Charging pile dynamic energy-saving method based on cooling algorithm

Abstract

The invention relates to the technical field of charging pile control, in particular to a charging pile dynamic energy-saving method based on a cooling algorithm, and the method comprises the following steps: carrying out the real-time data collection of a charging pile cooling device, and collecting the temperature and flow velocity of a cooling medium, and the temperature and voltage data of a battery in a charging state; according to the method, the energy consumption evaluation performance function is constructed based on the data, and the analysis of cooling energy consumption optimization is achieved in combination with the charging efficiency. The calculation of the gradient data provides an optimization direction in flow and temperature parameter adjustment, and the unreasonable phenomenon of cooling resource allocation is avoided. Iterative optimization of a parameter adjustment result is combined with continuous monitoring of energy consumption and equipment efficiency, and it is ensured that the cooling system can still operate in a dynamic optimization mode under complex conditions. The prediction analysis of the temperature change range in the peak period is combined with the real-time data to adjust the cooling resource allocation rule, and the adaptability of the cooling system in a complex scene is improved.

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: 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; 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; 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; 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.

[0006] Preferably, the steps of acquiring the real-time monitoring data are: Collect the cooling data of the liquid cooling device of the charging pile, collect the temperature and flow rate of the cooling medium through the temperature sensor and flow rate sensor, collect the temperature and voltage of the battery through the voltage sensor and temperature sensor, and timestamp all the collected sensor data to generate a marked 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 rate of the cooling medium and the temperature and voltage of the battery into real-time monitoring data.

[0007] Preferably, the steps of obtaining the performance evaluation function are: According to the real-time monitoring data, a performance function is constructed, and the formula is: ; in, is the performance function value, is the cooling medium flow rate, is the cooling medium temperature, is the total heat released during the charging process, is the set safety temperature threshold, and is the weight coefficient of charging efficiency and safety.

[0008] Preferably, the steps of 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.

[0009] Preferably, the steps of 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 ; in, The parameter adjustment value representing the cooling medium flow rate, The parameter adjustment value representing the cooling medium temperature, represents the partial derivative of the performance function associated with the flow, represents the partial derivative of the performance function with respect to 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 temperature, represents the initial flow value, Represents the current flow reference value, represents the initial temperature value, 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.

[0010] Preferably, the steps of obtaining the iterative optimization data are: 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.

[0011] Preferably, the steps of obtaining the optimization result 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: ; in, To achieve the performance target 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; 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.

[0012] Preferably, the steps of 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 rate 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 time information are integrated to calculate the cooling medium flow rate 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 cooling medium flow rate and the allocation rules of the pre-cooling resources during the peak period are set to obtain the scheduling strategy.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, an energy consumption evaluation performance function is constructed based on data, and combined with charging efficiency, an analysis of cooling energy consumption optimization is achieved. The calculation of gradient data provides an optimization direction in the adjustment of flow and temperature parameters, avoiding the unreasonable allocation of cooling resources. By iterative optimization of parameter adjustment results, combined with continuous monitoring of energy consumption and equipment efficiency, it is ensured that the cooling system can still operate dynamically and optimally under complex conditions. The predictive analysis of the temperature variation range during peak hours is combined with real-time data to adjust the cooling resource allocation rules, which improves the adaptability of the cooling system in complex scenarios and ensures the stable operation of charging piles under high load conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0016] See also Figure 1 The present invention provides a technical solution, a dynamic energy-saving method for a charging pile based on a cooling algorithm, comprising the following steps: Real-time data collection is performed on the cooling device of the charging pile 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; Using the performance evaluation function, performing gradient calculation to obtain gradient data; calculating the parameter adjustment value of the cooling medium flow rate and the parameter adjustment value of the cooling medium temperature based on the gradient data, and generating the 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 the performance improvement threshold has been reached and generate optimization results; Based on the optimization results and the temperature variation range of the charging piles during peak hours, the allocation rules of the cooling medium flow rate and pre-cooling resources during peak hours are set to obtain the scheduling strategy.

[0017] The steps for obtaining real-time monitoring data are as follows: Collect the cooling data of the liquid cooling device of the charging pile, collect the temperature and flow rate of the cooling medium through the temperature sensor and flow rate sensor, collect the temperature and voltage of the battery through the voltage sensor and temperature sensor, and timestamp all the collected sensor data to generate a marked sensor data set; Based on the labeled sensor data set, the format is standardized, the deviation value is corrected and the abnormal data is identified to generate the processed monitoring data set; Based on the processed monitoring data set, data fusion processing is performed to integrate the temperature and flow rate of the cooling medium and the temperature and voltage of the battery into real-time monitoring data.

