New energy charging pile remote control method considering battery health state

Abstract

The invention discloses a new energy charging pile remote control method considering a battery health state. The method comprises the following steps of obtaining battery full life cycle data, charging pile real-time working condition data and charging area power grid dynamic data, and performing preprocessing to generate battery health baseline data, pile body operation reference data and power grid dispatching reference data; extracting current health state parameters of the battery based on the health baseline data of the battery, calculating a charging adaptation coefficient in combination with the operation reference data of the pile body, analyzing the redundant capacity of the power grid, and constructing a charging control decision model; outputting an initial charging parameter combination based on the charging control decision model, performing health protection correction, and generating a basic remote control instruction; and issuing the basic remote control instruction to the target charging pile to calculate an instruction execution deviation degree, and performing dynamic iterative optimization to generate a new energy charging pile remote control scheme. According to the invention, collaborative optimization of safety and high efficiency of new energy vehicle charging and battery charging protection can be realized.

Description

Technical Field

[0001] This invention relates to the field of charging control technology, and more specifically, to a remote control method for new energy charging piles that takes into account the battery health status. Background Technology

[0002] With the surge in the number of new energy vehicles and the expansion of charging networks to multiple scenarios, higher demands are being placed on the accuracy of remote control of charging piles, battery protection, and grid adaptability. Remote control methods for new energy charging piles that consider battery health status, with their unique advantages of extending battery life, improving charging efficiency, and balancing grid load, are gradually overcoming the shortcomings of traditional remote control technologies and have become a key technological direction for the construction of smart charging networks. However, traditional remote control of charging piles mainly focuses on basic charging start / stop and power regulation, without fully considering battery health status. This makes them prone to overcharging and high-current surges that accelerate battery degradation. Simultaneously, they neglect grid load fluctuations and differences in charging pile operating conditions, causing power supply pressure during peak grid periods or resulting in low charging efficiency due to mismatched pile parameters. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a remote control method for new energy charging piles that considers battery health status, comprising the following steps: Step S1: Obtain the full life cycle data of the battery of the new energy vehicle, the real-time operating condition data of the charging pile, and the dynamic data of the power grid in the charging area. Perform attenuation trend fitting and time series completion processing on the full life cycle data of the battery to generate battery health baseline data. Perform abnormal operating condition identification and parameter standardization on the real-time operating condition data of the charging pile to generate the charging pile operation benchmark data. Perform load peak and valley identification and carrying capacity assessment processing on the dynamic data of the power grid in the charging area to generate power grid dispatch reference data. Step S2: Extract the current health status parameters of the battery based on the battery health baseline data, calculate the charging compatibility coefficient between the charging pile and the battery by combining the charging pile operation benchmark data, and analyze the grid redundancy capacity during the target charging period by using grid dispatch reference data to construct a charging control decision model that includes three dimensions: health priority, compatibility level, and grid capacity. Step S3: Output the initial charging parameter combination based on the charging control decision model, including the upper limit of charging current, the charging power adjustment gradient and the charging interval period, and perform health protection correction on the initial charging parameter combination by combining the capacity decay rate in the current health status parameters of the battery, and generate basic remote control commands. Step S4: Send basic remote control commands to the target charging pile, collect battery status feedback data and charging pile execution status data in real time during the charging process, calculate the command execution deviation based on the battery status feedback data and charging pile execution status data, dynamically iterate and optimize the basic remote control commands according to the command execution deviation, generate a new energy charging pile remote control scheme and realize the remote control of the charging pile.

[0004] The beneficial effects of this application are as follows: by fitting the degradation trend and completing the time series of battery data, a healthy baseline is accurately constructed, avoiding misjudgment of battery status (such as misestimation of remaining capacity) caused by missing or discrete data; anomaly identification and standardization of charging pile data are performed to eliminate interference from equipment fault data and ensure the authenticity and reliability of the charging pile operating parameters; load peak and valley identification and load assessment of power grid data are performed to clarify the redundancy capacity of the power grid at different times and avoid blind charging that ignores the power grid load. The three types of high-quality datasets generated eliminate the impact of data quality problems on control decisions from the source, providing reliable support for subsequent consideration of battery health, charging pile adaptation and power grid safety, and making up for the deficiency of weak foundation of traditional control data. Secondly, by extracting current health parameters based on the battery health baseline, the system ensures that decisions prioritize battery status. It calculates the compatibility coefficient between charging stations and batteries to avoid low charging efficiency due to mismatches between station parameters and battery requirements. It analyzes grid redundancy capacity to ensure charging avoids peak grid periods, reducing power supply pressure. This three-dimensional model incorporates health priority, compatibility level, and grid capacity into a unified decision-making framework, moving away from the traditional "one-size-fits-all" power adjustment and allowing for dynamic trade-offs based on actual conditions—for example, matching healthy batteries with highly compatible charging stations during grid off-peak hours to improve efficiency, and reducing power for degraded batteries during peak hours to balance health and grid safety. Then, by combining initial parameter combinations based on the decision model output, compatibility and grid capacity are initially considered. Further adjustments based on battery capacity degradation rate allow for personalized solutions for batteries in different health states—for example, for batteries with high capacity degradation rates, the upper limit of charging current is reduced and the interval period is extended to avoid high current surges exacerbating degradation; healthy batteries can maintain higher charging power within a safe range, balancing efficiency and protection. Compared to traditional fixed-parameter charging, this correction step makes the charging parameters more closely match the actual health condition of the battery, effectively reducing damage to the battery from overcharging and high-current surges, extending battery life, and avoiding excessively low charging efficiency due to over-protection, thus achieving a balance between "health protection" and "charging efficiency." Finally, by collecting real-time feedback on battery status and charging pile execution status after the basic commands are issued, deviations between commands and actual operating conditions can be detected in a timely manner; the deviation degree is calculated and commands are dynamically adjusted, such as reducing charging power based on battery temperature rise and correcting the current upper limit based on charging pile execution error, ensuring that commands are always adapted to real-time operating conditions. This closed-loop optimization mechanism avoids the charging efficiency decline or health risks caused by changes in operating conditions (such as grid voltage fluctuations and sudden battery temperature rise) in traditional open-loop control; the resulting remote control scheme can continuously balance battery health, charging pile compatibility, and grid safety during the charging process, completely solving the shortcomings of traditional static control commands in dealing with dynamic operating conditions, thereby significantly improving the safety, efficiency, and reliability of charging. Attached Figure Description

[0005] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of the remote control method for new energy charging piles that takes into account the battery health status in this embodiment; Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2. Detailed Implementation

[0006] The following drawings disclose several embodiments of the present invention. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. Furthermore, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0007] To further understand the invention's content, features, and effects, the following embodiments are provided, and detailed descriptions are given below in conjunction with the accompanying drawings: Reference Figure 1 , Figure 1 This is a flowchart of the remote control method for new energy charging piles that considers battery health status in this embodiment. The remote control method for new energy charging piles that considers battery health status in this embodiment includes the following steps: Step S1: Obtain the full life cycle data of the battery of the new energy vehicle, the real-time operating condition data of the charging pile, and the dynamic data of the power grid in the charging area. Perform attenuation trend fitting and time series completion processing on the full life cycle data of the battery to generate battery health baseline data. Perform abnormal operating condition identification and parameter standardization on the real-time operating condition data of the charging pile to generate the charging pile operation benchmark data. Perform load peak and valley identification and carrying capacity assessment processing on the dynamic data of the power grid in the charging area to generate power grid dispatch reference data. In this embodiment of the invention, the following data are obtained: full life cycle data of a new energy vehicle (lithium battery capacity 70kWh, cycle life 300 times) (capacity change per cycle 0.09-0.12kWh, discharge efficiency at 25℃ 92%, single cell voltage 3.2-3.3V), real-time operating data of charging piles (output voltage 218-222V, current fluctuation ±0.3A, contact resistance 0.02Ω), and dynamic data of the power grid in the charging area (total load 80MW, line 1 load rate 124.4%). The battery data was fitted with the least squares method to fit the capacity decay curve (y=70-0.05x), and the missing voltage data of the 8th cycle (3.26V) was filled in to generate battery health baseline data (capacity retention rate 78.57%, corrected discharge efficiency 83%). For charging pile data, abnormal operating conditions such as voltage fluctuation (0.5V exceeding the threshold) were identified, communication delay data (79ms) was filled in and standardized to generate charging pile operation benchmark data (parameter matching rate 83%). For grid data, a 1-hour sliding window was used to identify load peaks and valleys (peak 10:00-14:00), the line load rate was calculated, and grid dispatch reference data (recommended charging period 00:00-06:00) was generated.

[0008] Step S2: Extract the current health status parameters of the battery based on the battery health baseline data, calculate the charging compatibility coefficient between the charging pile and the battery by combining the charging pile operation benchmark data, and analyze the grid redundancy capacity during the target charging period by using grid dispatch reference data to construct a charging control decision model that includes three dimensions: health priority, compatibility level, and grid capacity. In this embodiment of the invention, the current health status parameters (actual usable capacity 55kWh, voltage balance score 99.01, remaining cycles 900) are extracted from the battery health baseline data; combined with the charging pile operation benchmark data (voltage fluctuation 218-222V, current accuracy ±0.3A) and battery parameters (rated voltage 220V, current 30A), the charging adaptation coefficient (voltage coefficient 0.9973, current coefficient 0.1167, power coefficient 0.8714, weighted to 0.6954) is calculated; and the grid dispatch reference data is analyzed to calculate the average redundant capacity of 17.23MW during the target charging period (00:00-06:00). A charging control decision model was constructed, with the following dimensions: health priority (available capacity score 23.57, voltage balance 39.60, remaining cycles 22.5, total score 85.67), adaptation level (score 69.54), and grid capacity (score 14.36). The model output is: when health priority ≥ 80, adaptation ≥ 60, and grid capacity ≥ 10, the maximum allowable charging power is 6.6kW.

[0009] Step S3: Output the initial charging parameter combination based on the charging control decision model, including the upper limit of charging current, the charging power adjustment gradient and the charging interval period, and perform health protection correction on the initial charging parameter combination by combining the capacity decay rate in the current health status parameters of the battery, and generate basic remote control commands. In this embodiment of the invention, an initial charging parameter combination (charging current limit 30A, power adjustment gradient 0.22 / 0.25 / 0.24kW, intermittent period of 10 minutes charging + 60 seconds pause) is output based on the charging control decision model. Combined with the battery's current health status parameters, including a capacity decay rate of 21.43% (health risk level 3), the charging current limit is corrected to 30×(1-3×0.05)=25.5A; a power tolerance coefficient of 0.8 is extracted, and the power adjustment step size correction value (0.044 / 0.05 / 0.048kW) is calculated, which are then superimposed to generate a health protection gradient (0.264 / 0.3 / 0.288kW); the heat dissipation efficiency is correlated to 72.86%, and an intermittent duration compensation of 12 seconds is calculated. The optimized intermittent period is 10 minutes of charging + 72 seconds of pause. The three types of correction parameters are integrated and converted into standardized control codes (current "CUR25.5", power "PWR0.264,0.3,0.288", intermittent "INT600,72") according to the charging pile communication protocol (ASCII code), and the basic remote control command "CTRL|CUR25.5|PWR0.264,0.3,0.288|INT600,72|END" is generated.

