Modularized new energy automobile charging pile and charging method

Abstract

The invention discloses a modularly-designed new energy automobile charging pile and a charging method, and relates to the technical field of new energy automobiles. Battery parameters, power grid parameters and media parameters in the automobile charging process are collected, collected battery data features are extracted, a battery health assessment mechanism is established, load analysis is carried out, and a fuzzy rule is established; according to the method, social media real-time event data are introduced, the data dimension is expanded, logic judgment is innovated, multi-source data such as batteries, power grids and social media are fused, the load state of the power grids is comprehensively described, and the method is high in practicability and high in practicability. By means of the self-adaptive fuzzy control algorithm, the weight is dynamically adjusted, and the charging power is precisely regulated and controlled, so that the power supply safety is balanced, the charging cost of a user is reduced, the capacity of the charging pile for coping with complex scenes is enhanced, and the intelligence and comprehensive performance of the charging pile are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and particularly to a new energy vehicle charging pile with modular design and a charging method. Background Art

[0002] With the booming development of the new energy vehicle industry, as an important supporting infrastructure, the charging pile is facing increasingly complex charging demands and power grid operation environments. On the one hand, the battery performances of different vehicles vary significantly, and accurately evaluating the battery health status is crucial for ensuring charging safety and extending the battery life. On the other hand, the power grid load fluctuates frequently, and the uncertainty of new energy power generation increases, making it necessary for the charging pile to interact with the power grid more intelligently to achieve supply-demand balance.

[0003] Currently, Chinese Patent with application number CN202410181642.X discloses a current sharing method for charging modules of a charging pile and a charging pile. The method includes: obtaining the current error and current change rate of the charging module; performing fuzzy inference based on the first membership function, the second membership function, the current error, and the current change rate to obtain the first membership degree of the current error, the second membership degree of the current change rate, and the phase shift angle control rule of the charging module corresponding to the first membership degree and the second membership degree; performing an AND operation on the third membership function, the first membership degree, and the second membership degree based on the phase shift angle control rule to obtain the phase shift angle control fuzzy set of the charging module; processing the control fuzzy set based on the area bisecting method to obtain the phase shift angle adjustment amount of the charging module, and controlling the current output of the charging module according to the sum of the phase shift angle adjustment amount and the current phase shift angle control amount of the charging module. Each charging module outputs evenly, enabling the charging module to reach the best operating state and extending the service life of the charging module.

[0004] The above technology is difficult to expand the data dimension and innovate the logical judgment by introducing social media real-time event data, which affects the use effect of the charging pile. Summary of the Invention

[0005] The technical problem solved by the present invention is that the existing technology is difficult to expand the data dimension and innovate the logical judgment by introducing social media real-time event data, which affects the use effect of the charging pile.

[0006] To solve the above technical problem, the present invention provides the following technical solutions:

[0007] A new energy vehicle charging pile with modular design includes a data acquisition module, a battery state analysis module, a power grid regulation module, a data integration and processing module, and a charging control output module;

[0008] The data acquisition module is used to collect battery parameters, power grid parameters, and media parameters during the vehicle charging process;

[0009] The battery status analysis module is used to extract the characteristics of the collected battery data, establish a battery health assessment mechanism, and output the battery health judgment result;

[0010] The power grid regulation module is used to perform load analysis and establish fuzzy rules, and output a charging adjustment instruction according to the load analysis information data and media parameters;

[0011] The data integration and processing module is used to perform logical integration to form a unified data processing flow;

[0012] The charging control output module is used to generate a control instruction according to the integration processing result.

[0013] Preferably, the data acquisition module includes a vehicle information acquisition unit, a battery parameter acquisition unit, a power grid data acquisition unit, and a media data acquisition unit;

[0014] The vehicle information acquisition unit is used to acquire vehicle identification code data and charging interface type data;

[0015] The battery parameter acquisition unit is used to acquire battery state of charge data, battery temperature data, voltage data, current data, and electrochemical impedance data;

[0016] The power grid data acquisition unit is used to acquire substation load rate data, electricity price signal data, and new energy output status data;

[0017] The media data acquisition unit is used to acquire social media text data in the region.