[0018] Specifically, based on the collation of the preliminary information of the liquid cooling device of the charging pile, the temperature and flow rate of the cooling medium are recorded respectively by the temperature sensor and the flow rate sensor, and then the temperature and voltage of the battery are recorded by the voltage sensor and the temperature sensor. First, it is determined that the sampling frequency can be set to five times per second and adjusted according to actual needs. The temperature range here can be set to 0°C to 90°C, the flow rate range can be set to 0 to 10. The unit is liters per minute, and the battery voltage range can be determined to be 0V to 24V. These ranges are formulated according to the basic values ​​given in the preliminary equipment tests and product manuals. Then, in the process of recording data, each recorded temperature value is compared with the set range one by one. If the temperature value falls between 0°C and 90°C, it is regarded as valid data. Then, the flow rate value is compared to see if it is between 0 and 10 to make the same judgment, and the voltage value is checked in the range of 0V to 24V. If the data is found to be out of the range, it is marked as needing to be checked. During the troubleshooting process, a simple numerical reference method is mainly used to check whether there is a temporary sensor failure or sampling abnormality. If necessary, a repeated test can be added on the basis of five samplings per second to confirm whether the deviation persists. Then all valid data are associated with the corresponding collection time, and a time tag of the current moment is added after each valid data. It should be noted here that the time tag must adopt a unified format to ensure that sorting and synchronous comparison can be achieved during subsequent processing. For the data marked as abnormal in the above judgment, the corresponding records must also be retained and associated with the time to support subsequent confirmation of faults or errors. After all detection and marking are completed, the temperature value, flow rate value, voltage value and corresponding time information are concatenated to form a traceable integrated structure. Finally, when the integration is completed, the marked sensor data set is generated.

[0019] Based on the marked sensor data set, first list all the collected data one by one and observe whether their formats are consistent with the requirements. For the temperature, flow rate and voltage fields, the corresponding numerical comparisons must be performed respectively. The temperature continues to use 0℃ to 90℃ 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 formulated in combination with the recommended ranges obtained in the actual tests in the previous article. If the format of any data is missing or does not meet the specifications, it will be marked as a format abnormality when recording, and then it will be determined whether the value itself is lost or the unit identification is wrong according to the specific situation. Next, the temperature, flow rate and voltage are corrected for deviation using the numerical deviation threshold set by experience. The deviation threshold is usually based on the sensor stability generated previously. The results of qualitative tests are used to estimate and set in combination with industry experience. For example, the temperature is allowed to fluctuate by 0.5°C, the flow rate is allowed to fluctuate by 0.2 liters per minute, and the voltage is allowed to fluctuate by 0.1V. When correcting the detected small deviations, the average interpolation method will be used or the average value of several valid data around the same time period will be referred to. If the deviation is found to exceed the set threshold, the record will be further marked as abnormal, and then continuous checks will be made for timestamp duplication or disordered sorting. If multiple records are found to share the same timestamp or there are contradictions in the order, they will be divided into abnormal rows and a reference list will be established internally to compare whether there are errors in sensor synchronization. After all calibration and detection operations are completed, the processed data will be verified again in segments to finally generate the processed monitoring data set.

[0020] Based on the processed monitoring data set, the temperature and flow rate of the cooling medium and the temperature and voltage of the battery need to be integrated. First, the calibrated and verified temperature, flow rate and voltage fields are read, and the corresponding values ​​are captured one by one from the same timestamp for grouping. Each group contains four data items: cooling medium temperature, cooling medium flow rate, battery temperature and battery voltage. At the same time, the timestamp is referenced to ensure their synchronization consistency. If one or more of the fields is missing at a certain timestamp, the missing timestamp is noted in the record, and the previously set intervals of 0℃ to 90℃, 0 to 10, and 0V to 24V are compared again to confirm whether the data is still within the available range. If it is out of range, it is corrected. Further judgment is made based on the size of the deviation. If the deviation is within the allowable range set in the previous article, it will be corrected. If it is far beyond the range, an abnormal mark will be added to the record. After completing the above processing, all qualified groups are collected into a unified data structure, and then based on these groups, the cooling medium and battery-related data are connected and compared in series to form a fusion result list. If more external information such as ambient temperature or charging current is needed, it can be associated with the environmental monitoring or related sensor data recorded previously. Finally, all fused groups are collected into the same time series structure to obtain real-time monitoring data integrating cooling medium temperature, cooling medium flow rate, battery temperature and battery voltage.

