Light storage and charging integrated new energy charging station

Abstract

The invention belongs to the technical field of new energy charging, and particularly relates to a light storage and charging integrated new energy charging station which comprises a photovoltaic power generation module, an energy storage module, a charging module and an intelligent control unit. The photovoltaic power generation module predicts power generation power through an LSTM neural network in combination with meteorological data, and dynamically adjusts a prediction time window based on a weather type; the energy storage module is directly connected with the photovoltaic direct current side to improve energy efficiency; the charging module obtains the SOC / SOH state of the vehicle and the user demand through the data acquisition unit, and generates a priority coefficient by using a scoring model. The system dynamically distributes photovoltaic power, energy storage power and power grid power through a demand grading module, and when it is detected that the photovoltaic output deviation exceeds a threshold value or the energy storage SOC is insufficient, a power redistribution and compensation mechanism is automatically triggered. According to the invention, efficient utilization of renewable energy sources, intelligent scheduling of an energy storage system and differentiated supply of charging services are realized, and the problems of low photovoltaic consumption rate and response lag of a traditional charging station are solved.

Description

Technical Field

[0001] This invention belongs to the field of new energy charging technology, specifically relating to an integrated photovoltaic, energy storage and charging new energy charging station. Background Technology

[0002] Photovoltaic-energy-storage-charging integration, as an important application direction in the new energy field, achieves efficient utilization and intelligent scheduling of renewable energy by integrating photovoltaic power generation, energy storage systems, and electric vehicle charging facilities. With the rapid popularization of electric vehicles, how to optimize the energy management of charging stations and improve system operating efficiency has become a key research focus in the industry.

[0003] Existing patent document CN118763670A discloses a new energy storage cloud platform technology solution for integrated photovoltaic, energy storage, and charging. This solution mainly achieves intelligent management of charging stations through a data acquisition module, an energy output regulation and analysis module, and a charging control module. The system monitors changes in the energy storage utilization rate of charging piles, analyzes its decreasing acceleration value, and redistributes energy based on preset thresholds, allocating surplus energy to charging piles with lower utilization rates to improve overall charging efficiency. This technology uses the ratio of charging pile working / non-working time combined with temperature monitoring data to evaluate energy storage utilization rate and establishes a relationship model between the time ratio and energy storage utilization rate. The system can dynamically adjust the output power according to the vehicle charging status, optimizing the energy utilization efficiency of the charging station to a certain extent.

[0004] While the aforementioned solution possesses certain technical advantages in charging power allocation, it still suffers from significant limitations: First, its energy storage utilization assessment method is overly simplistic, relying solely on the percentage of charging pile operating time and temperature change curves. It fails to comprehensively consider key factors such as battery aging, ambient temperature fluctuations, and charge-discharge cycle history, leading to a significant discrepancy between the assessment results and actual energy storage performance. Second, the system's dynamic response capability is insufficient. The power allocation strategy is based on static data analysis with a fixed monitoring period (e.g., 10-minute power change rate), unable to adapt in real-time to minute-level fluctuations in photovoltaic power generation and sudden changes in charging demand. Third, the solution lacks consideration for multi-energy synergy, failing to establish a dynamic coordination mechanism between photovoltaic power generation, energy storage systems, and the power grid. This can easily lead to energy waste or insufficient power supply when photovoltaic output changes abruptly. Furthermore, the system's ability to identify personalized user needs is weak, allocating power solely based on simple reservation information without considering differentiated factors such as vehicle battery type and the user's charging urgency, impacting user experience and the system's operational economy. Summary of the Invention

[0005] The purpose of this invention is to provide an integrated photovoltaic, energy storage and charging new energy charging station to overcome the technical defects of existing integrated photovoltaic, energy storage and charging systems, such as single energy storage assessment, lagging dynamic response and insufficient multi-energy coordination.