[0018] Preferably, the battery status analysis module includes a battery feature extraction unit and an evaluation mechanism establishment unit;

[0019] The battery feature extraction unit is used to perform noise filtering, timestamp alignment, and missing value filling on the battery state of charge data, voltage data, current data, battery temperature data, and electrochemical impedance data, and output the processed data. The time series data sliding window mechanism and a preset empirical feature template are used to extract features from the processed data. The features include the rate of change of battery state of charge, the voltage drop amplitude, the initial charging temperature rise rate, and the amplitude change of the low-frequency band feature points in the electrochemical impedance spectrum, and output a battery feature vector;

[0020] The evaluation mechanism establishment unit is used to establish a battery health assessment mechanism, and the battery health assessment mechanism is as follows:

[0021] The battery feature vectors are formed into a feature sequence in chronological order, input into a long short-term memory network model, and a health score value is output according to a preset hierarchical scoring mechanism. The health score value is analyzed:

[0022] If the health score value is greater than the preset first score value, it is determined that the battery is healthy and can be charged at high power;

[0023] If the health score value is between the first score value and the preset second score value, it is determined that the battery is slightly aged and can be charged at medium power;

[0024] If the health score value is less than the second score value, it is determined that the battery is aged and can be charged at low power;

[0025] Output the battery health judgment result.

[0026] Preferably, the power grid regulation module includes a load analysis unit and an optimization mechanism unit;

[0027] The load analysis unit is used to align the substation load rate data, electricity price signal data, and new energy output status data according to the time stamp, construct the power grid operation data stream, extract the characteristics of the power grid operation data stream through the time series data sliding window mechanism, and the characteristics include the peak period duration, valley period duration, load rising rate, load falling rate, electricity price change cycle, and electricity price fluctuation amplitude, and output it as a structured load status vector. Classify and label the structured load status vector according to the historical load model and the preset dispatching strategy experience library, and the classification labels include peak period - high load, valley period - low electricity price, and renewable energy surplus period, and output it as load analysis information data.

[0028] The optimization mechanism unit is used to perform text sentiment analysis and event keyword recognition on the social media text data, and output it as an event activation factor, and the event activation factor is the influence intensity of emergencies in the current area;

[0029] Establish fuzzy rules, and the fuzzy rules are:

[0030] When the event activation factor is greater than the preset event activation factor standard value and the classification label is peak period - high load, output a signal to reduce the charging power and a signal to increase the charging duration;

[0031] When the event activation factor is less than the preset event activation factor standard value and the classification label is valley period - low electricity price, output a signal to increase the charging power and a signal to reduce the charging duration;

[0032] When the classification label is renewable energy surplus period, output a signal to increase the charging power and a signal to increase the utilization rate of new energy electric energy;

[0033] Output the charging adjustment command.

[0034] Preferably, the optimization mechanism unit obtains the actual initial temperature rise rate of charging in real time, calculates the error index between the actual initial temperature rise rate of charging and the initial temperature rise rate of charging, and adjusts the weight parameters of the health score value and the event activation factor in the fuzzy rule according to the error index. The relationship between the weight parameter and the charging adjustment instruction is as follows:

[0035] The higher the weight parameter corresponding to the health score value, the higher the degree of decrease and increase of the corresponding charging power signal;

[0036] The higher the weight parameter corresponding to the event activation factor, the higher the degree of decrease and increase of the corresponding charging duration signal;

[0037] Update the charging adjustment instruction according to the health score value and the event activation factor.

[0038] Preferably, the data integration and processing module includes a logic fusion unit and a processing flow construction unit;

[0039] The logic fusion unit is used to perform association processing on the evaluation mechanism establishment unit and the optimization mechanism unit;

[0040] The processing flow construction unit is used to establish a unified data input-output structure and continuously track and trace the source of all data.

[0041] Preferably, the data integration and processing module is provided with a log record structure to record all input data, processing nodes and output results for subsequent analysis and call.

[0042] Preferably, the charging control output module includes a control signal generation unit and a user feedback unit;

[0043] The control signal generation unit is used to generate a real-time power control instruction according to the charging adjustment instruction and send it to the charging actuator;

[0044] The user feedback unit is used to display charging progress data, battery health status data, optimization prompt information and warning reminders on the user interface.

[0045] Preferably, the charging control output module further includes a remote data transmission interface, which supports uploading the complete data processing flow to the server for centralized analysis and remote maintenance.