[0021] The steps to obtain the performance evaluation function are: According to the real-time monitoring data, a performance function is constructed, and the formula is: ; in, is the performance function value, is the cooling medium flow rate, is the cooling medium temperature, is the total heat released during the charging process, is the set safety temperature threshold, and is the weight coefficient of charging efficiency and safety.

[0022] Specifically, the formula is beneficial in that by combining and , which can simultaneously reflect the impact of cooling medium flow on heat release and the cost of cooling medium temperature deviating from the safe temperature threshold, thus taking into account both efficiency and safety requirements in the control process.

[0023] The parameter acquisition steps are as follows: through the previously recorded charging efficiency and corresponding cooling parameter history data, the charging efficiency curve of each test is sorted out, and the charging time and heat dissipation under different flow and temperature conditions are counted, and the change sequence of charging efficiency in multiple tests is extracted, and then weighted average processing is performed and the recommended value in the technical manual is referred to, and the value area is located between 0.5 and 2.0. Finally, the sensitivity of the charging efficiency is monitored and the Determined to be 1.20.

[0024] The parameter acquisition steps are as follows: based on the safety impact indicators and temperature deviations extracted previously, count the temperature anomalies that occur when the flow rate changes or the temperature changes exceed a certain range, record the duration of these anomalies and organize them into a temperature deviation data sequence, and compare the temperature deviation with the safety performance requirements and perform weighted operations to obtain a reference interval between 0.01 and 0.05. Determined to be 0.03.

[0025] The parameter acquisition step is to use the cumulative value of heat released during the charging period recorded above to calculate the total heat by multiplying the thermal power by the charging time. The thermal power input under typical working conditions is 100 watts and lasts for one hour. joule.

[0026] The parameter acquisition step is to obtain the average value of the cooling medium flow rate from the above real-time monitoring data, combine it with the instantaneous flow rate measured multiple times during the stable operation stage of the equipment, take the value with smaller fluctuation as a reference, and finally obtain Liters per minute.

[0027] The parameter acquisition step is to observe the average value of the cooling medium temperature during a stable charging time through a temperature sensor, extract and statistically record the cooling medium temperature distribution between 0°C and 90°C, select the temperature value corresponding to the distribution peak as the representative, and obtain .

[0028] The steps to obtain the parameters are as follows: determine the strategy based on the safety temperature threshold recorded in the previous text and combine it with the safety upper limit temperature of 45°C in the equipment manual to obtain as a determined value.

[0029] Calculation process: The first step is to calculate : ; The second step is to calculate : ; Step 3: Calculate : ; Step 4: Calculate : ; Step 5: Substitute into the formula to obtain : ; The results show that When the value reaches 215993.25, it means that under the current combination of flow and temperature, the performance evaluation function achieves this value. If the value is maintained at a high level, it means that both efficiency and the degree of penalty for temperature deviation are taken into account under the current conditions. If there is a significant decline in subsequent monitoring, it is necessary to readjust the cooling parameters and calculate the performance function again to determine whether the adjustment effect is reasonable.

[0030] The steps to obtain gradient data are: Based on the performance evaluation function, the variable terms of flow rate and temperature in the performance function are analyzed, the expressions associated with flow rate and temperature are extracted, and the partial derivative expression of the performance function is established; According to the partial derivative expression of the performance function, the partial derivative values ​​of the performance function relative to the flow rate and temperature are calculated point by point using the flow rate and temperature parameters in the real-time monitoring data to obtain the partial derivative data; Based on the partial derivative data, all calculation results are sorted to form gradient data.

[0031] 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.

