Liquid cooling control method and system of battery control device

Abstract

The invention relates to the technical field of temperature regulation and control, in particular to a liquid cooling control method and system of a battery control device, in the liquid cooling control method and system, through real-time collection and area screening of temperature and power data, accurate recognition of a high-temperature area is achieved, a clear cooling requirement partition is provided, and the cooling requirement of a battery is met. A dynamic adjustment strategy of pressure and flow parameters of the liquid cooling pump is optimized through a genetic algorithm, the cooling response speed and control precision are improved while energy consumption and cooling efficiency are effectively balanced, the pump speed and the valve opening degree are adjusted in real time, temperature changes are rapidly responded through automatic control, gradual implementation of a cooling target is ensured, and the cooling efficiency is improved. The hysteresis in the heat management process is reduced, the temperature control effect is continuously evaluated through a fuzzy control algorithm, the cooling intensity is dynamically adjusted according to the monitoring result, efficient cooling is ensured, meanwhile, equalization of temperature distribution is achieved, and a systematic heat management framework is established through long-term data monitoring and continuous optimization of cooling operation. And efficient and stable operation of temperature management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and particularly to a liquid cooling control method and system for a battery control device. Background Art

[0002] The technical field of temperature control involves monitoring, managing, and regulating the temperature of equipment, systems, and components to ensure operation in the optimal state. The goal is to prevent overheating and overcooling from having an adverse impact on equipment performance, lifespan, and safety through precise heat management.

[0003] The purpose of the liquid cooling control method for a battery control device is to quickly dissipate the heat generated during battery operation through effective thermal management means, so as to avoid performance degradation or safety risks caused by overheating, extend the battery lifespan, improve the overall system operation efficiency and reliability, achieve uniform distribution of battery temperature, avoid damage caused by local overheating or overcooling, and achieve the effect of optimizing battery performance and ensuring safe operation.

[0004] Existing technologies lack the ability to accurately identify specific areas and dynamically partition in temperature control, unable to perform differential cooling for different temperature regions, prone to resource waste or insufficient local cooling, and unable to adapt to rapid changes in heat distribution in real time, which may lead to overheating or overcooling, having an adverse impact on equipment safety and operation efficiency. Existing technologies lack the ability of long-term dynamic adjustment, unable to optimize based on historical data. The non-uniformity of temperature distribution easily causes local thermal runaway or performance degradation, resulting in high energy consumption costs, increasing equipment maintenance frequency, and affecting the stability and economy of system operation. Summary of the Invention

[0005] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose a liquid cooling control method and system for a battery control device.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A liquid cooling control method for a battery control device, comprising the following steps: Step 1: Collect the temperature and power output data of each battery unit through a sensor network, screen out the high-temperature areas and normal working areas according to the temperature threshold, determine the cooling demand partition by comparing the real-time data with the safety standard, and generate a temperature control partition strategy; Step 2: Based on the temperature control partition strategy, calculate the deviation between the high-temperature area and the overall average temperature, set an accurate temperature drop target, and adjust the cooling parameters through real-time monitoring to generate cooling target setting details; Step 3: Based on the cooling target setting details, adopt a genetic algorithm, and through a dynamic adjustment control mechanism, adjust the pressure and flow rate of the liquid cooling pump, while balancing energy consumption and efficiency, to generate flow pressure optimization parameters; Step 4: According to the flow pressure optimization parameters, adjust the pump speed and valve opening through automatic control, monitor the temperature change after adjustment in real time, and generate the monitoring result of the adjustment effect; Step 5: According to the monitoring result of the adjustment effect, adopt the fuzzy control algorithm to evaluate the temperature control effect of each battery unit. If the preset target is not reached, re-enhance the cooling intensity, optimize the cooling strategy, and generate the optimized cooling strategy plan; Step 6: Based on the optimized cooling strategy plan, update the control parameters of the liquid cooling, optimize the cooling operation through long-term data monitoring, and continuously adjust to generate the continuous operation control framework.

[0007] As a further solution of the present invention, the specific steps for generating the temperature control zoning strategy are as follows: Collect the temperature and power output data of each battery unit through the sensor network. During the classification process, use the preset temperature threshold to screen the high-temperature area and the normal working area, record the classification results item by item, and complete the collection of regional temperature data in sequence to generate the temperature area division result; Based on the temperature area division result, compare the deviation between the high-temperature area and the safety standard point by point. By accumulating and statistically calculating the deviation amount, delimit the cooling demand range, calibrate the cooling demand level of each area item by item, and generate the cooling demand zoning result; Based on the cooling demand zoning result, match the cooling demand levels of each area, allocate the temperature control strategy parameters in sequence, establish the temperature control parameter list of each area according to the demand in sequence, record the parameter table after the allocation is completed, and generate the temperature control zoning strategy.

