A Dynamic Energy Saving Method for Charging Piles Based on Cooling Algorithm

Abstract

The present invention relates to the technical field of charging pile control, and specifically to a dynamic energy-saving method for charging piles based on a cooling algorithm, which includes the following steps: Real-time data collection is carried out on the charging pile cooling device to collect the temperature and flow rate of the cooling medium, as well as the battery temperature and voltage data of the charging state. In the present invention, an energy consumption evaluation performance function is constructed based on the data, and combined with the charging efficiency, an analysis of optimizing the cooling energy consumption is achieved. The calculation of the gradient data provides an optimization direction in the adjustment of the flow rate and temperature parameters, avoiding unreasonable phenomena in the allocation of cooling resources. By using the iterative optimization of the parameter adjustment results and combining with the continuous monitoring of the energy consumption and equipment efficiency, it is ensured that the cooling system can still operate dynamically optimized under complex conditions. The predictive analysis of the temperature change range during peak hours combined with real-time data adjusts the cooling resource allocation rules, improving the adaptability of the cooling system in complex scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile control, and in particular to a dynamic energy-saving method for a charging pile based on a cooling algorithm. Background Art

[0002] The field of charging pile control technology mainly focuses on the management and optimization of electric vehicle charging facilities, including energy scheduling, charging power regulation, equipment operation status monitoring and dynamic resource allocation during the charging process. The dynamic energy-saving method of charging piles is a technical means to achieve energy efficiency improvement and rational resource utilization by dynamically optimizing and regulating the cooling system and energy consumption process of charging piles.

[0003] However, the existing technology lacks data support for the dynamic changes of the cooling system in the operation control of the charging pile. The control strategy of the cooling medium flow rate and temperature is usually based on preset parameters, which is difficult to flexibly adjust according to the actual needs under complex conditions, and it is easy to cause uneven distribution of cooling resources. Therefore, improvement is needed. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a dynamic energy-saving method for a charging pile based on a cooling algorithm.

[0005] In order to achieve the above object, the present invention adopts the following technical solution, a dynamic energy-saving method for charging piles based on a cooling algorithm, comprising the following steps:

[0006] Real-time data collection is performed on the charging pile cooling device to collect the temperature and flow rate of the cooling medium and the battery temperature and voltage data of the charging state to obtain real-time monitoring data; based on the real-time monitoring data, a performance function for energy consumption evaluation is constructed to obtain a performance evaluation function;

[0007] Using the performance evaluation function, performing gradient calculation to obtain gradient data; calculating a parameter adjustment value of a cooling medium flow rate and a parameter adjustment value of a cooling medium temperature based on the gradient data, and generating a parameter adjustment result;

[0008] Based on the parameter adjustment results, iteratively optimize the cooling parameters, continuously monitor energy consumption and equipment efficiency during the process, and generate iterative optimization data; analyze the iterative optimization data to determine whether the performance improvement threshold is reached, and generate optimization results;

[0009] Based on the optimization results and in combination with the temperature variation range of the charging pile during the peak period, the allocation rules of the cooling medium flow rate and pre-cooling resources during the peak period are set to obtain the scheduling strategy.

[0010] Preferably, the steps of acquiring the real-time monitoring data are:

[0011] Collect the data of the liquid cooling device of the charging pile. Collect the temperature and flow rate of the cooling medium through temperature sensors and flow sensors, and collect the temperature and voltage of the battery using voltage sensors and temperature sensors. Timestamp all the collected sensor data to generate a timestamped sensor data set.

[0012] Based on the timestamped sensor data set, perform format standardization, correct deviation values, and identify abnormal data to generate a processed monitoring data set.

[0013] Based on the processed monitoring data set, perform data fusion processing to integrate the temperature and flow rate of the cooling medium and the temperature and voltage of the battery into real-time monitoring data.

[0014] Preferably, the steps for obtaining the performance evaluation function are as follows:

[0015] According to the real-time monitoring data, construct a performance function, the formula is:

[0016] ;

[0017] Where, is the performance function value, is the cooling medium flow rate, is the cooling medium temperature, is the total heat released during charging, is the set safety temperature threshold, and are the weight coefficients for the impact of charging efficiency and safety.

[0018] Preferably, the steps for obtaining the gradient data are as follows:

[0019] Based on the performance evaluation function, analyze the variable terms of flow rate and temperature in the performance function, extract the expressions associated with flow rate and temperature, and establish the partial derivative expression of the performance function.

[0020] According to the partial derivative expression of the performance function, use the flow rate and temperature parameters in the real-time monitoring data to calculate the partial derivative values of the performance function with respect to flow rate and temperature point by point to obtain partial derivative data.

[0021] Based on the partial derivative data, organize all the calculation results to form gradient data.

[0022] Preferably, the steps for obtaining the parameter adjustment result are as follows:

[0023] Based on the gradient data, calculate the parameter adjustment value of the cooling medium flow rate and the parameter adjustment value of the cooling medium temperature, and the calculation formulas are:

[0024] and ;

[0025] Among them, represents the parameter adjustment value of the cooling medium flow rate, represents the parameter adjustment value of the cooling medium temperature, represents the partial derivative value of the performance function associated with the flow rate, represents the partial derivative value of the performance function associated with the temperature, represents the total heat released during the charging process, represents the cooling medium flow rate, represents the upper limit of the safe adjustment range of the flow rate, represents the cooling medium temperature, represents the upper limit of the safe adjustment range of the temperature, represents the initial flow rate value, represents the current flow rate reference value, represents the initial temperature value, represents the current temperature reference value;

[0026] Based on the parameter adjustment value of the cooling medium flow rate and the parameter adjustment value of the cooling medium temperature, update the flow rate and temperature of the cooling medium to obtain the parameter adjustment result.

[0027] Preferably, the step of obtaining the iterative optimization data is as follows:

[0028] According to the parameter adjustment result, set the initial value of the cooling parameter as the starting point of the optimization iteration. Monitor the current cooling medium flow rate and temperature according to the real-time monitoring data, adjust the cooling parameter and record the energy consumption to generate a cooling parameter adjustment record;

[0029] Based on the cooling parameter adjustment record, conduct a comparative analysis of the energy consumption and equipment efficiency, determine whether the current cooling parameter meets the threshold of performance improvement. If not, continue to adjust the cooling parameter, and at the same time record the change trend of each parameter adjustment to form performance parameter update data;

[0030] According to the performance parameter update data, combined with the historical trend of the cooling parameter adjustment record, continuously adjust the cooling parameter and conduct iteration to obtain the iterative optimization data.