[0032] Based on the partial derivative expression separated above, the specific data points for flow and temperature in the collected real-time monitoring data are read. The flow data is first controlled in the range of 0 liters per minute to 10 liters per minute and the actual sampling value is recorded every minute. The temperature data is recorded degree by degree in the range of 0°C to 90°C and paired with the time tag. Then, the moments that meet the stability conditions are extracted from these paired data. If the flow rate is close to 10 liters per minute or the temperature is close to 90°C at a certain moment, additional checks are performed and the data are placed in the abnormal queue. After eliminating obvious abnormalities, the data in the normal queue are selected and brought into the partial derivative expression point by point. For each point of data, it is necessary to first confirm that the flow rate and temperature are both in the available range, and then replace the flow rate with the flow variable position and the temperature with the temperature variable position. Then the partial derivative expression is calculated. During the calculation process, the specific value represented by each symbol is decomposed step by step, including referring to the total heat obtained before and the safety temperature threshold and other parameters. After completing the calculation of the partial derivative value of each data point, the result values ​​are arranged in chronological order. If it is found that the value is greater than the partial derivative critical value set empirically, the moment is marked. The partial derivative critical value can be set through previous test records. For example, the partial derivative value under the condition of flow rate of 1 liter per minute and temperature of 30°C is observed and compared with the partial derivative value under the condition of flow rate of 9 liters per minute and temperature of 50°C. The above multiple tests are combined and statistically obtained to obtain an average partial derivative level, and then a certain safety factor is added to form the critical value. After all the calculations are completed, the obtained partial derivative values ​​are sorted and summarized one by one to finally obtain the partial derivative data.

[0033] After all the flow and temperature partial derivative data have been obtained in the previous stage, these partial derivatives are first compared with the aforementioned flow and temperature original records according to the time label, and the matching partial derivative values ​​are combined with their corresponding flow and temperature conditions. Then, the results of multiple operations are sorted in chronological order, and the partial derivative values ​​higher than the previously set critical value are extracted separately and recorded in the abnormal sequence. At the same time, all normal sections below the critical value are summarized in sections. If the cumulative partial derivative values ​​of temperature continue to increase or the cumulative partial derivative values ​​of flow continue to decrease in some sections, they also need to be compared with the previously obtained safe temperature threshold and minimum flow limit. Once it is confirmed that the trend of these partial derivatives is within the controllable range, they are included in the normal gradient sequence and the fluctuation part is processed by exponential smoothing or constant smoothing. Then, all the sorted normal partial derivative sequences and abnormal partial derivative sequences are listed separately, and these partial derivative results are finally revised according to time segmentation or other conditions. If the value jumps too much in some time periods, it is compared with the monitoring data again to confirm its rationality. Finally, all the results are integrated to form a complete gradient data.

[0034] The steps to obtain the parameter adjustment results 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. The calculation formula is: and ; in, The parameter adjustment value representing the cooling medium flow rate, The parameter adjustment value representing the cooling medium temperature, represents the partial derivative of the performance function associated with the flow, represents the partial derivative of the performance function with respect to 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 temperature, represents the initial flow value, Represents the current flow reference value, represents the initial temperature value, 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.

[0035] Specifically, the formula is beneficial in that the total heat released during the charging process is taken into account by considering both the flow rate derivative and the temperature derivative in the same calculation framework. , real-time traffic and real-time temperature and the upper limit of the safety adjustment range and Factors such as and The calculation takes into account both the thermal dimension and the temperature safety boundary.

[0036] The parameter acquisition step is to screen the gradient data of the performance function under different flow rates based on the previously obtained performance function, extract the distribution of partial derivative values ​​in each time period by recording the corresponding relationship between the flow change and the performance function value change at each time node, and then use the average value of multiple consecutive measurements as a reference and correct the extreme value to finally determine .

[0037] The steps of obtaining the parameters are as follows: referring to the temperature gradient data obtained above, performing segmented statistics on the performance function values ​​at different temperature levels, retaining several time records within the normal range (e.g., temperature between 0°C and 90°C), reading the partial derivative changes and integrating multiple measurement results, and comprehensively calculating . The parameter acquisition step is to combine the integral operation of the time-divided thermal power multiplied by the duration of the heat monitoring, collect the heat release process under typical conditions and average multiple measurement values ​​to obtain the value joule.

[0038] The parameter acquisition step is to calculate the average value of the range of 0 liters per minute to 10 liters per minute obtained by the cooling medium flow sensor during the stable operation of the equipment in the section with small fluctuations, and confirm that the average value has no abnormal drift. .

[0039] The parameter acquisition step is to select the most common temperature value without large fluctuations in the current period from the previously recorded temperature collection data as a representative, and check whether the temperature is within the range of 0℃ to 90℃, and finally obtain .