[0010] Step S4: Send basic remote control commands to the target charging pile, collect battery status feedback data and charging pile execution status data in real time during the charging process, calculate the command execution deviation based on the battery status feedback data and charging pile execution status data, dynamically iterate and optimize the basic remote control commands according to the command execution deviation, generate a new energy charging pile remote control scheme and realize the remote control of the charging pile.

[0011] In this embodiment of the invention, basic remote control commands are sent to the target charging pile, and charging process data is collected in real time (one set every 10 seconds, for a total of 6 sets): battery status feedback data (real-time voltage 219.5-220.3V, current 25.2-25.5A, SOC change rate 0.79%-0.83% / min) and charging pile execution status data (target voltage 220V, current 25.5A, power 5.61kW, response delay 0.6-0.9s). A deviation analysis dataset is generated by aligning the data according to timestamps, calculating the voltage deviation ratio (25%, allowable threshold 1V), current deviation ratio (30%, allowable threshold 0.5A), SOC deviation impact weight (0.0456, sensitivity coefficient 1.2), and response delay deviation coefficient (0.75, preset threshold 1s). A deviation calculation matrix is ​​constructed, and the weighted sum (voltage 0.1, current 0.09, SOC 0.0137) is multiplied by the delay coefficient to obtain a command execution deviation of 0.1528 (low deviation). Since the deviation meets the requirements, no adjustment instructions are needed. The final remote control solution for new energy charging piles is generated. The solution includes basic instructions and a deviation monitoring mechanism. Through the remote communication module, the charging piles are operated according to the corrected parameters to ensure battery health and grid stability.

[0012] Furthermore, step S1 includes the following steps: Step S11: Obtain the full life cycle data of the new energy vehicle's battery, the real-time operating condition data of the charging pile, and the dynamic data of the power grid in the charging area; among which, the full life cycle data of the battery includes the number of historical charge and discharge cycles, the capacity change in each cycle, the discharge efficiency at different temperatures, and the consistency data of the battery cell voltage; the real-time operating condition data of the charging pile includes the output voltage fluctuation value, the output current stability, the internal temperature distribution of the charging pile, the contact resistance of the charging interface, and the communication link delay data; the dynamic data of the power grid in the charging area includes the real-time total load of the area, the load ratio of each branch line, the voltage fluctuation amplitude, and the frequency stability coefficient. In this embodiment of the invention, the full life cycle data of a new energy vehicle (400km range, 70kWh lithium battery capacity) is obtained. This includes 300 historical charge-discharge cycles, with capacity changes of 0.12kWh, 0.11kWh…0.09kWh (300th cycle) for each cycle. Discharge efficiency at different temperatures is 92% at 25℃, 78% at 0℃, and 65% at -10℃. Battery cell voltage consistency data includes the voltage values ​​of 16 cells (range 3.2-3.3V). Real-time operating data of the charging pile is also obtained, including output voltage fluctuation ±0.5V, output current stability (fluctuation amplitude ≤1A), internal temperature distribution of the pile (core components 45℃, outer shell 32℃), charging interface contact resistance 0.02Ω, and communication link delay 80ms. Dynamic data of the power grid in the charging area is also obtained, including a real-time total load of 80MW, load percentage of each branch line (135%, 228%, 337%), voltage fluctuation ±0.03kV, and frequency stability coefficient 0.98.

[0013] Step S12: Based on the historical charge-discharge cycle count and the capacity change in each cycle, a battery capacity decay trend curve is generated by fitting the data, and the current capacity retention rate parameter of the battery is calculated; based on the discharge efficiency at different temperatures, a temperature-efficiency correlation model is constructed, and the discharge efficiency is corrected in real time by combining the current ambient temperature parameter to generate the corrected discharge efficiency parameter; missing values ​​are detected in the battery cell voltage consistency data, and interpolation based on the voltage change pattern of adjacent cycles is used to complete the data, and the maximum deviation rate parameter of the cell voltage is calculated; the capacity retention rate parameter, the corrected discharge efficiency parameter, and the maximum deviation rate parameter of the cell voltage are integrated to generate battery health baseline data that includes battery health status evaluation indicators and decay trend prediction. In this embodiment of the invention, the battery capacity decay trend curve is fitted using the least squares method based on 300 charge-discharge cycles and the corresponding capacity change. The fitted equation is y = 70 - 0.05x (where x is the number of cycles and y is the current capacity). At the 300th cycle, the current capacity is 70 - 0.05 × 300 = 55 kWh, and the capacity retention rate is 55 / 70 × 100% ≈ 78.57%. A temperature-efficiency correlation model is constructed based on the discharge efficiency at different temperatures. The model formula is η = 92% - 0.6 × (25 - T) (where T is the current ambient temperature). The current ambient temperature is 10℃, and the corrected discharge efficiency is 92% - 0.6 × (25 - 10) = 92% - 9% = 83%. Battery cell voltage consistency data testing revealed missing data for the 8th cycle. This was supplemented by interpolation using the voltage variation patterns from the 7th (3.25V) and 9th (3.27V) cycles to arrive at 3.26V. The maximum deviation rate of the 16 cell voltages was calculated as (3.3-3.2) / 3.25×100%≈3.08%. Integrated battery health baseline data was generated, including a capacity retention rate of 78.57%, a corrected discharge efficiency of 83%, a maximum cell voltage deviation rate of 3.08%, and a predicted capacity decay over the next 50 cycles (down to 52.5kWh).

[0014] Step S13: Identify abnormal fluctuations in the real-time operating data of the charging pile based on the preset normal operating parameter range, and mark the time nodes and parameter types corresponding to the abnormal data; classify the causes of the marked abnormal data. If it is caused by a temporary sensor failure, cross-validate and complete the data using other related sensor data from the same time period; if it is caused by abnormal equipment operation, record the characteristics of the abnormal operating conditions and remove invalid data; perform parameter standardization processing on the filtered normal operating data to convert parameters of different dimensions into a unified standard score; calculate the matching degree between the standardized operating parameters and the rated parameters of the charging pile, and generate the pile operation benchmark data including parameter matching rate and operating condition stability score; In this embodiment of the invention, by pre-setting the normal operating parameters range of the charging pile: output voltage fluctuation ±0.3V, output current fluctuation ≤0.8A, core temperature of the pile body ≤40℃, contact resistance ≤0.015Ω, and communication delay ≤50ms, abnormal fluctuation conditions are identified in the real-time data: output voltage fluctuation of 0.5V (out of range), core temperature of the pile body of 45℃ (out of range), contact resistance of 0.02Ω (out of range), and communication delay of 80ms (out of range). The time node is marked as 14:25, and the parameter types are voltage, temperature, resistance, and communication. The causes of abnormal data are classified. The communication delay of 80ms is verified to be a temporary sensor failure. Cross-validation is performed using communication delay data from adjacent charging piles at the same time (75ms, 82ms) to complete it to 79ms. Abnormal output voltage, temperature, and resistance indicate abnormal equipment operation. The characteristics (voltage fluctuation accompanied by temperature increase) are recorded, and invalid data (voltage fluctuation of 0.5V, resistance of 0.02Ω) are removed. The normal data after screening (current fluctuation 0.8A, casing temperature 32℃, communication delay after completion 79ms) were standardized and converted according to "(actual value - minimum value) / (maximum value - minimum value) × 100". The standardized scores were 80 for current fluctuation, 60 for casing temperature, and 79 for communication delay. The matching rate between each parameter and the rated parameters (voltage 220V, current 30A, temperature 35℃, resistance 0.01Ω, delay 50ms) was calculated. The matching rate was 97% for current, 91% for temperature, and 62% for communication. The benchmark data for pile operation was generated, with a parameter matching rate of 83% and a working condition stability score of 75.

[0015] Step S14: Use a sliding window to perform trend analysis on the real-time total load of the region, identify peak load periods, valley periods, and flat periods, and calculate the load duration and load change rate for each period; based on the load ratio of each branch line and combined with the rated carrying capacity of the line, calculate the current load rate of each line, and identify lines with overload risk and lines with load redundancy; according to the voltage fluctuation amplitude and frequency stability coefficient, use the fuzzy comprehensive evaluation method to score the current operation stability of the power grid and generate a power grid stability index; integrate the load period division results, line load rate data, and power grid stability index to generate power grid dispatch reference data that includes charging period recommendations and line selection suggestions.

[0016] In this embodiment of the invention, a trend analysis of the real-time total load of 80MW in the region is performed using a 1-hour sliding window to identify the peak load period of 10:00-14:00 (load 95-100MW), the valley period of 00:00-06:00 (load 40-50MW), and the flat periods of 06:00-10:00 and 14:00-24:00 (load 60-80MW). The peak period lasts for 4 hours with a load change rate of 5MW / h, the valley period lasts for 6 hours with a change rate of 3MW / h, and the flat periods each last for 4 hours with a change rate of 2MW / h. Based on the load proportions of each branch line, Line 1 has a rated capacity of 25MW and a current load of 35% × 80 = 28MW, with a load factor of 28 / 25 × 100% = 112% (overload risk); Line 2 has a rated capacity of 28MW and a current load of 28% × 80 = 22.4MW, with a load factor of 80% (normal); Line 3 has a rated capacity of 30MW and a current load of 37% × 80 = 29.6MW, with a load factor of 98.7% (close to overload). Line 1 is identified as an overload risk line and has no load redundancy. Based on the voltage fluctuation range of ±0.03kV (standard ≤ ±0.05kV) and the frequency stability coefficient of 0.98 (standard ≥ 0.95), the fuzzy comprehensive evaluation method is used, with a voltage score of 85 and a frequency score of 90. The power grid stability index is (85 × 0.4 + 90 × 0.6) = 88. The integrated power grid dispatch reference data recommends charging during off-peak hours (00:00-06:00) and suggests prioritizing line 2, followed by line 3, while avoiding line 1.

[0017] Furthermore, step S14, which involves calculating the current load rate of each line based on the load percentage of each branch line and the line's rated carrying capacity, and identifying lines at risk of overload and lines with redundant loads, includes the following steps: Real-time load data and basic parameters of each branch line are extracted from the dynamic data of the power grid in the charging area. The real-time load value of a single line is calculated based on the real-time load data. The real-time load ratio of the line is calculated by combining the total load data of the area, and the initial load ratio parameters of each branch line are generated. In this embodiment of the invention, real-time load data (line 1 is 140A, line 2 is 112A, and line 3 is 118A) and basic line parameters (line 1 voltage level 0.4kV, power factor 0.9; line 2 is the same as line 1; line 3 is the same as line 1) of three branch lines are extracted from the dynamic data of the charging area power grid. The real-time load value of a single line is calculated based on the real-time load data, using the formula "real-time load value = √3 × voltage × current × power factor". Line 1 = 1.732 × 0.4 × 140 × 0.9 ≈ 84.67MW, line 2 = 1.732 × 0.4 × 112 × 0.9 ≈ 67.74MW, and line 3 = 1.732 × 0.4 × 118 × 0.9 ≈ 71.22MW. Based on the total regional load data of 80MW (Note: This total load is a statistical value, and the difference from the calculated value of the lines is due to other loads in the power grid; the subsequent calculation of the percentage will be based on the statistical total load), the real-time load percentage of each line is calculated. The percentage of line 1 is 84.67 / (84.67+67.74+71.22)×100%≈35%, line 2 is 67.74 / 223.63×100%≈28%, and line 3 is 71.22 / 223.63×100%≈37%. The initial load percentage parameters of each branch line are generated (line 1 35%, line 2 28%, line 3 37%).

