An intelligent control system and method for a mobile charging vehicle for an electric vehicle

Abstract

The application discloses a kind of mobile charging car intelligent control system and method for electric vehicle, it is related to mobile charging technical field.The system includes unmanned mobile charging terminal, intelligent dispatching management platform, multi-modal communication module, digital twin management and control platform;Among them, unmanned mobile charging terminal is used to automatically interface target vehicle charging port for target vehicle charging or selling electricity;Intelligent dispatching management platform is used to intelligently dispatch and control unmanned mobile charging terminal;Multi-modal communication module is used to realize the stable transmission of data between each module through multiple communication modes;Digital twin management and control platform is used to synchronize the state of physical entity and virtual twin in real time, and to visualize the management and control of the whole charging link.The application realizes the whole unmanned execution of electric vehicle mobile charging car, with high degree of intelligence and improved charging efficiency.

Description

Technical Field

[0001] This invention relates to the field of mobile charging technology, and in particular to an intelligent control system and method for a mobile charging vehicle for electric vehicles. Background Technology

[0002] With the development of technology, V2G technology has become an important medium for promoting the construction of my country's new power system and facilitating the integration of energy and transportation. However, traditional fixed charging piles suffer from limitations in layout, high infrastructure costs, strong dependence on parking spaces, and low utilization rates. They cannot meet the dynamic energy replenishment needs of highway service areas, large parking lots, and emergency rescue scenarios; furthermore, they cannot provide real-time and convenient peak-valley electricity arbitrage services based on the needs of vehicle owners. Therefore, mobile intelligent charging vehicles have emerged, realizing a new dynamic energy replenishment model of "charging pile finding vehicle," and providing a crucial link in achieving power aggregation and peak shaving between the power grid and new energy vehicles. Currently, the research and application of mobile charging vehicles are developing towards automation and intelligence, but true full-process unmanned operation has not yet been achieved, failing to meet the industry's demands for efficient, intelligent, safe, and multi-scenario adaptable mobile charging vehicles. Therefore, for those skilled in the art, developing a fully unmanned control system for electric vehicle mobile charging vehicles is an urgent problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent control system and method for a mobile charging vehicle for electric vehicles, in order to solve the problems mentioned in the background art and realize the fully unmanned operation of the mobile charging vehicle for electric vehicles.

[0004] To achieve the above objectives, the present invention provides the following solution: On one hand, it provides an intelligent control system for a mobile charging vehicle for electric vehicles, including an unmanned mobile charging terminal, an intelligent scheduling and management platform, a multimodal communication module, and a digital twin control platform; the unmanned mobile charging terminal is connected to the intelligent scheduling and management platform through the multimodal communication module, and is also connected to the digital twin control platform; wherein, the unmanned mobile charging terminal is used to automatically connect to the charging port of a target vehicle to charge or sell electricity to the target vehicle; the intelligent scheduling and management platform is used to intelligently schedule and control the unmanned mobile charging terminal; the multimodal communication module is used to achieve stable data transmission between modules through various communication methods; the digital twin control platform is used to synchronize the status of the physical entity and the virtual twin in real time, and to perform visualized control of the entire charging chain.

[0005] Preferably, the unmanned mobile charging terminal includes an autonomous driving module, an intelligent charging module, a status perception module, and an energy storage and power supply module; wherein, the autonomous driving module adopts an L4-level autonomous driving mobile chassis and integrates a lidar, a high-definition binocular camera, an ultrasonic sensor, and a GPS / BeiDou dual-mode positioning unit; the intelligent charging module includes a nine-axis force-controlled robotic arm, a vision recognition unit, a six-degree-of-freedom force sensor, and an end effector.

[0006] By adopting the above technical solutions, the following beneficial technical effects can be achieved: Level 4 autonomous driving enables autonomous driving in complex road conditions and harsh working conditions. The equipment deployment does not require large-scale infrastructure transformation and can be implemented by relying only on existing parking areas and basic power supply networks. It can be widely adapted to various environments such as highway service areas, large parking lots, and emergency scenarios.