[0006] The present invention achieves the above objectives through the following technical solutions: This invention proposes an integrated photovoltaic, energy storage, and charging new energy charging station, comprising a photovoltaic power generation module, an energy storage module, and a charging module. The charging station also includes: The data acquisition module is used to extract charging status information when the vehicle and the charging module interact in real time. Based on the charging status information and user input information, the vehicle charging demand data is determined, including power priority. The photovoltaic forecasting module is used to predict the photovoltaic power sequence within a set time window based on the photovoltaic historical data of the photovoltaic power generation module and the real-time meteorological information. Combined with a preset adjustment strategy, the window width of the time window is dynamically adjusted according to the weather type in the meteorological information to update the photovoltaic power sequence. The demand grading module is used to dynamically adjust the power priority by combining the real-time power information of the energy storage module, the charging demand data, and the updated photovoltaic power sequence.

[0007] Furthermore, the data acquisition module includes a first acquisition unit and a second acquisition unit; the first acquisition unit is used to acquire the charging status parameters of the vehicle battery in real time through the communication interface of the charging module, including the current state of charge (SOC) value, the battery health (SOH) value, and the maximum allowable charging power; the second acquisition unit is used to receive user input information, including an emergency charging indicator obtained through the human-machine interface of the charging module and / or the scheduled charging time and / or the user-set charging limit value transmitted through the user's mobile terminal APP.

[0008] Furthermore, the data acquisition module also includes a charging demand scoring unit with a preset charging demand scoring model. The charging demand scoring unit is used to dynamically set the power allocation coefficient for the vehicle based on the charging status parameters and user input information, and transmit the power allocation coefficient to the charging module for charging power control. The charging demand scoring model includes the following formula: Score = aSOC + βSOH f +yU Where Score is the rating value; SOC is the standardized SOC value; SOH is the score. f = 1 + (SOH-80) / 100; U is assigned a value based on the user's input information: when "emergency charging" is input, U=X1; when "regular charging" is input, U=X2; when "delayed charging" is input, U=X3; X1>X2>X3, and α, β, and y are weighting coefficients. The charging demand scoring unit is also used to output the power allocation coefficient corresponding to the interval in which the score value (Score) is located.

[0009] Furthermore, the photovoltaic prediction module includes: The third acquisition unit is used to acquire the output voltage and current values ​​of the photovoltaic power generation module and calculate the photovoltaic power data; The fourth data acquisition unit is used to measure the temperature distribution data on the surface of the photovoltaic power generation module; The fifth acquisition unit is used to acquire cloud cover information, extract cloud coverage rate by combining image recognition algorithm, and determine the weather type based on cloud coverage rate. Weather types include "sunny", "cloudy" and "rainy". The prediction unit is used to build a photovoltaic power prediction model using an LSTM neural network. It takes photovoltaic power data, temperature distribution data and weather type as input and outputs a photovoltaic power prediction sequence within a set time window. The dynamic window adjustment unit is used to adjust the window width and data update frequency of the prediction time window in the photovoltaic power prediction sequence according to the real-time weather type and the preset adjustment strategy.

[0010] Furthermore, the step of extracting cloud coverage using image recognition algorithms and determining weather type based on cloud coverage includes: Sky images are captured in real time by an image acquisition device pre-installed on the photovoltaic power generation module; A convolutional neural network is used to process sky images, identify cloud regions, and calculate cloud coverage. Weather type is determined based on a preset cloud cover threshold range: The convolutional neural network is trained in advance using a set of sky image samples labeled with cloud regions.

[0011] Furthermore, the preset adjustment strategy includes: When the weather type is "sunny", the first preset window width and the first update frequency are used; When the weather type is "cloudy", a second preset window width smaller than the first preset window width and a second update frequency higher than the first update frequency are used; When the weather type is "cloudy and rainy", a third preset window width that is smaller than the second preset window width and a third update frequency that is higher than the second update frequency are used. The ratio of the window width to the update frequency increases as the weather type changes from sunny to rainy.

