Intelligent charging gun and charging method

Abstract

The invention relates to the technical field of new energy, in particular to an intelligent charging gun and a charging method. An off-line adaptation algorithm unit identifies CP signal characteristics of a vehicle through a built-in standard library of a CP signal identification module and outputs a vehicle type identifier, and a protocol compatibility algorithm library matches charging protocol characteristic parameters according to the vehicle type identifier; after the protocol conversion unit receives the parameters, voltage thresholds, PWM duty ratios and communication message formats among different protocol standards are converted in real time through a preset mapping table; when the off-line adaptation fails, the system triggers the on-line auxiliary adaptation algorithm unit, and the fault detection module monitors handshake failure, identifies protocol incompatibility characteristics and outputs a fault signal; the data recording and broadcasting module records data in the charging starting process, the cloud communication module uploads the data to the algorithm repairing platform and receives an updating package, and finally the protocol compatibility algorithm library is updated. According to the invention, intelligent conversion and continuous learning of the charging protocol are realized, and charging compatibility and system adaptability are significantly improved.

Description

Technical Field

[0001] The present application relates to the technical field of new energy, and in particular to an intelligent charging gun and a charging method. Background Art

[0002] With the global development of the new energy vehicle industry, electric vehicle charging infrastructure is rapidly spreading worldwide. However, charging protocol standards adopted by different countries and regions vary significantly, such as China's GB / T standard, Japan's CHAdeMO standard, and the European and American CCS standards. These standards differ in signal voltage, communication protocols, handshake processes, and safety requirements, leading to increasingly prominent cross-border charging compatibility issues.

[0003] Existing charging guns typically only support charging standards in specific countries or regions. Connecting vehicles with different standards often results in signal mismatches, communication failures, and other issues. Existing charging guns require different charging ports to achieve cross-standard charging, a costly and inconvenient approach that needs further improvement. Summary of the Invention

[0004] In order to solve the problem that existing charging guns need to replace different charging interfaces to achieve cross-standard charging, which is costly and inconvenient to use, this application provides a smart charging gun and charging method, which adopts the following technical solutions: In a first aspect, the present application provides a smart charging gun, comprising: The charging gun body is used to connect the charging pile and the electric vehicle; A control module, built into the charging gun body, is used to implement protocol conversion and intelligent adaptation; A protocol conversion unit is provided in the control module, and the protocol conversion unit realizes signal conversion between different charging protocol standards through a preset protocol mapping table; The offline adaptation algorithm unit is provided in the control module and includes: CP signal recognition module, with built-in CP signal standard library, is used to identify the CP signal characteristics of the vehicle and output the vehicle type identification; A protocol compatibility algorithm library, used to match corresponding charging protocol characteristic parameters according to the vehicle type identifier; The online auxiliary adaptation algorithm unit is provided in the control module and includes: Fault detection module, used to monitor charging handshake failure events and identify protocol incompatibility characteristics, and output fault type signals; A data recording and broadcasting module is used to record corresponding charging startup process data according to the fault type signal; A cloud communication module, configured to upload the charging startup process data to the algorithm repair platform and receive an algorithm update package; Among them, the offline adaptation algorithm unit outputs the charging protocol characteristic parameters to the protocol conversion unit. When the offline adaptation algorithm unit fails to successfully start charging, the online auxiliary adaptation algorithm unit is automatically triggered. The online auxiliary adaptation algorithm unit updates the protocol compatibility algorithm library in the offline adaptation algorithm unit through the algorithm update package.

[0005] By adopting the above technical solution, since traditional charging guns use fixed protocol configurations and lack an online learning mechanism, they can only rely on manual maintenance when encountering new standards or protocol changes. The offline adaptation algorithm unit of the present application first identifies the CP signal characteristics of the connected vehicle through the CP signal standard library built into the CP signal recognition module and outputs a vehicle type identifier. The protocol compatibility algorithm library then matches the corresponding charging protocol characteristic parameters based on the identifier. After receiving these parameters, the protocol conversion unit converts the voltage threshold, PWM duty cycle, and communication message format between different charging protocol standards in real time through a preset protocol mapping table. When offline adaptation fails to successfully start charging, the system automatically triggers the online auxiliary adaptation algorithm unit. The fault detection module monitors the charging handshake failure event and identifies the protocol incompatibility characteristics, outputting a fault type signal. The data recording and broadcasting module records the corresponding charging startup process data based on the fault signal. The cloud communication module uploads this data to the algorithm repair platform and receives the algorithm update package. Finally, the protocol compatibility algorithm library in the offline adaptation algorithm unit is updated through the algorithm update package. This realizes the intelligent conversion and continuous learning capabilities of the charging protocol, not only solving the problem of cross-border standard adaptation, but also realizing algorithm self-optimization through the cloud feedback mechanism, significantly improving charging compatibility and system adaptability.