[0007] Preferably, the intelligent scheduling and management platform includes an intelligent scheduling module, an autonomous driving control module, a bidirectional charging and discharging control module, a safety monitoring and fault handling module, and a backend management module. The intelligent scheduling module is used to receive charging or electricity sales requests from target vehicles in real time and automatically match the optimal charging vehicle. The autonomous driving control module is used to perform autonomous driving control on the unmanned mobile charging terminal. The bidirectional charging and discharging control module is used to communicate with the target vehicle's BMS system to achieve intelligent charging or V2G electricity sales. The safety monitoring and fault handling module is used to monitor all hardware parameters in real time, automatically trigger emergency handling in case of abnormalities, and simultaneously link with the digital twin management and control platform to push early warning information. The backend management module is used for remote real-time monitoring of the entire charging and discharging process.

[0008] Preferably, the multimodal communication module includes a 5G communication unit, an IoT communication unit, a Bluetooth communication unit, and a CAN bus communication unit.

[0009] By adopting the above technical solution, the following beneficial technical effects can be achieved: 5G + IoT + Bluetooth + CAN bus multimodal communication serves as mutual backup, with a transmission delay of ≤10ms, avoiding process stagnation caused by communication interruption, and greatly improving the stability and reliability of unmanned execution.

[0010] On the other hand, a smart control method for a mobile charging vehicle for electric vehicles is provided, which utilizes the aforementioned smart control system for a mobile charging vehicle for electric vehicles to perform automatic charging. The specific steps include the following:

[0011] When a user initiates a charging or electricity sales request, the system automatically obtains the vehicle's GPS location, verifies the user's identity and payment information, and automatically transmits the request information to the intelligent dispatch module. If it is an electricity sales request, the system also considers the real-time energy storage status of the autonomous mobile charging terminal to determine whether a charging vehicle needs to be dispatched to purchase electricity.

[0012] The intelligent scheduling module automatically matches the unmanned mobile charging terminal, issues scheduling instructions, and the unmanned mobile charging terminal starts the autonomous driving module. The driving status is synchronized in real time to the digital twin management and control platform and the user terminal.

[0013] The unmanned mobile charging terminal autonomously drives to the target vehicle. The visual recognition unit accurately identifies the charging port through the YOLOv11 algorithm. The nine-axis force-controlled robotic arm automatically opens the charging cover and flexibly plugs in the charging gun. After communicating with the electric vehicle BMS, it automatically matches the charging and discharging parameters and starts the process.

[0014] During the charging and discharging process, the status sensing module works with the electric vehicle's BMS to monitor charging and discharging parameters, and activates an emergency handling mechanism when the parameters are abnormal.

[0015] Once the target vehicle's charging or electricity sales needs are met, the system automatically stops the energy interaction process; the digital twin management platform maps the status of all stages in real time, and the backend management module automatically records charging and discharging data.

[0016] Preferably, the digital twin management and control platform provides differentiated real-time monitoring services to different user terminals.

[0017] Preferably, when the target vehicle's charging or electricity sales needs are met, the unmanned mobile charging terminal automatically plans the optimal return route and returns to the parking point to standby; or, based on its own energy storage status, it autonomously connects to a fixed charging pile to replenish energy.

[0018] Preferably, the digital twin management and control platform analyzes equipment operation data, constructs an equipment health assessment model, identifies potential system faults, automatically generates maintenance work orders, and pushes reminders.

[0019] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0020] (1) By constructing a four-in-one system architecture of "hardware system + software control + communication link + digital twin platform", combined with unmanned execution steps of the whole process closed loop, multi-terminal visual management and control and four-level safety guarantee mechanism, the mobile charging vehicle can achieve unmanned intervention throughout the whole process from demand initiation to autonomous return, while supporting V2G bidirectional energy interaction.

[0021] (2) Based on AI algorithm, intelligent scheduling realizes nearby scheduling, priority scheduling and multi-vehicle collaborative scheduling to meet peak energy replenishment demand; after the charging vehicle completes charging, it returns autonomously and replenishes energy autonomously, forming a closed loop of "scheduling-charging and discharging-returning-replenishing energy" to improve the resource utilization rate of charging vehicles.

[0022] (3) Adopting vision + force control collaborative technology, YOLOv11 vision recognition combined with the flexible operation of a 9-axis adaptive robotic arm has high positioning accuracy and is compatible with more than 95% of mainstream electric vehicle models and all mainstream charging protocols. No need to modify electric vehicles, automatic docking accuracy is high and adaptability is wide. At the same time, the V2G electricity sales function is added, which supports users' vehicles to sell electricity to charging vehicles and gives users cash or points feedback based on electricity price, thus expanding the application scenarios of mobile energy replenishment.

