automatic charging control method for an autonomous vehicle as described above.

[0008] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below: A computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the automatic charging control method for an autonomous vehicle as described above.

[0009] Compared with the prior art, the embodiments of this application have the following main advantages: The automatic charging control method for autonomous vehicles disclosed in this application connects the following steps into a continuous automated process: charging triggering, charging station selection, automatic positioning, identity verification, charging cover opening and docking, power supply switching, and settlement and departure. This enables autonomous vehicles to complete charging operations without human intervention. By coordinating control between the vehicle and the charging station, the efficiency losses caused by manually plugging and unplugging the charging gun, manually verifying the vehicle's identity, and manually switching the power supply method can be reduced, thereby improving the continuity of charging for autonomous vehicles and the efficiency of charging station utilization in actual operating scenarios. Attached Figure Description

[0010] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of one embodiment of the automatic charging control method for an autonomous vehicle according to this application; Figure 2 This is a schematic diagram of the structure of an embodiment of the automatic charging control system for an autonomous vehicle according to this application; Figure 3 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0013] refer to Figure 1 A flowchart of an embodiment of an automatic charging control method for an autonomous vehicle according to this application is shown. The automatic charging control method for the autonomous vehicle includes the following steps: Step S101: Obtain the remaining battery power information of the target vehicle, and when the remaining battery power information is lower than the preset charging threshold, send a charging request to the cloud intelligent dispatching platform; Step S102: The cloud intelligent scheduling platform determines the target charging station based on the location information of the target vehicle, the station status information and green energy output information of the candidate charging stations, and sends a navigation entry command to the target vehicle. Step S103: Control the target vehicle to drive to the charging station of the target charging station, and after detecting the target vehicle entering the station, collect the vehicle license plate information and vehicle Bluetooth authentication information of the target vehicle for identity verification. Step S104: After identity verification is passed, a charging port cover opening command is sent to the target vehicle, and after the charging port cover is detected to be open, the robotic arm is controlled to dock the charging gun to the charging port of the target vehicle according to the visual positioning result. Step S105: After detecting that the charging gun has completed docking and receiving the charging request from the target vehicle, control the green energy allocation module to switch to the vehicle charging path and prioritize the use of green energy to supply power to the target vehicle. When green energy is insufficient, control at least one of the energy storage device and the public power grid to supplement the power supply. Step S106: After detecting that charging is complete, control to stop power supply, control the robotic arm to pull out the charging gun and reset, close the charging port cover of the target vehicle, generate charging settlement information, and control the target vehicle to drive away from the target charging station.

[0014] This application provides an automatic charging control method for autonomous vehicles, applicable to unmanned charging scenarios where autonomous vehicles and smart charging stations cooperate. The autonomous vehicle side can be configured with a battery management unit, an autonomous driving control unit, a positioning and navigation unit, an in-vehicle Bluetooth communication unit, and an electric opening and closing mechanism for the charging port cover. The smart charging station side can be configured with a cloud-based intelligent dispatching platform, a charging pile camera, a Bluetooth communication link, a robotic arm, a green energy allocation module, an energy storage device, and a public grid interface. The charging pile camera can be installed at the bottom and top of the charging pile to capture images of the vehicle entering its position and its license plate. The robotic arm can be a six-degree-of-freedom collaborative arm, with a charging gun and a visual positioning module installed at its end. The green energy allocation module connects to the photovoltaic power generation unit, wind power generation unit, energy storage device, and public grid within the station to perform switching control between different power supply paths. By pre-configuring the execution components on both the vehicle and station sides, a unified hardware foundation can be provided for the subsequent unmanned charging process, thus avoiding the need for manual intervention during charging. As an optional setting, the preset energy replenishment threshold can be set to about 10%, the docking accuracy of the robotic arm can be controlled within ±2 millimeters, and the power supply path switching response time can be controlled within one second.

[0015] During the operation of an autonomous vehicle, the onboard battery management unit continuously outputs information about the remaining battery charge. This remaining charge information can be represented by the State of Charge (SOC) value or converted into remaining driving range based on the vehicle's current energy consumption. When the remaining charge is detected to be below a preset charging threshold, the onboard controller sends a charging request to the cloud-based intelligent dispatch platform. Upon receiving the charging request, the cloud-based intelligent dispatch platform, based on the target vehicle's current location, retrieves the status information and green energy output information of multiple candidate charging stations, determines the target charging station, and then issues a navigation entry command to the target vehicle, enabling the vehicle to autonomously proceed to the corresponding station for charging. The station status information mainly reflects whether the candidate charging station currently meets the conditions for receiving vehicles for charging, such as whether there are available work bays, whether the equipment is functioning properly, and whether the station is in an available state; the green energy output information is used to characterize the available power of wind and solar power within the station at the current time. In this way, the energy replenishment demand, the station's carrying capacity, and the energy supply capacity are all taken into consideration during the station selection stage, rather than making decisions solely based on the closest distance. This helps reduce situations where vehicles wait at the station, arrive at the wrong station, or fail to make full use of green energy.