[0006] Optionally, the protocol conversion unit includes: A protocol mapping table stores the correspondence between the voltage threshold, PWM duty cycle, and communication message format of the preset protocol type; The real-time conversion processor retrieves the corresponding voltage threshold, PWM duty cycle and communication message format from the protocol mapping table to perform signal conversion according to the charging protocol characteristic parameters output by the protocol compatibility algorithm library.

[0007] By adopting the above technical solution, due to the essential differences in the underlying signal definitions of charging standards in various countries, traditional methods cannot achieve dynamic parameter mapping and real-time conversion; the protocol mapping table of this application pre-stores the correspondence between the voltage thresholds, PWM duty cycles and communication message formats of various charging protocol types, forming a complete parameter conversion database; when the system is running, the real-time conversion processor receives the charging protocol characteristic parameters output by the protocol compatibility algorithm library, which contain the specific protocol type and parameter requirements required by the target vehicle; the processor then searches and retrieves the corresponding voltage threshold, PWM duty cycle and communication message format from the protocol mapping table, and then performs a signal conversion operation to adjust the output signal of the charging pile to the protocol format required by the vehicle; through the structured mapping table design and real-time processing mechanism, seamless conversion between multiple protocol standards is achieved.

[0008] Optionally, also include: A power management unit, connected to the control module, for monitoring voltage and current parameters during charging; A temperature acquisition module, connected to the control module, for collecting charging temperature data in real time; The fault detection module performs comprehensive fault diagnosis based on the voltage parameter, current parameter and charging temperature data.

[0009] By adopting the above technical solution, in order to solve the technical problem that traditional fault detection relies on only a single signal source to cause misjudgment and missed judgment, since protocol incompatibility failures are often accompanied by complex electrical and thermodynamic phenomena, simple communication level detection cannot accurately locate the root cause of the fault; for example, when a European car model is interrupted in charging on an Asian charging pile, it may manifest as multiple symptoms such as CP signal abnormality, charging current fluctuation, local overheating caused by increased contact resistance, etc. The traditional method only detects communication handshake failure and cannot distinguish whether it is protocol incompatibility or hardware failure, resulting in the wrong direction of algorithm update; the power management unit of this application is connected to the control module to continuously monitor the voltage parameters and current parameters during the charging process. The temperature acquisition module is also connected to the control module to collect charging temperature data in real time and monitor the thermal status changes of the charging interface, cable and controller. The fault detection module receives the voltage and current parameters from the power management unit and the charging temperature data from the temperature acquisition module, and comprehensively analyzes and processes these three key parameters. Through correlation analysis of multi-dimensional data such as voltage fluctuation amplitude, current change rate, and temperature rise rate, the fault detection module can accurately identify protocol incompatibility features and distinguish them from other fault types such as hardware faults and environmental factors, thereby outputting accurate fault type signals. This effectively avoids invalid algorithm updates caused by misdiagnosis.

[0010] Optionally, the control module further includes: A charging strategy optimization unit is used to obtain charging status information and generate a charging control strategy based on the charging status information. The charging status information includes current battery status information, ambient temperature information, grid load information and maintenance service mode information.

[0011] By adopting the above technical solution, the charging strategy optimization unit first obtains charging status information, including current battery status information, ambient temperature information, grid load information and maintenance service mode information; the charging strategy optimization unit comprehensively analyzes and processes this multi-dimensional information, and combines the identified vehicle protocol type and the current protocol conversion status to generate a targeted charging control strategy; this not only ensures the success rate of protocol conversion, but also improves charging efficiency and safety through comprehensive optimization of multiple factors, providing a truly personalized charging service experience for vehicles of different national standards.

[0012] Optionally, the charging strategy optimization unit includes: a standard parameter generating subunit, configured to generate standard charging curve parameters according to the current battery status information; a personalized adjustment subunit, configured to calculate a personalized adjustment factor according to the ambient temperature information, the grid load information, and the maintenance service mode information, and adjust the standard charging curve parameters based on the personalized adjustment factor to obtain a charging control strategy; The maintenance service mode information includes local repair response time requirements, spare parts supply cycle and fault prevention strategy parameters.

[0013] By adopting the above technical solution, the standard parameter generation subunit first performs a basic analysis based on the current battery status information. Based on these battery status data and combined with the standard charging requirements corresponding to the identified vehicle protocol type, the subunit generates standard charging curve parameters; then, the personalized adjustment subunit receives ambient temperature information, grid load information and maintenance service mode information, where the maintenance service mode information includes in detail the local repair response time requirements, spare parts supply cycle and fault prevention strategy parameters, and calculates the personalized adjustment factor based on these external condition information. Based on the calculated personalized adjustment factor, the subunit makes fine adjustments to the standard charging curve parameters, including temperature-related power attenuation, grid-friendly charging timing optimization and conservative strategy adjustment based on maintenance convenience, thereby obtaining the final charging control strategy; it not only ensures basic charging safety and protocol compatibility, but also realizes intelligent adaptation to local environment and service conditions.