[0031] Preferably, the step of obtaining the optimization result is as follows:

[0032] Based on the iterative optimization data, extract the energy consumption and equipment efficiency information after the cooling parameter adjustment, calculate the change trend of the index, and generate a performance change record;

[0033] According to the performance change record, calculate the performance target achievement value, and the calculation formula is:

[0034] ;

[0035] Among them, is the achieved value of the performance target, is the equipment efficiency of the current iteration, is the initial equipment efficiency, is the energy consumption of the current iteration, is the initial energy consumption;

[0036] Based on the achieved value of the performance target, it is judged whether a predetermined performance improvement threshold is reached. If satisfied, the iteration is terminated to obtain the optimization result.

[0037] Preferably, the obtaining steps of the scheduling strategy are as follows:

[0038] Based on the optimization result, extract the temperature change range during the peak period of the charging pile, analyze the influence on the flow rate of the cooling medium and the precooling time, and generate a preliminary cooling resource allocation framework;

[0039] According to the preliminary cooling resource allocation framework, integrate the real-time monitored temperature change data and the peak period information, calculate the allocation priorities of the cooling medium flow rate and the precooling resources under different conditions, and form a cooling resource adjustment record;

[0040] Based on the cooling resource adjustment record, set the allocation rules for the cooling medium flow rate and the precooling resources during the peak period to obtain the scheduling strategy.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0042] In the present invention, an energy consumption evaluation performance function is constructed based on data, and combined with the charging efficiency, the analysis of optimizing the cooling energy consumption is achieved. The calculation of the gradient data provides an optimization direction in the adjustment of the flow rate and temperature parameters, avoiding the unreasonable phenomenon of cooling resource allocation. By using the iterative optimization of the parameter adjustment results and combining the continuous monitoring of the energy consumption and the equipment efficiency, it is ensured that the cooling system can still operate dynamically optimized under complex conditions. The predictive analysis of the temperature change range during the peak period combined with the real-time data to adjust the cooling resource allocation rules improves the adaptability of the cooling system in complex scenarios and ensures the stable operation of the charging pile under high load conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] Please refer to Figure 1 , the present invention provides a technical solution, a dynamic energy-saving method for a charging pile based on a cooling algorithm, including the following steps:

[0046] Collect real-time data of the charging pile cooling device, collect the temperature, flow rate of the cooling medium, and the temperature and voltage data of the battery in the charging state to obtain real-time monitoring data; based on the real-time monitoring data, construct a performance function for energy consumption evaluation to obtain a performance evaluation function;

[0047] Use the performance evaluation function to perform gradient calculation to obtain gradient data; based on the gradient data, calculate the parameter adjustment value of the cooling medium flow rate and the parameter adjustment value of the cooling medium temperature to generate a parameter adjustment result;

[0048] Based on the parameter adjustment result, iteratively optimize the cooling parameters, continuously monitor the energy consumption and equipment efficiency during the process to generate iterative optimization data; analyze the iterative optimization data to determine whether the performance improvement threshold is reached to generate an optimization result;

[0049] Based on the optimization result, combined with the temperature change range during the peak period of the charging pile, set the distribution rules of the cooling medium flow rate and pre-cooling resources during the peak period to obtain a scheduling strategy.

[0050] The steps for obtaining real-time monitoring data are as follows:

[0051] Collect the cooling data of the liquid cooling device of the charging pile, collect the temperature and flow rate of the cooling medium through temperature sensors and flow rate sensors, collect the temperature and voltage of the battery using voltage sensors and temperature sensors, and mark the time stamps for all the collected sensor data to generate a marked sensor data set;

[0052] Based on the marked sensor data set, perform format standardization, correct deviation values and identify abnormal data to generate a processed monitoring data set;

[0053] Based on the processed monitoring data set, perform data fusion processing to integrate the temperature and flow rate of the cooling medium and the temperature and voltage of the battery into real-time monitoring data.

[0054] Specifically, based on the collation of the preliminary information of the liquid cooling device for charging piles, the temperature and flow rate of the cooling medium are recorded by a temperature sensor and a flow rate sensor respectively. Then, a voltage sensor and a temperature sensor are used to record the temperature and voltage of the battery. First, it is determined that the sampling frequency can be set to five times per second and adjusted according to actual requirements. The temperature range here can be initially set from 0°C to 90°C, the flow rate range can be set from 0 to 10 (the unit of this value is liters per minute), and the battery voltage range can be initially determined from 0V to 24V. These ranges are formulated based on the basic values given in the previous equipment tests and product manuals. Subsequently, during the process of recording data, each recorded temperature value is compared one by one with the set range. If the temperature value falls between 0°C and 90°C, it is regarded as valid data. Then, the same judgment is made by comparing whether the flow rate value is between 0 and 10, and the voltage value is checked within the range of 0V to 24V. If it is found that the data exceeds the range, it is marked as needing to be investigated. During the investigation process, a simple numerical reference method is mainly used to check whether there is a temporary malfunction of the sensor or abnormal sampling. If necessary, an additional repeated detection can be added on the basis of five samplings per second to confirm whether the deviation persists. Immediately afterwards, all valid data is associated with the corresponding acquisition time, and the time tag of the current moment is added after each piece of valid data. It should be noted here that the time tag should adopt a unified format to ensure sorting and synchronous comparison during subsequent processing. For the data marked as abnormal in the foregoing judgment, the corresponding records should also be retained and associated with the time to support the subsequent confirmation of faults or errors. After all detections and markings are completed, the temperature values, flow rate values, voltage values, and the corresponding time information are concatenated to form a traceable integrated structure. Finally, when the integration is completed, the marked sensor data set is generated.