[0018] Furthermore, the line model and laying method data in the basic parameters of the line are retrieved, the preset line parameter database is queried, the rated carrying capacity benchmark value of the line matching the line model and laying method is obtained, and the rated carrying capacity benchmark value of the line is corrected by aging coefficient in combination with the line operation years data to generate the actual rated carrying capacity of each line. In this embodiment of the invention, the basic parameters of three lines are retrieved: Line 1 is a YJV22-0.6 / 1kV-3×240 line, laid directly underground; Line 2 is the same as Line 1; Line 3 is a YJV22-0.6 / 1kV-3×300 line, also laid directly underground. A query of the preset line parameter database shows that the rated carrying capacity of the YJV22-0.6 / 1kV-3×240 line laid directly underground is 25MW, and the rated carrying capacity of the YJV22-0.6 / 1kV-3×300 line laid directly underground is 30MW. Obtain the line operation years data (Line 1: 5 years, Line 2: 23 years, Line 3: 34 years). The aging factor calculation formula is "Aging Factor = 1 - 0.02 × Operation Years" (Aging factor is 0.8 after 10 years of operation, with linear decay). Line 1 aging factor = 1 - 0.02 × 5 = 0.9, Line 2 = 1 - 0.02 × 3 = 0.94, Line 3 = 1 - 0.02 × 4 = 0.92. Calculate the actual rated carrying capacity: Line 1 = 25 × 0.9 = 22.5MW, Line 2 = 25 × 0.94 = 23.5MW, Line 3 = 30 × 0.92 = 27.6MW.

[0019] Furthermore, the initial load percentage parameter is correlated with the predicted total load value of the region to calculate the real-time absolute load value of each line. Then, the ratio of the real-time absolute load value to the actual rated carrying capacity is calculated to generate the current load rate of each line. In this embodiment of the invention, by obtaining the predicted total load value of the region (stable at 80MW within the next hour), the initial load percentage parameter is correlated with the predicted total load value to calculate the absolute value of the real-time load for each line: Line 1 = 80 × 35% = 28MW, Line 2 = 80 × 28% = 22.4MW, and Line 3 = 80 × 37% = 29.6MW. The ratio of the absolute value of the real-time load to the actual rated carrying capacity is calculated to generate the current load factor: Line 1 load factor = 28 / 22.5 × 100% ≈ 124.4%, Line 2 = 22.4 / 23.5 × 100% ≈ 95.3%, and Line 3 = 29.6 / 27.6 × 100% ≈ 107.2%.

[0020] Furthermore, a line load risk assessment model is constructed based on the current load rate. Historical overload frequency data and current ambient temperature data are introduced. The current load rate is compared with the load rate threshold during historical overloads to calculate the overload risk coefficient of the line. At the same time, the load redundancy of the line is calculated by combining the difference between the current load rate and the actual rated carrying capacity. In this embodiment of the invention, a line load risk assessment model is constructed based on the current load rate. The model inputs are the current load rate, historical overload frequency, and current ambient temperature (28°C). Historical overload frequency data (5 times for line 1 in the past 3 months, 1 time for line 2, and 33 times for line 3) and the load rate thresholds during historical overloads (1120% for line 1, 100% for line 2, and 105% for line 3) are retrieved. The overload risk coefficient is calculated using the formula: "Overload Risk Coefficient = Current Load Rate / Historical Overload Threshold × (1 + 0.1 × Historical Overload Frequency)". For Line 1: 124.4% / 120% × (1 + 0.1 × 5) ≈ 1.037 × 1.5 ≈ 1.556; for Line 2: 95.3% / 100% × (1 + 0.1 × 1) ≈ 0.953 × 1.1 ≈ 1.048; for Line 3: 107.2% / 105% × (1 + 0.1 × 3) ≈ 1.021 × 1.3 ≈ 1.327. The load redundancy is calculated using the formula: "Load Redundancy = Actual Rated Capacity - Real-time Absolute Load Value". For Line 1: 22.5 - 28 = -5.5MW (negative redundancy indicates overload); for Line 2: 23.5 - 22.4 = 1.1MW; for Line 3: 27.6 - 29.6 = -2MW (negative redundancy).

[0021] Furthermore, an overload risk coefficient threshold and a load redundancy threshold are set. If the overload risk coefficient exceeds the overload risk coefficient threshold, the line is marked as having an overload risk; if the load redundancy exceeds the load redundancy threshold, the line is marked as having a load redundancy, and line load rate data is generated by combining the current load rate of each line.

[0022] In this embodiment of the invention, an overload risk coefficient threshold of 1.2 and a load redundancy threshold of 2MW are set. Line 1 has an overload risk coefficient of 1.556 > 1.2 and is marked as an overload risk line; Line 2 has an overload risk coefficient of 1.048 < 1.2 and a load redundancy of 1.1MW < 2MW, and is not marked as a redundant line; Line 3 has an overload risk coefficient of 1.327 > 1.2 and is marked as an overload risk line, but has no load redundancy (Line 2's redundancy does not reach the threshold). The marking results are integrated with the current load rate of each line to generate line load rate data: Line 1 (current load rate 124.4%, overload risk line), Line 2 (95.3%, normal line), and Line 3 (107.2%, overload risk line), providing data support for line selection in remote control of charging piles.

[0023] Furthermore, step S2 includes the following steps: Step S21: Retrieve the capacity retention rate parameter from the battery health baseline data, and calculate the actual available capacity parameter of the battery at present by combining it with the battery rated capacity data; calculate the theoretical charging time required for the battery to reach the target capacity based on the corrected discharge efficiency parameter combined with the current ambient temperature and the target charging capacity. In this embodiment of the invention, the actual usable capacity parameter is calculated by retrieving the capacity retention rate parameter of 78.57% from the battery health baseline data and the battery rated capacity of 70kWh. The formula is "Actual usable capacity = Rated capacity × Capacity retention rate". Substituting the data, we get 70 × 78.57% ≈ 55kWh. Based on the corrected discharge efficiency parameter of 83%, the current ambient temperature is 10℃, and the target charging capacity is from 30% to 80% (required replenishment capacity = 70 × (80% - 30%) = 35kWh). The theoretical charging time is calculated by the formula "Theoretical charging time = Required replenishment capacity / (Charging pile rated output power × Corrected discharge efficiency)". The rated output power of the charging pile is 6.6kW. Substituting the values, we get 35 / (6.6 × 83%) ≈ 35 / (5.478) ≈ 6.39 hours, rounded to two decimal places, which is 6.4 hours.

[0024] Step S22: Extract the maximum deviation rate parameter of the single cell voltage and combine it with the number of battery cells and the safe voltage range to calculate the current voltage balance score of the battery; In this embodiment of the invention, the maximum deviation rate parameter of the single cell voltage is extracted as 3.08%, the number of battery cells is 16, and the safe voltage range is 3.0-3.6V. A voltage balance scoring model is constructed, with a scoring range of 0-100 points. The formula is "Voltage Balance Score = 100 - (Maximum Deviation Rate of Single Cell Voltage × Number of Cells × 2)" (for every 1% increase in deviation rate or every additional single cell, 2 points are deducted from the score). Substituting the data, we get 100 - (3.08% × 16 × 2) = 100 - (0.0308 × 32) = 100 - 0.9856 ≈ 99.01 points. Rounded to two decimal places, 99.01 points is obtained. This score reflects the balance of the voltage distribution of the battery cells; the higher the score, the better the balance.

[0025] Step S23: Based on the number of charge-discharge cycles in the battery's full life cycle data and combined with the battery's designed cycle life, calculate the battery's remaining cycle life parameters; integrate the actual usable capacity parameters, theoretical charging time, voltage balance score, and remaining cycle life parameters to generate the battery's current health status parameters; In this embodiment of the invention, the remaining cycle life parameter is calculated by obtaining 300 charge-discharge cycles and a designed cycle life of 1200 cycles from the battery's full life cycle data. The formula is "Remaining cycle life = Designed cycle life - Number of cycles already completed". Substituting the data, we get 1200 - 300 = 900 cycles. The actual usable capacity parameter of 55kWh, theoretical charging time of 6.4 hours, voltage balance score of 99.01, and remaining cycle life of 900 cycles are integrated and organized in the format of "parameter name-value-unit" to generate the battery's current health status parameters. These parameters include the actual usable capacity of 55kWh, theoretical charging time of 6.4 hours, voltage balance score of 99.01, and remaining cycle life of 900, comprehensively reflecting the battery's current health level.

[0026] Step S24: Calculate the charging compatibility coefficient between the charging pile and the battery by combining the charging pile operation benchmark data, and analyze the grid redundancy capacity during the target charging period by using grid dispatch reference data; In this embodiment of the invention, the charging compatibility coefficient is calculated by combining the charging pile's operating baseline data (output voltage fluctuation 218-222V, output current adjustment accuracy ±0.3A, power conversion efficiency 90%-93%, interface contact resistance 0.02Ω) with the battery health status parameters (rated voltage 220V, rated current 30A, optimal power 6.0-6.6kW, internal resistance 0.08Ω). First, the voltage compatibility base coefficient is calculated as 0.9973, the current compatibility base coefficient as 0.1167, and the power compatibility base coefficient as 0.8714. Then, the total resistance of the charging circuit is calculated as 0.1Ω (with an impact correction coefficient of 1). The weighted fusion (voltage 0.4, current 0.3, power 0.3) yields 0.9973×0.4+0.1167×0.3+0.8714×0.3≈0.6954. Analyzing the grid dispatch reference data during the target charging period (00:00-06:00), the calculated grid redundancy capacities are 15.43MW, 13.58MW, 17.03MW, 19.12MW, 21.21MW, and 17.03MW, with an average redundancy capacity of 17.23MW.

[0027] Step S25: Based on the current battery health status parameters, charging adaptation coefficient, and grid redundancy capacity, construct a charging control decision model that includes three dimensions: health priority, adaptation level, and grid capacity.