[0040] The parameters are obtained by comparing the maximum allowable cooling flow rate listed in the equipment manual with the high flow rate conditions monitored by the test, and correcting them according to the industrial standards. . The parameter acquisition step is to calculate the upper limit range of the temperature that can be safely adjusted from the sensor data under multiple high temperature conditions according to the safe temperature adjustment range, and confirm that it is within the range that the normal cooling system can withstand. .

[0041] , , , The parameter acquisition step is to record the initial flow rate and temperature information obtained previously and the current reference flow rate and temperature information one by one. For example, in the early stage of execution, the flow rate can be initially set by the device. , reference flow , the initial temperature value , current temperature reference value ,The above values ​​are obtained through real-time monitoring or records in the scheduling strategy.

[0042] Calculation process: The first step is to calculate and sum: ; Step 2: Calculate the denominator : ; The third step is to merge : ; Step 4: Multiply the result by the negative sign and multiply by : ; Therefore .

[0043] Similarly, for Operations: The first step is to calculate and sum: ; Step 2: Calculate the denominator : ; The third step is to merge : ; Step 4: Multiply the result by the negative sign and multiply by : ; Therefore .

[0044] The results show that is about -2.001 and When it is about -7.15, it means that under the current environment, the cooling medium flow rate needs to be adjusted down by about 2.001 liters per minute and the cooling medium temperature needs to be adjusted down by about 7.15℃. If the value is out of bounds after adjustment, the boundary correction can be combined with the flow safety range and temperature safety range obtained previously. The results of different value ranges will indicate the required parameter adjustment range. After completing the above calculations, through the previously obtained and The values ​​correspond to the flow rate and temperature range of the cooling medium respectively. The flow rate part is first compared with the range of 0 liters per minute to 10 liters per minute. If the correction amount calculated this time is greater than 4 liters per minute, it is marked as an oversized adjustment and needs to be confirmed again. If the correction amount is between 1 and 4, it is considered a medium adjustment. If it is less than 1, it is considered a slight adjustment. All actual correction values ​​are uniformly recorded in an update queue. The temperature adjustment also adopts the same idea. First, compare the temperature range of 0℃ to 90℃. If the correction amount is between 5℃ and 15℃, it is considered a medium adjustment. If it exceeds 15℃, further verification is performed to see if it is listed as a feasible value in the temperature safety adjustment range table compiled earlier. Then query the relevant measurement sequence to confirm whether the temperature correction is still within the compliance range. All confirmed correction data are sorted in sequence and correspond to the time label one by one, and then connected in series with the previously obtained flow and temperature reference values. Finally, the parameter update operation is completed according to these values ​​and the new flow and temperature levels are registered to obtain the parameter adjustment results.

[0045] The steps for obtaining iterative optimization data are: According to the parameter adjustment results, 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 parameters are adjusted and the energy consumption is recorded, and the cooling parameter adjustment record is generated; Based on the cooling parameter adjustment records, 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 adjusted continuously. At the same time, the 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.

[0046] Specifically, according to the parameter adjustment results obtained previously, the current cooling medium flow value and temperature value are configured as the starting reference point. First, the initial value of the cooling parameter can be read from the previously determined cooling medium flow and temperature, and the corresponding energy consumption basic level and the initial reference level of equipment efficiency are recorded. Then, the initial value of the cooling parameter is gradually monitored according to the time series, including observing the flow and temperature values ​​at fixed time intervals to see whether they are still in the range of 0 liters per minute to 10 liters per minute and 0°C to 90°C, and then comparing these values ​​with the energy consumption benchmark values ​​obtained previously. If the energy consumption and equipment efficiency corresponding to the current flow are significantly different from the historical data collected previously, check whether the flow deviation exceeds 2 liters per minute or the temperature deviation exceeds Over 5℃, these critical values ​​can be set by referring to the safety fluctuation limits in the product manual and combining with the median fluctuations of previous tests. If it is found that the data does not deviate excessively in comparison, continue to use this flow 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 increasing or decreasing the temperature in steps of 2℃ and record the energy consumption again. When summarizing these adjustment results, add a time label to each record. During this period, pay attention to whether the parameter distortion occurs when the flow or temperature is close to 0 or 90℃. At the same time, confirm that it remains in a reasonable range through the equipment efficiency comparison table. After summarizing all the records in the adjustment process, you can get a cooling parameter adjustment record.