[0012] Furthermore, the demand grading module includes a power dynamic allocation unit and a real-time adjustment unit; The power dynamic allocation unit is used to calculate the total available power Pt of the charging station based on the real-time SOC value and maximum charging and discharging power of the energy storage module, as shown in the following formula: Pt = Pp + Pe + Pg Where Pp is the predicted photovoltaic power, Pe is the available energy storage power, and Pg is the grid dispatchable power; The power dynamic allocation unit is also used to allocate the target power Pa of each charging module proportionally according to the power allocation coefficient, as shown in the following formula: Pa i =(A i / ∑At)×Pt×η Among them, A i Let ∑At be the power allocation coefficient of the i-th charging module, ∑At be the sum of the power allocation coefficients of all charging modules, and η be the efficiency coefficient of the charging module. The real-time adjustment unit is used to recalculate the total available power Pt of the charging station and adjust the target power Pa of each charging module when the deviation between the updated value of the photovoltaic power sequence and the original predicted value exceeds the first threshold; when the real-time SOC value of the energy storage module is lower than the second threshold, it reduces the available energy storage power Pe and triggers the redistribution of charging demand.

[0013] Furthermore, the demand grading module also includes a dynamic compensation unit, used for: The deviation ΔP between the actual output power of each charging module and the target power Pa is monitored in real time. When |ΔP| / Pa > the third threshold, power compensation is performed in the next scheduling cycle. The compensation amount Pc is: Pc = k×ΔP, where k is the compensation coefficient. Record historical compensation amount η old And dynamically adjust the efficiency coefficient η of the charging module. new As shown in the following formula: η new = η old × (1 - |ΣΔP| / (ΣPa)) Where ΣΔP is the cumulative power deviation and ΣPa is the cumulative target power.

[0014] The beneficial effects of this invention are as follows: This invention presents an integrated photovoltaic, energy storage, and charging new energy charging station that achieves multi-module collaborative control. The photovoltaic power generation module, combined with dynamic prediction technology, can adapt to power fluctuations under different weather conditions. The direct coupling design between the energy storage module and the photovoltaic DC side reduces energy conversion losses, while intelligent charging and discharging strategies extend battery life. The data acquisition module accurately acquires vehicle status and user needs, and, in conjunction with the priority scheduling algorithm of the demand classification module, realizes differentiated charging services, ensuring priority power supply for emergency vehicles while optimizing system load balancing. A dynamic compensation mechanism corrects power deviations in real time, ensuring the stability and accuracy of the charging process. This system overcomes the shortcomings of traditional charging stations, such as high dependence on the power grid and slow response speed, through the closed-loop linkage of photovoltaic prediction, energy storage scheduling, and charging control. Attached Figure Description

[0015] Figure 1 This is a system block diagram of an integrated photovoltaic, energy storage and charging new energy charging station proposed in this invention; Figure 2 This is a flowchart of photovoltaic power prediction in this invention. Detailed Implementation

[0016] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0017] Please see Figure 1 and Figure 2 In one specific embodiment, an integrated photovoltaic-storage-charging new energy charging station is proposed, including a photovoltaic power generation module, an energy storage module, and a charging module. The photovoltaic power generation module can adopt a distributed photovoltaic array, which is responsible for converting part of the solar energy in the charging station area into electrical energy and storing it in the energy storage module. Obviously, the power generation efficiency of the photovoltaic power generation module is easily affected by the weather. The energy storage module can adopt a battery system with power conversion function to store excess photovoltaic energy or release electrical energy to ensure charging during different weather periods. The charging module is specifically a charging pile, including fast charging piles and slow charging piles.

[0018] In this embodiment, the charging station also includes a data acquisition module, a photovoltaic (PV) prediction module, and a demand classification module. The data acquisition module extracts charging status information during real-time information interaction between the vehicle and the charging module, and determines vehicle charging demand data based on the charging status information and user input information. This charging demand data includes power priority. The PV prediction module predicts the PV power sequence within a set time window based on historical PV data from the PV power generation module and real-time monitored meteorological information. It then dynamically adjusts the window width of the time window according to the weather type in the meteorological information, using a preset adjustment strategy, to update the PV power sequence. The demand classification module dynamically adjusts the power priority by combining real-time power information from the energy storage module, charging demand data, and the updated PV power sequence.

[0019] In photovoltaic-storage-charging systems, photovoltaic power generation is intermittent, and energy storage capacity is limited. Furthermore, the charging needs of different vehicles vary in urgency (e.g., commercial vehicles require rapid charging, while private cars can be flexibly recharged). This application quantifies power priority across three dimensions: battery status (e.g., charging should be prioritized when SOC is below 20% to prevent battery damage); user needs (emergency charging indicators or scheduled charging times); and system load (to avoid localized overload). For example, a scoring model (Score = 0.6 × SOC + 0.3 × SOH + 0.1 × urgency coefficient) categorizes vehicles into high, medium, and low priority levels. High-priority vehicles can utilize backup energy storage power, while low-priority vehicles automatically reduce power or queue when photovoltaic output is insufficient.