[0055] Based on the labeled sensor data set, first list all the collected data item by item and observe whether their formats are consistent with the requirements. For the temperature, flow rate, and voltage fields, corresponding numerical comparisons should be made separately. For temperature, the range of 0°C to 90°C is still used as the verification interval. The flow rate is checked against 0 to 10, and the voltage is checked against 0V to 24V. These interval values are determined based on the recommended ranges obtained from the actual tests in the previous text. If there is a situation where the format of the data is missing or does not conform to the specification, it is first marked as format abnormal during recording, and then it is determined whether the numerical value itself is missing or the unit identifier is incorrect according to the specific situation. Next, the numerical deviation threshold set by experience is used to correct the deviation of temperature, flow rate, and voltage. This deviation threshold is usually estimated based on the sensor stability detection results generated previously and set in combination with industry experience. For example, the temperature allows a fluctuation of 0.5°C, the flow rate allows a fluctuation of 0.2 liters per minute, and the voltage allows a fluctuation of 0.1V. When correcting small detected deviations, the average interpolation method or the average value of several valid data in the surrounding area during the same time period will be used as a reference. If it is found that the deviation exceeds the set threshold, the corresponding record will be further marked as abnormal. After that, continuously check for duplicate timestamps or incorrect sorting. If it is found that multiple records share the same timestamp or there is a contradiction in the order, they will be classified into the abnormal rows and a reference list will be established internally to compare whether there is an error in sensor synchronization. After all the correction and detection operations are completed, the processed data is verified in segments again, and finally the processed monitoring data set is generated.

[0056] Based on the processed monitoring data set, it is necessary to fuse the temperature and flow rate of the cooling medium as well as the temperature and voltage of the battery. First, read the corrected and verified temperature, flow rate, and voltage fields, and grab the corresponding values one by one from the same timestamp for grouping. Each group contains four data items: the temperature of the cooling medium, the flow rate of the cooling medium, the temperature of the battery, and the voltage of the battery. At the same time, reference the timestamp to ensure their synchronization and consistency. If one or more fields are missing at a certain timestamp, note the missing situation at that timestamp in the record, and then compare again with the previously set ranges of 0°C to 90°C, 0 to 10, and 0V to 24V to confirm whether the data still belongs to the available range. In case of out-of-range situations, further judgment is made based on the magnitude of the deviation. If the deviation is within the previously set allowable range, it is corrected; if it far exceeds the range, an abnormal flag is added to that record. After the above processing, all eligible groups are aggregated into a unified data structure. Then, based on these groups, the data related to the cooling medium and the battery are concatenated and compared to form a fused result list. If more external information such as ambient temperature or charging current needs to be incorporated, it can be associated according to the previously recorded environmental monitoring or relevant sensor data. Finally, all the fused groups are incorporated into the same time series structure, and the real-time monitoring data integrating the temperature of the cooling medium, the flow rate of the cooling medium, the temperature of the battery, and the voltage of the battery can be obtained.

[0057] The steps to obtain the performance evaluation function are as follows:

[0058] Based on the real-time monitoring data, construct a performance function, and the formula is:

[0059] ;

[0060] Where, is the performance function value, is the flow rate of the cooling medium, is the temperature of the cooling medium, is the total heat released during the charging process, is the set safety temperature threshold, and are the weight coefficients for the impact of charging efficiency and safety.

[0061] Specifically, the advantage of the formula is that by combining and in the same expression, it can simultaneously reflect the impact of the cooling medium flow rate on heat release and the cost brought by the deviation of the cooling medium temperature from the safety temperature threshold. Therefore, the requirements of both efficiency and safety are taken into account during the control process.

[0062] The steps for obtaining the parameter are as follows: Based on the previously recorded charging efficiency and the corresponding historical data of cooling parameters, organize the charging efficiency curves for each test, and count the charging duration and heat dissipation under different flow rate and temperature conditions. Extract the change sequence of the charging efficiency in multiple tests, and then perform weighted average processing and refer to the recommended values in the technical manual to locate the numerical range between 0.5 and 2.0. Finally, after monitoring the sensitivity of the charging efficiency, it is determined to be 1.20.

[0063] The steps for obtaining the parameter are as follows: Based on the previously extracted safety impact indicators and temperature deviation conditions, count the temperature abnormal points that occur when the flow rate or temperature changes exceed a certain range, record the duration of these abnormal points and organize them into a temperature deviation data sequence. By comparing the temperature deviation with the safety performance requirements and performing weighted operations, a reference interval with a numerical range between 0.01 and 0.05 is obtained. Finally, it is determined to be 0.03.

[0064] The steps for obtaining the parameter are as follows: Utilize the cumulative value of the heat released during the charging period recorded previously, calculate the total heat through the product of the heat power and the charging duration. Refer to the heat power input of 100 watts and continuous operation for one hour under typical working conditions to obtain Joules.

[0065] The steps for obtaining the parameter are as follows: Obtain the average value of the cooling medium flow rate from the aforementioned real-time monitoring data, combine with the instantaneous flow rates measured multiple times during the stable operation stage of the equipment, and take the value with relatively small fluctuations as a reference. Finally, take liters per minute.

[0066] The steps for obtaining the parameter are as follows: Observe the average value of the cooling medium temperature within a certain period of stable charging through the temperature sensor, extract and count the cooling medium temperature distribution within the recording interval from 0°C to 90°C, and select the temperature value corresponding to the distribution peak as a representative to obtain .

[0067] The steps for obtaining the parameter are as follows: According to the safety temperature threshold determination strategy recorded previously and combined with the safety upper limit temperature of 45°C in the equipment manual, take as the determined value.

[0068] Calculation process:

[0069] The first step is to calculate :

[0070] ;

[0071] In the second step, calculate :

[0072] ;

[0073] In the third step, calculate :

[0074] ;

[0075] In the fourth step, calculate :

[0076] ;

[0077] In the fifth step, substitute into the formula to obtain :

[0078] ;

[0079] This result indicates that when the value of reaches 215993.25, it means that under the current flow rate and temperature combination, the performance evaluation function obtains this value. If this value remains at a high level, it indicates that both efficiency and the penalty degree for temperature deviation are considered under the current conditions. If there is a significant decrease in subsequent monitoring, the cooling parameters need to be readjusted and the performance function needs to be recalculated to determine whether the adjustment effect is reasonable.

[0080] The steps for obtaining gradient data are as follows:

[0081] Based on the performance evaluation function, analyze the variable terms of flow rate and temperature in the performance function, extract the expressions associated with flow rate and temperature, and establish the partial derivative expression of the performance function;

[0082] According to the partial derivative expression of the performance function, use the flow rate and temperature parameters in the real-time monitoring data to calculate the partial derivative values of the performance function with respect to flow rate and temperature point by point to obtain partial derivative data;

[0083] Based on the partial derivative data, organize all calculation results to form gradient data.