[0046] A modular design new energy vehicle charging method, which is applied to the modular design new energy vehicle charging pile described above, includes the following steps:

[0047] Step S1, collect battery parameters, grid parameters and media parameters during the vehicle charging process;

[0048] Step S2: Extract the collected battery data features, establish a battery health assessment mechanism, and output the battery health judgment result;

[0049] Step S3: Conduct load analysis and establish fuzzy rules, and output a charging adjustment instruction according to the load analysis information data and media parameters;

[0050] Step S4: Conduct logical integration to form a unified data processing flow;

[0051] Step S5: Generate a control instruction according to the integrated processing result.

[0052] Advantages of the present invention: The present invention introduces real-time social media event data, expands the data dimension, innovates logical judgment, integrates multi-source data such as batteries, power grids, and social media, comprehensively describes the power grid load status, and dynamically adjusts the weight by virtue of the adaptive fuzzy control algorithm to accurately control the charging power, thereby balancing power supply safety, reducing the user's charging cost, enhancing the ability of the charging pile to cope with complex scenarios, and significantly improving the intelligence and comprehensive performance of the charging pile. Brief Description of the Drawings

[0053] Figure 1 It is a schematic diagram of the basic process of a modular-designed new energy vehicle charging pile provided by an embodiment of the present invention;

[0054] Figure 2 It is a step flowchart of a modular-designed new energy vehicle charging method provided by an embodiment of the present invention. Detailed Embodiments

[0055] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention is made in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments.

[0056] Embodiment 1, referring to Figure 1 , a modular-designed new energy vehicle charging pile is provided, including a data acquisition module, a battery state analysis module, a power grid regulation module, a data integration and processing module, and a charging control output module.

[0057] The data acquisition module is used to collect battery parameters, power grid parameters, and media parameters during the vehicle charging process.

[0058] The battery state analysis module is used to extract the collected battery data features, establish a battery health assessment mechanism, and output the battery health judgment result.

[0059] The power grid regulation module is used to conduct load analysis and establish fuzzy rules, and output a charging adjustment instruction according to the load analysis information data and media parameters.

[0060] The data integration and processing module is used for logical integration to form a unified data processing flow.

[0061] The charging control output module is used to generate control instructions according to the integration processing result.

[0062] The data acquisition module includes a vehicle information acquisition unit, a battery parameter acquisition unit, a grid data acquisition unit, and a media data acquisition unit.

[0063] The vehicle information acquisition unit is used to acquire vehicle identification code data and charging interface type data.

[0064] The vehicle information acquisition unit can acquire vehicle identification code data and charging interface type data, providing key information for the charging system to accurately identify the vehicle identity and match the appropriate charging interface, ensuring the smooth start of the charging process and avoiding charging failures caused by mismatched vehicle information.

[0065] The battery parameter acquisition unit is used to acquire battery state of charge data, battery temperature data, voltage data, current data, and electrochemical impedance data.

[0066] The battery parameter acquisition unit real-time acquires multi-dimensional data such as battery state of charge, temperature, voltage, current, and electrochemical impedance, grasps the battery state, and provides an important basis for reasonably controlling the charging power, preventing overcharging and over-discharging of the battery, and ensuring the safety and life of the battery.

[0067] The grid data acquisition unit is used to acquire substation load rate data, electricity price signal data, and new energy output status data.

[0068] The grid data acquisition unit acquires data such as substation load rate, electricity price signal, and new energy output status, helping the system understand the real-time operating conditions of the grid, formulating an economic and efficient charging strategy in combination with electricity price information, reasonably allocating the charging load according to the new energy output situation, promoting new energy consumption, and maintaining grid stability.

[0069] The media data acquisition unit is used to acquire social media text data within the region.

[0070] The media data acquisition unit collects social media text data within the region, provides external information reference, helps to predict in advance changes in charging demand, user feedback, etc., provides an auxiliary decision-making basis for the optimization and adjustment of charging piles, and enhances adaptability and flexibility.

[0071] The data acquisition module obtains key data related to vehicles, batteries, grids, and media during the charging process. These rich and accurate data provide a solid data foundation for the subsequent formulation of charging strategies, monitoring and control of the operating status of charging piles, helping to improve the intelligent level, operating efficiency, and safety of charging piles, and optimizing the user experience.

[0072] The battery status analysis module includes a battery feature extraction unit and an evaluation mechanism establishment unit.