[0020] More specifically, when an electric vehicle is connected to the charging gun, the data acquisition module acquires the battery status parameters transmitted by the vehicle's BMS in real time, including the current SOC value, SOH health level, and battery temperature gradient data. This process takes place during real-time information interaction between the vehicle and the charging module. User input information is collected through dual channels: the emergency charging indicator directly input on the charging pile touch screen (which generates a priority flag when triggered), and the reservation information transmitted from the mobile APP background (such as the expected charging completion time and power requirements).

[0021] Based on the above, in a preferred embodiment, the data acquisition module of this application includes a first acquisition unit and a second acquisition unit; the first acquisition unit is used to acquire the charging status parameters of the vehicle battery in real time through the communication interface of the charging module, including the current state of charge (SOC) value, the battery health (SOH) value, and the maximum allowable charging power; the second acquisition unit is used to receive user input information, including an emergency charging indicator obtained through the human-machine interface of the charging module and / or the scheduled charging time and / or the user-set charging limit value transmitted through the user's mobile terminal APP.

[0022] In a preferred embodiment, the data acquisition module of this application further includes a charging demand scoring unit with a preset charging demand scoring model. The charging demand scoring unit is used to dynamically set the power allocation coefficient for the vehicle based on charging status parameters and user input information, and transmit the power allocation coefficient to the charging module for charging power control. The charging demand scoring unit is also used to output the power allocation coefficient for the corresponding interval based on the interval of the score value Score. The charging demand scoring model includes the following formula: Score = aSOC + βSOH f +yU Where Score is the rating value; SOC is the standardized SOC value; SOH is the score. f = 1 + (SOH-80) / 100; U is assigned a value based on the user's input information: when "emergency charging" is input, U=X1; when "regular charging" is input, U=X2; when "delayed charging" is input, U=X3; X1>X2>X3, and α, β, and y are weighting coefficients.

[0023] Understandably, the charging demand scoring unit in this application achieves power allocation through a quantification model. The charging demand scoring model of this unit adopts a linear weighted algorithm: Score = 0.5SOC + 0.3SOH + 0.2U, where SOC is the standardized value of the current state of charge (SOC / 100), and SOH = 1 + (SOH-80) / 100 (reset to zero when SOH < 80). The user's urgency level U is differentiated through a triple assignment: for example, emergency charging (U=1.0) corresponds to special vehicles such as ambulances, regular charging (U=0.6) is for ordinary pre-booked users, and delayed charging (U=0.3) is suitable for flexible loads at night.

[0024] In addition, the score is mapped to three power levels. For example, when Score ≥ 0.8, the allocation coefficient K = 1.5 (full-power fast charging); when 0.5 ≤ Score < 0.8, K = 1.0 (standard power); and when Score < 0.5, K = 0.5 (limited power). This model updates the score every 10 seconds by acquiring BMS data in real time and combining it with emergency indicators input through the human-machine interface. The weighting coefficients α, β, and γ in this dynamic adjustment can be adjusted by the backend management system according to seasonal strategies (e.g., setting α = 0.6 in summer to emphasize battery protection).

[0025] Please see Figure 2 In a preferred embodiment, the photovoltaic prediction module of this application includes a third acquisition unit, a fourth acquisition unit, a prediction unit, and a dynamic window adjustment unit. The third acquisition unit is used to acquire the output voltage and current values ​​of the photovoltaic power generation module and calculate the photovoltaic power data. The third acquisition unit can use a Hall sensor to monitor the output voltage and current of the photovoltaic array in real time. The fourth acquisition unit is used to measure the temperature distribution data on the surface of the photovoltaic power generation module. The fourth acquisition unit can use an infrared thermal imager array. The fifth acquisition unit is used to acquire cloud coverage information. The fifth acquisition unit can use an all-sky imager and combine it with an image recognition algorithm to extract the cloud coverage rate and determine the weather type based on the cloud coverage rate. The weather type includes "sunny", "cloudy", and "rainy". The prediction unit is used to construct a photovoltaic power prediction model using an LSTM neural network. It takes photovoltaic power data, temperature distribution data, and weather type as input and outputs a photovoltaic power prediction sequence within a set time window. The dynamic window adjustment unit is used to adjust the window width and data update frequency of the prediction time window in the photovoltaic power prediction sequence according to the real-time weather type and a preset adjustment strategy.