[0014] Optionally, the cloud communication module includes: A data uploading unit, configured to upload the charging startup process data to the algorithm repair platform in a preset format; an algorithm downloading unit, configured to receive and verify the integrity of the algorithm update package from the algorithm repair platform; The standard switching unit is used to automatically obtain the corresponding protocol standard parameter package from the cloud based on the charging gun's geographical location information and switch the local protocol configuration; A temperature control strategy management unit, used to switch heat dissipation control strategies based on the temperature rise limit requirements of different charging standards and optimize the thermal management algorithm through online temperature rise monitoring; The insulation detection standard management unit is used to switch the detection algorithm according to the insulation resistance test method of different charging standards, and dynamically adjust the judgment threshold based on the actual detection results.

[0015] By adopting the above technical solution, the data upload unit structures the charging startup process data according to a preset format and uploads it to the algorithm repair platform. The algorithm download unit receives the algorithm update package from the algorithm repair platform and verifies the integrity of the algorithm update package through checksum verification, digital signature authentication, etc. The standard switching unit automatically obtains the protocol standard parameter package of the corresponding region from the cloud based on the geographical location information of the charging gun, and performs seamless switching of the local protocol configuration; the temperature control strategy management unit automatically switches the corresponding heat dissipation control strategy according to the temperature rise limit requirements of different charging standards, and at the same time collects actual operation data through online temperature rise monitoring to continuously optimize the control accuracy and response speed of the thermal management algorithm; the insulation detection standard management unit switches the corresponding insulation resistance test method according to the currently applicable charging standard, including adjustments to the test voltage, test duration and test sequence, and dynamically adjusts the judgment threshold based on statistical analysis of actual test results to adapt to different environments and equipment aging status; through real-time data feedback and algorithm optimization mechanisms, the accuracy and adaptability of various safety detection and control strategies are continuously improved.

[0016] Optionally, the temperature control strategy management unit includes: The temperature control standard switching subunit is used to preset the temperature rise limit and heat dissipation design parameters of each charging standard, and automatically switch the corresponding temperature control strategy according to the currently adapted charging protocol; Real-time temperature rise monitoring subunit, used to continuously monitor the temperature rise curve changes during the charging process; The thermal management optimization subunit is used to dynamically adjust the heat dissipation control parameters and update the thermal management algorithm based on the deviation between the actual temperature rise curve and the preset standard.

[0017] By adopting the above technical solution, the temperature control standard switching subunit pre-stores the temperature rise limit and heat dissipation design parameters of each charging standard. When the system identifies the currently adapted charging protocol type, the subunit automatically searches for and switches to the temperature control strategy of the corresponding standard. The real-time temperature rise monitoring subunit continuously monitors the changes in the temperature rise curve of key parts during the charging process through multi-point temperature sensors, including real-time temperature data of charging connectors, cable connections, power devices and radiators, forming a complete thermal distribution status monitoring. The thermal management optimization subunit receives real-time temperature rise data, compares and analyzes the actual temperature rise curve with the preset temperature rise model of the current standard, and calculates the temperature rise deviation and change trend; based on the deviation analysis results, the subunit dynamically adjusts the heat dissipation control parameters, and at the same time feeds back the optimized control parameters to the thermal management algorithm to achieve continuous improvement and adaptive optimization of the temperature control strategy; through real-time monitoring and dynamic optimization mechanisms, the accuracy and adaptability of thermal management are significantly improved.

[0018] In a second aspect, the present application provides a charging method for a smart charging gun, which is applied to the above-mentioned smart charging gun and includes the following steps: S1: Identify the CP signal characteristics of the connected vehicle through the CP signal recognition module and output the vehicle type identification; S2: The protocol compatibility algorithm library matches the corresponding charging protocol characteristic parameters according to the vehicle type identifier; S3: The protocol conversion unit retrieves the corresponding voltage threshold, PWM duty cycle, and communication message format from the protocol mapping table according to the charging protocol characteristic parameters to perform signal conversion; S4: Start the charging process. If charging is successful, continue charging. If charging fails, go to step S5. S5: The fault detection module monitors the charging handshake failure event and identifies the protocol incompatibility characteristics, and outputs a fault type signal; S6: The data recording and broadcasting module records the corresponding charging startup process data according to the fault type signal; S7: The cloud communication module uploads the charging start process data to the algorithm repair platform and receives an algorithm update package; S8: Update the protocol compatibility algorithm library through the algorithm update package and re-execute steps S2 to S4.

[0019] Optionally, the signal conversion process in step S3 includes the following steps: S31: Determine the target protocol type according to the characteristic parameters of the charging protocol; S32: searching the protocol mapping table for the voltage threshold, PWM duty cycle, and communication message format corresponding to the target protocol type; S33: The real-time conversion processor converts and outputs the current signal in real time according to the found parameters.

[0020] Optionally, the fault detection process in step S5 includes the following steps: S51: monitoring voltage parameters and current parameters during the charging process through the power management unit; S52: Collect charging temperature data in real time through the temperature acquisition module; S53: Comprehensively analyzing the voltage parameters, current parameters, and charging temperature data, identifying protocol incompatibility features, and outputting a fault type signal.