[0073] The battery feature extraction unit is used to perform noise filtering, timestamp alignment, and missing value filling on the battery state of charge data, voltage data, current data, battery temperature data, and electrochemical impedance data, and output the processed data. It extracts features from the processed data using a time series data sliding window mechanism and a preset empirical feature template. The features include the rate of change of the battery state of charge, the voltage drop amplitude, the initial temperature rise rate during charging, and the amplitude change of the low-frequency segment feature points in the electrochemical impedance spectrum, and outputs a battery feature vector.

[0074] The battery feature extraction unit performs noise filtering, timestamp alignment, and missing value filling on various original data of the battery, and outputs the processed data to ensure the accuracy and integrity of the data, providing a high-quality data basis for subsequent feature extraction. Using a time series data sliding window mechanism and a preset empirical feature template, it extracts features such as the rate of change of the battery state of charge, the voltage drop amplitude, the initial temperature rise rate during charging, and the amplitude change of the low-frequency segment feature points in the electrochemical impedance spectrum from the processed data, and outputs a battery feature vector, providing key feature information for battery health assessment.

[0075] The evaluation mechanism establishment unit is used to establish a battery health evaluation mechanism. The battery health evaluation mechanism is as follows:

[0076] The battery feature vectors are formed into a feature sequence in chronological order and input into a long short-term memory network model, and a health score value is output according to a preset hierarchical scoring mechanism. Analyze the health score value:

[0077] If the health score value is greater than a preset first score value, it is determined that the battery is healthy and can be charged at high power.

[0078] If the health score value is between the first score value and a preset second score value, it is determined that the battery is slightly aged and can be charged at medium power.

[0079] If the health score value is less than the second score value, it is determined that the battery is aged and can be charged at low power.

[0080] Output the battery health judgment result.

[0081] The evaluation mechanism establishment unit establishes a battery health evaluation mechanism, forms the battery feature vectors into a feature sequence in chronological order, inputs them into a long short-term memory network model, outputs a health score value according to a preset hierarchical scoring mechanism, analyzes the health score value, accurately determines the health state of the battery, and outputs the battery health judgment result, providing a scientific basis for the use and management of the battery.

[0082] The battery status analysis module can process and extract features of various battery data, and use the long short-term memory network model and the preset hierarchical scoring mechanism to evaluate the battery health status, output the battery health judgment results, and provide a basis for the battery charging power selection.

[0083] The power grid control module includes a load analysis unit and an optimization mechanism unit.

[0084] The load analysis unit is used to align the substation load rate data, electricity price signal data and new energy output status data according to timestamps, construct a power grid operation data stream, and extract the characteristics of the power grid operation data stream through the time series data sliding window mechanism. The characteristics include the duration of the peak period, the duration of the valley period, the load increase rate, the load drop rate, the electricity price change cycle and the electricity price fluctuation range. The output is a structured load state vector. The structured load state vector is labeled with classification labels according to the historical load model and the preset scheduling strategy experience library. The classification labels include peak period-high load, valley period-low electricity price and renewable energy surplus period, and the output is load analysis information data.

[0085] The load analysis unit aligns the substation load rate data, electricity price signal data and new energy output status data by timestamp, constructs a power grid operation data stream, and uses the time series data sliding window mechanism to extract features such as peak period duration, valley period duration, load rise rate, load fall rate, electricity price change cycle and electricity price fluctuation range, and outputs a structured load state vector to provide rich feature information for subsequent analysis. According to the historical load model and the preset dispatch strategy experience library, the structured load state vector is classified and labeled, including peak period-high load, valley period-low electricity price and renewable energy surplus period, and load analysis information data is output to provide a key basis for the optimization mechanism unit to formulate charging adjustment instructions.

[0086] The optimization mechanism unit is used to perform text sentiment analysis and event keyword recognition on social media text data, and the output is the event activation factor, which is the impact intensity of the sudden event in the current area.

[0087] Establish fuzzy rules, the fuzzy rules are:

[0088] When the event activation factor is greater than the preset event activation factor standard value and the classification label is peak period-high load, a signal to reduce charging power and a signal to increase charging time are output.

[0089] When the event activation factor is less than the preset event activation factor standard value and the classification label is off-peak period-low electricity price, a signal to increase charging power and a signal to reduce charging time are output.