[0008] As a further solution of the present invention, the specific steps for generating the cooling target setting details are as follows: Based on the temperature control zoning strategy, gradually measure the difference between the high-temperature area and the overall average temperature, and calculate the regional target temperature in combination with the preset target temperature adjustment range. Record the adjustment target data in sequence to generate the temperature adjustment target parameters; Based on the temperature adjustment target parameters, monitor the temperature change process in real time, modify the cooling parameters item by item to achieve temperature adjustment, optimize the parameter settings according to the monitoring data during the adjustment process, record the adjusted parameters in sequence, and generate the cooling target setting details; Based on the cooling target setting details, analyze the adjusted regional temperature change data point by point, compare it with the target value range, correct the cooling parameters with large regional deviations, and gradually complete the correction of all areas to generate the cooling target setting details.

[0009] As a further solution of the present invention, the specific steps for generating the flow pressure optimization parameters are as follows: Based on the above-mentioned cooling target setting details, the genetic algorithm is adopted to measure the initial states of the liquid cooling pump pressure and flow rate. Through gradually adjusting the pressure and flow rate ratio for preliminary setting, and recording the parameter data after setting, an initial parameter setting result is generated. Based on the above-mentioned initial parameter setting result, gradually adjust the pressure value and flow rate value of the pump. By synchronously measuring and recording data, calibrate the matching relationship between the two, and record the energy consumption during the adjustment process to generate dynamic adjustment optimization data. Based on the above-mentioned dynamic adjustment optimization data, recalibrate the distribution of the liquid cooling pump flow rate and pressure in different regions. By repeating tests and corrections, record the final configuration values of the flow rate and pressure in each region to generate flow rate-pressure optimization parameters.

[0010] As a further solution of the present invention, the genetic algorithm is calculated according to the formula: Where: represents the current pressure value of the liquid cooling pump, represents the current flow rate value of the liquid cooling pump, represents the ratio weight parameter between pressure and flow rate, represents the heat load coefficient of the cooling target, represents the heat load weight parameter, represents the energy consumption efficiency ratio of the liquid cooling pump, represents the energy consumption efficiency weight parameter, represents the comprehensive evaluation result.

[0011] As a further solution of the present invention, the specific steps for generating the adjustment effect monitoring result are as follows: Based on the above-mentioned flow rate-pressure optimization parameters, gradually adjust the initial value of the pump speed. By manually adjusting the opening degree of the valve step by step for matching, record each state value after the pump speed adjustment to generate pump speed and valve adjustment data. Based on the above-mentioned pump speed and valve adjustment data, measure the real-time change values of the temperatures in each region. Gradually correct the opening degree of the valve to match the temperature change requirements, and record the temperature change situations in each region to generate regional temperature monitoring data. Based on the above-mentioned regional temperature monitoring data, compare the measured values with the target values. Gradually correct the adjustment parameters of the pump speed and valve, and complete the verification through real-time data recording to generate the adjustment effect monitoring result.

[0012] As a further solution of the present invention, the specific steps for generating the cooling strategy optimization plan are as follows: Based on the above-mentioned adjustment effect monitoring result, measure the temperatures in each region item by item. Analyze by recording the deviation between the temperature data and the preset target, and gradually classify the unqualified regions, and record the temperature deviation data after classification to generate a temperature evaluation result. Based on the temperature evaluation results, a fuzzy control algorithm is adopted to gradually increase the cooling intensity. By adjusting the output pressure of the flow control device and adjusting the cooling flow rate, the cooling effect of each area is re-measured, the changes in cooling parameters are recorded item by item, and a cooling parameter adjustment record is generated. Based on the cooling parameter adjustment record, the cooling strategy is optimized one by one. By gradually adjusting the cooling flow rate and the area allocation scheme, an area cooling strategy configuration table is established item by item and the results are recorded, and a cooling strategy optimization scheme is generated.

[0013] As a further solution of the present invention, the fuzzy control algorithm is calculated according to the formula: Where: is the improved cooling intensity, is the target temperature, is the currently measured temperature, is the current cooling flow rate, is the temperature difference adjustment coefficient, is the flow rate adjustment coefficient, is the non-uniformity of heat distribution, is the heat distribution weight coefficient, is the flow velocity of the cooling medium, is the flow velocity weight coefficient.