[0028] In this embodiment of the invention, a charging control decision model is constructed based on the battery's current health status parameters, a charging adaptation coefficient of 0.6954, and an average grid redundancy capacity of 17.23MW. The health priority dimension includes: actual available capacity of 55kWh (weight 0.3, score 55 / 70×100×0.3≈23.57), voltage balance of 99.01 points (weight 0.4, score 99.01×0.4≈39.60), and 900 remaining cycles (weight 0.3, score 900 / 1200×100×0.3≈22.5), resulting in a total health priority score of ≈85.67. The adaptation level dimension includes: a charging adaptation coefficient of 0.6954 (score 0.6954×100≈69.54). The grid capacity dimension includes: an average redundancy capacity of 17.23MW (grid rated capacity 120MW, score 17.23 / 120×100≈14.36). The model takes three-dimensional scores as input and outputs a charging power regulation strategy (e.g., when health priority ≥80, adaptation ≥60, and grid capacity ≥10, the maximum allowed charging power is 6.6kW).

[0029] Furthermore, the step S24, which involves calculating the charging compatibility coefficient between the charging pile and the battery by combining the pile's operational reference data, includes the following steps: The output voltage fluctuation range, output current regulation accuracy and power conversion efficiency parameters of the charging pile are extracted from the charging pile operation benchmark data. At the same time, the rated charging voltage, rated charging current and optimal charging power range of the battery are obtained from the current health status parameters of the battery. The two types of data are time-series aligned to generate the basic dataset for adaptation calculation. In this embodiment of the invention, the output voltage fluctuation range (218-222V), output current adjustment accuracy (±0.3A), and power conversion efficiency (90%-93%) of a charging pile are extracted from the charging pile's operational baseline data. The rated charging voltage (220V), rated charging current (30A), and optimal charging power range (6.0-6.6kW) of the new energy vehicle battery (calculated from the rated voltage and current, corresponding to a current of 27.3-30A) are obtained from the battery's current health status parameters. The two types of data are time-aligned at 1-minute intervals to ensure that each minute simultaneously includes both the charging pile's real-time output parameters and the battery's rated parameters. For example, in the first minute, the charging pile's output voltage is 219V, current is 28A, and efficiency is 91%, corresponding to a battery rated voltage of 220V, current of 30A, and power range of 6.0-6.6kW. This generates 60 sets of time-aligned data, forming the basic dataset for adaptation calculation.

[0030] Furthermore, based on the output voltage fluctuation range and rated charging voltage in the basic dataset of the adaptation calculation, the voltage adaptation deviation rate is calculated, specifically the percentage difference between the voltage fluctuation range boundary and the rated charging voltage. Then, combined with the voltage stability weight, the basic voltage adaptation coefficient is generated. In this embodiment of the invention, the voltage adaptation deviation rate is calculated based on the output voltage fluctuation range of 218-222V and the rated charging voltage of 220V in the adaptation calculation basic dataset. The upper limit deviation rate = (222-220) / 220×100%≈0.91%, the lower limit deviation rate = (220-218) / 220×100%≈0.91%, and the maximum deviation rate of 0.91% is taken. The voltage stability weight is set to 0.3 (determined according to the degree of influence of charging pile voltage fluctuation on battery charging). The formula for calculating the voltage adaptation basic coefficient is "voltage adaptation basic coefficient = 1 - maximum deviation rate × voltage stability weight". Substituting the data, we get 1 - 0.91% × 0.3 ≈ 1 - 0.00273 = 0.99727, which is 0.9973 rounded to four decimal places.

[0031] Furthermore, based on the output current regulation accuracy and rated charging current in the adaptation calculation base dataset, a current regulation matching model is constructed, and the coverage of the current regulation range and rated charging current is calculated through the model. Combined with the current response speed parameter, the current adaptation base coefficient is generated. In this embodiment of the invention, a current regulation matching model is constructed based on the output current regulation accuracy of ±0.3A and the rated charging current of 30A in the adaptation calculation dataset. The model takes the current regulation range (29.7-30.3A, determined by the rated current ± regulation accuracy) and the battery's optimal charging current range (27.3-30A, calculated from the optimal power range and rated voltage) as inputs to calculate the coverage. The overlap between the current regulation range and the optimal charging current range is 29.7-30A, and the coverage is calculated as follows: (30-29.7) / (30-27.3)×100%≈0.3 / 2.7×100%≈11.11%. Extract the current response speed parameter as 0.5A / s (it takes 5.4 seconds for the charging pile current to increase from 27.3A to 30A), set the current response speed weight to 0.2, and calculate the current adaptation base coefficient using the formula "current adaptation base coefficient = coverage × (1 + current response speed × 0.1)". Substituting the data, we get 11.11% × (1 + 0.5 × 0.1) = 0.1111 × 1.05 ≈ 0.11665, which is 0.1167 after rounding to four decimal places.

[0032] Furthermore, by utilizing the power conversion efficiency and optimal charging power range in the adaptation calculation base dataset, the average power conversion efficiency within the optimal power range is calculated, and then combined with the power loss threshold, a power adaptation base coefficient is generated. In this embodiment of the invention, the average power conversion efficiency within the optimal power range is calculated by utilizing the power conversion efficiency of 90%-93% in the basic dataset for adaptation calculation and the optimal charging power range of 6.0-6.6kW. The optimal power range corresponds to an efficiency of 90% at 6.0kW, 91.5% at 6.3kW, and 93% at 6.6kW. The average value is calculated using linear interpolation: (90%+91.5%+93%) / 3=91.5%. A power loss threshold of 5% is set (exceeding this threshold will affect battery charging efficiency). The formula for calculating the power adaptation base coefficient is "Power adaptation base coefficient = average efficiency / (1 + power loss threshold)". Substituting the data, we get 91.5% / (1+5%) = 0.915 / 1.05≈0.8714, which is rounded to four decimal places as 0.8714.

[0033] Furthermore, by introducing the interface contact resistance parameter and battery internal resistance parameter from the charging pile's operating reference data, the influence correction coefficient of the total resistance of the charging circuit on the compatibility is calculated. This influence correction coefficient is then weighted and fused with the voltage, current, and power compatibility base coefficients to generate the charging compatibility coefficient between the charging pile and the battery.

[0034] In this embodiment of the invention, the interface contact resistance of 0.02Ω is extracted from the pile's operational reference data, and the battery internal resistance of 0.08Ω is obtained from the battery health status parameters. The total resistance of the charging circuit is calculated as 0.02 + 0.08 = 0.1Ω. A total resistance threshold of 0.15Ω is set, and the influence correction coefficient is calculated using the formula: "Influence correction coefficient = 1 - (Total resistance - Threshold) / Threshold (takes 1 when total resistance ≤ threshold)". Since 0.1Ω ≤ 0.15Ω, the influence correction coefficient is 1. The basic coefficients for voltage, current, and power adaptation are set to have weights of 0.4, 0.3, and 0.3, respectively. The weighted fusion formula is "Charging adaptation coefficient = voltage coefficient × 0.4 + current coefficient × 0.3 + power coefficient × 0.3". Substituting the data, we get 0.9973 × 0.4 + 0.1167 × 0.3 + 0.8714 × 0.3 = 0.39892 + 0.03501 + 0.26142 = 0.69535. Multiplying this by the influence correction coefficient of 1, the final charging adaptation coefficient is 0.6954.

[0035] Furthermore, the analysis of grid redundancy capacity during the target charging period using grid dispatch reference data in step S24 includes the following steps: Extract the regional total load forecast, grid rated total capacity data and load rate data of each branch line for the target charging period from the grid dispatch reference data. Then, correlate and match the three types of data according to the time dimension to generate a basic dataset for redundancy capacity analysis. In this embodiment of the invention, the predicted total regional load (40MW, 42MW, 41MW, 39MW, 38MW, and 40MW per hour) for the target charging period (00:00-06:00, a total of 6 hours), the rated total grid capacity of 120MW, and the load factor data of three branch lines (85%, 86%, 84%, 83%, 82%, and 84% per hour for line 1; 78%, 79%, 77%, 76%, 75%, and 77% for line 2; and 90%, 91%, 89%, 88%, 87%, and 89% for line 3) are extracted from the grid dispatch reference data. The three types of data are matched and correlated according to the hourly time dimension. For example, 00:00 corresponds to a total load of 40MW, a rated capacity of 120MW, and a load factor of 85% for line 1, 78% for line 2, and 90% for line 3. This sequentially completes the data matching for six time nodes, generating six sets of time-aligned redundant capacity analysis base datasets.

[0036] Furthermore, based on the regional total load forecast and the grid rated total capacity data in the redundancy capacity analysis basic dataset, the theoretical remaining capacity parameters of the grid are calculated, and then combined with the historical load fluctuation characteristics of the grid during the same period, the initial value of theoretical redundancy capacity is generated. In this embodiment of the invention, the theoretical remaining capacity parameter of the power grid per hour is calculated based on the regional total load forecast value and the rated total capacity of the power grid of 120MW in the redundancy capacity analysis basic dataset. The formula is "theoretical remaining capacity = rated total capacity - total load forecast value". The theoretical remaining capacity at 00:00 is 120-40=80MW, at 01:00 it is 120-42=78MW, and the subsequent time periods are 79MW, 81MW, 82MW and 80MW respectively. Load fluctuation characteristic data of the power grid in the same period (same season and same date) for the past 3 years were retrieved. The maximum fluctuation range of the load in the same period was ±3MW, and the fluctuation probability was 15%. The initial value of theoretical redundancy capacity was generated by the formula "initial value = theoretical remaining capacity - maximum fluctuation range × fluctuation probability". The initial value at 00:00 was 80 - 3 × 0.15 = 79.55MW, and at 01:00 it was 78 - 3 × 0.15 = 77.55MW. The values ​​for the other time periods were calculated as follows: 78.55MW, 80.55MW, 81.55MW, and 79.55MW.

[0037] Furthermore, based on the load rate data of each branch line in the redundancy capacity analysis basic dataset, the load redundancy ratio of each line is calculated, and the load redundancy ratio of all lines is integrated through a weighted average algorithm to generate a comprehensive line redundancy coefficient. In this embodiment of the invention, the hourly load redundancy ratio of each line is calculated based on the load rate data of each branch line in the redundancy capacity analysis basic dataset. The formula is "load redundancy ratio = 1 - load rate". For line 1, the redundancy ratio at 00:00 is 1 - 85% = 15%, at 01:00 it is 14%, and subsequently 16%, 17%, 18%, and 16%; for line 2 it is 22%, 21%, 23%, 24%, 25%, and 23%; and for line 3 it is 10%, 9%, 11%, 12%, 13%, and 11%. Based on the weighted average of the line rated capacity (line 1 25%, line 2 25%, line 3 50%), a weighted average algorithm is used for integration. The comprehensive redundancy coefficient for line 00:00 is 15% × 25% + 22% × 25% + 10% × 50% = 3.75% + 5.5% + 5% = 14.25%, and the remaining time periods are calculated as 13.5%, 15.25%, 16.25%, 17.25%, and 15.25% respectively.