[0090] When the classification label is the period of renewable energy surplus, output signals for increasing the charging power and improving the utilization rate of new energy electric energy.

[0091] Output a charging adjustment command.

[0092] The optimization mechanism unit continuously obtains the actual initial charging temperature rise rate of the actual feedback, calculates the error index between the actual initial charging temperature rise rate and the initial charging temperature rise rate, and adjusts the weight parameters of the health score value and the event activation factor in the fuzzy rule according to the error index. The relationship between the weight parameter and the charging adjustment command is as follows:

[0093] The higher the weight parameter corresponding to the health score value, the higher the degree of decrease and increase of the corresponding charging power signal.

[0094] The higher the weight parameter corresponding to the event activation factor, the higher the degree of decrease and increase of the corresponding charging duration signal.

[0095] Update the charging adjustment command according to the health score value and the event activation factor.

[0096] The optimization mechanism unit performs text sentiment analysis and event keyword recognition on social media text data, and outputs an event activation factor. This factor reflects the impact intensity of emergencies in the current area and provides an important reference factor for the judgment of fuzzy rules. Based on the event activation factor and the classification label output by the load analysis unit, charging adjustment commands are generated according to the preset fuzzy rules, such as reducing the charging power signal, increasing the charging duration signal, increasing the charging power signal and reducing the charging duration signal, and increasing the utilization rate of new energy electric energy, etc., to achieve the preliminary regulation of charging behavior. Continuously obtain the actual initial charging temperature rise rate, calculate its error index with the expected value, dynamically adjust the weight parameters of the health score value and the event activation factor in the fuzzy rule according to the error index, and update the charging adjustment command according to the adjusted weight parameters, so that the charging adjustment is more accurate and flexible, and can better adapt to the changes in the battery health status and the external environment. Among them, the higher the weight parameter corresponding to the health score value, the higher the degree of decrease and increase of the corresponding charging power signal, and the higher the weight parameter corresponding to the event activation factor, the higher the degree of decrease and increase of the corresponding charging duration signal.

[0097] The power grid regulation module can comprehensively utilize substation load rate data, electricity price signal data, new energy output status data, and social media text data, extract and classify the power grid operation characteristics through the load analysis unit, perform text sentiment analysis and event keyword recognition with the help of the optimization mechanism unit, generate charging adjustment commands according to the fuzzy rules, and dynamically adjust the weight parameters according to the error index between the actual initial charging temperature rise rate and the expected value, and then update the charging adjustment command to achieve the precise regulation of the power grid charging behavior to adapt to different power grid operation states and external environment changes.

[0098] The data integration and processing module includes a logical fusion unit and a processing flow construction unit.

[0099] The logical fusion unit is used to perform an association process between the evaluation mechanism establishment unit and the optimization mechanism unit.

[0100] The logical fusion unit can perform an association process between the evaluation mechanism establishment unit and the optimization mechanism unit, break the information barrier between the two units, promote data circulation and collaborative work between different mechanisms, make the operation of the charging pile more coordinated and efficient, and lay a foundation for the subsequent unified data processing flow.

[0101] The processing flow construction unit is used to establish a unified data input and output structure, and perform continuous tracking and processing traceability on all data.

[0102] The processing flow construction unit establishes a unified data input and output structure, standardizes the data flow mode in the charging pile, ensures that data from different sources and types can be input and output in a consistent format, improves data compatibility and the usability of the charging pile, performs continuous tracking and processing traceability on all data, can clearly understand the flow and changes of data during the entire processing process, facilitates timely discovery and solution of problems occurring during the data processing process, and ensures the accuracy and reliability of data processing.

[0103] The data integration and processing module is provided with a log record structure, which records all input data, processing nodes, and output results for subsequent analysis and call.

[0104] The log record structure records all input data, processing nodes, and output results, completely preserves the whole process information of data processing, provides rich data support for subsequent data analysis, problem troubleshooting, and charging pile optimization, and the recorded data can be used for subsequent analysis and call. By deeply analyzing the log data, the rules and trends behind the data can be mined, providing a strong basis for the improvement and decision-making of the charging pile.

[0105] The charging control output module includes a control signal generation unit and a user feedback unit.

[0106] The control signal generation unit is used to generate a real-time power control instruction according to the charging adjustment instruction and send it to the charging actuator.