[0014] As a further solution of the present invention, the specific steps for generating the continuous operation regulation framework are as follows: Based on the cooling strategy optimization scheme, the flow rate and pressure control parameters of each cooling area are gradually updated. By batch importing the parameters and testing the area cooling performance one by one, the updated values of the parameters are recorded, and a parameter update result is generated. Based on the parameter update result, the temperature data of each area is continuously collected. By regularly collecting and recording the operating parameters during the cooling process, the changing relationship between the temperature and energy consumption data among the areas is gradually analyzed, and long-term monitoring data is generated. Based on the long-term monitoring data, the cooling parameters are gradually corrected. By optimizing the cooling distribution and flow rate adjustment of each area, the stability of the cooling operation is verified item by item and the final data is recorded, and a continuous operation regulation framework is generated.

[0015] A liquid cooling control system for a battery control device. The liquid cooling control system of the battery control device is used to execute the liquid cooling control method of the battery control device. The system includes: Temperature control zoning module: Collect the temperature and power output data of each battery unit through a sensor network, use the temperature threshold to screen the high-temperature area and the normal working area, compare the temperature of the high-temperature area with the safety standard, and partition the cooling requirements according to the comparison result to generate a temperature control zoning strategy. Cooling target setting module: Based on the temperature control zoning strategy, calculate the deviation between the temperature in the high-temperature area and the overall average temperature, compare the temperature deviation in the high-temperature area with the preset temperature drop target, set the temperature drop target for each area, and generate the cooling target setting details by monitoring the dynamic distribution of cooling flow rate and pressure in real time; Flow rate and pressure optimization module: Based on the cooling target setting details, use the genetic algorithm to generate an initial cooling strategy solution, compare the ratio of the pressure value to the flow rate value of the liquid cooling pump with the optimization target of the cooling demand, record the energy consumption and cooling effect after iterative adjustment of the pressure and flow rate values of the liquid cooling pump, map the optimized pressure and flow rate distribution relationship to the regional cooling strategy table, and generate the flow rate and pressure optimization parameters; Cooling adjustment module: Based on the flow rate and pressure optimization parameters, adjust the pump speed and valve opening through automatic control, compare the adjusted pump speed value with the regional cooling demand, monitor the changes in the adjusted flow rate and regional temperature in real time, record the cooling effect data for each area, and generate the adjustment effect monitoring result; Cooling strategy evaluation module: Based on the adjustment effect monitoring result, use the fuzzy control algorithm to evaluate the cooling effect, classify the cooling demand values in the non-compliant areas and the adjusted cooling resource allocation values, rearrange the resource allocation priorities in the non-compliant areas, and adjust the resource allocation plan to generate the cooling strategy optimization plan; Continuous regulation module: Based on the cooling strategy optimization plan, analyze the historical operation data and current control parameters, compare the cooling allocation mode in the historical data with the current cooling parameters, classify the long-term optimization data of regional resource allocation, establish a long-term liquid cooling control model, and generate the continuous operation regulation framework.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: 1. In the present invention, through the real-time collection and regional screening of temperature and power data, the accurate identification of the high-temperature area is realized, a clear cooling demand zoning is provided, and the deviation between the high-temperature area and the overall average temperature is calculated to clarify the temperature drop target. The dynamic adjustment of cooling parameters is used to optimize the utilization efficiency of cooling resources and enhance the control force over the heat distribution; 2. In the present invention, the dynamic adjustment strategy of the pressure and flow rate parameters of the liquid cooling pump is optimized through the genetic algorithm. While effectively balancing the energy consumption and cooling efficiency, the cooling response speed and control accuracy are improved. The pump speed and valve opening are adjusted in real time, and the temperature change is quickly responded through automatic control to ensure the gradual realization of the cooling target and reduce the hysteresis in the heat management process; 3. In the present invention, the temperature control effect is continuously evaluated through a fuzzy control algorithm, and the cooling intensity is dynamically adjusted according to the monitoring results to ensure efficient cooling while achieving the equalization of temperature distribution. Through long-term data monitoring and continuous optimization of cooling operations, a systematic thermal management framework is established to achieve efficient and stable operation of temperature management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the working process of the present invention; Figure 2 It is a detailed flowchart of S1 of the present invention; Figure 3 It is a detailed flowchart of S2 of the present invention; Figure 4 It is a detailed flowchart of S3 of the present invention; Figure 5 It is a detailed flowchart of S4 of the present invention; Figure 6 It is a detailed flowchart of S5 of the present invention; Figure 7 It is a detailed flowchart of S6 of the present invention; Figure 8 It is a system flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, 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.