[0038] Furthermore, the power grid stability index in the power grid dispatch reference data is introduced, and the power grid stability index is converted into a redundancy capacity correction weight. Then, combined with the initial value of theoretical redundancy capacity and the comprehensive coefficient of line redundancy, the preliminary redundancy capacity is calculated. In this embodiment of the invention, by introducing the grid stability index from the grid dispatch reference data (the target charging time periods are 92, 91, 93, 94, 95, and 93 per hour respectively), the stability index is converted into a redundancy capacity correction weight. The conversion formula is "correction weight = stability index / 100". At 00:00, the correction weight is 92 / 100 = 0.92; at 01:00, it is 0.91; and subsequently, it is 0.93, 0.94, 0.95, and 0.93. Combining the initial theoretical redundancy capacity value and the line redundancy comprehensive coefficient, the preliminary redundancy capacity is calculated using the formula "preliminary redundancy capacity = initial theoretical redundancy capacity × line redundancy comprehensive coefficient × correction weight". At 00:00, the preliminary redundancy capacity is 79.55 × 14.25% × 0.92 ≈ 79.55 × 0.1311 ≈ 10.43 MW. The remaining time periods are calculated as 9.58 MW, 11.03 MW, 12.12 MW, 13.21 MW, and 11.03 MW respectively.

[0039] Furthermore, by combining the predicted output data of renewable energy generation during the target charging period, the amount of renewable energy generation supplementing the grid's redundant capacity is calculated. This supplement is then superimposed on the initial redundant capacity to generate the grid's redundant capacity for the target charging period.

[0040] In this embodiment of the invention, by acquiring the predicted output data of new energy power generation (photovoltaic + wind power) during the target charging period (5MW, 4MW, 6MW, 7MW, 8MW, and 6MW per hour respectively), the amount of supplementary capacity of new energy power generation to the grid redundancy is calculated. The supplementary amount is equal to the predicted output value of new energy power generation (new energy power generation can directly offset part of the load demand, increasing the actual redundancy). The supplementary amount is superimposed with the preliminary redundancy capacity to generate the grid redundancy capacity for the target charging period: 00:00=10.43+5=15.43MW, 01:00=9.58+4=13.58MW, 02:00=11.03+6=17.03MW, 03:00=12.12+7=19.12MW, 04:00=13.21+8=21.21MW, 05:00=11.03+6=17.03MW. This data provides the grid capacity basis for the power allocation of the charging pile remote control.

[0041] Furthermore, step S3 includes the following steps: Step S31: Extract the remaining cycle life from the battery's current health status parameters, the pile power regulation accuracy from the pile operation benchmark data, and the grid redundancy capacity from the grid dispatch reference data from the input data of the charging control decision model. Standardize the three types of data to generate the model input feature vector. In this embodiment of the invention, the following parameters are extracted from the input data of the charging control decision model: the remaining cycle life of 560 cycles (initial cycle life of 800 cycles, 240 cycles already completed) from the battery's current health status parameters; the charging pile's power adjustment accuracy of ±0.3kW from the charging pile's operating baseline data; and the grid redundancy capacity of 120kW from the grid dispatch reference data (current grid total load of 380kW, rated load of 500kW). These three types of data are standardized: the remaining cycle life is calculated as "standardized value = remaining cycle life / initial cycle life", resulting in 560 / 800 = 0.7; the charging pile's power adjustment accuracy is calculated as "standardized value = absolute value of adjustment accuracy / rated power of charging pile", with a rated power of 6.6kW, resulting in 0.3 / 6.6 ≈ 0.0455; and the grid redundancy capacity is calculated as "standardized value = redundancy capacity / rated load of grid", resulting in 120 / 500 = 0.24. The standardized values ​​of 0.7, 0.0455, and 0.24 are combined in the order of "remaining cycle life - pile power regulation accuracy - power grid redundancy capacity" to generate the model input feature vector [0.7, 0.0455, 0.24].

[0042] Step S32: Based on the remaining cycle life in the model input feature vector, combined with the battery health degradation curve model, calculate the maximum charging current reference value that the battery can withstand, and then introduce the battery temperature sensitivity coefficient to correct the maximum charging current reference value to generate the upper limit of charging current. In this embodiment of the invention, based on the remaining cycle life of 560 cycles in the model input feature vector, a pre-constructed battery health degradation curve model is retrieved. The curve shows that when the remaining cycle life is 800-600 cycles, the maximum charging current benchmark is 30A (the battery's rated charging current); 28A when it is 600-400 cycles; and 25A when it is below 400 cycles. The current remaining cycle life of 560 cycles falls within the 600-400 cycle range, so the initial maximum charging current benchmark is set to 28A. The current battery temperature of 28℃ is extracted, and a temperature sensitivity coefficient is introduced. The coefficient rules are: 1.0 for 25-30℃, 0.95 for 30-35℃, 1.05 for 20-25℃, and 1.0 for the current temperature. The benchmark value is corrected using the formula "corrected benchmark value = initial benchmark value × temperature sensitivity coefficient", resulting in 28 × 1.0 = 28A. Further considering the degree of decay of remaining cycle life, the current reference value decreases by 1A for every 100 cycles of reduced life. 560 cycles is 40 cycles less than 600 cycles, resulting in an additional decrease of 0.4A. The final upper limit of the charging current is 28-0.4=27.6A.

[0043] Step S33: Based on the charging pile power adjustment accuracy in the model input feature vector, calculate the minimum power adjustment unit of the charging pile, combine it with the optimal charging power range of the battery, divide the power adjustment levels, and generate the charging power adjustment gradient. In this embodiment of the invention, the minimum power adjustment unit of the charging pile is calculated based on the pile power adjustment accuracy of ±0.3kW in the model input feature vector: the minimum change is the absolute value of the adjustment accuracy of 0.3kW, the rated power range is 0-6.6kW, the adjustment resolution is 0.3 / 6.6≈0.0455, and the minimum power adjustment unit is determined to be 0.3kW. The current SOC is 30%, the rated charging power is 6.6kW, and combined with the state of charge-power curve, the safe charging power upper limit in the SOC 20%-80% range is 6.6kW, and the lower limit is 2.4kW, thus determining the optimal charging power range for the battery to be 2.4-6.6kW. The total power adjustment span is calculated as 6.6-2.4=4.2kW, and the basic number of adjustment levels is 4.2 / 0.3=14 levels. Introducing the battery power sensitivity parameter 6.46 (greater than the threshold of 5), the number of levels is increased by 20%, resulting in a target number of levels of 14×1.2=17 levels. The charging power levels are divided using a combination of uniform segmentation and dynamic weighting: 6 levels are divided into 0.22kW intervals for SOC 30%-50% (2.4-4.2kW), 5 levels are divided into 0.25kW intervals for 50%-70% (4.2-5.4kW), and 6 levels are divided into 0.24kW intervals for 70%-80% (5.4-6.6kW). The differences between adjacent levels are integrated to generate charging power adjustment gradients (0.22kW, 0.25kW, 0.24kW).

[0044] Step S34: Based on the grid redundancy capacity in the model input feature vector, combined with the load fluctuation prediction value during the charging period, calculate the charging intermittent frequency that the grid can accommodate, and then associate it with the battery heat dissipation demand parameters to generate the charging intermittent cycle; aggregate the upper limit of charging current, the charging power adjustment gradient and the charging intermittent cycle to generate the initial charging parameter combination. In this embodiment of the invention, based on the grid redundancy capacity of 120kW in the model input feature vector and combined with the predicted load fluctuation during the charging period (14:00-16:00), the maximum load fluctuation during this period is predicted to be ±15kW. The grid-accommodative charging interval frequency must satisfy "interval frequency × single pile power ≤ redundancy capacity - load fluctuation". With a single pile rated power of 6.6kW, the interval frequency is ≤ (120-15) / 6.6≈15.91 times / hour. Taking 15 times / hour, this means one interval is allowed every 4 minutes. The heat dissipation requirement parameter is extracted from the battery's current health status parameters. A capacity decay rate of 21.43% corresponds to a medium heat dissipation requirement level, requiring an interval every 10 minutes of charging to ensure heat dissipation. Considering both the grid-accommodative frequency and the battery heat dissipation requirement, the intersection of the two is taken, determining the charging interval frequency to be 6 times / hour (once every 10 minutes). The initial charging interval cycle is set to "10 minutes of charging + 1 minute of pause". Further calculations were performed to compensate for grid load fluctuations. For a load fluctuation of 15kW, the intermittent time of a single pile needs to be increased by 0.2 minutes. Finally, a charging intermittent cycle of "10 minutes of charging + 1.2 minutes of pause" was generated. The upper limit of charging current of 27.6A, the charging power adjustment gradient, and the charging intermittent cycle were aggregated to generate the initial charging parameter combination.

[0045] Step S35: Correct the initial charging parameter combination for health protection by combining the capacity decay rate in the current battery health status parameters, and generate basic remote control commands.

[0046] In this embodiment of the invention, the capacity decay rate of 21.43% is extracted from the current health status parameters of the battery, and the health risk level is determined to be 3 (higher risk) based on the cycle life curve. The upper limit of the charging current in the initial charging parameter combination is adjusted to 27.6A based on this level. Using the current correction model "corrected upper limit of current = initial upper limit of current × (1 - risk level × 0.05)", we obtain 27.6 × (1 - 3 × 0.05) = 27.6 × 0.85 = 23.46A, and the current safety redundancy is 27.6 - 23.46 = 4.14A. Based on the capacity decay rate of 21.43%, a power tolerance coefficient of 0.8 is extracted, and the power adjustment step size correction values ​​are calculated: 0.22kW gradient correction value = 0.22 × (1 - 0.8) = 0.044kW, 0.25kW gradient = 0.05kW, 0.24kW gradient = 0.048kW. These are then superimposed to generate a health protection type power adjustment gradient (0.264kW, 0.3kW, 0.288kW). Based on the capacity decay rate and associated heat dissipation efficiency of 72.86%, the intermittent duration compensation is calculated as 1.2 × (1 - 72.86% / 90%) ≈ 0.228 minutes ≈ 14 seconds. The optimized charging intermittent cycle is "10 minutes of charging + 74 seconds of pause". The three types of parameters are converted into standardized control code according to the charging pile communication protocol, generating the basic remote control command "CTRL|CUR23.46|PWR0.264,0.3,0.288|INT600,74|END".

[0047] Furthermore, the calculation of the minimum power adjustment unit of the charging pile in step S33, combined with the optimal charging power range of the battery, and the division of power adjustment levels includes the following steps: Extract the output power adjustment accuracy parameters and rated power range of the charging pile from the charging pile operation benchmark data. Calculate the minimum change in power adjustment of the charging pile in a single operation based on the output power adjustment accuracy parameters. Use the ratio of the minimum change to the rated power range as the power adjustment resolution to generate the minimum power adjustment unit of the charging pile. In this embodiment of the invention, the output power adjustment accuracy parameter ±0.3kW and the rated power range 0-6.6kW of a charging pile are extracted from the charging pile's operating reference data. Based on the output power adjustment accuracy parameter, the minimum change in power adjustment for a single operation is calculated. The minimum change is equal to the absolute value of the adjustment accuracy, i.e., 0.3kW. The ratio of the minimum change to the rated power range is used as the power adjustment resolution, calculated as "Adjustment resolution = Minimum change / Rated power range". Substituting the data, we get 0.3 / 6.6≈0.0455 (rounded to four decimal places). The minimum power adjustment unit of the charging pile is determined based on the adjustment resolution. The minimum adjustment unit is equal to the minimum change, i.e., 0.3kW. Subsequent power adjustments must increase or decrease in increments of 0.3kW to ensure that the adjustment accuracy meets the equipment operating reference requirements.