[0021] In summary, this application includes at least one of the following beneficial technical effects: 1. The offline adaptation algorithm unit of this application identifies the vehicle's CP signal characteristics through the built-in standard library of the CP signal recognition module, outputs the vehicle type identification, and the protocol compatibility algorithm library matches the charging protocol characteristic parameters accordingly; after receiving the parameters, the protocol conversion unit converts the voltage threshold, PWM duty cycle and communication message format between different protocol standards in real time through a preset mapping table; when the offline adaptation fails, the system triggers the online auxiliary adaptation algorithm unit, the fault detection module monitors the handshake failure and identifies the protocol incompatibility characteristics, and outputs a fault signal; the data recording and broadcasting module records the charging startup process data, and the cloud communication module uploads the data to the algorithm repair platform and receives the update package, and finally updates the protocol compatibility algorithm library, realizing intelligent conversion and continuous learning of the charging protocol, significantly improving charging compatibility and system adaptability; 2. The power management unit of this application is connected to the control module to continuously monitor the voltage and current parameters during the charging process and capture abnormal changes in electrical signals. The temperature acquisition module is connected to the control module to collect charging temperature data in real time and monitor thermal state changes of the charging interface, cable, and controller. The fault detection module receives the voltage and current parameters of the power management unit and the charging temperature data of the temperature acquisition module, and performs comprehensive analysis and processing on these three key parameters. Through correlation analysis of multi-dimensional data such as voltage fluctuation amplitude, current change rate, and temperature rise rate, the fault detection module accurately identifies protocol incompatibility characteristics, distinguishes them from other fault types such as hardware failure and environmental factors, and outputs a precise fault type signal, effectively avoiding invalid algorithm updates caused by misdiagnosis. 3. The charging strategy optimization unit first obtains charging status information, including current battery status, ambient temperature, grid load, and maintenance service mode information. The charging strategy optimization unit comprehensively analyzes and processes this multi-dimensional information, combining the identified vehicle protocol type and the current protocol conversion status to generate a targeted charging control strategy. This not only ensures the success rate of protocol conversion, but also improves charging efficiency and safety through multi-factor comprehensive optimization, providing a truly personalized charging service experience for vehicles meeting different national standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a core architecture diagram of a smart charging gun according to an embodiment of the present application; Figure 2 This is a structural diagram of a power strategy optimization unit in an intelligent charging gun according to an embodiment of the present application; Figure 3 This is a structural diagram of a cloud communication module in a smart charging gun according to an embodiment of the present application; Figure 4 This is a flow chart of a charging method for a smart charging gun according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0024] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0025] In the first aspect, the present application provides a smart charging gun, whose hardware architecture is closely integrated with software functional modules to achieve cross-standard compatible smart charging control. Figure 1 The core architecture shown in the figure and the specific embodiments are described as follows: The smart charging gun includes a charging gun body, which is used to connect the charging pile and the electric vehicle. Its internal integrated hardware system consists of the following core modules: The PLC module (CP / PLC) is located at the top level of the hardware architecture. It is connected to the charging pile and electric vehicle through the charging gun cable. It realizes the transmission of control guidance signals (CP signals) and network data based on power line carrier technology (CP). It supports physical layer communications of domestic standards (such as GB / T) and European, American and Japanese standards (such as IEC 61851), and provides the underlying link for protocol conversion.

[0026] The MCU module is the control module, which is built into the charging gun body and is used to implement protocol conversion and intelligent adaptation. The MCU module includes: The protocol conversion unit is used to realize signal conversion between different charging protocol standards through a preset protocol mapping table; the protocol conversion unit includes a protocol conversion algorithm, which is based on standards such as ISO15118 and DIN70121 to realize signal parsing and mapping between different protocol standards; the protocol conversion unit includes a protocol mapping table and a real-time conversion processor; the protocol mapping table stores the correspondence between the voltage threshold, PWM duty cycle and communication message format of the preset protocol type; the real-time conversion processor retrieves the corresponding voltage threshold, PWM duty cycle and communication message format from the protocol mapping table for signal conversion based on the charging protocol characteristic parameters output by the protocol compatibility algorithm library.

[0027] The offline adaptation algorithm unit integrates an adaptive control algorithm, including a CP signal recognition module and a protocol compatibility algorithm library. The CP signal recognition module has a built-in CP signal standard library. It identifies the connected vehicle type by analyzing the CP signal characteristics received from the PLC module and outputs the vehicle type identification. The protocol compatibility algorithm library matches the corresponding charging protocol characteristic parameters based on the vehicle type identification, including key parameters such as voltage threshold, PWM duty cycle, and communication message format. The online auxiliary adaptation algorithm unit includes a fault detection module, a data recording and broadcasting module, and a cloud communication module. The fault detection module monitors charging handshake failure events, comprehensively analyzes the voltage and current parameters of the power management unit and the temperature data of the temperature acquisition module, identifies protocol incompatibility characteristics, and outputs a fault type signal. The data recording and broadcasting module records the charging startup process data based on the fault type signal. The cloud communication module uploads data to the algorithm repair platform via the wireless communication module's 4G network and receives algorithm update packages.