[0019] Please refer to Figure 1 , the present invention provides a technical solution: a liquid cooling control method for a battery control device, including the following steps: S1: Collect the temperature and power output data of each battery unit through a sensor network, screen out the high-temperature area and the normal working area according to the temperature threshold, determine the cooling demand partition by comparing the real-time data with the safety standard, and generate a temperature control partition strategy; S2: Based on the temperature control partition strategy, calculate the deviation between the high-temperature area and the overall average temperature, set an accurate temperature drop target, and adjust the cooling parameters through real-time monitoring to generate the details of the cooling target setting; S3: Based on the details of the cooling target setting, adopt a genetic algorithm and adjust the pressure and flow rate of the liquid cooling pump through a dynamic adjustment control mechanism, while balancing energy consumption and efficiency, to generate flow pressure optimization parameters; S4: According to the flow pressure optimization parameters, adjust the pump speed and valve opening through automatic control, and monitor the temperature change after adjustment in real time to generate the monitoring result of the adjustment effect; S5: According to the monitoring results of the adjustment effect, use the fuzzy control algorithm to evaluate the temperature control effect of each battery unit. If the preset target is not reached, re-enhance the cooling intensity, optimize the cooling strategy, and generate an optimized cooling strategy plan. S6: Based on the optimized cooling strategy plan, update the control parameters of the liquid cooling, optimize the cooling operation through long-term data monitoring, and continuously adjust to generate a continuous operation control framework.

[0020] Please refer to Figure 2 , and the specific steps to generate the temperature control zone strategy are as follows: S101: Collect the temperature and power output data of each battery unit through the sensor network. During the classification process, use the preset temperature threshold to screen the high-temperature area and the normal working area, record the classification results item by item, and complete the regional temperature data collection in sequence to generate the temperature zone division result. S102: Based on the temperature zone division result, compare the deviation between the high-temperature area and the safety standard point by point. By accumulating and statistically calculating the deviation amount, delimit the cooling demand range, calibrate the cooling demand level of each area item by item, and generate the cooling demand zone division result. S103: Based on the cooling demand zone division result, match the cooling demand levels of each area, allocate the temperature control strategy parameters in sequence, establish the temperature control parameter list of each area according to the demand in sequence, record the parameter table after the allocation is completed, and generate the temperature control zone strategy. Use the sensor network to collect the temperature and power output data of each battery unit in real time. Gradually record the collected data through the node distributed collection algorithm, and use the temperature data parsing method to parse the data format, including setting the temperature sampling rate to 1 second and the data output format to CSV file. Uniformly organize the recorded collected data, classify each battery unit one by one according to the preset temperature threshold, and after completing the classification of the high-temperature area and the normal working area through the conditional screening command, generate the classification results recorded item by item, and complete the regional temperature data collection in sequence through the regional marking algorithm, output the sorted data table, and generate the temperature zone division result. Based on the temperature zone division result, compare the deviation between the high-temperature area and the safety standard using the regional data, use the linear deviation calculation method to accumulate and statistically calculate the deviation value point by point, determine the deviation amount through the step-by-step cumulative statistical operation, delimit the cooling demand range according to the area for the statistical deviation amount, and calibrate the cooling demand level of each area through the regional priority allocation algorithm. The level division range includes the first-level emergency cooling demand to the third-level low cooling demand. After calibrating the cooling demand levels of all areas in sequence, generate the cooling demand zone division result. Based on the results of the cooling demand zoning, a dynamic parameter allocation algorithm is used to match the policy parameters for the cooling demand levels of each region. The temperature control policy parameters are allocated item by item through a look-up table method. The parameters include the set value of the coolant flow rate range from 2 to 10 liters per minute, the set value of the cooling pressure range from 0.5 to 2 MPa, and the set value of the cooling time period range from 30 to 120 seconds. After the parameters are matched in sequence, a regional temperature control parameter list is established, the parameter table data is recorded, and a temperature control zoning policy is generated.