[0026] In a preferred embodiment, the cloud cover rate is extracted by combining an image recognition algorithm, and the weather type is determined according to the cloud cover rate, including: collecting sky images in real time through an image acquisition device preset on the photovoltaic power generation module; processing the sky images by using a convolutional neural network to identify the cloud area and calculate the cloud cover rate; determining the weather type according to a preset cloud cover rate threshold range: wherein, the convolutional neural network is pre-trained by a sky image sample set marked with cloud areas.

[0027] In a preferred embodiment, the preset calibration strategy includes: when the weather type is "sunny", a first preset window width and a first update frequency are adopted; when the weather type is "cloudy", a second preset window width smaller than the first preset window width and a second update frequency higher than the first update frequency are adopted; when the weather type is "rainy or overcast", a third preset window width smaller than the second preset window width and a third update frequency higher than the second update frequency are adopted; wherein, the ratio of the window width to the update frequency increases as the weather type changes from sunny to rainy or overcast.

[0028] In specific implementation, the improved U-Net convolutional neural network is used to analyze the sky images, output the cloud cover rate C, and divide the weather type according to the threshold. As an example: when C≤30%, it is sunny; when 30%<C≤70%, it is cloudy; when C>70%, it is rainy or overcast (including rain, snow and other weather). The prediction unit adopts a double-layer LSTM neural network, and the input features include the photovoltaic power sequence within a historical set period, the current temperature distribution statistics (mean / variance / ΔT) and the weather type code (sunny = 0, cloudy = 1, rainy or overcast = 2), and outputs the power prediction sequence within the future time window. The dynamic window adjustment unit executes the preset calibration strategy. As an example: in the sunny mode, a 30-minute prediction window (update frequency: 10 minutes) is adopted; in the cloudy mode, it is shortened to a 15-minute window (update frequency: 5 minutes); in the rainy or overcast mode, a 5-minute rolling window (updated every minute) is enabled, and the ratio of the window width to the update frequency increases step by step according to the ratio of 1.5:1 (sunny), 3:1 (cloudy), 5:1 (rainy or overcast).

[0029] It is important to emphasize that the preset adjustment strategy optimizes the accuracy of photovoltaic power prediction by dynamically adjusting the prediction time parameters. The system automatically matches differentiated window configurations based on the real-time weather type: under stable illumination conditions, a longer prediction window and a moderate update frequency are used to fully utilize the regularity of photovoltaic output; when the weather turns cloudy, the prediction window is shortened while the update frequency is increased to enhance the tracking ability of intermittent irradiance changes; during cloudy or rainy weather, the window width is further compressed and the data refresh rate is significantly increased to quickly respond to drastically fluctuating power generation conditions. In particular, the ratio of window width to update frequency is designed to monotonically increase with the severity of weather deterioration. This ratio reflects the system's dynamic trade-off between prediction stability and real-time performance, using higher-frequency local predictions in severe weather to offset errors caused by environmental uncertainties.

[0030] The following is an example of dynamic adjustment in the dynamic window adjustment unit: When the system suddenly changes from sunny to cloudy: Window compression: The forecast window is reduced from 30 minutes to 15 minutes (reducing reliance on historical data); Frequency increase: Update interval reduced from 10 minutes to 5 minutes (faster response to new data); Strategy switching: Maintain a 3:1 bandwidth ratio to ensure forecast continuity.

[0031] When it worsens further into rainy weather: Window further compressed: 15 minutes → 5 minutes (focusing on ultra-short-term forecasts); Frequency doubled: 5 minutes → 1 minute (real-time tracking of sudden changes); Wideband ratio adjustment: 3:1 → 5:1 (prioritizing predicted refresh rate).