[0084] Specifically, according to the performance evaluation function obtained previously and the cooling medium flow and temperature parameters contained therein, the items corresponding to the flow variable and the temperature variable are first decomposed from the recorded function form. For the flow item, the possible range of 0 liters per minute to 10 liters per minute can be first enumerated, and each enumeration value is compared with the heat parameter and weight parameter in the function point by point. In the process, the set conditions are first compared, such as whether it is between 0 and 10 and there is no large fluctuation in a short period of time, and then the temperature item is gradually extracted against the range of 0°C to 90°C. If the temperature value falls within this interval, other parameters at the corresponding time will continue to be collected so that they can be matched one by one in subsequent calculations. According to the previously recorded safety temperature threshold and flow mean, the values ​​that meet the conditions are marked and put into the calculation queue one by one, and then in this calculation queue, the values ​​are counted and compared. The partial expressions associated with flow and the partial expressions associated with temperature are separated from the column respectively, and the charging efficiency weight or safety weight contained therein is identified and distinguished. If it is found that the temperature exceeds 45°C or the flow is less than 1 liter per minute in certain periods, it is necessary to continue to compare the previously obtained flow and temperature detection results to verify whether these values ​​still have obvious deviations. The threshold set according to experience can also be used to determine whether excessive deviations need to be investigated. For example, if the temperature is more than 5°C higher than 45°C, it is classified as a serious deviation. The temperature data sequence formed previously is called again to compare its change trend point by point. Once all flow and temperature items within the normal range are identified, these related expressions are taken out, arranged and symbolically distinguished. Then, each item is corrected and merged, and irrelevant factors are eliminated. Finally, the partial derivative expression of the function is obtained.

[0085] Based on the partial derivative expressions isolated previously, specific data points for flow rate and temperature are read from the collected real-time monitoring data. The flow rate data is first controlled within the range of 0 liters per minute to 10 liters per minute, and the actual sampling values are recorded every minute. The temperature data is recorded degree by degree within the range of 0°C to 90°C and paired with time tags. Then, the moments that meet the stable conditions are extracted from these paired data. If the flow rate approaches 10 liters per minute or the temperature approaches 90°C at a certain moment, additional checks are performed and they are put into the abnormal queue. After excluding obvious anomalies, the data in the normal queue is selected and substituted into the partial derivative expression point by point. For each data point, it is necessary to first confirm that both the flow rate and temperature fall within the available range, then substitute the flow rate into the flow rate variable position and the temperature into the temperature variable position, and then perform the operation on the partial derivative expression. During the operation process, the specific numerical values represented by each symbol are decomposed step by step, including parameters such as the total heat obtained previously and the safety temperature threshold. After calculating the partial derivative values for each data point, the result values are arranged in chronological order. If it is found that the value is greater than the empirically set partial derivative critical value, that moment is marked with emphasis. This partial derivative critical value can be set through previous test records. For example, observe the partial derivative value at a flow rate of 1 liter per minute and a temperature of 30°C, and compare it with the partial derivative value at a flow rate of 9 liters per minute and a temperature of 50°C. After comprehensively considering multiple tests and statistics, an average partial derivative level is obtained, and then a certain safety factor is added to form this critical value. After all the calculations are completed, the obtained partial derivative values are sorted out and summarized one by one, and finally the partial derivative data is obtained.

[0086] After obtaining all the partial derivative values of flow rate and temperature in the previous stage, first, according to the time tags, these partial derivative values are vertically compared with the original records of flow rate and temperature mentioned above. The matched partial derivative values are combined with their corresponding flow rate and temperature conditions, and then the multiple calculation results are sorted in chronological order. The partial derivative values higher than the previously set critical value are separately extracted and recorded in the abnormal sequence. At the same time, for all normal sections below the critical value, segmented induction is performed. If the cumulative partial derivative value of temperature continues to increase or the cumulative partial derivative value of flow rate continues to decrease in some sections, it is also necessary to compare with the previously obtained safety temperature threshold and the minimum flow rate limit. Once it is confirmed that the trend of these partial derivative values is within the controllable range, they are classified into the normal gradient sequence and the fluctuating part is processed by exponential smoothing or constant smoothing. Then, the fully sorted normal partial derivative sequence and abnormal partial derivative sequence are listed separately, and the partial derivative results are finally revised according to time segments or other conditions. If the values in individual time periods fluctuate too much, the monitoring data is compared again to confirm its reasonableness. Finally, all the results are integrated to form a complete gradient data.

[0087] The steps to obtain the parameter adjustment results are as follows:

[0088] Based on the gradient data, calculate the parameter adjustment value of the cooling medium flow rate and the parameter adjustment value of the cooling medium temperature. The calculation formula is as follows:

[0089] and ;

[0090] wherein, represents the parameter adjustment value of the cooling medium flow rate, represents the parameter adjustment value of the cooling medium temperature, represents the partial derivative value of the performance function related to the flow rate, represents the partial derivative value of the performance function related to the temperature, represents the total heat released during the charging process, represents the cooling medium flow rate, represents the upper limit of the safe adjustment range of the flow rate, represents the cooling medium temperature, represents the upper limit of the safe adjustment range of the temperature, represents the initial flow rate value, represents the current flow rate reference value, represents the initial temperature value, represents the current temperature reference value;

[0091] Based on the parameter adjustment value of the cooling medium flow rate and the parameter adjustment value of the cooling medium temperature, update the flow rate and temperature of the cooling medium to obtain the parameter adjustment result.

[0092] Specifically, the advantage of the formula is that by simultaneously considering the flow rate partial derivative term and the temperature partial derivative term in the same calculation framework, the total heat released during the charging process , the real-time flow rate and the real-time temperature are jointly introduced into the operation process together with the upper limits of the safe adjustment range and and other factors, so that the calculation of and takes into account both the heat dimension and the temperature safety boundary.

[0093] The steps for obtaining the parameters are as follows: Based on the gradient data of the performance function at different flow rates obtained previously, perform screening. By recording the corresponding relationship between the flow rate change and the performance function value change at each time node, extract the distribution of partial derivative values in each time period, and then use the average value of continuous multiple measurements as a reference and correct the extreme values. Finally, determine .

[0094] The steps for obtaining the parameter are as follows: referring to the temperature gradient data obtained previously, conduct segmented statistics on the numerical values of the performance function at different temperature levels, retain the records at several moments within the normal range (for example, between 0°C and 90°C), read the change in the partial derivative and fuse the results of multiple measurements, and comprehensively calculate . The steps for obtaining the parameter are as follows: combine the integral operation of multiplying the time-segmented thermal power obtained from heat monitoring by the duration, collect the heat release process under typical conditions and average the multiple measurement values to obtain the numerical value Joule.