[0028] The PMU power management unit connects the control module and the underlying battery unit, continuously monitors the voltage and current parameters during the charging process, captures abnormal change trends of electrical signals, and provides power status data for the fault detection module.

[0029] The temperature acquisition module collects temperature data of the charging port, cable, and controller in real time, monitors changes in thermal status, and ensures thermal safety during the charging process.

[0030] The wireless communication module supports 4G and other wireless communication protocols to achieve data interaction and remote monitoring functions with the cloud-based algorithm repair platform.

[0031] During the workflow, the offline adaptation algorithm unit outputs the characteristic parameters of the charging protocol to the protocol conversion unit for real-time conversion. When the offline adaptation algorithm unit fails to successfully start charging, it automatically triggers the online auxiliary adaptation algorithm unit and updates the protocol compatibility algorithm library through the algorithm update package to achieve intelligent conversion and continuous learning capabilities of the charging protocol.

[0032] In one embodiment, the control module further includes: The charging strategy optimization unit is used to obtain charging status information and generate a charging control strategy based on the charging status information.

[0033] The charging status information includes current battery status information, ambient temperature information, grid load information, and maintenance service mode information.

[0034] In this embodiment, the charging strategy optimization unit refers to an intelligent decision-making module that dynamically adjusts charging parameters based on multi-dimensional status information. It achieves precise control and efficiency optimization of the charging process by comprehensively analyzing battery characteristics, external environment, and system maintenance requirements. It adopts a hierarchical decision-making architecture to separate standardized parameter generation from personalized adjustment, ensuring that the charging strategy meets both basic safety requirements and can adapt to the differentiated needs of specific application scenarios.

[0035] Specifically, refer to Figure 2 , the charging strategy optimization unit includes: The standard parameter generation subunit is used to generate standard charging curve parameters according to the current battery status information.

[0036] In this embodiment, the standard parameter generation subunit establishes a battery feature recognition rule library and matches the corresponding standard charging curve parameters by analyzing the battery model identification, rated capacity range, rated voltage level, and current SOC status reported by the battery management system (BMS). The subunit has a built-in three-stage charging parameter template library, which includes the current upper limit value for the constant current charging stage, the voltage set value for the constant voltage charging stage, and the current lower limit value for the trickle charging stage. The power allocation ratio for each stage is determined by mapping the battery capacity to the charge rate. When the battery SOC is detected in different ranges, the corresponding charging strategy template is automatically called to generate a standard parameter combination including the voltage set value, current limit value, and charging time estimate.

[0037] The personalized adjustment subunit is used to calculate the personalized adjustment factor according to the ambient temperature information, the grid load information and the maintenance service mode information, and adjust the standard charging curve parameters based on the personalized adjustment factor to obtain the charging control strategy.

[0038] The maintenance service model information includes local maintenance response time requirements, spare parts supply cycle and fault prevention strategy parameters.

[0039] In this embodiment, the personalized adjustment subunit establishes a multi-factor adjustment matrix, dividing ambient temperature information into five adjustment levels based on the temperature range: severe cold, low temperature, normal temperature, high temperature, and extremely hot. Each level corresponds to a different power attenuation factor and charging time extension factor. Grid load information is obtained through a communication interface with the grid dispatching system to obtain real-time load levels. A multi-level load response strategy is established, with each level corresponding to a different power allocation ratio and charging priority setting. Maintenance service mode information includes local repair response time requirements, spare parts supply cycle, and fault prevention strategy parameters. Equipment reliability coefficients are calculated using a maintenance risk assessment algorithm. When the system is in a maintenance-restricted state, charging power is automatically reduced and the protection detection cycle is extended to ensure the safety and controllability of the charging process.

[0040] Furthermore, maintenance service model information is accurately identified and dynamically adjusted by establishing a regional vehicle maintenance service history database. The database collects and analyzes the maintenance service records of other electric vehicles in the same area, including fault type distribution, actual repair response time, spare parts acquisition cycle, and maintenance cost statistics. By analyzing the temporal distribution characteristics of vehicle maintenance data in the region, the system identifies maintenance service pressure and resource constraints in different time periods and establishes a service capability assessment model based on historical maintenance data. When a new vehicle is connected to the charger, the system predicts the maintenance service availability and response time expectations for the current period based on the maintenance service statistics of similar vehicles in the area, and dynamically adjusts the conservativeness of the charging strategy and the fault prevention parameter settings.

[0041] In one possible embodiment, the maintenance service mode information also integrates real-time status feedback on vehicle maintenance services in the area. By sharing data with other charging equipment and vehicle management systems, it obtains the number of vehicles currently undergoing maintenance services, the length of the maintenance service queue, and the busy status of technicians. The system establishes a maintenance service load forecasting algorithm to identify time windows with tight maintenance service resources in advance based on the historical peak periods of vehicle maintenance requests in the area, seasonal changes, and emergency impact patterns. When a maintenance service peak period or insufficient resources are predicted, the system automatically enables preventive protection mode for the current charging vehicle. By appropriately reducing charging power, extending the equipment self-test interval, and enhancing fault warning sensitivity, the probability of maintenance needs is reduced, ensuring charging safety and continuity under maintenance service-restricted conditions.