[0047] According to the cooling parameter adjustment records generated previously, first extract the flow and temperature values ​​in each record and compare them with the corresponding energy consumption and equipment efficiency values. Arrange the flow and temperature values ​​before and after each adjustment on the same timeline and check their changing trends. If energy consumption continues to rise while equipment efficiency continues to decline, further check whether the flow and temperature at that time have deviated from the set reasonable range. For example, check the flow range of 0 to 10 and the temperature range of 0℃ to 90℃ to see if they are out of bounds. Then check whether the minimum reference value or energy consumption upper limit threshold specified by the equipment efficiency is reached. These preset thresholds are determined by combining the average level of previous multiple tests with industry standards, and are usually set The critical point is the value where the energy consumption is 20% higher than the average value. If the current data has crossed the critical point, the flow-temperature pair at that moment and its corresponding energy efficiency will be marked as failing to meet the performance improvement standard and the parameter correction for the next stage will be considered. Otherwise, the existing flow and temperature will be considered to be temporarily maintained, and the records will be sorted and summarized into a new trend sequence. The commonalities will be found by comparing the change amplitude of the flow and the change amplitude of the temperature after each adjustment. If the directions of multiple adjustments are similar, they will be considered to be summarized into an integrated trend. The performance parameters will be updated according to these trends and the new and old values ​​will be differentially analyzed to clarify the impact of each change on energy consumption and equipment efficiency, thereby ultimately forming performance parameter update data.

[0048] On the basis of the previously obtained performance parameter update data, first combine the time sequence and adjustment range of each parameter update information to summarize it, and then associate it with the previous cooling parameter adjustment records for viewing. Each time the association is made, the current flow rate and temperature need to be matched 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 certain periods and the energy consumption has not decreased significantly, then in the next iteration, priority will be given to adjusting the flow item significantly. 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 flow changes on energy consumption is not significant enough, then compare the amplitude of temperature correction to evaluate its sensitivity, refer to the fluctuation amplitude of equipment efficiency when the temperature increases or decreases between 2°C and 10°C, and compare the temperature response curve listed in the industry statistics to determine whether the change is a reasonable value. If the temperature still exceeds the safety boundary after the change, the adjustment step is further reduced according to the previously registered maximum or minimum temperature tolerance range. All these operations are continuously cycled and the latest adjustment results of flow and temperature are recorded again in the extended time series. Compared with the change trajectory of energy consumption changes and equipment efficiency, all records generated by multiple cycles are finally sorted and summarized to obtain iterative optimization data.

[0049] The steps to obtain the optimization results are: Based on iterative optimization data, extract the energy consumption and equipment efficiency information after cooling parameter adjustment, calculate the change trend of indicators, and generate performance change records; According to the performance change records, the performance target achievement value is calculated using the following formula: ; in, To achieve the performance target 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; Based on the performance target achievement value, determine whether the predetermined performance improvement threshold is reached. If it is met, terminate the iteration and obtain the optimization result.

[0050] Specifically, based on the iterative optimization data obtained previously and the registered cooling parameters, first summarize the energy consumption corresponding to the most recent flow and temperature after adjustment, compare the energy consumption value in each time period with the previously recorded equipment efficiency, and screen out abnormal data points, such as flow suddenly exceeding 10 liters per minute or temperature exceeding 90°C, and then match these available data in time series to identify the energy consumption distribution area under the current flow and temperature. If energy consumption is concentrated in a section that is 20% higher than the original average energy consumption level, then compare the judgment standard corresponding to the equipment efficiency to see if it has dropped to 10% or more below the initial efficiency. These percentage intervals are combined with the median values ​​of multiple tests in the previous article and the general industry standards. If it has been confirmed that the energy consumption is high and the equipment efficiency has also declined significantly, it is marked. This period is the "high energy consumption and low efficiency" data point and is summarized in the same list. Then, other points with little deviation or still within 10% above and below the average value are put into the "stable period" list. Then all the data in these two lists and their time tags are concentrated in a mapping table, so that the changing trends of energy consumption and equipment efficiency can be observed on the same timeline, and it can be identified whether there are three or more consecutive records showing an upward or downward trend. Once these continuous fluctuations are found, they are further screened against the reference interval set previously. If multiple changes are within the same interval and do not exceed the safety threshold set in the previous article, a corresponding mark is added to the list to indicate that follow-up statistics will be continued. Finally, after the energy consumption and equipment efficiency change trajectories in all cycles are sorted out, a performance change record can be formed.

[0051] The benefit of the formula is that by combining the change in equipment efficiency and the change in energy consumption in the same expression, a comprehensive measurement of both efficiency and energy consumption can be made, and the offsets of the two can be normalized separately and then added together, thereby promptly revealing the overall degree of deviation between the current iteration state and the initial benchmark.