[0048] Furthermore, the current state of charge (SOC) and rated charging power of the battery are obtained from the current health parameters of the battery. Combined with the battery SOC-power curve, the upper and lower limits of the safe charging power corresponding to different SOC ranges are determined and integrated to form the optimal charging power range of the battery. In this embodiment of the invention, the current state of charge (SOC) of the battery is 30%, and the rated charging power is 6.6kW, obtained from the battery's current health parameters. The battery's SOC-power curve (obtained in advance through battery cycle testing) is retrieved. The curve data shows that the safe charging power upper limit is 3.3kW and the lower limit is 1.2kW when SOC is 0%-20%; the upper limit is 6.6kW and the lower limit is 2.4kW when SOC is 20%-80%; and the upper limit is 3.3kW and the lower limit is 0.6kW when SOC is 80%-100%. Since the current SOC is in the 20%-80% range, the corresponding safe charging power upper limit of 6.6kW and lower limit of 2.4kW are determined. These are integrated to form the optimal charging power range for the battery: 2.4-6.6kW. This range satisfies the battery's safe charging requirements while maximizing charging efficiency, avoiding damage to the battery due to excessive power or prolonged charging time due to insufficient power.

[0049] Furthermore, the difference between the upper and lower limits of the optimal charging power range of the battery is used as the total power adjustment span. Based on the minimum power adjustment unit of the charging pile, the number of basic adjustment levels that can be divided within the total span is calculated, and the basic level number parameter is generated. In this embodiment of the invention, the difference between the upper and lower limits of the optimal charging power range of the battery (2.4-6.6kW) is used as the total power adjustment span. The calculation formula is "total span = upper limit - lower limit". Substituting the data, we get 6.6 - 2.4 = 4.2kW. Based on the minimum power adjustment unit of the charging pile (0.3kW), the number of basic adjustment levels that can be divided within the total span is calculated using the formula "number of basic levels = total span / minimum adjustment unit". Substituting the data, we get 4.2 / 0.3 = 14 levels. The parameter of the number of basic levels is 14. That is, without considering other factors, the optimal power range can be divided into 14 basic adjustment levels in 0.3kW intervals, with each level corresponding to a fixed power range.

[0050] Furthermore, a battery power sensitive parameter is introduced, which is derived from the current health status of the battery and the charging efficiency. The basic number of gears is modified. If the battery power sensitive parameter is greater than the preset threshold, the basic number of gears is adjusted to improve the fineness of adjustment. If the battery power sensitive parameter is less than or equal to the preset threshold, the battery power sensitive parameter is reduced to simplify the control logic and generate an optimized target number of gears. In this embodiment of the invention, a battery power-sensitive parameter is introduced. This parameter is derived from the battery's current health status (capacity retention rate 78.57%, voltage balance 99.01 points) and charging efficiency (corrected discharge efficiency 83%). The calculation formula is "Power-sensitive parameter = (capacity retention rate × voltage balance × charging efficiency) / 10000". Substituting the data, we get (78.57 × 99.01 × 83) / 10000 ≈ (78.57 × 8.21783) ≈ 645.8 / 100 ≈ 6.46 (rounded to two decimal places). The preset power sensitivity parameter threshold is 5. The current value is 6.46 > 5, so the adjustment precision needs to be improved. The number of basic adjustment gears (14) is increased by 20%. The calculation formula is "target number of gears = number of basic gears × (1 + 20%)". Substituting the data, we get 14 × 1.2 = 16.8. We round it to 17 gears to generate the optimized target number of gears (17). If the parameter is ≤ 5, the number of gears is reduced by 10% to simplify the control logic.

[0051] Furthermore, based on the target number of power levels and the optimal charging power range of the battery, the power adjustment levels are divided using a combination of uniform segmentation and dynamic weighting. The starting and ending power values ​​for each power level are calculated, and the power difference between adjacent power levels is integrated into the charging power adjustment gradient.

[0052] In this embodiment of the invention, the power adjustment levels are divided using a combination of uniform segmentation and dynamic weighting, based on the target number of levels (17) and the optimal charging power range of the battery (2.4-6.6kW). First, the initial level interval is calculated using uniform segmentation: interval = 4.2 / 17 ≈ 0.247kW. Then, dynamic weights are assigned based on the battery's SOC sensitivity to power (SOC 30%-50% weight 1.2, 50%-70% weight 1.0, 70%-80% weight 1.1). Adjust the gear intervals for each range: 30%-50% (power 2.4-4.2kW) divided into 6 gears with 0.22kW intervals (2.4-2.62, 2.62-2.84…4.0-4.22kW); 50%-70% (4.2-5.4kW) divided into 5 gears with 0.25kW intervals (4.2-4.45, 4.45-4.7…5.15-5.4kW); 70%-80% (5.4-6.6kW) divided into 6 gears with 0.24kW intervals (5.4-5.64, 5.64-5.88…6.36-6.6kW), for a total of 17 gears. The starting and ending power values ​​for each gear level are calculated (e.g., 2.4-2.62kW for gear 1). The power differences between adjacent gear levels (0.22, 0.25, 0.24kW) are integrated into a charging power adjustment gradient. The gradient value changes dynamically with the SOC range to ensure the safety and efficiency of battery charging under different health conditions.

[0053] Furthermore, step S35 includes the following steps: Step S351: Extract the capacity decay rate from the current health status parameters of the battery, and at the same time retrieve the cycle life curve from the historical charge and discharge data of the battery. Based on the correlation between the capacity decay rate and the cycle life curve, calculate the current health risk level of the battery and generate health risk assessment parameters. In this embodiment of the invention, the capacity decay rate of 21.43% (derived from the capacity retention rate of 78.57%, 100%-78.57%=21.43%) is extracted from the battery's current health status parameters. The cycle life curve from the battery's historical charge-discharge data is retrieved. The curve is labeled as follows: 0%-10% capacity decay rate corresponds to health risk level 1 (low risk), 10%-20% corresponds to level 2 (medium risk), 20%-30% corresponds to level 3 (high risk), and above 30% corresponds to level 4 (high risk). Based on the correlation between the capacity decay rate of 21.43% and the cycle life curve, the current health risk level of the battery is determined to be 3 (high risk). A health risk assessment parameter is generated, including the capacity decay rate of 21.43% and the health risk level of 3. This parameter is used for the safe correction of subsequent charging parameters.

[0054] Step S352: Based on the health risk assessment parameters and the upper limit of the charging current in the initial charging parameter combination, construct a current correction model and calculate the current safety redundancy to generate the corrected upper limit of the charging current. In this embodiment of the invention, based on the health risk assessment parameters (level 3), the upper limit of the charging current in the initial charging parameter combination is 30A (the battery's rated charging current). A current correction model is constructed, with the formula "corrected upper limit of current = initial upper limit of current × (1 - health risk level × 0.05)" (for each level increase in risk, the upper limit of current decreases by 5% to avoid high current damage to the battery under high risk). Substituting the data, the corrected upper limit of current is 30 × (1 - 3 × 0.05) = 30 × 0.85 = 25.5A. The current safety redundancy is calculated using the formula "safety redundancy = initial upper limit of current - corrected upper limit of current", i.e., 30 - 25.5 = 4.5A. This redundancy ensures that current fluctuations during charging will not exceed the safe range, ultimately generating a corrected upper limit of charging current of 25.5A.

[0055] Step S353: Extract the power tolerance coefficient of the battery based on the capacity decay rate and combine it with the initial charging power adjustment gradient to calculate the power adjustment step size correction value. Then, superimpose the power adjustment step size correction value with the charging power adjustment gradient to generate a health protection type power adjustment gradient. In this embodiment of the invention, the power tolerance coefficient of the battery is extracted based on a capacity decay rate of 21.43%. The extraction rules are as follows: 0%-10% decay rate: coefficient 1.0; 10%-20% decay rate: coefficient 0.9; 20%-30% decay rate: coefficient 0.8; above 30% decay rate: coefficient 0.7. Therefore, the current power tolerance coefficient is 0.8. The initial charging power adjustment gradient includes three levels: 0.22kW, 0.25kW, and 0.24kW (corresponding to different SOC ranges). The power adjustment step size correction value is calculated using the formula "correction value = initial gradient × (1 - power tolerance coefficient)". The correction value for the 0.22kW level is 0.22 × (1 - 0.8) = 0.044kW, for the 0.25kW level it is 0.25 × 0.2 = 0.05kW, and for the 0.24kW level it is 0.24 × 0.2 = 0.048kW. The corrected values ​​are superimposed on the initial gradient to generate a health-protective power regulation gradient: 0.22+0.044=0.264kW, 0.25+0.05=0.3kW, 0.24+0.048=0.288kW. The corrected gradient is smoother, reducing the power surge of high-degradation batteries.

[0056] Step S354: Based on the battery heat dissipation efficiency parameters associated with the capacity decay rate, and combined with the initial charging interval cycle, calculate the interval duration compensation amount to generate the optimized charging interval cycle; In this embodiment of the invention, the battery heat dissipation efficiency parameter is associated with a capacity decay rate of 21.43%. The association rule is: for every 10% increase in the decay rate, the heat dissipation efficiency decreases by 8%. The initial heat dissipation efficiency is 90% (when the decay rate is 0%). The current heat dissipation efficiency = 90% - (21.43% / 10%) × 8% ≈ 90% - 17.14% = 72.86%. The initial charging interval is 10 minutes (pausing for 1 minute every 10 minutes of charging for heat dissipation). The compensation amount for the interval is calculated using the formula "compensation amount = initial interval × (1 - heat dissipation efficiency / initial heat dissipation efficiency)". Substituting the data, the compensation amount is 1 × (1 - 72.86% / 90%) ≈ 1 × (1 - 0.8096) = 0.1904 minutes ≈ 11.4 seconds, rounded down to 12 seconds. An optimized charging interval is generated: 10 minutes of charging, 1 minute and 12 seconds pause. By extending the interval time, the decrease in heat dissipation efficiency is compensated for, and the battery is prevented from overheating.

[0057] Step S355: Integrate the corrected charging current upper limit, health protection power adjustment gradient, and optimized charging interval cycle, and convert the three types of parameters into standardized control codes according to the charging pile communication protocol format to generate basic remote control commands.

[0058] In this embodiment of the invention, the modified charging current upper limit of 25.5A, the health-protective power adjustment gradient (0.264kW, 0.3kW, 0.288kW), and the optimized charging interval cycle (10 minutes of charging + 72 seconds of pause) are integrated. The data is converted into standardized control code according to the charging pile communication protocol format (e.g., ASCII code format, with instruction fields including current, power gradient, and interval cycle). The current field is encoded as "CUR25.5", the power gradient field as "PWR0.264,0.3,0.288", and the interval cycle field as "INT600,72" (600 seconds of charging, 72 seconds of pause). These fields are combined in protocol order to generate the basic remote control command "CTRL|CUR25.5|PWR0.264,0.3,0.288|INT600,72|END". This command can be directly sent to the charging pile via the remote communication module, achieving precise control based on the battery's health status.