[0016] Furthermore, when determining the target charging station, the cloud-based intelligent dispatch platform can obtain information on available charging bays, navigation routes, and green energy output from each candidate charging station, and conduct a comprehensive evaluation in conjunction with the remaining battery power of the target vehicle. Available charging bay information reflects the number of available charging bays within the station and their occupancy status; navigation route information reflects the navigation distance, estimated travel time, and real-time traffic conditions between the target vehicle's current location and the candidate charging station; green energy output information reflects the available wind and solar power at the candidate charging station during the current or predicted period. The cloud-based intelligent dispatch platform can assign higher accessibility efficiency weights to target vehicles with lower remaining battery power according to preset evaluation rules, and assign higher priority to candidate charging stations that simultaneously possess high green energy output and available charging bays, thereby determining the candidate charging station with the best evaluation results as the target charging station. This comprehensive evaluation method improves the matching degree between the selected charging station and the vehicle's actual charging needs, enabling vehicles to recharge relatively stably even with limited remaining battery power.

[0017] As the target vehicle travels to the target charging station, the cloud-based intelligent dispatch platform continuously acquires the station's status information to dynamically determine its availability. This status information includes not only idle workstation status but also equipment malfunction, temporary shutdown, maintenance, or communication anomalies. When the target charging station is detected to be temporarily unavailable—for example, if a workstation is occupied by another vehicle, the robotic arm malfunctions, the charging pile stops operating abnormally, or the station enters maintenance mode—the cloud-based intelligent dispatch platform selects an updated target charging station from the candidate stations and issues an updated navigation and entry command to the target vehicle, causing it to change its route and proceed to the new station for charging. This avoids vehicles being stranded at the original target station for an extended period and improves the continuity and task completion rate of the entire unmanned charging process.

[0018] Once the target vehicle arrives at the target charging station and enters the charging bay, the station begins vehicle identity verification. This verification preferably employs a dual verification method combining license plate recognition and in-vehicle Bluetooth authentication. Specifically, the charging station's camera captures images of the vehicle and its license plate. The information processing unit recognizes the license plate image to obtain the target vehicle's license plate information. Simultaneously, the charging station's Bluetooth communication module establishes a short-range communication connection with the target vehicle's in-vehicle Bluetooth communication unit, reading the corresponding in-vehicle Bluetooth authentication information. Subsequently, the information processing unit matches the license plate information and in-vehicle Bluetooth authentication information with pre-stored vehicle binding information. This pre-stored vehicle binding information can be stored in a cloud intelligent dispatch platform or the station's local database, used to establish a correspondence between specific license plate information and specific in-vehicle Bluetooth authentication information. Identity verification is considered successful only when both license plate information and in-vehicle Bluetooth authentication information match successfully; failure to match either information results in identity verification failure. This dual verification method can reduce misjudgments caused by the influence of light, occlusion or communication fluctuations on the single identification method, improve the accuracy of vehicle-pile binding, and thus provide more reliable prerequisites for subsequent automatic opening and automatic docking.

[0019] After successful identity verification, the station control system sends a charging port cover opening command to the target vehicle. Upon receiving this command, the vehicle-side interaction module drives the electric opening and closing mechanism of the charging port cover to open it and sends feedback on the opening status to the station control system. The robotic arm only enters the docking phase after detecting that the charging port cover is open. If identity verification fails, the station control system generates a charging rejection command, prohibiting the robotic arm from performing subsequent docking actions and controlling the target vehicle to leave the current charging station. By basing both the opening and docking actions on successful identity verification, unauthorized vehicles can be prevented from mistakenly occupying charging stations or triggering automatic charging processes, thereby improving the safety and controllability of the entire charging process.