[0042] In one embodiment, referring to Figure 3 , the cloud communication module includes: The data uploading unit is used to upload the charging startup process data to the algorithm repair platform in a preset format.

[0043] The algorithm download unit is used to receive and verify the integrity of the algorithm update package from the algorithm repair platform.

[0044] The standard switching unit is used to automatically obtain the corresponding protocol standard parameter package from the cloud according to the charging gun's geographical location information and switch the local protocol configuration.

[0045] In this embodiment, the standard switching unit refers to an intelligent module that automatically identifies and switches charging protocol standards based on geographic location information. Through real-time interaction with the cloud-based standard parameter library, it ensures that charging equipment in different countries and regions can adopt protocol configurations that comply with local standards. The geographic location and standard mapping method is used to avoid the complexity and error risks of manual configuration.

[0046] The temperature control strategy management unit is used to switch the heat dissipation control strategy according to the temperature rise limit requirements of different charging standards, and optimize the thermal management algorithm through online temperature rise monitoring.

[0047] In this embodiment, the temperature control strategy management unit refers to a thermal management module that dynamically adjusts the heat dissipation strategy based on the temperature control requirements of different charging standards, ensuring thermal safety and temperature control compliance during the charging process through real-time monitoring and online optimization mechanisms.

[0048] The insulation detection standard management unit is used to switch the detection algorithm according to the insulation resistance test method of different charging standards, and dynamically adjust the judgment threshold based on the actual detection results.

[0049] Because different countries and regions have different specific requirements for insulation resistance testing, in this embodiment, the insulation detection standard management unit establishes a multi-standard insulation detection method library to store the specific requirements of different charging standards for insulation resistance testing, including test voltage level, test time length, resistance threshold range, and test frequency settings. The unit automatically selects the corresponding detection algorithm based on the currently adapted charging standard and adjusts the test voltage output, resistance measurement accuracy, and judgment time window parameters. A statistical analysis mechanism for insulation detection results is established. By collecting the distribution characteristics of long-term detection data, the trend of insulation performance changes under different environmental conditions and equipment aging conditions is identified. The insulation resistance judgment threshold is dynamically adjusted based on the statistical analysis results. This avoids overly conservative misjudgments while ensuring safety, thereby improving the accuracy and applicability of insulation detection.

[0050] In one embodiment, the temperature control strategy management unit includes: The temperature control standard switching subunit is used to preset the temperature rise limit and heat dissipation design parameters of each charging standard, and automatically switch the corresponding temperature control strategy according to the currently adapted charging protocol.

[0051] In this embodiment, the temperature control standard switching subunit refers to a dedicated module that automatically matches and switches heat dissipation strategies based on the temperature control requirements of different charging standards. It ensures the consistency and compliance of temperature control strategies with charging standards through a preset standard parameter library and protocol identification mechanism. It adopts a combination of standardized parameter storage and rapid retrieval to achieve seamless switching of temperature control strategies between different standards, thereby avoiding safety risks caused by mismatch of temperature control parameters.

[0052] Specifically, the temperature control standard switching subunit establishes a full-standard temperature control parameter database, which stores the temperature rise limit requirements and heat dissipation design specifications of various national standards. The database establishes a parameter matrix based on four dimensions: connector temperature rise limit, cable temperature rise limit, controller temperature rise limit, and ambient temperature correction coefficient. Each standard corresponds to a different temperature rise threshold range and heat dissipation power configuration requirements. By linking with the protocol identification module, the subunit automatically queries the corresponding standard temperature control parameters when the charging protocol switch is detected, including fan start temperature, power derating trigger point, thermal protection action threshold, and temperature sensor calibration parameters, to achieve standardized matching and real-time switching of temperature control strategies.

[0053] The real-time temperature rise monitoring subunit is used to continuously monitor the temperature rise curve changes during the charging process.

[0054] The thermal management optimization subunit is used to dynamically adjust the heat dissipation control parameters and update the thermal management algorithm based on the deviation between the actual temperature rise curve and the preset standard.

[0055] In this embodiment, the thermal management optimization subunit establishes a standard temperature rise model library, storing theoretical temperature rise curve templates under different charging power, ambient temperature and load conditions as a comparison benchmark for actual temperature rise data. The subunit uses a deviation analysis algorithm to calculate the degree of deviation between the actual temperature rise curve and the standard model in real time, and identify characteristic patterns of high, low or abnormal temperature rise. Based on the deviation analysis results, the heat dissipation control parameters are dynamically adjusted, including fan speed adjustment strategy, power limit trigger conditions and thermal protection action time, and the heat dissipation effect is optimized through a feedback control mechanism. A thermal management algorithm learning mechanism is established, and the optimized control parameters and effect evaluation results are stored as empirical data. The control logic and parameter settings of the thermal management algorithm are continuously updated to achieve self-optimization and performance improvement of the thermal management strategy.