[0095] The steps for obtaining the parameter are as follows: for the range of 0 liters per minute to 10 liters per minute obtained from the cooling medium flow sensor, select the section with less fluctuation during the stable operation of the equipment to calculate the average value, and obtain it after confirming that the average value does not show abnormal drift .

[0096] The steps for obtaining the parameter are as follows: select the most common temperature value without significant fluctuation in the current period from the previously recorded temperature acquisition data as a representative, and check whether the temperature is within the range of 0°C to 90°C, and finally obtain .

[0097] The steps for obtaining the parameter are as follows: compare the maximum allowable cooling flow upper limit listed in the equipment manual with the high-flow condition monitored in the test, and make corrections according to the industrial standard, and take . The steps for obtaining the parameter are as follows: according to the safe temperature adjustment range, statistically obtain an upper limit range of the temperature that can be safely callback from the sensing data under multiple high-temperature conditions, and confirm that it is within the range that the normal cooling system can withstand, and then .

[0098] , , , The steps for obtaining the parameter are as follows: record item by item according to the initial flow rate and temperature information and the current reference flow rate and temperature information obtained previously. For example, during the initial stage of execution, the initial flow rate set by the equipment can be , reference flow rate , the initial temperature value , the current temperature reference value , and the above numerical values are all obtained through real-time monitoring or records in the scheduling strategy.

[0099] Calculation process:

[0100] First step, calculate and sum:

[0101] ;

[0102] Step 2: Calculate the denominator :

[0103] ;

[0104] Step 3: Combine and find :

[0105] ;

[0106] Step 4: Multiply the result by a negative sign and multiply by :

[0107] ;

[0108] Therefore .

[0109] Similarly, for the operation of :

[0110] Step 1: Calculate the sum of and :

[0111] ;

[0112] Step 2: Calculate the denominator :

[0113] ;

[0114] Step 3: Combine and find :

[0115] ;

[0116] Step 4: Multiply the result by a negative sign and multiply by :

[0117] ;

[0118] Therefore .

[0119] This result indicates that is approximately -2.001 and When it is approximately -7.15, it means that under the current environment, the cooling medium flow rate needs to be adjusted downward by approximately 2.001 liters per minute and the cooling medium temperature needs to be adjusted downward by approximately 7.15 °C respectively. If an out-of-bounds situation occurs after the numerical adjustment, the boundary can be corrected by combining the flow safety range and temperature safety range obtained previously. The results in different numerical ranges will indicate the magnitude of the required parameter adjustment. After completing the above calculations, through the and values respectively correspond to the flow rate and temperature range of the cooling medium. First, compare the flow rate part with the range from 0 liters per minute to 10 liters per minute. If the correction amount obtained from this calculation is greater than 4 liters per minute, it is marked as an extremely large adjustment and needs to be confirmed again. If the correction amount is between 1 and 4, it is regarded as a medium adjustment. If it is less than 1, it is regarded as a minor adjustment. And all actual correction values are uniformly recorded in an update queue. The temperature adjustment also follows the same idea. First, compare the temperature range from 0 °C to 90 °C. If the correction amount is between 5 °C and 15 °C, it is regarded as a medium adjustment. If it exceeds 15 °C, further verification is carried out to check whether it is listed as a feasible value in the previously compiled temperature safety adjustment range table. Subsequently, query the relevant measurement sequence to confirm whether the temperature correction is still within the compliance range. All the verified correction data are sorted in sequence and corresponding to the time tags one by one, and then concatenated with the previously obtained flow rate and temperature reference values. Finally, the parameter update operation is completed according to these values and the new flow rate and temperature levels are registered, so as to obtain the parameter adjustment result.

[0120] The steps for obtaining iteratively optimized data are as follows:

[0121] According to the parameter adjustment result, set the initial value of the cooling parameter as the starting point for optimization iteration. Monitor the current cooling medium flow rate and temperature according to the real-time monitoring data, adjust the cooling parameter and record the energy consumption, and generate a cooling parameter adjustment record;

[0122] Based on the cooling parameter adjustment record, conduct a comparative analysis of the energy consumption and equipment efficiency, and judge whether the current cooling parameter meets the threshold for performance improvement. If it does not meet, continue to adjust the cooling parameter, and at the same time record the change trend of each parameter adjustment to form performance parameter update data;

[0123] According to the performance parameter update data, combined with the historical trend of the cooling parameter adjustment record, continuously adjust the cooling parameter and conduct iteration to obtain iteratively optimized data.

[0124] Specifically, according to the parameter adjustment results obtained previously, configure the current cooling medium flow rate value and temperature value as the starting reference point. First, determine that the initial value of the cooling parameter can be read from the previously determined cooling medium flow rate and temperature, and record the corresponding initial reference levels of the energy consumption base level and equipment efficiency. Subsequently, gradually monitor the initial value of this cooling parameter according to the time series, including observing whether the values of the flow rate and temperature are still within the range of 0 liters per minute to 10 liters per minute and 0 °C to 90 °C at fixed intervals. Then, compare these values with the energy consumption benchmark value obtained previously. If there is a significant difference in the energy consumption and equipment efficiency corresponding to the current flow rate compared to the previously collected historical data, check whether the deviation of the flow rate exceeds 2 liters per minute or the deviation of the temperature exceeds 5 °C. These critical values can be set by referring to the safety fluctuation limit in the product manual and combining the median fluctuation situation of previous multiple tests. If it is found that the data does not show excessive deviation, continue to use this flow rate and temperature in the next cycle and record the energy consumption again. Otherwise, make fine adjustments in the current cycle by increasing or decreasing the flow rate in steps of 0.5 liters per minute or raising or lowering the temperature in steps of 2 °C and record the energy consumption again. When summarizing these adjustment results, add a time tag to each record. During this period, pay attention to whether parameter distortion occurs when the flow rate or temperature approaches 0 or 90 °C, and at the same time confirm through the equipment efficiency comparison table that it still remains within a reasonable range. After summarizing all the records during the adjustment process, a cooling parameter adjustment record can be obtained.