[0020] The process of the robotic arm docking the charging gun to the target vehicle's charging port can be completed collaboratively by a vision positioning module and a robotic arm controller. First, the vision positioning module acquires image information of the target vehicle's charging port area and extracts the port's contour, edge, or marker features to generate charging port position data. This position data may include the charging port's center position, attitude angle, and spatial offset relative to the robotic arm's end effector. Subsequently, the robotic arm controller controls the robotic arm to perform visual calibration and pose adjustment based on the aforementioned position data, ensuring the charging gun's insertion direction aligns with the charging port's opening direction, and gradually completing the alignment and docking. After docking, the system continues to monitor the charging interface connection status and the vehicle's battery status. The former determines whether a stable connection has been formed between the charging gun and the charging port, while the latter determines whether the power battery currently meets charging safety conditions, such as whether the battery temperature, voltage state, and insulation state are within permissible ranges. When the detection results meet the preset charging conditions, the system outputs a charging start signal to enter the subsequent power supply path switching and charging control stages. In this way, the robotic arm does not simply perform the insertion action, but starts charging after positioning, calibration, and status confirmation. This helps to improve the success rate of automatic docking and reduce the risk of mis-insertion and charging in abnormal conditions.

[0021] During the charging control phase, the green energy allocation module is responsible for switching control between the vehicle charging path, energy storage path, and grid interaction path. Specifically, the green energy allocation module acquires real-time information on wind power output, photovoltaic output, energy storage status, and grid interaction status. Wind power output and photovoltaic output information reflect the actual output power of the wind and photovoltaic power generation units within the power station at the current moment; energy storage status information reflects the remaining capacity, charging and discharging capacity, and current operating mode of the energy storage device; and grid interaction status information reflects the grid connection status between the power station and the public grid, as well as the exchangeable power. When no charging request from the target vehicle is received and the green energy output meets the surplus conditions, the green energy allocation module controls the surplus green energy to be preferentially connected to the energy storage device; when the energy storage device reaches the preset energy storage limit or no longer has the conditions for continued energy storage, the surplus green energy is then connected to the public grid. After detecting that the charging gun has completed docking and receiving a charging request from the target vehicle, the green energy allocation module switches to the vehicle charging path, prioritizing the direct supply of green energy to the target vehicle. When the green energy output is insufficient to meet the current charging power demand, the energy storage device or at least one of the public power grids will supplement the power supply. Through this approach, the on-site absorption capacity of wind and solar power at the power station can be improved during non-charging periods, while green energy can be prioritized to supplement vehicle power during charging periods, thus balancing the stability of power supply and the efficiency of clean energy utilization.

[0022] When the system detects that the target vehicle has completed charging, the control system first stops supplying power, then controls the robotic arm to remove the charging gun and return to its initial position. Subsequently, the vehicle-side interaction module closes the charging port cover. The cloud-based intelligent scheduling platform generates charging settlement information based on the charging amount and corresponding billing rules, and issues a departure command to the target vehicle. Upon receiving the departure command, the target vehicle's autonomous driving control unit controls the vehicle to leave the current charging station and continue its subsequent driving tasks. This completes the fully automated, closed-loop charging process, from charging triggering, target station identification, automatic positioning, identity verification, cover opening and docking, green energy supply, to settlement and departure. By connecting each step in a predetermined sequence, interruptions and uncertainties caused by human intervention can be reduced, thereby improving the charging efficiency of autonomous vehicles and the operational efficiency of charging stations in real-world scenarios.

[0023] For example, in a specific application scenario, during the vehicle's journey, the battery management unit continuously outputs SOC information. When the SOC drops below 10%, the vehicle controller sends a power replenishment request to the cloud intelligent dispatch platform. The cloud intelligent dispatch platform, considering the vehicle's current location, the availability of available charging bays at candidate charging stations, and the photovoltaic and wind power output within the stations, identifies a target charging station with available bays and high current green energy output, and issues a navigation entry command to the vehicle. Upon arrival at the station, the charging station's camera recognizes the license plate, and the Bluetooth communication link reads the vehicle's authentication information. After successful matching between these two with pre-stored binding information, the charging port cover automatically opens on the vehicle. A robotic arm, based on visual positioning, docks the charging gun. Once it detects a normal interface connection and that the vehicle's battery status meets charging requirements, it outputs a charging start signal. At this point, if the available wind and photovoltaic power within the station is sufficient to meet the vehicle's current charging needs, the green energy allocation module controls green energy to directly supply power to the vehicle; if the available green energy is insufficient, it is supplemented by energy storage devices or the public grid. After charging is complete, the system automatically shuts off power, the robotic arm removes and resets, the charging port cover on the vehicle closes, the cloud-based intelligent dispatch platform generates settlement information, and issues a departure command to the target vehicle, which then autonomously leaves the depot. Thus, vehicle recharging, depot dispatching, and green energy utilization can be coordinated and controlled within the same process.