[0107] Based on the charging adjustment instruction, the control signal generation unit can accurately generate a real-time power control instruction, ensure that the control instruction matches the current charging demand and system state, provide an accurate control basis for the charging actuator, quickly and stably send the generated real-time power control instruction to the charging actuator, realize real-time regulation of the charging process, and ensure the efficient and stable progress of the charging process.

[0108] The user feedback unit is used to display charging progress data, battery health status data, optimization prompt information, and warning reminders on the user interface.

[0109] The user feedback unit clearly and intuitively displays the charging progress data on the user interface, enabling users to understand the completion status of charging in real time, reasonably arrange the usage time, accurately present the battery health status data, enabling users to promptly grasp the battery's performance status, providing a reference for battery maintenance and usage, providing optimization prompt information to help users optimize their charging behavior, improve charging efficiency and battery service life. At the same time, it promptly issues warning reminders, allowing users to be aware of possible abnormal situations in advance and take corresponding measures to ensure charging safety.

[0110] The charging control output module also includes a remote data transmission interface, which supports uploading the complete data processing process to the server for centralized analysis and remote maintenance.

[0111] The remote data transmission interface supports uploading the complete data processing process to the server, ensuring that the server can obtain comprehensive and accurate data information, providing a solid data foundation for centralized analysis. The uploaded data can be used by the server for centralized analysis. Through in-depth mining and analysis of a large amount of data, potential problems and optimization spaces in the system operation can be discovered, providing strong support for the improvement and upgrade of the charging pile. Technical personnel can remotely diagnose charging pile faults based on the uploaded data, perform operations such as software updates and parameter adjustments, improve maintenance efficiency, reduce maintenance costs, and ensure the stable operation of the charging pile.

[0112] The charging control output module accurately generates real-time power control instructions according to the charging adjustment instructions and sends them to the charging actuator to achieve real-time and effective control of the charging process. At the same time, it intuitively displays key information such as charging progress, battery health status, optimization prompts, and warning reminders on the user interface, providing users with comprehensive and real-time feedback on the charging status. In addition, through the remote data transmission interface, the complete data processing process can be uploaded to the server for centralized analysis, facilitating the optimization of the charging pile and supporting remote maintenance to ensure the stable operation of the charging pile.

[0113] This charging pile innovatively introduces social media text data as one of the reference factors for charging decisions. A large amount of information related to regional emergencies, special events, etc. is contained in social media, and this information can reflect potential changes in electricity demand within the region. By performing text sentiment analysis and event keyword recognition on social media text data, event activation factors are output and incorporated into the generation logic of charging adjustment instructions, realizing the expansion of data dimensions. Based on the introduced event activation factors and grid load status data, an adaptive fuzzy rule is established. The fuzzy rule comprehensively considers different combinations of event activation factors and grid load status, and outputs corresponding charging adjustment instructions. This logical judgment method is more in line with the complex situations in actual charging scenarios, improving the scientificity and rationality of charging decisions and integrating multi-dimensional data such as vehicle battery parameters, grid parameters, and social media parameters. Battery parameters include state of charge, temperature, voltage, current, and electrochemical impedance, etc., which can accurately reflect the health status of the battery; grid parameters cover substation load rate, electricity price signal, and new energy output status, etc., which help to understand the operation status of the grid; social media parameters provide real-time event information within the region.

[0114] By processing and analyzing these multi-dimensional data, the grid load status can be comprehensively described from multiple perspectives. At the same time, the social media event activation factor can reflect the impact of regional emergencies on electricity demand, further enriching the description dimensions of the grid load status and providing more comprehensive information support for charging decisions. The optimization mechanism unit real-time obtains the actual initial charging temperature rise rate, calculates the error index between it and the preset initial charging temperature rise rate, and dynamically adjusts the weight parameters of the health score value and the event activation factor in the fuzzy rule according to the error index. The higher the weight parameter corresponding to the health score value, the higher the reduction degree and increase degree of the corresponding charging power signal; the higher the weight parameter corresponding to the event activation factor, the higher the reduction degree and increase degree of the corresponding charging duration signal. Through the adaptive fuzzy control algorithm, the charging pile can adjust the charging power and charging duration in real time according to different grid environments and battery states, realizing precise control of the charging power.