[0032] In a preferred embodiment, the demand grading module includes a power dynamic allocation unit and a real-time adjustment unit; the power dynamic allocation unit is used to calculate the total available power Pt of the charging station based on the real-time SOC value and maximum charging / discharging power of the energy storage module, as shown in the following formula: Pt = Pp + Pe + Pg Where Pp is the predicted photovoltaic power, Pe is the available energy storage power, and Pg is the grid dispatchable power; The power dynamic allocation unit is also used to proportionally allocate the target power Pa of each charging module according to the power allocation coefficient, as shown in the following formula: Pa i =(A i / ∑At)×Pt×η Among them, A i Let ∑At be the power allocation coefficient of the i-th charging module, ∑At be the sum of the power allocation coefficients of all charging modules, and η be the efficiency coefficient of the charging module. The real-time adjustment unit is used to recalculate the total available power Pt of the charging station and adjust the target power Pa of each charging module when the deviation between the updated value of the photovoltaic power sequence and the original predicted value exceeds the first threshold; when the real-time SOC value of the energy storage module is lower than the second threshold, it reduces the available energy storage power Pe and triggers the redistribution of charging demand.

[0033] In this application, the power dynamic allocation unit continuously monitors three power inputs: the expected power generation Pp provided by the photovoltaic prediction module, the available charge / discharge power Pe calculated by the energy storage system based on the current SOC state (considering battery health and temperature constraints), and the dispatchable power Pg from the grid interface (limited by distribution capacity). The system calculates the total available power Pt = Pp + Pe + Pg in real time and normalizes the power allocation coefficient A. i Mapped to actual target power Pa i The efficiency coefficient η dynamically reflects the operating losses of the charging pile (including line loss and conversion efficiency).

[0034] In addition, the real-time adjustment unit has a dual triggering mechanism: when the deviation between the actual output of photovoltaic power and the predicted value exceeds the preset tolerance, the total available power Pt of the charging station is immediately recalculated and the power is redistributed globally; if the energy storage SOC drops to the protection threshold, the output capacity of Pe is linearly reduced, and the charging queue is reorganized in descending order according to the Score value of the scoring model, that is, high-priority vehicles are kept powered, and low-priority vehicles enter the buffer waiting.

[0035] In a preferred embodiment, the demand grading module further includes a dynamic compensation unit, used for: real-time monitoring of the deviation ΔP between the actual output power of each charging module and the target power Pa; when |ΔP| / Pa > a third threshold, performing power compensation in the next scheduling cycle, with the compensation amount Pc being: Pc = k×ΔP, where k is the compensation coefficient; and recording the historical compensation amount η. old And dynamically adjust the charging module efficiency coefficient η new As shown in the following formula: η new =η old ×(1 - |ΣΔP| / (ΣPa)) Where ΣΔP is the cumulative power deviation and ΣPa is the cumulative target power.

[0036] Understandably, in this application, the dynamic compensation unit continuously compares the actual output power of each charging pile with the target value. When the relative deviation exceeds the allowable range, it generates a correction command: the compensation amount Pc is linearly related to the deviation value ΔP, and its compensation coefficient k is dynamically adjusted according to the system stability requirements. For charging piles with frequent fluctuations, a smaller k value is used to avoid over-adjustment, while for equipment with lagging response, the k value is increased to enhance the compensation strength. Simultaneously, the system uses a sliding time window to statistically analyze the cumulative deviation ΣΔP and the cumulative target power ΣPa, and recursively updates the efficiency coefficient η accordingly. new The above design can automatically correct for efficiency degradation caused by equipment aging, increased contact resistance, etc., ensuring that the power distribution model always matches the actual operating conditions. When the |ΣΔP / ΣPa| of a charging pile is continuously high for several consecutive cycles, a maintenance alarm will be triggered, indicating possible hardware failure or calibration requirements.

[0037] In practice, the setting of each threshold is based on a balance between the safety and performance of the charging station system: the first threshold (photovoltaic prediction deviation) is determined based on the statistical analysis of historical photovoltaic fluctuations, and exceeding it will trigger the correction of the prediction model; the second threshold (energy storage SOC protection) is set in the range of 20%-25% according to the battery characteristics to prevent over-discharge from damaging the battery life; the third threshold (power compensation trigger) is calibrated through experiments.