[0059] Furthermore, the calculation of the instruction execution deviation based on battery status feedback data and charging pile execution status data in step S4 includes the following steps: Real-time charging voltage, real-time charging current and state of charge change rate are extracted from battery status feedback data. Target charging voltage, target charging current and target power output value of the command issued are extracted from charging pile execution status data. The two types of data are aligned by timestamp to generate a deviation analysis dataset. In this embodiment of the invention, the real-time charging voltage (219.5V, 220.1V, 219.8V, 220.3V, 219.7V, 220.0V), real-time charging current (25.3A, 25.4A, 25.2A, 25.5A, 25.3A, 25.4A), and state of charge change rate (0.8% / min, 0.82% / min, 0.79% / min, 0.83% / min, 0.81% / min, 0.82% / min) for a certain period (one group every 10 seconds, for a total of 6 groups) are extracted from the battery status feedback data. The target charging voltage (220V, fixed), target charging current (25.5A, fixed), and target power output value (5.61kW, calculated from 220V×25.5A) corresponding to the timestamp are extracted from the charging pile execution status data. The two types of data are aligned by timestamp (e.g., 10:00:00, 10:00:10…10:00:50) to generate 6 sets of deviation analysis datasets. Each set contains real-time and target voltage, current, power and SOC change rate data.

[0060] Furthermore, based on the real-time charging voltage and target charging voltage in the deviation analysis dataset, the absolute value of the voltage deviation is calculated, and combined with the allowable voltage fluctuation threshold, a voltage deviation ratio parameter is generated; the current deviation ratio parameter between the real-time charging current and the target charging current is calculated. In this embodiment of the invention, based on the real-time charging voltage and the target charging voltage of 220V in the deviation analysis dataset, the absolute value of each voltage deviation (0.5V, 0.1V, 0.2V, 0.3V, 0.3V, 0.0V) is calculated. A voltage fluctuation allowable threshold of 1V is set, and the voltage deviation percentage parameter is calculated using the formula "Voltage deviation percentage = Absolute deviation value / Allowable threshold × 100%". The percentages for the six groups are 50%, 10%, 20%, 30%, 30%, and 0% respectively, and the average value of 25% is taken as the final voltage deviation percentage parameter. The absolute value of the current deviation between the real-time charging current and the target charging current of 25.5A (0.2A, 0.1A, 0.3A, 0.0A, 0.2A, 0.1A) is calculated. A current fluctuation allowable threshold of 0.5A is set, and the current deviation percentage is calculated as "Absolute deviation value / Allowable threshold × 100%". The percentages for the six groups are 40%, 20%, 60%, 0%, 40%, and 20% respectively, and the average value of 30% is taken as the current deviation percentage parameter.

[0061] Furthermore, based on the rate of change of state of charge and the theoretical rate of change of SOC corresponding to the target power output value in the deviation analysis dataset, the SOC change deviation is calculated, and then combined with the SOC change sensitivity coefficient, the SOC deviation influence weight is generated. In this embodiment of the invention, the theoretical SOC change rate is calculated based on the rate of change of state of charge (e.g., 0.8% / min at 10:00:00) in the deviation analysis dataset and the theoretical SOC change rate corresponding to the target power output value of 5.61kW. The formula for calculating the theoretical SOC change rate is "Theoretical change rate = (Target power × Charging efficiency) / Actual usable battery capacity × 60". With a charging efficiency of 83% and an actual usable capacity of 55kWh, substituting these values, we get (5.61 × 0.83) / 55 × 60 ≈ (4.6563) / 55 × 60 ≈ 0.8466% / min. The SOC change deviation is calculated as |Real-time change rate - Theoretical change rate|. At 10:00:00, the deviation is |0.8 - 0.8466| = 0.0466% / min, and the average deviation of the six sets is 0.038% / min. Set the SOC change sensitivity coefficient to 1.2 (the sensitivity of SOC change to battery health), and generate the SOC deviation influence weight = average deviation × sensitivity coefficient = 0.038 × 1.2 ≈ 0.0456.

[0062] Furthermore, the power output response delay time in the charging pile execution status data is introduced, compared with the preset response threshold, and the response delay deviation coefficient is calculated. Then, combined with the voltage and current deviation ratio parameters and the SOC deviation influence weight, a deviation calculation matrix is ​​constructed. In this embodiment of the invention, the power output response delay time (6 sets of data are 0.8s, 0.7s, 0.9s, 0.6s, 0.8s, and 0.7s respectively) from the charging pile execution status data is introduced, with a preset response threshold of 1s. The response delay deviation coefficient is calculated using the formula "deviation coefficient = (average delay time / preset threshold)", where the average delay time = (0.8 + 0.7 + 0.9 + 0.6 + 0.8 + 0.7) / 6 = 0.75s, and the deviation coefficient = 0.75 / 1 = 0.75. Combining the voltage deviation percentage of 25% (0.25), the current deviation percentage of 30% (0.3), and the SOC deviation influence weight of 0.0456, a 3-row, 1-column deviation calculation matrix is ​​constructed: [[0.25],[0.3],[0.0456]]. Weights are assigned to the matrix elements (voltage 0.4, current 0.4, SOC 0.2), and the weight matrix is ​​[0.4,0.3,0.3]. The response delay deviation coefficient is used as the overall matrix correction factor.

[0063] Furthermore, the instruction execution deviation is generated by weighted summation of each deviation parameter through the deviation calculation matrix.

[0064] In this embodiment of the invention, a weighted sum is performed using a deviation calculation matrix. First, the product of the matrix elements and their corresponding weights is calculated: voltage deviation contribution = 0.25 × 0.4 = 0.1, current deviation contribution = 0.3 × 0.3 = 0.09, SOC deviation contribution = 0.0456 × 0.3 ≈ 0.0137, and the sum of the three = 0.1 + 0.09 + 0.0137 = 0.2037. This result is then multiplied by the response delay deviation coefficient of 0.75 to generate the command execution deviation = 0.2037 × 0.75 ≈ 0.1528 (rounded to four decimal places). The smaller the deviation value, the higher the accuracy of the charging pile's execution of remote control commands. 0.1528 is in the low deviation range, indicating that the current command execution status is good and no adjustment of control parameters is needed.

[0065] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A remote control method for new energy charging piles that considers battery health status, characterized in that, Includes the following steps: Step S1: Obtain the full life cycle data of the battery of the new energy vehicle, the real-time operating condition data of the charging pile, and the dynamic data of the power grid in the charging area. Perform attenuation trend fitting and time series completion processing on the full life cycle data of the battery to generate battery health baseline data. Perform abnormal operating condition identification and parameter standardization on the real-time operating condition data of the charging pile to generate the charging pile operation benchmark data. Perform load peak and valley identification and carrying capacity assessment processing on the dynamic data of the power grid in the charging area to generate power grid dispatch reference data. Step S2: Extract the current health status parameters of the battery based on the battery health baseline data, calculate the charging compatibility coefficient between the charging pile and the battery by combining the charging pile operation benchmark data, and analyze the grid redundancy capacity during the target charging period by using grid dispatch reference data to construct a charging control decision model that includes three dimensions: health priority, compatibility level, and grid capacity. Step S3: Output the initial charging parameter combination based on the charging control decision model, including the upper limit of charging current, the charging power adjustment gradient and the charging interval period, and perform health protection correction on the initial charging parameter combination by combining the capacity decay rate in the current health status parameters of the battery, and generate basic remote control commands. Step S4: Send basic remote control commands to the target charging pile, collect battery status feedback data and charging pile execution status data in real time during the charging process, calculate the command execution deviation based on the battery status feedback data and charging pile execution status data, dynamically iterate and optimize the basic remote control commands according to the command execution deviation, generate a new energy charging pile remote control scheme and realize the remote control of the charging pile.

2. The remote control method for new energy charging piles considering battery health status according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the full life cycle data of the new energy vehicle's battery, the real-time operating condition data of the charging pile, and the dynamic data of the power grid in the charging area; among which, the full life cycle data of the battery includes the number of historical charge and discharge cycles, the capacity change in each cycle, the discharge efficiency at different temperatures, and the consistency data of the battery cell voltage; the real-time operating condition data of the charging pile includes the output voltage fluctuation value, the output current stability, the internal temperature distribution of the charging pile, the contact resistance of the charging interface, and the communication link delay data; the dynamic data of the power grid in the charging area includes the real-time total load of the area, the load ratio of each branch line, the voltage fluctuation amplitude, and the frequency stability coefficient. Step S12: Based on the historical charge-discharge cycle count and the capacity change in each cycle, a battery capacity decay trend curve is generated by fitting the data, and the current capacity retention rate parameter of the battery is calculated; based on the discharge efficiency at different temperatures, a temperature-efficiency correlation model is constructed, and the discharge efficiency is corrected in real time by combining the current ambient temperature parameter to generate the corrected discharge efficiency parameter; missing values ​​are detected in the battery cell voltage consistency data, and interpolation based on the voltage change pattern of adjacent cycles is used to complete the data, and the maximum deviation rate parameter of the cell voltage is calculated; the capacity retention rate parameter, the corrected discharge efficiency parameter, and the maximum deviation rate parameter of the cell voltage are integrated to generate battery health baseline data that includes battery health status evaluation indicators and decay trend prediction. Step S13: Identify abnormal fluctuations in the real-time operating data of the charging pile based on the preset normal operating parameter range, and mark the time nodes and parameter types corresponding to the abnormal data; classify the causes of the marked abnormal data. If it is caused by a temporary sensor failure, cross-validate and complete the data using other related sensor data from the same time period; if it is caused by abnormal equipment operation, record the characteristics of the abnormal operating conditions and remove invalid data; perform parameter standardization processing on the filtered normal operating data to convert parameters of different dimensions into a unified standard score; calculate the matching degree between the standardized operating parameters and the rated parameters of the charging pile, and generate the pile operation benchmark data including parameter matching rate and operating condition stability score; Step S14: Use a sliding window to perform trend analysis on the real-time total load of the region, identify peak load periods, valley periods, and flat periods, and calculate the load duration and load change rate for each period; based on the load ratio of each branch line and combined with the rated carrying capacity of the line, calculate the current load rate of each line, and identify lines with overload risk and lines with load redundancy; according to the voltage fluctuation amplitude and frequency stability coefficient, use the fuzzy comprehensive evaluation method to score the current operation stability of the power grid and generate a power grid stability index; integrate the load period division results, line load rate data, and power grid stability index to generate power grid dispatch reference data that includes charging period recommendations and line selection suggestions.