[0125] According to the previously generated cooling parameter adjustment records, first extract the flow rate and temperature values in each record and compare them with the corresponding energy consumption and equipment efficiency values. Arrange the flow rate and temperature values before and after each adjustment on the same time axis and observe their change trends. If the energy consumption shows a continuous increase while the equipment efficiency continuously declines, further check whether the flow rate and temperature at that time have deviated from the set reasonable range. For example, check whether it exceeds the boundary by comparing the range of flow rate from 0 to 10 and temperature from 0°C to 90°C. Then check whether it reaches the minimum reference value specified by the equipment efficiency or the upper limit threshold of energy consumption. These preset thresholds are determined by superimposing the average level of previous multiple tests and industry standards, and are usually set at around 20% higher than the average energy consumption ratio as the judgment critical point. If the current data has crossed the critical point, mark the flow rate-temperature pair at that moment and its corresponding energy consumption and efficiency situation as not meeting the performance improvement standard and consider parameter correction in the next stage. Otherwise, consider temporarily maintaining the existing flow rate and temperature. Then sort and summarize the records into a new trend sequence. By comparing the change amplitudes of the flow rate and temperature for each adjustment, find the commonalities among them. If the adjustment directions are similar for multiple times, it is considered that they can be summarized into an integrated trend. Then update the performance parameters according to these trends and perform differential analysis on the old and new values to clarify the impact degree of each change on energy consumption and equipment efficiency, so as to finally form the performance parameter update data.

[0126] Based on the previously obtained performance parameter update data, first summarize it by combining the time sequence and adjustment amplitude of each parameter update information, and view it in association with the previous cooling parameter adjustment records. When making each association, match the current flow rate and temperature with the previous historical trends. If it is found that the flow rate has increased by 0.5 liters per minute for more than three times in some time periods and the energy consumption has not significantly decreased, then give priority to significantly adjusting this flow rate item in the next iteration. At the same time, observe the fluctuation of energy consumption while keeping the temperature unchanged or making small adjustments. If multiple records show that the impact of the flow rate change on energy consumption is not significant enough, then compare the amplitude of the temperature correction to evaluate its sensitivity. Refer to the fluctuation amplitude of the equipment efficiency when the temperature increases or decreases between 2°C and 10°C, and compare with the temperature response curve listed in the industry statistics to judge whether this change amount is a reasonable value. If the temperature still exceeds the safety boundary after the change, further narrow the adjustment step according to the previously registered maximum or minimum temperature tolerance range. All these operations are continuously cycled and the latest adjustment results of the flow rate and temperature are recorded again in the extended time sequence. By comparing the energy consumption change and the change trajectory of the equipment efficiency, finally organize and summarize all the records generated by multiple cycles to obtain the iterative optimization data.

[0127] The steps to obtain the optimization results are as follows:

[0128] Based on the iteratively optimized data, extract the energy consumption and equipment efficiency information after adjusting the cooling parameters, calculate the change trend of the indicators, and generate a performance change record;

[0129] According to the performance change record, calculate the performance target achievement value, and the calculation formula is:

[0130] ;

[0131] Among them, is the performance target achievement value, is the equipment efficiency of the current iteration, is the initial equipment efficiency, is the energy consumption of the current iteration, is the initial energy consumption;

[0132] Based on the performance target achievement value, judge whether the predetermined performance improvement threshold is reached. If it is satisfied, terminate the iteration to obtain the optimization result.

[0133] Specifically, based on the iteratively optimized data obtained previously and the registered cooling parameters, first summarize the energy consumption corresponding to the adjusted flow rate and temperature in the most recent time. Compare the energy consumption values within each time period with the previously recorded equipment efficiency, and screen out abnormal data points such as the flow rate suddenly exceeding 10 liters per minute or the temperature breaking through 90°C. Then match these available data in chronological order to identify the energy consumption distribution area at the current flow rate and temperature. If the energy consumption is concentrated in a section that is 20% higher than the original average energy consumption level, then check whether the equipment efficiency drops below 10% or more of the initial efficiency according to the corresponding judgment criteria. These percentage intervals are combined from the median values of multiple previous tests and the industry general standards. If it is confirmed that the energy consumption is high and the equipment efficiency also significantly declines, then mark this time period as a "high energy consumption and low efficiency" data point and summarize it into the same list. Subsequently, for other points with a small deviation or still within 10% of the average value, put them into the "stable period" list. Then, concentrate all the data in these two lists together with their time tags in a mapping table, so that the change trends of energy consumption and equipment efficiency can be observed on the same time line, and whether there are three or more consecutive records showing an upward or downward trend can be identified. Once these continuous fluctuations are found, further screen them according to the previously set reference intervals. If multiple changes are within the same interval and do not cross the previously set safety threshold, add corresponding marks in the list to indicate continuous follow-up statistics in the future. Finally, after sorting out the change trajectories of energy consumption and equipment efficiency in all cycles, a performance change record can be formed.

[0134] The advantage of the formula is that by combining the change in device efficiency and the change in energy consumption in the same expression, a comprehensive measurement can be carried out from both the aspects of efficiency and energy consumption. After normalizing the offsets of the two respectively and then adding them together, the overall deviation degree between the current iteration state and the initial benchmark can be revealed in a timely manner.

[0135] The steps to obtain the parameter are as follows: referring to the previously saved device efficiency records, collect the working state and output completion degree of the device under test in each time period, and combine the actual test performance of the device within the stable range. Perform weighted processing with indicators such as the completion degree and the ratio of additional energy requirements in each time period, and finally obtain a time series, and select the corresponding value at that moment as the current device efficiency when iterating a certain round. For example, during the latest round of observation and statistics, determine (this value is between 0 and 1, and is more commonly in the form of a percentage of work efficiency or completion degree).

[0136] The steps to obtain the parameter are as follows: through integrating and analyzing the test data in the initial stage when the system is put into operation, confirm the starting efficiency level that the device reaches when there is no fault and the working conditions are better, and extract the mean value in multiple repeated tests to determine .

[0137] The steps to obtain the parameter are as follows: based on collecting the energy consumption distribution in the current iteration period and continuously observing the energy usage situation in this period, by accumulating the energy consumption per minute and comparing it with the previous rated energy consumption of the device, obtain the actual energy consumption value. After several sampling comparisons and removing abnormal peaks, obtain watt-hours.

[0138] The steps to obtain the parameter are as follows: before the device starts running, conduct multiple rounds of collection of the basic energy consumption level, record the electric power consumed in the initial period respectively, obtain multiple effective statistical values after removing several interference fluctuations, and then select the weighted mean value among them as the initial energy consumption to determine watt-hours.