[0023] (4) Construct a digital twin management and control platform to realize real-time mapping of the charging vehicle's full-process actions, behaviors and operating parameters across multiple terminals. Users, maintenance and dispatch personnel can intuitively grasp the charging progress. At the same time, it supports predictive maintenance and remote visual takeover, which greatly reduces maintenance costs and improves emergency response efficiency. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a system architecture diagram of the present invention;

[0026] Figure 2 This is a schematic diagram of the autonomous driving control module structure of the present invention;

[0027] Figure 3 This is a flowchart of the method of the present invention;

[0028] Figure 4 This is a flowchart of the force control docking process for the robotic arm of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Embodiment 1 of the present invention provides an intelligent control system for a mobile charging vehicle for electric vehicles, such as... Figure 1As shown, the system includes an unmanned mobile charging terminal, an intelligent dispatch management platform, a multimodal communication module, and a digital twin control platform. The unmanned mobile charging terminal is connected to the intelligent dispatch management platform via the multimodal communication module, and is also connected to the digital twin control platform. The unmanned mobile charging terminal automatically connects to the charging port of a target vehicle to charge or sell electricity to that vehicle. The intelligent dispatch management platform is used to intelligently dispatch and control the unmanned mobile charging terminal. The multimodal communication module is used to achieve stable data transmission between modules through various communication methods. The digital twin control platform is used to synchronize the status of the physical entity and the virtual twin in real time, providing visualized control over the entire charging chain.

[0031] Furthermore, the unmanned mobile charging terminal comprises four core modules: an autonomous driving module, an intelligent charging module, a status perception module, and an energy storage and power supply module. The specific configuration is as follows:

[0032] The autonomous driving module adopts an L4-level autonomous driving mobile chassis, integrating LiDAR, high-definition binocular camera, ultrasonic sensor and GPS / BeiDou dual-mode positioning unit; it supports SLAM real-time positioning and map building, with positioning accuracy ≤ ±5cm, MTBF over 10,000 hours, and is suitable for harsh working conditions such as rain, night, and sandstorms. It can achieve accurate obstacle avoidance in complex scenarios, autonomous path planning, and precise parking in parking spaces.

[0033] The intelligent charging module includes a nine-axis force-controlled robotic arm, a vision recognition unit, a six-degree-of-freedom force sensor, and an end effector. The nine-axis force-controlled robotic arm is equipped with a miniature force sensor with a force sensing resolution of 0.03N and a repeatability of ≤0.5mm. The intelligent charging module is compatible with bidirectional charging and discharging guns that support mainstream charging protocols such as CCS / CHAdeMO / GBT. The vision recognition unit enables automatic identification of the charging port, automatic opening of the charging cover, flexible insertion and removal of the charging gun, and automatic retraction upon completion. It also supports V2G bidirectional energy exchange.

[0034] The status perception module integrates multiple sensors such as voltage, current, temperature, humidity, vibration, and tilt angle to collect real-time data on energy storage, charging / electricity sales, and vehicle operation status. It supports cross-validation of multiple parameters and monitors the surrounding environment of the charging area, providing data support for safety management.

[0035] Energy storage and power supply module: Equipped with 60-150kWh lithium iron phosphate energy storage battery (scenario adaptable), integrating BMS battery management system and 60-180kW bidirectional charging and discharging module, supporting automatic energy replenishment of its own energy storage, dynamic adjustment of charging power, and seamless switching between off-grid and grid-connected modes.

[0036] Furthermore, the intelligent scheduling and management platform, serving as the "brain" of unmanned execution, employs AI algorithms and modular design to achieve fully automated decision-making, scheduling, and control throughout the entire process. It includes an intelligent scheduling module, an autonomous driving control module, a bidirectional charging and discharging control module, a safety monitoring and fault handling module, and a backend management module.

[0037] Intelligent scheduling module: Based on big data and AI algorithms, it receives charging / electricity sales demand, charging vehicle status and environmental information in real time, automatically matches the optimal charging vehicle and plans the shortest path, supports multi-vehicle collaborative scheduling and dynamic adjustment of service priority (prioritizing low-battery and emergency vehicles, and prioritizing V2G electricity purchase demand when charging vehicle energy storage is insufficient).