[0024] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0025] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0026] Further reference Figure 2 As a response to the above Figure 1 The implementation of the method shown in this application provides an embodiment of an automatic charging control system for autonomous vehicles, which is similar to... Figure 1 Corresponding to the method embodiments shown, the system can be specifically applied to various electronic devices.

[0027] like Figure 2 As shown, the automatic charging control system 200 for autonomous vehicles described in this embodiment includes: an acquisition module 201, a determination module 202, a first control module 203, a second control module 204, a third control module 205, and a fourth control module 206. Wherein: The acquisition module 201 is used to acquire the remaining battery power information of the target vehicle, and send a power replenishment request to the cloud intelligent scheduling platform when the remaining battery power information is lower than a preset power replenishment threshold. The determination module 202 is used by the cloud intelligent scheduling platform to determine the target charging station based on the location information of the target vehicle, the station status information of the candidate charging stations and the green energy output information, and to issue a navigation entry command to the target vehicle. The first control module 203 is used to control the target vehicle to drive to the charging station of the target charging station, and after detecting that the target vehicle has entered the station, to collect the license plate information and vehicle Bluetooth authentication information of the target vehicle for identity verification. The second control module 204 is used to send a charging port cover opening command to the target vehicle after the identity verification is passed, and after detecting that the charging port cover is open, control the robotic arm to dock the charging gun to the charging port of the target vehicle according to the visual positioning result. The third control module 205 is used to control the green energy allocation module to switch to the vehicle charging path after detecting that the charging gun has completed docking and receiving the charging request from the target vehicle, and to give priority to using green energy to supply power to the target vehicle. When green energy is insufficient, it controls at least one of the energy storage device and the public power grid to supplement the power supply. The fourth control module 206 is used to control the power supply to stop after detecting that charging is complete, and to control the robotic arm to pull out the charging gun and reset, close the charging port cover of the target vehicle, generate charging settlement information, and control the target vehicle to leave the target charging station.

[0028] The automatic charging control system for autonomous vehicles provided in this embodiment of the invention can realize all the processes of the automatic charging control method for autonomous vehicles in the above embodiments. The functions and technical effects of each module in the device are the same as those of the automatic charging control method for autonomous vehicles in the above embodiments, and will not be repeated here.

[0029] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0030] The computer device 3 includes a memory 31, a processor 32, and a network interface 33 that are interconnected via a system bus. It should be noted that only the computer device 3 with components 31-33 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0031] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0032] The memory 31 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 31 may be an internal storage unit of the computer device 3, such as the hard disk or memory of the computer device 3. In other embodiments, the memory 31 may also be an external storage device of the computer device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 3. Of course, the memory 31 may also include both the internal storage unit and its external storage device of the computer device 3. In this embodiment, the memory 31 is typically used to store the operating system and various application software installed on the computer device 3, such as computer-readable instructions for an automatic charging control method for an autonomous vehicle. In addition, the memory 31 can also be used to temporarily store various types of data that have been output or will be output.

[0033] In some embodiments, the processor 32 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 32 is typically used to control the overall operation of the computer device 3. In this embodiment, the processor 32 is used to execute computer-readable instructions stored in the memory 31 or to process data, such as executing computer-readable instructions for the automatic charging control method of the autonomous vehicle.

[0034] The network interface 33 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 3 and other electronic devices.

[0035] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the automatic charging control method for an autonomous vehicle as described above.

[0036] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0037] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An automatic charging control method for an autonomous vehicle, characterized in that, include: Obtain the remaining battery power information of the target vehicle, and when the remaining battery power information is lower than a preset charging threshold, send a charging request to the cloud intelligent dispatching platform; The cloud-based intelligent dispatch platform determines the target charging station based on the location information of the target vehicle, the station status information and green energy output information of the candidate charging stations, and issues a navigation entry command to the target vehicle. The system controls the target vehicle to drive autonomously to the charging station of the target charging station, and after detecting that the target vehicle has entered the station, it collects the vehicle's license plate information and vehicle Bluetooth authentication information for identity verification. After identity verification is passed, a charging port cover opening command is sent to the target vehicle, and after the charging port cover is detected to be open, the robotic arm is controlled to dock the charging gun to the charging port of the target vehicle according to the visual positioning result. After detecting that the charging gun has completed docking and receiving the charging request from the target vehicle, the green energy allocation module is controlled to switch to the vehicle charging path and prioritize the use of green energy to supply power to the target vehicle. When green energy is insufficient, at least one of the energy storage device and the public power grid is controlled to supplement the power supply. Upon detection that charging is complete, the system stops supplying power, controls the robotic arm to remove and reset the charging gun, closes the charging port cover of the target vehicle, generates charging settlement information, and controls the target vehicle to leave the target charging station.