[0021] Please refer to Figure 3 , and the specific steps to generate the cooling target setting details are as follows: S201: Based on the temperature control zoning policy, gradually measure the difference between the high-temperature region and the overall average temperature, and calculate the regional target temperature in combination with the preset target temperature adjustment range. Record the adjustment target data in sequence to generate the temperature adjustment target parameters; S202: Based on the temperature adjustment target parameters, monitor the temperature change process in real time, modify the cooling parameters item by item to achieve temperature adjustment, optimize the parameter settings according to the monitoring data during the adjustment process, record the adjusted parameters in sequence, and generate the cooling target setting details; S203: Based on the cooling target setting details, analyze the adjusted regional temperature change data point by point, compare it with the target value range, correct the cooling parameters with large regional deviations, and gradually complete the correction of all regions to generate the cooling target setting details; Based on the temperature control zoning policy, use the point-by-point data comparison algorithm to gradually measure the difference between the high-temperature region and the overall average temperature. The specific steps include extracting the real-time temperature data of the high-temperature region, comparing each data point with the overall average temperature, and gradually recording the difference between each high-temperature region and the average temperature using the difference calculation formula. Combine the preset target temperature adjustment range and use the dynamic target allocation algorithm to calculate the regional target temperature. The specific operations of the dynamic target allocation algorithm include setting the adjustment range from 2 to 5 degrees Celsius, allocating the adjustment range to the target temperature of each region one by one, recording the adjustment target data of all regions in sequence, and generating the temperature adjustment target parameters; Based on the temperature adjustment target parameters, use the real-time monitoring algorithm to monitor the temperature change process item by item. Specifically, set the monitoring interval to 5 seconds, the monitoring range to all data points of each high-temperature region, gradually analyze the data collected per second and update the temperature change trend, and modify the cooling parameters using the monitoring data in combination with the parameter optimization algorithm. The parameter optimization algorithm includes dynamically adjusting the coolant flow rate range from 2 to 10 liters per minute, adjusting the coolant temperature setting range from 10 to 20 degrees Celsius, and adjusting the pressure range from 0.5 to 2 MPa. Complete the real-time adjustment of the cooling parameters one by one, and record the parameters after each adjustment to generate the cooling target setting details; Based on the cooling target setting details, the regional data comparison algorithm is used to analyze the adjusted regional temperature change data point by point. Specifically, it includes extracting the temperature change data of each region and comparing it with the target value range point by point, and using the deviation correction algorithm to correct the cooling parameters. The operations of the deviation correction algorithm include gradually locating the regions where the deviation exceeds 2 degrees Celsius, and gradually adjusting the ratio of the flow rate parameter to the pressure parameter in the order of cooling priority. The adjustment range of the flow rate parameter is no more than 1 liter per minute increase or decrease, and the pressure adjustment range is no more than 0.2 MPa increase or decrease. Gradually complete the correction operations for all regions, record the corrected parameter data, and generate the cooling target setting details.

[0022] Please refer to Figure 4 , and the specific steps to generate the optimized flow rate and pressure parameters are as follows: S301: Based on the cooling target setting details, use the genetic algorithm to measure the initial states of the liquid cooling pump pressure and flow rate, make a preliminary setting by gradually adjusting the pressure and flow rate ratio, record the set parameter data, and generate the initial parameter setting result; S302: Based on the initial parameter setting result, gradually adjust the pressure value and flow rate value of the pump, calibrate the matching relationship between the two through synchronous measurement and data recording, and record the energy consumption during the adjustment process to generate the dynamic adjustment optimization data; S303: Based on the dynamic adjustment optimization data, recalibrate the distribution of the liquid cooling pump flow rate and pressure in different regions, record the final configuration values of the flow rate and pressure in each region through repeated testing and correction, and generate the optimized flow rate and pressure parameters; Based on the cooling target setting details, use the genetic algorithm to measure the initial states of the liquid cooling pump pressure and flow rate. Initialize the pressure value and flow rate value of the liquid cooling pump using the random population generation method. The scale of the random population is set to 50, the pressure value range is set to 0.5 to 2 MPa, and the flow rate value range is set to 2 to 10 liters per minute. Gradually adjust the ratio of the pressure and flow rate, and use the fitness function to evaluate the fitness of the current parameter combination. The parameters of the fitness function include the pressure stability coefficient, the flow rate uniformity coefficient, and the energy consumption record coefficient. Eliminate the 10% individuals with the lowest fitness, and use single-point crossover and mutation operations to generate a new population. Record the pressure value and flow rate value generated after crossover and mutation to generate the initial parameter setting result; Based on the initial parameter setting result, use the synchronous adjustment algorithm to gradually adjust the pressure value and flow rate value of the pump. Set the adjustment amplitude of the pressure value to 0.1 MPa each time, and the adjustment amplitude of the flow rate value to 0.5 liters per minute. Measure the real-time ratio of the pressure value to the flow rate through synchronous sampling. The sampling frequency is set to 10 seconds each time. Use the data recording module to gradually record each group of pressure and flow rate data during the adjustment process, and combine the real-time energy consumption monitoring module to record the current energy consumption data item by item. Integrate the sampling data and the energy consumption record to generate the dynamic adjustment optimization data; Optimize data based on dynamic adjustment. Use the recalibration algorithm to re-calibrate item by item the distribution of the flow rate and pressure of the liquid cooling pump in different regions. Measure point by point the flow rate and pressure data of each region through the multi-region test method. Each measurement is set to three repeated samplings, and gradually average the sampling data of each group to reduce accidental errors. Gradually correct the data with a deviation exceeding 5% according to the calibration results. The adjustment and correction range includes a flow rate value change range of 0.2 to 1 liter per minute and a pressure value change range of 0.1 to 0.3 MPa. After completing the calibration of all regions, record the final configuration values of the flow rate and pressure in each region, and generate the flow rate and pressure optimization parameters.