[0139] Calculation process:

[0140] The first step is to calculate :

[0141] ;

[0142] The second step is to calculate :

[0143] ;

[0144] The third step is to combine and obtain :

[0145] ;

[0146] This result indicates that at the current iteration moment, the device efficiency has increased by 6.25% compared with the initial efficiency, while the energy consumption has decreased by 10% compared with the starting level. The normalized sum of the two is 0.1625. If this value continues to drop below a certain predetermined standard in subsequent iterations, the adjustment process will stop. If it is higher than the predetermined standard, further monitoring is required and the next iteration is carried out.

[0147] After obtaining the performance target achievement value and comparing it with the previously set performance improvement threshold, it is necessary to first retrieve the efficiency and energy consumption fluctuation ranges of the device in historical tests. If the current value is less than the threshold, it is considered to have entered an ideal performance state. Therefore, the continuous iteration can be stopped and the cooling parameters are no longer corrected. If it is still greater than the threshold, then continue to evaluate whether there is a sudden over-limit situation by referring to the previous energy consumption and efficiency change sequences. When evaluating, first list the difference between the current and the result of the previous iteration. If this difference continuously exceeds 0.02 and does not show a decline in three records, it is marked as a large deviation, and additional adjustment of the flow rate or temperature is required. When the adjustment amplitude exceeds the specified 5% flow rate limit or a 10°C temperature change, then observe the trend of the corresponding device efficiency in the subsequent cycle records again to confirm whether it has approached or exceeded the previously given optimization threshold. If it still does not meet the standard, continue to accumulate new data points and update the value for re-comparison. The above process is repeated in sequence, and whether the standard has been achieved is rechecked after each new R value is obtained. Once it meets the standard, the flow rate, temperature, and corresponding energy consumption in this round of state can be sorted out together to obtain the optimization result.

[0148] The steps to obtain the scheduling strategy are as follows:

[0149] Based on the optimization result, extract the temperature change range during the peak period of the charging pile, analyze the influence on the flow rate of the cooling medium and the precooling time, and generate a preliminary cooling resource allocation framework;

[0150] According to the preliminary cooling resource allocation framework, integrate the real-time monitored temperature change data and peak period information, calculate the allocation priorities of the flow rate of the cooling medium and the precooling resources under different conditions, and form a cooling resource adjustment record;

[0151] Based on the cooling resource adjustment record, set the allocation rules for the flow rate of the cooling medium and the precooling resources during the peak period to obtain the scheduling strategy.

[0152] Specifically, based on the optimization results, first, all temperature fluctuation data within the peak period previously identified are screened out from the peak period records. Then, a validity check is performed on each temperature record between 0°C and 90°C. If a certain temperature record exceeds this range, it is marked and summarized in the abnormal row. After that, among the remaining normal records, the difference between the maximum and minimum temperatures is observed according to the time sequence. If the difference between the maximum and minimum temperatures exceeds 10°C, cross-confirmation is carried out in combination with the previously obtained flow fluctuation range to see whether the flow rate also shows a synchronous increase or decrease trend when the temperature rises. These corresponding moments need to be marked and recorded in a temperature-flow correspondence list. Then, from this list, it is observed in segments whether the temperature ranges of several peak periods are close to the upper limit of 90°C. If the temperature approaches 90°C multiple times, see how long it lasts in each peak period, such as continuously for 5 minutes or 10 minutes, and compare it with the empirical threshold collected in previous rounds, for example, if it exceeds 5 minutes each time, it is regarded as a high-temperature continuous period. The occurrence frequency of such high-temperature continuous periods is counted and summarized in the temperature statistics entry. Then, these data are matched with the previously recorded pre-cooling resource periods. Since pre-cooling usually takes at least 5 minutes or more to complete, if the temperature has climbed close to 90°C in a short time, this pre-cooling time period is marked as a high-priority pre-cooling stage, and the available flow resources are initially allocated in combination with the flow rate changes in the follow-up. Once the maximum temperature, minimum temperature, and duration are sorted out, corresponding settings are made according to the previous temperature statistics entry and the existing pre-cooling duration, and these associated information is saved to affect the flow rate of the cooling medium and the pre-cooling time, generating a preliminary cooling resource allocation framework.

[0153] According to the preliminary cooling resource allocation framework, first select the temperature extreme points determined in the previous stage and compare them item by item with the peak period information. List the temperature data within the peak period in chronological order and retrieve the flow rate sampling values together. If it is found that the temperature fluctuates by more than 10°C within a short period of time in some periods and the flow rate also changes by more than 1 liter per minute, then combine the statistical items obtained previously to determine whether this situation occurs during the morning peak or the evening peak period, and record the matching data of temperature and flow rate. Then refer to the set priority division strategy. For example, a situation where the temperature fluctuation is greater than 10°C and the flow rate fluctuation is greater than 2 liters per minute is regarded as a high priority that requires more pre-cooling resources. In addition, when the temperature fluctuation is less than 5°C but the flow rate fluctuation is close to 3 liters per minute, it is regarded as a secondary priority. These specific values are all derived from the statistical results of the cooling pressure during the peak period in multiple monitors. Each pair of temperature and flow rate matching records needs to indicate the time mark after the corresponding item, and attach the peak period or non-peak period information of that moment on the same line. After completion, file the flow rate and temperature data marked as high priority or secondary priority. If there are multiple high-priority records during the peak period, connect them in chronological order during filing, and group records of the same type together. Finally, after all groups are calibrated, the allocation reference of flow rate and pre-cooling resources under each priority can be calculated to form a cooling resource adjustment record.

[0154] Based on the cooling resource adjustment record, first list the high-priority data of each group in sequence and attach the time sequence label of each record. Then, in the same window, view the temperature, flow rate, and their cumulative pre-cooling duration corresponding to each group of records. If the temperature reaches above 80°C multiple times and the flow rate tends to approach 10 liters per minute or changes continuously within a short time, check whether it has reached the predetermined condition for forced cooling according to the previously set criteria. Forced cooling can usually be selected from methods such as increasing the flow rate to 8 liters per minute or increasing the pre-cooling time to 10 minutes. Regarding this, it can be decided whether to give priority to increasing the flow rate or increasing the pre-cooling duration according to the aforementioned archived information. If the peak period duration is less than 30 minutes, usually the flow rate will be increased first. If the peak period duration exceeds 30 minutes, both the flow rate and the pre-cooling duration should be increased. All these operations should be combined with the confirmed temperature and flow rate corresponding grading criteria. The temperature is compared in the range of 0°C to 90°C, and approaching 90°C is defined as the priority to trigger pre-cooling. For the flow rate, it is compared in the range of 0 to 10 liters per minute. If the continuously recorded flow rate has exceeded 8 liters per minute, it is also marked as an emergency handling item. After integrating the above data, set the upper limit of the cooling medium flow rate and the corresponding shortest pre-cooling duration for this peak period, and mark these configurations together in the period scheduling table to finally obtain the scheduling strategy.