2. The method according to claim 1, characterized in that, The cloud-based intelligent scheduling platform acquires the available workstation information, navigation path information, and green energy output information of each candidate charging station, and combines this with the remaining battery power information of the target vehicle to conduct a comprehensive evaluation of each candidate charging station, so as to determine the candidate charging station with the best evaluation results as the target charging station.

3. The method according to claim 2, characterized in that, During the process of the target vehicle driving toward the target charging station, the station status information of the target charging station is continuously acquired; when the station status information indicates that the target charging station is temporarily unavailable, an updated target charging station is determined from the candidate charging stations, and an updated navigation entry command is issued to the target vehicle.

4. The method according to claim 1, characterized in that, In the step of controlling the target vehicle to autonomously drive to the charging station of the target charging station, and collecting the vehicle license plate information and vehicle Bluetooth authentication information of the target vehicle for identity verification after detecting that the target vehicle has entered the station, the identity verification includes: The license plate information of the target vehicle is collected by the camera at the end of the pile. The vehicle's Bluetooth authentication information is read via the Bluetooth communication link. The license plate information and the vehicle Bluetooth authentication information are matched with the pre-stored vehicle binding information, and the identity verification result is determined based on the matching result.

5. The method according to claim 4, characterized in that, When the identity verification result indicates that the verification is successful, a charging port cover opening command is sent to the target vehicle; If the identity verification result indicates that the verification has failed, a charging refusal command is generated, and the target vehicle is controlled to leave the current charging station.

6. The method according to claim 5, characterized in that, The controlled robotic arm connects the charging gun to the charging port of the target vehicle based on the visual positioning result, including: Obtain the location data of the charging port of the target vehicle; The robotic arm is controlled to perform visual calibration and pose adjustment based on the charging port position data, so that the charging gun is aligned with the charging port; After docking is completed, the charging interface connection status and vehicle battery status are checked, and a charging start signal is output when the test results meet the preset charging conditions.

7. The method according to claim 1, characterized in that, The control green energy allocation module switches to the vehicle charging path, including: Real-time acquisition of wind power output information, photovoltaic power output information, energy storage status information, and grid interaction status information; When no charging request is received from the target vehicle and the green energy output information meets the surplus condition, the surplus green energy is controlled to be connected to the energy storage device or connected to the public power grid. Upon receiving a charging request from the target vehicle, priority is given to controlling green energy to supply power to the target vehicle, and if green energy is insufficient, at least one of the energy storage device and the public power grid is controlled to supplement the power supply to the target vehicle.

8. An automatic charging control system for an autonomous vehicle, characterized in that, include: The acquisition module is used to acquire the remaining battery power information of the target vehicle, and send a power replenishment request to the cloud intelligent dispatch platform when the remaining battery power information is lower than a preset power replenishment threshold. The determination module is used by the cloud intelligent scheduling platform to determine the target charging station based on the location information of the target vehicle, the station status information and green energy output information of the candidate charging stations, and to issue a navigation entry command to the target vehicle. The first control module is used to control the target vehicle to drive autonomously to the charging station of the target charging station, and after detecting that the target vehicle has entered the station, to collect the license plate information and vehicle Bluetooth authentication information of the target vehicle for identity verification. The second control module is used to send a charging port cover opening command to the target vehicle after the identity verification is passed, and after detecting that the charging port cover is open, control the robotic arm to dock the charging gun to the charging port of the target vehicle according to the visual positioning result. The third control module is used to control the green energy allocation module to switch to the vehicle charging path after detecting that the charging gun has completed docking and receiving the charging request from the target vehicle, and to give priority to using green energy to supply power to the target vehicle. When green energy is insufficient, it controls at least one of the energy storage device and the public power grid to supplement the power supply. The fourth control module is used to control the power supply to stop after detecting that charging is complete, control the robotic arm to pull out the charging gun and reset it, close the charging port cover of the target vehicle, generate charging settlement information, and control the target vehicle to leave the target charging station.

9. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the automatic charging control method for an autonomous vehicle as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the automatic charging control method for an autonomous vehicle as described in any one of claims 1 to 7.

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