[0056] In a second aspect, the present application provides a charging method for a smart charging gun. The charging method for the smart charging gun of the present application is described below in conjunction with the above-mentioned smart charging gun.

[0057] Reference Figure 4 , a charging method for a smart charging gun, comprising the following steps: S1: Identify the CP signal characteristics of the connected vehicle through the CP signal recognition module and output the vehicle type identification; S2: The protocol compatibility algorithm library matches the corresponding charging protocol characteristic parameters according to the vehicle type identification; S3: The protocol conversion unit retrieves the corresponding voltage threshold, PWM duty cycle, and communication message format from the protocol mapping table according to the charging protocol characteristic parameters to perform signal conversion; S4: Start the charging process. If charging is successful, continue charging. If charging fails, go to step S5. S5: The fault detection module monitors the charging handshake failure event and identifies the protocol incompatibility characteristics, and outputs a fault type signal; S6: The data recording and broadcasting module records the corresponding charging startup process data according to the fault type signal; S7: The cloud communication module uploads the charging startup process data to the algorithm repair platform and receives the algorithm update package; S8: Update the protocol compatibility algorithm library through the algorithm update package and re-execute steps S2 to S4.

[0058] In one embodiment, the signal conversion process in step S3 includes the following steps: S31: Determine the target protocol type according to the charging protocol characteristic parameters; S32: Searching the voltage threshold, PWM duty cycle, and communication message format corresponding to the target protocol type from the protocol mapping table; S33: The real-time conversion processor converts and outputs the current signal in real time according to the found parameters.

[0059] In one embodiment, the fault detection process in step S5 includes the following steps: S51: monitoring voltage parameters and current parameters during the charging process through the power management unit; S52: Collect charging temperature data in real time through the temperature acquisition module; S53: Comprehensively analyze the voltage parameters, current parameters, and charging temperature data, identify protocol incompatibility features, and output a fault type signal.

[0060] In one embodiment, the charging strategy optimization step is further included: S101: Acquiring charging status information, including current battery status information, ambient temperature information, grid load information, and maintenance service mode information, wherein the maintenance service mode information includes local maintenance response time requirements, spare parts supply cycle, and fault prevention strategy parameters; S102: The standard parameter generation subunit generates standard charging curve parameters according to the current battery status information; S103: The personalized adjustment subunit calculates a personalized adjustment factor based on the ambient temperature information, the grid load information, and the maintenance service mode information; S104: Adjusting standard charging curve parameters based on the personalized adjustment factor to obtain a charging control strategy; S105: Execute the charging process according to the charging control strategy.

[0061] In one embodiment, the cloud communication process in step S7 includes the following steps: S71: The data uploading unit standardizes the charging startup process data according to a preset format and uploads it to the algorithm repair platform; S72: The algorithm download unit receives the algorithm update package from the algorithm repair platform and verifies the integrity; S73: The standard switching unit automatically obtains the corresponding protocol standard parameter package from the cloud based on the charging gun's geographical location information; S74: The temperature control strategy management unit switches the heat dissipation control strategy according to the temperature rise limit requirements of different charging standards; S75: The insulation detection standard management unit switches the detection algorithm according to the insulation resistance test method of different charging standards.

[0062] In one embodiment, the temperature control strategy management process in step S74 includes the following steps: S741: The temperature control standard switching subunit automatically switches to the corresponding temperature control strategy based on the currently adapted charging protocol, and retrieves the preset temperature rise limit and heat dissipation design parameters. S742: The real-time temperature rise monitoring subunit continuously monitors the temperature rise curve changes of key parts during the charging process; S743: The thermal management optimization subunit analyzes the deviation between the actual temperature rise curve and the preset standard; S744: Dynamically adjust the heat dissipation control parameters and update the thermal management algorithm based on the deviation analysis results.

[0063] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0064] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A smart charging gun, characterized in that: include: The charging gun body is used to connect the charging pile and the electric vehicle; A control module, built into the charging gun body, is used to implement protocol conversion and intelligent adaptation; A protocol conversion unit is provided in the control module, and the protocol conversion unit realizes signal conversion between different charging protocol standards through a preset protocol mapping table; The offline adaptation algorithm unit is provided in the control module and includes: CP signal recognition module, with built-in CP signal standard library, is used to identify the CP signal characteristics of the vehicle and output the vehicle type identification; A protocol compatibility algorithm library, used to match corresponding charging protocol characteristic parameters according to the vehicle type identifier; The online auxiliary adaptation algorithm unit is provided in the control module and includes: Fault detection module, used to monitor charging handshake failure events and identify protocol incompatibility characteristics, and output fault type signals; A data recording and broadcasting module is used to record corresponding charging startup process data according to the fault type signal; A cloud communication module, configured to upload the charging startup process data to the algorithm repair platform and receive an algorithm update package; Among them, the offline adaptation algorithm unit outputs the charging protocol characteristic parameters to the protocol conversion unit. When the offline adaptation algorithm unit fails to successfully start charging, the online auxiliary adaptation algorithm unit is automatically triggered. The online auxiliary adaptation algorithm unit updates the protocol compatibility algorithm library in the offline adaptation algorithm unit through the algorithm update package.