[0023] Genetic algorithm, according to the formula: Where: represents the current pressure value of the liquid cooling pump, represents the current flow rate value of the liquid cooling pump, represents the ratio weight parameter between pressure and flow rate, represents the heat load coefficient of the cooling target, represents the heat load weight parameter, represents the energy consumption efficiency ratio of the liquid cooling pump, represents the energy consumption efficiency weight parameter, represents the comprehensive evaluation result; Execution process: First, measure the pressure value and the flow rate value of the liquid cooling pump. Real-time monitor the operating state of the liquid cooling pump through sensors, and input the collected pressure and flow rate data as the basic parameters of the formula. Subsequently, set the weight parameter of the pressure-to-flow rate ratio, with the initial value set to 0.5, and gradually adjust to achieve the dynamic balance of pressure and flow rate, and obtain the preliminary liquid cooling setting value. Then, combined with the heat load state of the cooling target battery pack, measure the heat generated by the battery and calculate the heat load coefficient . Use the thermal sensor to collect temperature data in real time, and combine with the heat exchange model to obtain the specific value. At the same time, set the weight parameter of the heat load, with the initial value of 0.3, and optimize and adjust the specific value according to the experiment. Then calculate the energy consumption efficiency ratio of the liquid cooling pump. Record the power consumption of the liquid cooling pump within a unit time through the energy consumption monitoring module, combine with the pressure and flow rate of the liquid cooling pump, evaluate its efficiency index, and set the energy consumption efficiency weight parameter with the initial value that can be taken as 0.2, and optimize and adjust according to the operating performance. Finally, substitute into the formula for comprehensive calculation, generate the initial parameter setting result of the liquid cooling control system, and store the calculation result through the recording device to provide a reference basis for the subsequent liquid cooling control process.

[0024] Please refer to Figure 5 , and the specific steps for generating the adjustment effect monitoring result are as follows: S401: Based on the flow pressure optimization parameters, gradually adjust the initial value of the pump speed, match it by manually adjusting the valve opening step by step, record each state value after the pump speed adjustment, and generate the pump speed and valve adjustment data; S402: Based on the pump speed and valve adjustment data, measure the real-time change value of the temperature in each area, gradually correct the valve opening to match the temperature change requirement, record the temperature change situation in each area, and generate the area temperature monitoring data; S403: Based on the area temperature monitoring data, compare the measured value with the target value, gradually correct the adjustment parameters of the pump speed and the valve, complete the verification through real-time data recording, and generate the adjustment effect monitoring result; Based on the flow pressure optimization parameters, gradually adjust the initial value of the pump speed using the step-by-step adjustment algorithm. Manually set the pump speed adjustment range to increase or decrease by 100 revolutions per minute each time. The initial pump speed setting range is 500 to 1500 revolutions per minute. Record the pump speed value after adjustment through synchronous sampling, and gradually adjust the valve opening using the valve opening matching algorithm. The adjustment range is to increase or decrease by 5% each time. Record all state data corresponding to the valve opening and the pump speed, and generate the pump speed and valve adjustment data; Based on the pump speed and valve adjustment data, gradually measure the real-time change value of the temperature in each area using the real-time temperature measurement method. Set the sampling frequency of the high-frequency sampling module to once per second, collect temperature data point by point and store it in the database, and gradually correct the matching degree between the valve opening and the temperature change using the dynamic adjustment algorithm. The valve opening correction range is set to increase or decrease by 2% each time. Adjust the valve opening of all areas in sequence, record the temperature change situation after each adjustment, and generate the area temperature monitoring data; Based on the area temperature monitoring data, compare the measured value with the target value using the error comparison algorithm. Calculate the error between the measured value and the target value in real time. The allowable error range is set to ±0.5 degrees Celsius. Gradually correct the adjustment parameters of the pump speed and the valve for the areas outside the error range. The pump speed correction range is to increase or decrease by 50 revolutions per minute each time, and the valve adjustment correction range is to increase or decrease by 1% each time. Use the real-time data recording module to synchronously record all correction operations and result data, and generate the adjustment effect monitoring result.