[0052] The steps for obtaining parameters are as follows: referring to the previously saved equipment efficiency records, collecting the working status and output completion of the tested device in each period, combining the actual test performance of the equipment in the stable range, and weighting the completion degree and additional energy demand ratio of each time period to finally obtain The time series of the current equipment efficiency is selected as the corresponding value at a certain time in a certain round of iteration. For example, statistics are taken in the latest round of observations to determine (This value is between 0 and 1 and is more commonly expressed as a percentage of work efficiency or completion).

[0053] The parameters are obtained by integrating and analyzing the test data at the beginning of the system operation, confirming the initial efficiency level achieved by the equipment when there is no fault and the working conditions are good, and extracting the mean value from repeated tests to determine the initial efficiency level achieved by the equipment when there is no fault and the working conditions are good. .

[0054] The parameter acquisition step is to collect the energy consumption distribution of the current iteration period and continuously observe the energy usage of the period, and obtain the actual energy consumption value by accumulating the energy consumption per minute and comparing it with the rated energy consumption of the previous equipment. After several sampling comparisons and eliminating abnormal peaks, the actual energy consumption value is obtained. Watt-hour.

[0055] The parameter acquisition step is to collect the basic energy consumption level for multiple rounds before the equipment starts running, record the electric power consumed in the initial period, remove some interference fluctuations to obtain multiple valid statistical values, and then select the weighted mean as the initial energy consumption to determine Watt-hour.

[0056] Calculation process: The first step is to calculate : ; The second step is to calculate : ; The third step is to merge : ; The results show that It means that at the current iteration, the equipment efficiency has increased by 6.25% compared with the initial efficiency, while the energy consumption has decreased by 10% compared with the initial level. The normalized sum of the two is 0.1625. If the value continues to drop below a certain predetermined standard in subsequent iterations, the adjustment process will be stopped. If it is higher than the predetermined standard, further monitoring is required and the next iteration will be carried out.

[0057] After the performance target has been achieved, when comparing it with the previously set performance improvement threshold, it is necessary to first retrieve the efficiency and energy consumption fluctuation range of the device in the historical test. If the value is less than the threshold, it is considered to have entered a relatively ideal performance state, so the continuous iteration can be stopped and the cooling parameters will no longer be corrected. If it is still greater than the threshold, the previous energy consumption and efficiency change sequence is compared to continue to evaluate whether there is a sudden over-limit situation. When evaluating, the current The difference with the result of the previous iteration. If this difference is continuously greater than 0.02 and does not fall back in three records, it is marked as a large deviation, and additional adjustments to the flow or temperature are required. When the adjustment range exceeds the specified 5% flow limit or 10°C temperature change, the trend of the corresponding equipment efficiency is observed again in the subsequent cycle records 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 above process is repeated and each time a new R value is obtained, it is rechecked whether the standard has been met. Once it is met, the flow rate, temperature and corresponding energy consumption under this state can be sorted out together to obtain the optimization result.

[0058] The steps to obtain 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 rate 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 rate and pre-cooling resource allocation priority under different conditions, and form a cooling resource adjustment record; Based on the cooling resource adjustment records, the cooling medium flow rate and pre-cooling resource allocation rules during peak hours are set to obtain the scheduling strategy.

[0059] Specifically, based on the optimization results, first filter out all temperature fluctuation data within the peak period from the previously identified records, and make a validity judgment on each temperature record between 0°C and 90°C. If a temperature record is found to be beyond this range, mark it and summarize it in the abnormal row. Then, check the difference between the maximum and minimum temperature values ​​in the remaining normal records in chronological order. If the difference between the maximum and minimum values ​​exceeds 10°C, cross-check with the flow fluctuation range obtained previously to see whether the flow rate also shows a trend of synchronous increase or decrease when the temperature rises. These corresponding moments need to be marked together and recorded in a temperature-flow corresponding list. Then, observe in segments from the list whether the temperature range of several peak periods is close to the upper limit of 90°C. If the temperature approaches 90°C multiple times, see whether it is at each peak. How long the period lasts, for example, 5 or 10 minutes in a row, and compared with the empirical thresholds collected in previous rounds, for example, each time it exceeds 5 minutes is considered a high temperature period. The frequency of occurrence of such high temperature periods is counted and summarized in the temperature statistics entry, and then these data are matched with the previously recorded pre-cooling resource periods. Because pre-cooling usually takes at least 5 minutes or more to complete, if the temperature has climbed to nearly 90°C in a short period of time, this pre-cooling time period is marked as a high-priority pre-cooling stage, and the available flow resources are preliminarily allocated in combination with the flow changes in the future. Once the maximum and minimum temperature values ​​and duration are sorted out, corresponding settings are made according to the previous temperature statistics entries and the existing pre-cooling time. Saving these related information can affect the cooling medium flow rate and pre-cooling time, and generate a preliminary cooling resource allocation framework.