[0155] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A dynamic energy-saving method for charging piles based on cooling algorithm, characterized in that: The following steps are involved: Real-time data collection is performed on the charging pile cooling device to collect the temperature and flow of the cooling medium and the battery temperature and voltage data of the charging status to obtain real-time monitoring data; Based on the real-time monitoring data, a performance function for energy consumption evaluation is constructed to obtain a performance evaluation function; Using the performance evaluation function, performing gradient calculation to obtain gradient data; Based on the gradient data, calculating a parameter adjustment value of a cooling medium flow rate and a parameter adjustment value of a cooling medium temperature, and generating a parameter adjustment result; Based on the parameter adjustment results, iteratively optimize the cooling parameters, continuously monitor energy consumption and equipment efficiency during the process, and generate iterative optimization data; Analyze the iterative optimization data to determine whether a performance improvement threshold is reached and generate an optimization result; Based on the optimization results, combined with the temperature variation range of the charging pile during the peak period, the allocation rules of the cooling medium flow and pre-cooling resources during the peak period are set to obtain the scheduling strategy; The steps for obtaining the parameter adjustment result are: Based on the gradient data, the parameter adjustment value of the cooling medium flow rate and the parameter adjustment value of the cooling medium temperature are calculated, and the calculation formula is: and Among them, ΔF represents the parameter adjustment value of the cooling medium flow rate, ΔT represents the parameter adjustment value of the cooling medium temperature, represents the partial derivative of the performance function associated with the flow, represents the partial derivative of the performance function associated with temperature, Q represents the total heat released during the charging process, F represents the cooling medium flow rate, G c represents the upper limit of the safe adjustment range of the flow rate, T represents the cooling medium temperature, G t represents the upper limit of the safe adjustment range of temperature, F0 represents the initial flow value, F c represents the current flow reference value, T0 represents the initial temperature value, T c Represents the current temperature reference value; Based on the parameter adjustment value of the cooling medium flow rate and the parameter adjustment value of the cooling medium temperature, the flow rate and the temperature of the cooling medium are updated to obtain a parameter adjustment result.

2. The dynamic energy-saving method for charging piles based on cooling algorithm according to claim 1 is characterized in that: The steps for obtaining the real-time monitoring data are as follows: Collect data from the liquid cooling device of the charging pile, collect the temperature and flow of the cooling medium through temperature sensors and flow sensors, collect the temperature and voltage of the battery using voltage sensors and temperature sensors, and timestamp all collected sensor data to generate a labeled sensor data set; Based on the labeled sensor data set, format standardization is performed, deviation values ​​are corrected, and abnormal data are identified to generate a processed monitoring data set; Based on the processed monitoring data set, data fusion processing is performed to integrate the temperature and flow of the cooling medium and the temperature and voltage of the battery into real-time monitoring data.

3. The dynamic energy-saving method for charging piles based on cooling algorithm according to claim 1 is characterized in that: The steps for obtaining the performance evaluation function are as follows: According to the real-time monitoring data, a performance function is constructed, and the formula is: Among them, P(F,T) is the performance function value, F is the cooling medium flow rate, T is the cooling medium temperature, Q is the total heat released during the charging process, T0 is the set safety temperature threshold, and α and β are the weight coefficients of the charging efficiency and safety.

4. The dynamic energy saving method for charging piles based on cooling algorithm according to claim 1 is characterized in that: The steps for obtaining the gradient data are: Based on the performance evaluation function, the variable terms of flow rate and temperature in the performance function are analyzed, expressions associated with flow rate and temperature are extracted, and partial derivative expressions of the performance function are established; According to the partial derivative expression of the performance function, using the flow rate and temperature parameters in the real-time monitoring data, the partial derivative values ​​of the performance function relative to the flow rate and temperature are calculated point by point to obtain partial derivative data; Based on the partial derivative data, all calculation results are sorted to form gradient data.

5. The dynamic energy-saving method for charging piles based on cooling algorithm according to claim 1 is characterized in that: The steps for obtaining the iterative optimization data are as follows: According to the parameter adjustment result, the initial value of the cooling parameter is set as the starting point of the optimization iteration, the current cooling medium flow and temperature are monitored according to the real-time monitoring data, the cooling parameter is adjusted and the energy consumption is recorded, and a cooling parameter adjustment record is generated; Based on the cooling parameter adjustment record, a comparative analysis of energy consumption and equipment efficiency is performed to determine whether the current cooling parameters meet the performance improvement threshold. If not, the cooling parameters are continuously adjusted, and the change trend of each parameter adjustment is recorded to form performance parameter update data; According to the performance parameter update data, combined with the historical trend of the cooling parameter adjustment record, the cooling parameters are continuously adjusted and iterated to obtain iterative optimization data.

6. The dynamic energy saving method for charging piles based on cooling algorithm according to claim 1 is characterized in that: The steps for obtaining the optimization results are: Based on the iterative optimization data, extract the energy consumption and equipment efficiency information after the cooling parameters are adjusted, calculate the change trend of the indicators, and generate a performance change record; According to the performance change record, the performance target achievement value is calculated, and the calculation formula is: Among them, R is the performance target achievement value, E t is the equipment efficiency of the current iteration, E0 is the initial equipment efficiency, C t is the energy consumption of the current iteration, C0 is the initial energy consumption; Based on the performance target achievement value, it is determined whether a predetermined performance improvement threshold is reached. If so, the iteration is terminated to obtain an optimization result.

7. The dynamic energy-saving method for charging piles based on cooling algorithm according to claim 1 is characterized in that: The steps for obtaining the scheduling strategy are: Based on the optimization results, the temperature variation range of the charging pile during the peak period is extracted, the impact on the cooling medium flow and precooling time is analyzed, and the preliminary cooling resource allocation framework is generated; According to the preliminary cooling resource allocation framework, the real-time monitored temperature change data and peak period information are integrated to calculate the cooling medium flow and the allocation priority of pre-cooling resources under different conditions, and form a cooling resource adjustment record; Based on the cooling resource adjustment record, the allocation rules of the cooling medium flow and pre-cooling resources during the peak period are set to obtain a scheduling strategy.

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