[0025] Please refer to Figure 6 , and the specific steps for generating are as follows: S501: Based on the adjustment effect monitoring result, measure the temperature in each area item by item, analyze by recording the deviation between the temperature data and the preset target, gradually classify the non-compliant areas, record the classified temperature deviation data, and generate the temperature evaluation result; S502: Based on the temperature evaluation result, adopt the fuzzy control algorithm to gradually increase the cooling intensity. By adjusting the output pressure of the flow control device and adjusting the cooling flow rate, re-measure the cooling effect of each area, record the changes in the cooling parameters item by item, and generate a cooling parameter adjustment record; S503: Based on the cooling parameter adjustment record, optimize the cooling strategy one by one. By gradually adjusting the cooling flow rate and the area allocation plan, establish an area cooling strategy configuration table item by item and record the results, and generate a cooling strategy optimization plan; Based on the adjustment effect monitoring result, use the point-by-point analysis algorithm to measure the temperature of each area item by item. Set the acquisition frequency of the high-frequency temperature acquisition module to once per second, record the real-time temperature data of each area, and use the difference calculation method to gradually calculate the deviation between the temperature data and the preset target. The difference calculation range is set to ±2 degrees Celsius. Gradually classify the areas that do not reach the deviation range. The classification standard is set as the high deviation area and the medium deviation area. Mark and record the classified temperature deviation data respectively, and generate a temperature evaluation result; Based on the temperature evaluation result, adopt the fuzzy control algorithm to gradually increase the cooling intensity. Set the output pressure adjustment range of the flow control device to 0.5 to 2 MPa through the fuzzy rule table, adjust the cooling flow rate in real time according to the area demand, and set the flow rate change range to increase or decrease by 0.5 liters per minute each time. Use the temperature feedback mechanism to measure the cooling effect of each area in real time, record the changes in the cooling parameters item by item, and the recorded content includes the real-time flow rate value, pressure value, and temperature value of the corresponding area, and generate a cooling parameter adjustment record; Based on the cooling parameter adjustment record, adopt the step-by-step optimization algorithm to optimize the cooling strategy one by one. Through the area allocation mechanism, gradually adjust the cooling flow rate and the area allocation plan. The allocation plan parameters include the flow rate allocation ratio and the area cooling priority level. The priority level is set to three levels: high, medium, and low. Gradually optimize the flow rate allocation for each level. The optimization content includes adjusting the allocation ratio to be between 10%, 20%, 30% and 50%. Establish an area cooling strategy configuration table item by item, and record the data after each optimization, and generate a cooling strategy optimization plan.

[0026] Fuzzy control algorithm, according to the formula: Where: is the improved cooling intensity, is the target temperature, is the currently measured temperature, is the current cooling flow rate, is the temperature difference adjustment coefficient, is the flow rate adjustment coefficient, is the non-uniformity of heat distribution, is the heat distribution weight coefficient, is the flow velocity of the cooling medium, is the flow rate weight coefficient;

[0027] Execution process: Use a temperature sensor to measure the current temperature of the cooling area in real time , and compare it with the target temperature to calculate the temperature difference . Set the temperature difference adjustment coefficient according to experimental data and historical operation records to quantify the impact of the temperature difference on the cooling intensity. The initial value can be obtained through experimental fitting and optimized in subsequent processes. Then use a flow sensor to measure the current cooling flow rate , and set the flow rate adjustment coefficient in combination with the cooling operation status . Dynamically adjust its value according to the actual operating conditions through an optimization method. Then calculate the non-uniformity of heat distribution based on the multi-point temperature data in the area . Use the standard deviation or the maximum temperature difference to quantify the change in heat distribution, and set the heat distribution weight coefficient to reflect the impact of heat distribution on the cooling effect. Subsequently, measure the flow rate of the cooling medium through a flow rate sensor , and set the flow rate weight coefficient in combination with the dynamic characteristics of the cooling fluid . Finally, substitute all parameters into the formula to calculate the comprehensive cooling intensity . Dynamically adjust the flow rate, flow velocity, and pressure parameters of the cooling system according to the calculation results to ensure that the cooling effect reaches the optimal state required for battery operation, and generate a cooling parameter adjustment record for system optimization and long-term monitoring.