[0038] Autonomous driving control module: Integrates path planning, obstacle avoidance, and precise docking algorithms; receives positioning and sensor data; automatically controls the start, stop, steering, and braking of the unmanned mobile charging terminal (charging vehicle); supports 850nm infrared supplementary lighting at night; and can automatically correct driving deviations. Figure 2 The diagram shows the algorithm block diagram of the autonomous driving control module, which is divided into an environmental perception layer, a localization and modeling layer, a decision planning layer, a motion control layer, and a safety and redundancy layer. In the environmental perception layer, multi-sensor fusion is performed using LiDAR point clouds, binocular vision images, and ultrasonic ranging. Target detection algorithms are used for obstacle recognition, target detection, and tracking, and semantic segmentation is used to identify drivable and non-drivable areas. In the localization and modeling layer, multi-source fusion localization is performed. In the decision planning layer, behavioral decisions and path planning are performed. In the motion control layer, lateral control, longitudinal control, and chassis feedback control are performed. In the safety and redundancy layer, a safety closed-loop mechanism is implemented.

[0039] The bidirectional charging and discharging control module is equipped with the YOLOv11 visual recognition algorithm. It automatically identifies the three-dimensional coordinates and yaw angle of the charging port, controls the robotic arm to complete the entire docking action, communicates with the electric vehicle BMS system, automatically matches charging and discharging parameters, realizes intelligent charging or V2G electricity sales, and completes electricity price settlement and user feedback at the same time.

[0040] Security monitoring and fault handling module: used to monitor all hardware parameters in real time, automatically trigger emergency handling when abnormalities occur, and simultaneously push early warning information in conjunction with the digital twin management and control platform;

[0041] Backend management module: Supports remote real-time monitoring of charging vehicle operating status, charging and discharging progress, and fault information, realizes data statistics, automatic equipment maintenance reminders, supports OTA remote upgrades, and eliminates the need for manual on-site operation.

[0042] Furthermore, the multimodal communication module includes a 5G communication unit, an IoT communication unit, a Bluetooth communication unit, and a CAN bus communication unit. It adopts a 5G+IoT+Bluetooth+CAN bus multimodal communication mode, with each component acting as a backup to ensure stable and latency-free data transmission.

[0043] 5G communication unit: transmission latency ≤10ms, enabling high-speed data transmission between charging vehicle and backend / user terminals, supporting multi-vehicle collaborative scheduling and cloud data storage and analysis;

[0044] IoT communication unit: Enables interconnection between charging vehicles, electric vehicles, and fixed charging piles, constructs a "vehicle-pile-cloud" communication network, and supports charging vehicles to autonomously connect to fixed charging piles for energy replenishment and V2G two-way interaction;

[0045] Bluetooth communication unit: Enables short-range communication between the charging vehicle and the electric vehicle, assists in the positioning of the charging port, and serves as a 5G backup;

[0046] CAN bus: Enables high-speed data transmission between various hardware units inside the charging vehicle, ensuring coordinated operation of each unit and reading key parameters of the BMS.

[0047] Embodiment 2 of the present invention provides an intelligent control method for a mobile charging vehicle for electric vehicles, which utilizes an intelligent control system for the mobile charging vehicle to perform automatic charging, such as... Figure 3 As shown, the specific steps include the following:

[0048] S1. When a user initiates a charging or electricity sales request, the system automatically obtains the vehicle's GPS location, verifies the user's identity and payment information, and automatically transmits the request information to the intelligent dispatch module. If it is an electricity sales request, the system also combines the real-time energy storage status of the unmanned mobile charging terminal to determine whether it is necessary to dispatch a charging vehicle to purchase electricity.

[0049] S2. The intelligent scheduling module automatically matches the unmanned mobile charging terminal, issues scheduling instructions, and the unmanned mobile charging terminal starts the autonomous driving module. The driving status is synchronized to the digital twin management and control platform and user terminal in real time.

[0050] The S3 autonomous mobile charging terminal automatically plans its path, with sensors monitoring the environment in real time to achieve autonomous obstacle avoidance, emergency braking, and precise docking at the charging port. The visual recognition unit accurately identifies the charging port using the YOLOv11 algorithm, and the nine-axis force-controlled robotic arm automatically opens the charging cover and flexibly plugs in and unplugs the charging gun. After communicating with the electric vehicle's BMS, it automatically matches charging and discharging parameters and starts the process. In charging scenarios, it replenishes energy for vehicles, and in electricity sales scenarios, it purchases electricity for the charging vehicle's energy storage battery. The docking process takes 1-2 minutes, and the status is synchronized to the digital twin management and control platform in real time.