[0115] By accurately evaluating the battery health status and real-time monitoring the grid load status, the charging pile can reasonably adjust the charging power, avoid safety problems caused by overcharging the battery or overloading the grid, and ensure power supply safety. According to the grid load situation and electricity price signal, the charging pile can guide users to charge during off-peak periods, reducing the charging cost of users and improving the charging economy of users. The modular design of the charging pile makes each functional module relatively independent, facilitating maintenance and upgrading. At the same time, the introduction of social media real-time event data and the adaptive fuzzy control algorithm enables the system to have a stronger intelligent level, be able to automatically adapt to different charging scenarios and grid environments, and provide more convenient and efficient charging services for users.

[0116] Embodiment 2. Refer to Figure 2 , and a new energy vehicle charging method with modular design is provided, including the following steps:

[0117] Step S1, collect battery parameters, grid parameters, and media parameters during the vehicle charging process.

[0118] Step S2, extract the collected battery data features, establish a battery health assessment mechanism, and output a battery health judgment result.

[0119] Step S3, conduct load analysis and establish fuzzy rules, and output a charging adjustment instruction according to the load analysis information data and media parameters.

[0120] Step S4, conduct logical integration to form a unified data processing flow.

[0121] Step S5, generate a control instruction according to the integration processing result.

[0122] By collecting battery parameters during the vehicle charging process and extracting data features to establish an evaluation mechanism, this method can accurately judge the battery health status. This helps users understand the battery performance in a timely manner, reasonably arrange the charging plan, avoid potential safety hazards caused by battery aging or failure, and at the same time provide a scientific basis for battery maintenance and replacement, extend the battery service life. Considering the load analysis information data and media parameters comprehensively, establish fuzzy rules and output charging adjustment instructions. This intelligent adjustment method can dynamically adjust the charging strategy according to factors such as the actual grid load and vehicle usage scenarios, realize the optimization of the charging process, improve the charging efficiency, reduce energy waste, and at the same time reduce the impact on the grid and ensure the stable operation of the grid. Conduct logical integration on various collected parameters to form a unified data processing flow, and generate a control instruction according to the integration result. This process realizes the centralized processing and efficient utilization of data, enables the charging to quickly respond to various changes, improves the stability and reliability, and provides users with a more stable and efficient charging service.

[0123] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disks, or optical disks. These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in the process Figure 1 in one process or multiple processes and / or boxes Figure 1 or the functions specified in multiple boxes.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A modularly designed new energy vehicle charging pile, characterized in that: It includes data acquisition module, battery status analysis module, power grid control module, data integration and processing module and charging control output module; The data acquisition module is used to collect battery parameters, grid parameters and media parameters during vehicle charging; The battery status analysis module is used to extract the collected battery data features, establish a battery health assessment mechanism, and output a battery health judgment result; The power grid control module is used to perform load analysis and establish fuzzy rules, and output charging adjustment instructions according to load analysis information data and media parameters; The data integration processing module is used to perform logical integration to form a unified data processing flow; The charging control output module is used to generate a control instruction according to the integrated processing result.

2. A modularly designed new energy vehicle charging pile as claimed in claim 1, characterized in that: The data acquisition module includes a vehicle information acquisition unit, a battery parameter acquisition unit, a power grid data acquisition unit and a media data acquisition unit; The vehicle information collection unit is used to collect vehicle identification code data and charging interface type data; The battery parameter acquisition unit is used to collect battery state of charge data, battery temperature data, voltage data, current data and electrochemical impedance data; The power grid data acquisition unit is used to collect substation load rate data, electricity price signal data and new energy output status data; The media data collection unit is used to collect social media text data in the area.

3. A modular design of a new energy vehicle charging pile as claimed in claim 2, characterized in that: The battery status analysis module includes a battery feature extraction unit and an evaluation mechanism establishment unit; The battery feature extraction unit is used to perform noise filtering, timestamp alignment and missing value filling on the battery state of charge data, voltage data, current data, battery temperature data and electrochemical impedance data, and outputs the processed data. The time series data sliding window mechanism and the preset empirical feature template are used to extract features from the processed data. The features include the battery state of charge change rate, voltage drop amplitude, initial charging temperature rise rate and amplitude change of low-frequency feature points in the electrochemical impedance spectrum, and the output is a battery feature vector; The evaluation mechanism establishment unit is used to establish a battery health evaluation mechanism, and the battery health evaluation mechanism is: The battery feature vectors are organized into feature sequences in chronological order and input into the long short-term memory network model. The health score values ​​are output according to the preset hierarchical scoring mechanism and analyzed: If the health score value is greater than the preset first score value, it is determined that the battery is healthy and can be charged at high power; If the health score value is between the first score value and the preset second score value, it is determined that the battery is slightly aged and can be charged at medium power; If the health score value is less than the second score value, it is determined that the battery is aged and can be charged at low power; Output the battery health judgment result.