[0038] Based on the charging station proposed in the above embodiments, a charging control method is implemented, including the following steps: acquiring vehicle charging status information and user input information through a data acquisition module to determine vehicle charging demand data, including power priority; predicting photovoltaic power sequence based on historical photovoltaic data and real-time meteorological information through a photovoltaic prediction module, and dynamically adjusting the window width of the prediction time window according to the weather type; and dynamically adjusting power priority by combining real-time power information from the energy storage module, charging demand data, and the updated photovoltaic power sequence through a demand grading module.

[0039] The working principle of this invention, the integrated photovoltaic, energy storage, and charging new energy charging station, is as follows: The charging station system converts solar energy into electricity through photovoltaic (PV) power generation modules, forming a multi-energy complementarity with energy storage modules and grid power supply. A data acquisition module obtains real-time vehicle battery status (SOC / SOH) and user charging demand, dynamically generating power priorities through a scoring model. The PV forecasting module predicts power generation based on meteorological data and an LSTM algorithm, adaptively adjusting the forecast time window and update frequency according to three weather types: sunny, cloudy, and rainy. The demand classification module integrates PV forecast power, energy storage SOC status, and grid capacity, calculating the total available power using the formula Pt=Pp+Pe+Pg, and then allocating power to each charging pile according to a priority coefficient. When the actual PV output deviation exceeds the first threshold or the energy storage SOC falls below the second threshold, power reallocation is triggered. Simultaneously, the dynamic compensation unit monitors the charging pile output deviation; if it exceeds the third threshold, proportional compensation is performed in the next cycle, and the efficiency coefficient η is recursively updated. Through this closed-loop control, the system maximizes the absorption of PV power generation and optimizes the scheduling of the energy storage system while ensuring high-priority charging demand.

[0040] It will be apparent to those skilled in the art that the embodiments of the present invention are not limited to the details of the exemplary embodiments described above, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the embodiments of the present invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the embodiments of the present invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be encompassed within the embodiments of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules, or devices recited in the system, apparatus, or terminal claims may also be implemented by the same unit, module, or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0041] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A photovoltaic-storage-charging integrated new energy charging station, comprising a photovoltaic power generation module, an energy storage module, and a charging module, characterized in that, The charging station also includes: The data acquisition module is used to extract charging status information when the vehicle and the charging module interact in real time. Based on the charging status information and user input information, the vehicle charging demand data is determined, including power priority. The photovoltaic forecasting module is used to predict the photovoltaic power sequence within a set time window based on the photovoltaic historical data of the photovoltaic power generation module and the real-time meteorological information. Combined with a preset adjustment strategy, the window width of the time window is dynamically adjusted according to the weather type in the meteorological information to update the photovoltaic power sequence. The demand grading module is used to dynamically adjust the power priority by combining the real-time power information of the energy storage module, the charging demand data, and the updated photovoltaic power sequence.

2. The integrated photovoltaic, energy storage, and charging new energy charging station according to claim 1, characterized in that, The data acquisition module includes a first acquisition unit and a second acquisition unit; the first acquisition unit is used to acquire the charging status parameters of the vehicle battery in real time through the communication interface of the charging module, including the current state of charge (SOC) value, the battery health (SOH) value, and the maximum allowable charging power. The second acquisition unit is used to receive user input information, including an emergency charging indicator obtained through the human-machine interface of the charging module and / or the scheduled charging time and / or the user-set maximum charging amount value transmitted through the user's mobile terminal APP.

3. The integrated photovoltaic, energy storage, and charging new energy charging station according to claim 2, characterized in that, The data acquisition module also includes a charging demand scoring unit with a preset charging demand scoring model. The charging demand scoring unit is used to dynamically set the power allocation coefficient for the vehicle based on the charging status parameters and user input information, and transmit the power allocation coefficient to the charging module for charging power control. The charging demand scoring model includes the following formula: Score = aSOC+βSOH f +yU; Where Score is the rating value; SOC is the standardized SOC value; SOH is the score. f = 1 + (SOH-80) / 100; U is assigned a value based on the user's input: when "emergency charging" is input, U=X1; when "regular charging" is input, U=X2; when "delayed charging" is input, U=X3; X1>X2>X3, and α, β, and y are weighting coefficients. The charging demand scoring unit is also used to output the power allocation coefficient corresponding to the interval in which the score value (Score) is located.