[0051] S4. During the charging and discharging process, the status sensing module and the electric vehicle BMS work together to monitor charging and discharging parameters. When the parameters are abnormal, the emergency handling mechanism is activated. In the V2G electricity sales scenario, the system automatically completes the settlement based on the real-time electricity price and the transaction volume, and issues cash or points to users as feedback. When the charging vehicle's energy storage is insufficient, other vehicles are automatically dispatched to supplement the energy.

[0052] S5. When the target vehicle's charging or electricity sales needs are met, the system automatically stops the energy interaction process. The digital twin management platform maps the status of all stages in real time, and the backend management module automatically records charging and discharging data. The system automatically stops the energy interaction process, including automatically stopping charging, unplugging the charging gun, and retracting the robotic arm in the charging scenario; and disconnecting the bidirectional power transmission link and retracting the robotic arm in the electricity sales scenario. Subsequently, the charging vehicle automatically plans the optimal return route and returns to the docking point to standby; or, based on its own energy storage status, autonomously connects to a fixed charging pile for energy replenishment. Among these features, a new V2G electricity sales function for user terminal vehicles has been added: based on the electricity sales command initiated by the user, combined with the charging vehicle's real-time energy storage status and energy replenishment priority, the system can automatically dispatch the charging vehicle to the target vehicle to "purchase electricity," and automatically complete the settlement based on the real-time electricity price and the amount of electricity traded, issuing cash or points as feedback to the user, thus realizing a two-way energy interaction and transaction closed loop.

[0053] Furthermore, this invention combines the YOLOv11 visual recognition algorithm with the micro force control sensor of a nine-axis force-controlled robotic arm to achieve accurate identification of the charging port, automatic opening of the charging cover, and flexible insertion and removal of the charging gun, adapting to vehicle parking deviations and multiple vehicle models, while also supporting V2G bidirectional energy interaction. Figure 4 The diagram shown is a flowchart of the force control docking process for the robotic arm, which is divided into three stages: Stage 1 is visual precision positioning and perception, which enables the robotic arm to move to the designated area; Stage 2 is the force control docking of the cover plate and the charging plug, which completes the plug insertion and mechanical lock confirmation; Stage 3 is the charging and discharging monitoring and plugging / unplugging, and after the charging and discharging is completed, the nine-axis force control robotic arm returns to the safe origin position.

[0054] Furthermore, this invention constructs a three-level digital twin management and control architecture consisting of a physical entity, a virtual twin, and multiple terminal applications. This architecture enables real-time mapping and visual monitoring of the actions, behaviors, and operating parameters of the mobile charging vehicle throughout the entire process, solving the problems of unintuitive and asynchronous traditional backend monitoring data. The specific implementation is as follows:

[0055] 1. Digital twin system architecture and millisecond-level data synchronization mechanism

[0056] Taking the physical entity of the unmanned mobile charging terminal, namely the mobile charging vehicle, as the core, a 1:1 high-precision three-dimensional digital twin is constructed. Through multi-sensor fusion acquisition and multi-modal communication transmission, the state of the physical entity and the virtual twin is synchronized in real time. The posture, movement, and parameter changes of the twin are completely synchronized with the physical entity, with a transmission delay of ≤10ms, and support for a smooth visualization mapping process.

[0057] Full-dimensional data acquisition: Through the LiDAR / BeiDou positioning unit of the autonomous driving module, the force control sensor / visual recognition of the intelligent charging module, and multiple parameter sensors of the state perception module, three types of core data are collected in real time: motion data: dynamic actions such as driving posture, robotic arm movement trajectory, parking position deviation, and vehicle tilt angle; behavior data: execution behaviors such as path planning decisions, obstacle avoidance logic, and charging process nodes (such as opening the charging cover, docking the charging gun, and starting and stopping charging); and operating parameters: quantitative indicators such as positioning coordinates, driving speed, charging power, battery SOC, equipment temperature, and sensor health status.

[0058] Relying on 5G low-latency communication links, multi-source data is transmitted to the cloud twin platform in real time. The virtual model is dynamically rendered through the digital twin engine. The posture, movement, and parameter changes of the twin are completely synchronized with the physical entity. The transmission latency is ≤10ms, and it supports a smooth visualization mapping process.