4. A modularly designed new energy vehicle charging pile as claimed in claim 3, characterized in that: The power grid control module includes a load analysis unit and an optimization mechanism unit; The load analysis unit is used to align the substation load rate data, electricity price signal data and new energy output status data according to timestamps, construct a power grid operation data stream, and extract the characteristics of the power grid operation data stream through a time series data sliding window mechanism. The characteristics include the duration of the peak period, the duration of the valley period, the load increase rate, the load drop rate, the electricity price change cycle and the electricity price fluctuation range. The output is a structured load state vector. The structured load state vector is labeled with classification labels according to the historical load model and the preset scheduling strategy experience library. The classification labels include peak period-high load, valley period-low electricity price and renewable energy surplus period, and the output is load analysis information data. The optimization mechanism unit is used to perform text sentiment analysis and event keyword recognition on social media text data, and the output is an event activation factor, which is the impact intensity of the sudden event in the current area; Establish fuzzy rules, the fuzzy rules are: When the event activation factor is greater than the preset event activation factor standard value and the classification label is peak period-high load, a signal to reduce the charging power and a signal to increase the charging time are output; When the event activation factor is less than the preset event activation factor standard value and the classification label is off-peak period-low electricity price, a signal to increase the charging power and a signal to reduce the charging time are output; When the classification label is a renewable energy surplus period, a signal for increasing charging power and a signal for increasing the utilization rate of new energy power are output; Output charging regulation command.

5. A modularly designed new energy vehicle charging pile as claimed in claim 4, characterized in that: The optimization mechanism unit obtains the actual initial charging temperature rise rate fed back in real time, calculates the error index between the actual initial charging temperature rise rate and the initial charging temperature rise rate, and adjusts the weight parameters of the health score value and the event activation factor in the fuzzy rule according to the error index. The relationship between the weight parameter and the charging adjustment instruction is: The higher the weight parameter corresponding to the health score, the higher the corresponding decrease and increase of the charging power signal; The higher the weight parameter corresponding to the event activation factor, the higher the corresponding decrease and increase of the charging duration signal; The charging regulation instructions are updated based on the health score value and event activation factor.

6. A modularly designed new energy vehicle charging pile as claimed in claim 5, characterized in that: The data integration processing module includes a logic fusion unit and a processing flow construction unit; The logic fusion unit is used to associate the evaluation mechanism establishment unit with the optimization mechanism unit; The processing flow construction unit is used to establish a unified data input and output structure to continuously track and trace the processing of all data.

7. A modularly designed new energy vehicle charging pile as claimed in claim 5, characterized in that: The data integration processing module is provided with a log recording structure to record all input data, processing nodes and output results for subsequent analysis and calling.

8. A modularly designed new energy vehicle charging pile as claimed in claim 7, characterized in that: The charging control output module includes a control signal generating unit and a user feedback unit; The control signal generating unit is used to generate a real-time power control instruction according to the charging regulation instruction and send it to the charging actuator; The user feedback unit is used to display charging progress data, battery health status data, optimization prompt information and early warning reminders on the user interface.

9. A modularly designed new energy vehicle charging pile as claimed in claim 8, characterized in that: The charging control output module also includes a remote data feedback interface, which supports uploading the complete data processing flow to the server for centralized analysis and remote maintenance.

10. A modular design of new energy vehicle charging method, which is applied to a modular design of new energy vehicle charging pile as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step S1, collecting battery parameters, grid parameters and media parameters during vehicle charging; Step S2, extracting the collected battery data features, establishing a battery health assessment mechanism, and outputting a battery health judgment result; Step S3, performing load analysis and establishing fuzzy rules, and outputting charging adjustment instructions according to load analysis information data and media parameters; Step S4, performing logical integration to form a unified data processing flow; Step S5, generating a control instruction according to the integration processing result.

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