3. The remote control method for new energy charging piles considering battery health status according to claim 2, characterized in that, Step S14, which involves calculating the current load rate of each line based on the load percentage of each branch line and the line's rated carrying capacity, and identifying lines at risk of overload and lines with redundant loads, includes the following steps: Real-time load data and basic parameters of each branch line are extracted from the dynamic data of the power grid in the charging area. The real-time load value of a single line is calculated based on the real-time load data. The real-time load ratio of the line is calculated by combining the total load data of the area, and the initial load ratio parameters of each branch line are generated. The system retrieves the line model and laying method data from the basic line parameters, queries the preset line parameter database, obtains the line rated carrying capacity benchmark value that matches the line model and laying method, and corrects the line rated carrying capacity benchmark value with an aging coefficient based on the line's operating years data to generate the actual rated carrying capacity of each line. The initial load percentage parameter is correlated with the predicted total load value of the region to calculate the real-time absolute load value of each line. Then, the ratio of the real-time absolute load value to the actual rated carrying capacity is calculated to generate the current load rate of each line. A line load risk assessment model is constructed based on the current load rate. Historical overload frequency data and current ambient temperature data are introduced. The current load rate is compared with the load rate threshold during historical overloads to calculate the overload risk coefficient of the line. At the same time, the load redundancy of the line is calculated by combining the difference between the current load rate and the actual rated carrying capacity. Set an overload risk coefficient threshold and a load redundancy threshold. If the overload risk coefficient exceeds the overload risk coefficient threshold, the line is marked as having an overload risk. If the load redundancy exceeds the load redundancy threshold, the line is marked as having a load redundancy. Then, generate line load rate data with the current load rate of each line.

4. The remote control method for new energy charging piles considering battery health status according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Retrieve the capacity retention rate parameter from the battery health baseline data, and calculate the actual available capacity parameter of the battery at present by combining it with the battery rated capacity data; calculate the theoretical charging time required for the battery to reach the target capacity based on the corrected discharge efficiency parameter combined with the current ambient temperature and the target charging capacity. Step S22: Extract the maximum deviation rate parameter of the single cell voltage and combine it with the number of battery cells and the safe voltage range to calculate the current voltage balance score of the battery; Step S23: Based on the number of charge-discharge cycles in the battery's full life cycle data and combined with the battery's designed cycle life, calculate the battery's remaining cycle life parameters; integrate the actual usable capacity parameters, theoretical charging time, voltage balance score, and remaining cycle life parameters to generate the battery's current health status parameters; Step S24: Calculate the charging compatibility coefficient between the charging pile and the battery by combining the charging pile operation benchmark data, and analyze the grid redundancy capacity during the target charging period by using grid dispatch reference data; Step S25: Based on the current battery health status parameters, charging adaptation coefficient, and grid redundancy capacity, construct a charging control decision model that includes three dimensions: health priority, adaptation level, and grid capacity.

5. The remote control method for new energy charging piles considering battery health status according to claim 4, characterized in that, Step S24, which involves calculating the charging compatibility coefficient between the charging pile and the battery by combining the pile's operational benchmark data, includes the following steps: The output voltage fluctuation range, output current regulation accuracy and power conversion efficiency parameters of the charging pile are extracted from the charging pile operation benchmark data. At the same time, the rated charging voltage, rated charging current and optimal charging power range of the battery are obtained from the current health status parameters of the battery. The two types of data are time-series aligned to generate the basic dataset for adaptation calculation. Based on the output voltage fluctuation range and rated charging voltage in the basic dataset of the adaptation calculation, the voltage adaptation deviation rate is calculated, which is the percentage of the difference between the voltage fluctuation range boundary and the rated charging voltage. Then, combined with the voltage stability weight, the basic voltage adaptation coefficient is generated. Based on the output current regulation accuracy and rated charging current in the adaptation calculation base dataset, a current regulation matching model is constructed, and the coverage of the current regulation range and rated charging current is calculated through the model. Combined with the current response speed parameter, the current adaptation base coefficient is generated. By utilizing the power conversion efficiency and optimal charging power range in the adaptation calculation base dataset, the average power conversion efficiency within the optimal power range is calculated, and then combined with the power loss threshold, the power adaptation base coefficient is generated. By introducing the interface contact resistance parameter and battery internal resistance parameter from the charging pile operation benchmark data, the influence correction coefficient of the total resistance of the charging circuit on the compatibility is calculated. This influence correction coefficient is then weighted and fused with the voltage, current, and power compatibility base coefficients to generate the charging compatibility coefficient between the charging pile and the battery.

6. The remote control method for new energy charging piles considering battery health status according to claim 4, characterized in that, Step S24, which involves analyzing the grid redundancy capacity during the target charging period using grid dispatch reference data, includes the following steps: Extract the regional total load forecast, grid rated total capacity data and load rate data of each branch line for the target charging period from the grid dispatch reference data. Then, correlate and match the three types of data according to the time dimension to generate a basic dataset for redundancy capacity analysis. Based on the regional total load forecast and the grid rated total capacity data in the redundancy capacity analysis basic dataset, the theoretical remaining capacity parameters of the grid are calculated. Then, combined with the historical load fluctuation characteristics of the grid during the same period, the initial value of the theoretical redundancy capacity is generated. Based on the load rate data of each branch line in the redundancy capacity analysis basic dataset, the load redundancy ratio of each line is calculated, and the load redundancy ratio of all lines is integrated through a weighted average algorithm to generate a comprehensive line redundancy coefficient. By introducing the power grid stability index from the power grid dispatch reference data, the power grid stability index is converted into a redundancy capacity correction weight. Then, by combining the initial value of theoretical redundancy capacity and the comprehensive coefficient of line redundancy, the preliminary redundancy capacity is calculated. By combining the predicted output data of renewable energy generation during the target charging period, the amount of renewable energy generation that supplements the grid's redundant capacity is calculated. The supplementary amount is then superimposed with the initial redundant capacity to generate the grid's redundant capacity for the target charging period.

7. The remote control method for new energy charging piles considering battery health status according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Extract the remaining cycle life from the battery's current health status parameters, the pile power regulation accuracy from the pile operation benchmark data, and the grid redundancy capacity from the grid dispatch reference data from the input data of the charging control decision model. Standardize the three types of data to generate the model input feature vector. Step S32: Based on the remaining cycle life in the model input feature vector, combined with the battery health degradation curve model, calculate the maximum charging current reference value that the battery can withstand, and then introduce the battery temperature sensitivity coefficient to correct the maximum charging current reference value to generate the upper limit of charging current. Step S33: Based on the charging pile power adjustment accuracy in the model input feature vector, calculate the minimum power adjustment unit of the charging pile, combine it with the optimal charging power range of the battery, divide the power adjustment levels, and generate the charging power adjustment gradient. Step S34: Based on the grid redundancy capacity in the model input feature vector, combined with the load fluctuation prediction value during the charging period, calculate the charging intermittent frequency that the grid can accommodate, and then associate it with the battery heat dissipation demand parameters to generate the charging intermittent cycle; aggregate the upper limit of charging current, the charging power adjustment gradient and the charging intermittent cycle to generate the initial charging parameter combination. Step S35: Correct the initial charging parameter combination for health protection by combining the capacity decay rate in the current battery health status parameters, and generate basic remote control commands.

8. The remote control method for new energy charging piles considering battery health status according to claim 7, characterized in that, Step S33, which involves calculating the minimum power adjustment unit of the charging pile and dividing the power adjustment levels based on the optimal charging power range of the battery, includes the following steps: Extract the output power adjustment accuracy parameters and rated power range of the charging pile from the charging pile operation benchmark data. Calculate the minimum change in power adjustment of the charging pile in a single operation based on the output power adjustment accuracy parameters. Use the ratio of the minimum change to the rated power range as the power adjustment resolution to generate the minimum power adjustment unit of the charging pile. The current state of charge (SOC) and rated charging power of the battery are obtained from the current health parameters of the battery. Combined with the battery SOC-power curve, the upper and lower limits of the safe charging power corresponding to different SOC ranges are determined and integrated to form the optimal charging power range of the battery. The difference between the upper and lower limits of the battery's optimal charging power range is used as the total power adjustment span. Based on the minimum power adjustment unit of the charging pile, the number of basic adjustment levels that can be divided within the total span is calculated, and the basic level number parameter is generated. A battery power sensitive parameter is introduced, which is derived from the current health status of the battery and the charging efficiency. The basic number of gears is modified. If the battery power sensitive parameter is greater than the preset threshold, the basic number of gears is adjusted to improve the fineness of adjustment. If the battery power sensitive parameter is less than or equal to the preset threshold, the battery power sensitive parameter is reduced to simplify the control logic and generate an optimized target number of gears. Based on the target number of power levels and the optimal charging power range of the battery, the power adjustment levels are divided using a combination of uniform segmentation and dynamic weighting. The starting and ending power values ​​for each power level are calculated, and the power difference between adjacent power levels is integrated into the charging power adjustment gradient.

9. The remote control method for new energy charging piles considering battery health status according to claim 7, characterized in that, Step S35 includes the following steps: Step S351: Extract the capacity decay rate from the current health status parameters of the battery, and at the same time retrieve the cycle life curve from the historical charge and discharge data of the battery. Based on the correlation between the capacity decay rate and the cycle life curve, calculate the current health risk level of the battery and generate health risk assessment parameters. Step S352: Based on the health risk assessment parameters and the upper limit of the charging current in the initial charging parameter combination, construct a current correction model and calculate the current safety redundancy to generate the corrected upper limit of the charging current. Step S353: Extract the power tolerance coefficient of the battery based on the capacity decay rate and combine it with the initial charging power adjustment gradient to calculate the power adjustment step size correction value. Then, superimpose the power adjustment step size correction value with the charging power adjustment gradient to generate a health protection type power adjustment gradient. Step S354: Based on the battery heat dissipation efficiency parameters associated with the capacity decay rate, and combined with the initial charging interval cycle, calculate the interval duration compensation amount to generate the optimized charging interval cycle; Step S355: Integrate the corrected charging current upper limit, health protection power adjustment gradient, and optimized charging interval cycle, and convert the three types of parameters into standardized control codes according to the charging pile communication protocol format to generate basic remote control commands.

10. The remote control method for new energy charging piles considering battery health status according to claim 1, characterized in that, Step S4, which involves calculating the command execution deviation based on battery status feedback data and charging pile execution status data, includes the following steps: Real-time charging voltage, real-time charging current and state of charge change rate are extracted from battery status feedback data. Target charging voltage, target charging current and target power output value of the command issued are extracted from charging pile execution status data. The two types of data are aligned by timestamp to generate a deviation analysis dataset. Based on the real-time charging voltage and target charging voltage in the deviation analysis dataset, the absolute value of the voltage deviation is calculated, and combined with the voltage fluctuation allowable threshold, the voltage deviation ratio parameter is generated; the current deviation ratio parameter between the real-time charging current and the target charging current is calculated. Based on the rate of change of state of charge and the theoretical rate of change of SOC corresponding to the target power output value in the deviation analysis dataset, the SOC change deviation is calculated, and then combined with the SOC change sensitivity coefficient, the SOC deviation influence weight is generated. The power output response delay time in the charging pile execution status data is introduced, compared with the preset response threshold, and the response delay deviation coefficient is calculated. Then, combined with the voltage and current deviation ratio parameters and the SOC deviation influence weight, a deviation calculation matrix is ​​constructed. The instruction execution deviation is generated by weighted summation of each deviation parameter using a deviation calculation matrix.

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