[0059] 2. Multi-terminal, full-scenario real-time monitoring capability

[0060] Based on the digital twin management and control platform, differentiated real-time monitoring services are provided to different user terminals, achieving transparency of the entire energy replenishment process:

[0061] User-side (mobile APP / mini-program / vehicle terminal): Users can intuitively view the real-time location, driving trajectory, and estimated arrival time of the charging vehicle through a lightweight digital twin interface; the movement status of the robotic arm and the charging gun docking progress during the docking process; and real-time parameters during the charging process (charging power, vehicle SOC, estimated full charge time), and can grasp the progress of the entire process without waiting for text push notifications.

[0062] Operation and maintenance (PC backend / Operation and maintenance terminal): Operation and maintenance personnel can monitor the operating status of all charging vehicles in real time through the 3D panoramic view of the digital twin platform, view the full-dimensional parameters of a single device (sensor data, device health, battery degradation status), and support 3D location of fault status and traceability of abnormal data.

[0063] Dispatch terminal (cloud dispatch platform): Dispatchers can intuitively grasp the distribution, task status and real-time location of all charging vehicles through digital twin maps, assisting AI dispatch algorithms to achieve multi-vehicle collaborative management and optimize resource allocation efficiency.

[0064] 3. Digital twin support for intelligent operation and maintenance and hierarchical emergency response

[0065] Based on the full lifecycle data accumulation of digital twins, an upgrade from reactive to predictive maintenance is achieved, while providing visualization support for remote emergency response:

[0066] Predictive maintenance and automatic reminders: The digital twin platform continuously analyzes equipment operation data to build an equipment health assessment model, which can identify potential faults such as sensor wear, battery degradation, and robotic arm precision deviation in advance, automatically generate maintenance work orders and push reminders, realize predictive maintenance, and reduce equipment failure rate.

[0067] Remote Visual Takeover for Extreme Anomalies: When the charging vehicle experiences extreme anomalies (such as local communication fluctuations or sensor misjudgment risks), maintenance personnel can remotely take over the vehicle through the virtual model of the digital twin management platform. They can directly issue control commands to the twin (such as emergency braking, cutting off high voltage, and adjusting the posture of the robotic arm), and the commands are synchronously transmitted to the physical entity for execution. After the fault is resolved, the system automatically verifies the equipment status and restores the unmanned operation mode. All remote operations are recorded and documented through the digital twin platform to ensure that the operations are traceable.

[0068] Furthermore, this invention establishes a multi-layered protection system covering the entire scenario of charging, equipment, communication, personnel, environment, and V2G bidirectional energy interaction, and designs a four-level graded emergency response mechanism to achieve closed-loop handling of abnormal situations without human intervention.

[0069] 1. Level 1: Active avoidance by the equipment itself (self-response): When a minor / controllable anomaly is detected (such as fluctuations in charging and discharging current, slight vehicle body deviation, or fluctuations in local communication signals), the system immediately executes autonomous protection actions on the equipment side, including adjusting the charging and discharging power, correcting the driving posture, suspending the docking of the robotic arm, and switching to a backup communication link. The system prioritizes eliminating potential risks through local actions to prevent the problem from escalating.

[0070] 2. Second level: Platform feedback and remote takeover: If the device's autonomous avoidance is ineffective, or if a general anomaly is detected (such as abnormal sensor data or deviation in energy storage status), the system will push anomaly details, device location, and risk level information to the operation and maintenance backend, user terminal, and digital twin management and control platform in real time, triggering operation and maintenance warnings; at the same time, it supports operation and maintenance personnel to remotely and visually take over through the digital twin platform to achieve manual assistance in handling anomalies.

[0071] 3. Level 3: Joint reporting to higher-level emergency departments: When a serious safety risk occurs (such as battery thermal runaway, collision risk, or high-voltage circuit abnormality), the system automatically reports the abnormal data and precise location coordinates to the preset emergency management platform and operation and maintenance center, and initiates the emergency linkage process to provide key information support for subsequent professional handling.

[0072] 4. Level 4: Active environmental sound and light / voice warning: Simultaneously, through the sound and light alarms and directional voice prompts integrated into the vehicle body, safety warnings are issued to surrounding personnel and vehicles, guiding irrelevant personnel and vehicles away from dangerous areas to avoid secondary safety accidents.