[0060] According to the preliminary cooling resource allocation framework, first select the temperature extreme points determined in the previous stage and compare them with the peak period information one by one, list the temperature data in the peak period in chronological order and call out the flow rate sampling values ​​together. If it is found that the temperature of some periods fluctuates by more than 10°C in a short period of time and the flow rate also fluctuates by more than 1 liter per minute, combine the statistical items obtained previously to determine whether the situation is during the morning peak or evening peak period, and record the matching data of temperature and flow rate, and then refer to the set priority division strategy. For example, the 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, in the case of temperature fluctuation When the temperature is less than 5℃ but the flow rate fluctuates close to 3 liters per minute, it is considered as a secondary priority. These specific values ​​are derived from the statistical results of cooling pressure during peak hours during multiple monitorings. Each pair of temperature and flow rate matching records needs to be marked with a time stamp after the corresponding entry, and the peak or non-peak time information of that moment is attached to the same line. After completion, the flow rate and temperature data marked as high priority or secondary priority are archived. If there are multiple high-priority records during peak hours, they are linked together in chronological order during archiving, and records of the same type are grouped. Finally, after all groups are calibrated, the allocation reference of flow rate and pre-cooling resources under each priority level can be calculated to form a cooling resource adjustment record.

[0061] Based on the cooling resource adjustment records, first list each group of high-priority data in sequence and attach a time sequence label to each record. Then view the temperature, flow rate and cumulative pre-cooling time corresponding to each group of records in the same window. If the temperature reaches above 80°C for many times and the flow rate tends to approach 10 liters per minute or changes continuously in a short time, check whether it has reached the predetermined conditions for forced cooling according to the previously set standards. Forced cooling can usually be selected by increasing the flow rate to 8 liters per minute or increasing the pre-cooling time to 10 minutes. For this, you can decide whether to give priority to increasing the flow rate or increasing the pre-cooling time according to the aforementioned archived information. If the peak time is short, If the peak time exceeds 30 minutes, the flow rate will usually be increased as a priority. If the peak time exceeds 30 minutes, the pre-cooling time should be added while increasing the flow rate. All these operations should be combined with the confirmed temperature and flow rate corresponding classification standards. The temperature is compared with the 0℃ to 90℃ range and the temperature close to 90℃ is defined as the priority trigger for pre-cooling. The flow rate is compared with the 0 to 10 liters per minute range. If the continuously recorded flow rate has exceeded 8 liters per minute, it is also marked as an emergency processing item. After integrating the above data, the corresponding cooling medium flow rate upper line and the corresponding minimum pre-cooling time are set for the peak period. These configurations are marked together in the time period scheduling table to finally obtain the scheduling strategy.

[0062] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls 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 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; 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 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.

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 the cooling data of the liquid cooling device of the charging pile, collect the temperature and flow rate of the cooling medium through the temperature sensor and flow rate sensor, collect the temperature and voltage of the battery through the voltage sensor and temperature sensor, and timestamp all the collected sensor data to generate a marked 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 rate 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: ； in, is the performance function value, is the cooling medium flow rate, is the cooling medium temperature, is the total heat released during the charging process, is the set safety temperature threshold, and is the weight coefficient of 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 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 ; in, The parameter adjustment value representing the cooling medium flow rate, The parameter adjustment value representing the cooling medium temperature, represents the partial derivative of the performance function associated with the flow, represents the partial derivative of the performance function with respect to 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 temperature, represents the initial flow value, Represents the current flow reference value, represents the initial temperature value, 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.

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 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.

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 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: ； in, To achieve the performance goal, 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; 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.

8. 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 rate 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 time information are integrated to calculate the cooling medium flow rate 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 cooling medium flow rate and the allocation rules of the pre-cooling resources during the peak period are set to obtain the scheduling strategy.

Images