2. The smart charging gun according to claim 1, characterized in that: The protocol conversion unit includes: A protocol mapping table stores the correspondence between the voltage threshold, PWM duty cycle, and communication message format of the preset protocol type; The real-time conversion processor retrieves the corresponding voltage threshold, PWM duty cycle and communication message format from the protocol mapping table to perform signal conversion according to the charging protocol characteristic parameters output by the protocol compatibility algorithm library.

3. The smart charging gun according to claim 1, characterized in that: Also includes: A power management unit, connected to the control module, for monitoring voltage and current parameters during charging; A temperature acquisition module, connected to the control module, for collecting charging temperature data in real time; The fault detection module performs comprehensive fault diagnosis based on the voltage parameter, current parameter and charging temperature data.

4. The smart charging gun according to claim 1, characterized in that: The control module further includes: A charging strategy optimization unit is used to obtain charging status information and generate a charging control strategy based on the charging status information. The charging status information includes current battery status information, ambient temperature information, grid load information and maintenance service mode information.

5. The smart charging gun according to claim 4, characterized in that: The charging strategy optimization unit includes: a standard parameter generating subunit, configured to generate standard charging curve parameters according to the current battery status information; a personalized adjustment subunit, configured to calculate a personalized adjustment factor according to the ambient temperature information, the grid load information, and the maintenance service mode information, and adjust the standard charging curve parameters based on the personalized adjustment factor to obtain a charging control strategy; The maintenance service mode information includes local repair response time requirements, spare parts supply cycle and fault prevention strategy parameters.

6. The smart charging gun according to claim 1, characterized in that: The cloud communication module includes: A data uploading unit, configured to upload the charging startup process data to the algorithm repair platform in a preset format; an algorithm downloading unit, configured to receive and verify the integrity of the algorithm update package from the algorithm repair platform; The standard switching unit is used to automatically obtain the corresponding protocol standard parameter package from the cloud based on the charging gun's geographical location information and switch the local protocol configuration; A temperature control strategy management unit, used to switch heat dissipation control strategies based on the temperature rise limit requirements of different charging standards and optimize the thermal management algorithm through online temperature rise monitoring; The insulation detection standard management unit is used to switch the detection algorithm according to the insulation resistance test method of different charging standards, and dynamically adjust the judgment threshold based on the actual detection results.

7. The smart charging gun according to claim 6, characterized in that: The temperature control strategy management unit includes: The temperature control standard switching subunit is used to preset the temperature rise limit and heat dissipation design parameters of each charging standard, and automatically switch the corresponding temperature control strategy according to the currently adapted charging protocol; Real-time temperature rise monitoring subunit, used to continuously monitor the temperature rise curve changes during the charging process; The thermal management optimization subunit is used to dynamically adjust the heat dissipation control parameters and update the thermal management algorithm based on the deviation between the actual temperature rise curve and the preset standard.

8. A charging method for an intelligent charging gun, characterized in that: The smart charging gun according to any one of claims 1 to 7 comprises the following steps: S1: Identify the CP signal characteristics of the connected vehicle through the CP signal recognition module and output the vehicle type identification; S2: The protocol compatibility algorithm library matches the corresponding charging protocol characteristic parameters according to the vehicle type identifier; S3: The protocol conversion unit retrieves the corresponding voltage threshold, PWM duty cycle, and communication message format from the protocol mapping table according to the charging protocol characteristic parameters to perform signal conversion; S4: Start the charging process. If charging is successful, continue charging. If charging fails, go to step S5. S5: The fault detection module monitors the charging handshake failure event and identifies the protocol incompatibility characteristics, and outputs a fault type signal; S6: The data recording and broadcasting module records the corresponding charging startup process data according to the fault type signal; S7: The cloud communication module uploads the charging start process data to the algorithm repair platform and receives an algorithm update package; S8: Update the protocol compatibility algorithm library through the algorithm update package and re-execute steps S2 to S4.

9. The charging method of the smart charging gun according to claim 8, characterized in that: The signal conversion process in step S3 includes the following steps: S31: Determine the target protocol type according to the characteristic parameters of the charging protocol; S32: searching the protocol mapping table for the voltage threshold, PWM duty cycle, and communication message format corresponding to the target protocol type; S33: The real-time conversion processor converts and outputs the current signal in real time according to the found parameters.

10. The charging method of the smart charging gun according to claim 8, characterized in that: The fault detection process in step S5 includes the following steps: S51: monitoring voltage parameters and current parameters during the charging process through the power management unit; S52: Collect charging temperature data in real time through the temperature acquisition module; S53: Comprehensively analyzing the voltage parameters, current parameters, and charging temperature data, identifying protocol incompatibility features, and outputting a fault type signal.

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