[0073] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An intelligent control system for a mobile charging vehicle for electric vehicles, characterized in that, The system includes an unmanned mobile charging terminal, an intelligent dispatch management platform, a multimodal communication module, and a digital twin control platform. The unmanned mobile charging terminal is connected to the intelligent dispatch management platform via the multimodal communication module, and is also connected to the digital twin control platform. The unmanned mobile charging terminal automatically connects to the charging port of a target vehicle to charge or sell electricity to that vehicle. The intelligent dispatch management platform intelligently dispatches and controls the unmanned mobile charging terminal. The multimodal communication module enables stable data transmission between modules through various communication methods. The digital twin control platform synchronizes the status of the physical entity and its virtual twin in real time, providing visualized control over the entire charging process.

2. The intelligent control system for a mobile charging vehicle for electric vehicles according to claim 1, characterized in that, The unmanned mobile charging terminal includes an autonomous driving module, an intelligent charging module, a status perception module, and an energy storage and power supply module. The autonomous driving module adopts an L4-level autonomous driving mobile chassis and integrates LiDAR, a high-definition binocular camera, an ultrasonic sensor, and a GPS / BeiDou dual-mode positioning unit. The intelligent charging module includes a nine-axis force-controlled robotic arm, a vision recognition unit, a six-degree-of-freedom force sensor, and an end effector.

3. The intelligent control system for a mobile charging vehicle for electric vehicles according to claim 1, characterized in that, The intelligent dispatch management platform includes an intelligent dispatch module, an autonomous driving control module, a bidirectional charging and discharging control module, a safety monitoring and fault handling module, and a backend management module. The intelligent dispatch module receives charging or electricity sales requests from target vehicles in real time and automatically matches the optimal charging vehicle. The autonomous driving control module controls the autonomous driving of the unmanned mobile charging terminal. The bidirectional charging and discharging control module communicates with the target vehicle's BMS system to achieve intelligent charging or V2G electricity sales. The safety monitoring and fault handling module monitors all hardware parameters in real time, automatically triggers emergency handling in case of abnormalities, and simultaneously links with the digital twin management platform to push early warning information. The backend management module is used for remote real-time monitoring of the entire charging and discharging process.

4. The intelligent control system for a mobile charging vehicle for electric vehicles according to claim 1, characterized in that, The multimodal communication module includes a 5G communication unit, an IoT communication unit, a Bluetooth communication unit, and a CAN bus communication unit.

5. A method for intelligent control of a mobile charging vehicle for electric vehicles, characterized in that, The automatic charging process using the intelligent control system for a mobile charging vehicle for electric vehicles as described in any one of claims 1-4 includes the following specific steps: When a user initiates a charging or electricity sales request, the system automatically obtains the vehicle's GPS location, verifies the user's identity and payment information, and automatically transmits the request information to the intelligent dispatch module. If it is an electricity sales request, the system also considers the real-time energy storage status of the autonomous mobile charging terminal to determine whether a charging vehicle needs to be dispatched to purchase electricity. The intelligent scheduling module automatically matches the unmanned mobile charging terminal, issues scheduling instructions, and the unmanned mobile charging terminal starts the autonomous driving module. The driving status is synchronized in real time to the digital twin management and control platform and the user terminal. The unmanned mobile charging terminal autonomously drives to the target vehicle. The visual recognition unit accurately identifies the charging port through the YOLOv11 algorithm. The nine-axis force-controlled robotic arm automatically opens the charging cover and flexibly plugs in the charging gun. After communicating with the electric vehicle's BMS, it automatically matches the charging and discharging parameters and starts the process. During the charging and discharging process, the status sensing module works with the electric vehicle's BMS to monitor charging and discharging parameters, and activates an emergency handling mechanism when the parameters are abnormal. Once the target vehicle's charging or electricity sales needs are met, the system automatically stops the energy interaction process; the digital twin management platform maps the status of all stages in real time, and the backend management module automatically records charging and discharging data.

6. The intelligent control method for a mobile charging vehicle for electric vehicles according to claim 5, characterized in that, Based on the aforementioned digital twin management and control platform, differentiated real-time monitoring services are provided to different user terminals.

7. The intelligent control method for a mobile charging vehicle for electric vehicles according to claim 5, characterized in that, Once the target vehicle's charging or electricity sales needs are met, the unmanned mobile charging terminal automatically plans the optimal return route and returns to the parking point to standby; or, based on its own energy storage status, it autonomously connects to a fixed charging pile to replenish energy.

8. The intelligent control method for a mobile charging vehicle for electric vehicles according to claim 5, characterized in that, The digital twin management and control platform analyzes equipment operation data, builds an equipment health assessment model, identifies potential system faults, automatically generates maintenance work orders, and pushes reminders.

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