4. The integrated photovoltaic, energy storage, and charging new energy charging station according to claim 3, characterized in that, The photovoltaic prediction module includes: The third acquisition unit is used to acquire the output voltage and current values ​​of the photovoltaic power generation module and calculate the photovoltaic power data; The fourth data acquisition unit is used to measure the temperature distribution data on the surface of the photovoltaic power generation module; The fifth acquisition unit is used to acquire cloud cover information and extract cloud coverage rate by combining image recognition algorithm. The weather type is determined based on the cloud coverage rate. The weather type includes "sunny", "cloudy" and "rainy". The prediction unit is used to build a photovoltaic power prediction model using an LSTM neural network. It takes photovoltaic power data, temperature distribution data and weather type as input and outputs a photovoltaic power prediction sequence within a set time window. The dynamic window adjustment unit is used to adjust the window width and data update frequency of the prediction time window in the photovoltaic power prediction sequence according to the real-time weather type and the preset adjustment strategy.

5. The integrated photovoltaic, energy storage, and charging new energy charging station according to claim 4, characterized in that, The step of extracting cloud cover rate using image recognition algorithms and determining weather type based on cloud cover rate includes: Sky images are captured in real time by an image acquisition device pre-installed on the photovoltaic power generation module; A convolutional neural network is used to process sky images, identify cloud regions, and calculate cloud coverage. Weather type is determined based on a preset cloud cover threshold range: The convolutional neural network is trained in advance using a set of sky image samples labeled with cloud regions.

6. The integrated photovoltaic-storage-charging new energy charging station according to claim 4, characterized in that, The preset adjustment strategy includes: When the weather type is "sunny", the first preset window width and the first update frequency are used; When the weather type is "cloudy", a second preset window width smaller than the first preset window width and a second update frequency higher than the first update frequency are used; When the weather type is "cloudy and rainy", a third preset window width that is smaller than the second preset window width and a third update frequency that is higher than the second update frequency are used; The ratio of the window width to the update frequency increases as the weather type changes from sunny to rainy.

7. The photovoltaic-storage-charging integrated new energy charging station according to claim 4, characterized in that, The demand grading module includes a power dynamic allocation unit and a real-time adjustment unit. The power dynamic allocation unit is used to calculate the total available power Pt of the charging station based on the real-time SOC value and maximum charging and discharging power of the energy storage module, as shown in the following formula: Pt = Pp + Pe + Pg; Where Pp is the predicted photovoltaic power, Pe is the available energy storage power, and Pg is the grid dispatchable power; The power dynamic allocation unit is also used to allocate the target power Pa of each charging module proportionally according to the power allocation coefficient, as shown in the following formula: Pa i =(A i / ∑At)×Pt×η; Among them, A i Let ∑At be the power allocation coefficient of the i-th charging module, ∑At be the sum of the power allocation coefficients of all charging modules, and η be the efficiency coefficient of the charging module. The real-time adjustment unit is used to recalculate the total available power Pt of the charging station and adjust the target power Pa of each charging module when the deviation between the updated value of the photovoltaic power sequence and the original predicted value exceeds the first threshold; when the real-time SOC value of the energy storage module is lower than the second threshold, it reduces the available energy storage power Pe and triggers the redistribution of charging demand.

8. The photovoltaic-storage-charging integrated new energy charging station according to claim 7, characterized in that, The demand grading module also includes a dynamic compensation unit, used for: The deviation ΔP between the actual output power of each charging module and the target power Pa is monitored in real time. When |ΔP| / Pa > the third threshold, power compensation is performed in the next scheduling cycle. The compensation amount Pc is: Pc = k×ΔP, where k is the compensation coefficient. Record historical compensation amount η old And dynamically adjust the efficiency coefficient η of the charging module. new As shown in the following formula: or new =h old ×(1 - |ΣΔP| / (ΣPa)); Where ΣΔP is the cumulative power deviation and ΣPa is the cumulative target power.

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