[0028] Please refer to Figure 7 , and the specific steps to generate the continuous operation control framework are as follows: S601: Based on the cooling strategy optimization plan, gradually update the flow rate and pressure control parameters of each cooling area. By importing parameters in batches and testing the cooling performance of each area one by one, record the updated values of the parameters and generate the parameter update results; S602: Based on the parameter update results, continuously collect the temperature data of each area. By regularly collecting and recording the operating parameters during the cooling process, gradually analyze the change relationship between the temperature and energy consumption data between areas and generate long-term monitoring data; S603: Based on the long-term monitoring data, gradually correct the cooling parameters. By optimizing the cooling distribution and flow rate adjustment of each area, verify the stability of the cooling operation item by item and record the final data to generate the continuous operation control framework; Based on the cooling strategy optimization plan, the batch import algorithm is adopted to gradually update the flow rate and pressure control parameters of each cooling area. The updated parameters are divided into 10 groups and loaded batch by batch through the parameter grouping import method. Each group of parameters includes a flow rate range set from 2 to 10 liters per minute and a pressure range set from 0.5 to 2 MPa. The single-region test method is used to verify the regional cooling performance one by one. The test content includes measuring the immediate response of the updated parameters to temperature changes and pressure stability, gradually recording the values after all parameter updates, and generating the parameter update results; Based on the parameter update results, the timed acquisition algorithm is used to continuously acquire the temperature data of each area. The acquisition time interval is set to once every 5 minutes. The temperature sensing network is used to record the flow rate, pressure, and temperature operation parameters during the cooling process in real time. The point-by-point comparison algorithm is used to gradually analyze the change trend of the temperature data between regions, and the correlation analysis is gradually carried out with the energy consumption data. The analysis parameters include unit energy consumption and average temperature difference range. The analysis results are recorded item by item and the data is integrated to generate long-term monitoring data; Based on the long-term monitoring data, the step-by-step correction algorithm is used to correct the cooling parameters one by one. The cooling distribution of each area is optimized through the flow rate adjustment module. The adjustment range includes the flow rate change set to increase or decrease by 1 liter per minute each time, and the pressure change set to increase or decrease by 0.1 MPa each time. The regional stability verification module is used to verify the stability of the cooling operation item by item. The verification content includes flow rate stability and temperature control accuracy. The final cooling parameter data is gradually recorded and integrated into an operation log to generate a continuous operation control framework.

[0029] Please refer to Figure 8 , a liquid cooling control system for a battery control device. The liquid cooling control system of the battery control device is used to execute the above-mentioned liquid cooling control method for the battery control device. The system includes: Temperature control zoning module: Collect the temperature and power output data of each battery unit through the sensor network, use the temperature threshold to screen the high-temperature area and the normal working area, compare the temperature of the high-temperature area with the safety standard, and divide the cooling requirements according to the comparison result to generate a temperature control zoning strategy; Cooling target setting module: Based on the temperature control zoning strategy, calculate the deviation between the temperature of the high-temperature area and the overall average temperature, compare the temperature deviation of the high-temperature area with the preset temperature drop target, set the temperature drop target for each area, and adjust the dynamic distribution of the cooling flow rate and pressure in real time to generate the cooling target setting details; Flow rate and pressure optimization module: Based on the cooling target setting details, use the genetic algorithm to generate an initial cooling strategy solution, compare the ratio of the pressure value to the flow rate value of the liquid cooling pump with the optimization target of the cooling requirement, iteratively adjust the pressure and flow rate values of the liquid cooling pump, record the energy consumption and cooling effect after adjustment, and map the optimized pressure and flow rate distribution relationship to the regional cooling strategy table to generate the flow rate and pressure optimization parameters; Cooling adjustment module: Based on the flow pressure optimization parameters, adjust the pump speed and valve opening through automated control, compare the adjusted pump speed value with the regional cooling demand, monitor the changes in the adjusted flow rate and regional temperature in real time, record the cooling effect data for each region, and generate the adjustment effect monitoring result; Cooling strategy evaluation module: Based on the adjustment effect monitoring result, use the fuzzy control algorithm to evaluate the cooling effect, classify the cooling demand values of the non-compliant regions and the adjusted cooling resource allocation values, rearrange the resource allocation priorities of the non-compliant regions, and adjust the resource allocation plan to generate the cooling strategy optimization plan; Continuous regulation module: Based on the cooling strategy optimization plan, analyze the historical operation data and current control parameters, compare the cooling distribution mode in the historical data with the current cooling parameters, classify the long-term optimization data of regional resource allocation, establish a long-term liquid cooling control model, and generate the continuous operation regulation framework.

[0030] The above are only the preferred embodiments of the present invention, and are not intended to 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 the technical solution content of the present invention is not departed from, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A liquid cooling control method for a battery control device, characterized in that: The following steps are involved: Step 1: Collect the temperature and power output data of each battery unit through the sensor network, filter out the high temperature area and normal working area according to the temperature threshold, determine the cooling demand zone by comparing the real-time data with the safety standard, and generate the temperature control zone strategy; Step 2: Based on the temperature control zoning strategy, calculate the deviation between the high temperature area and the overall average temperature, set an accurate temperature drop target, adjust the cooling parameters through real-time monitoring, and generate cooling target setting details; Step 3: Based on the cooling target setting details, a genetic algorithm is used to adjust the pressure and flow of the liquid cooling pump through a dynamic adjustment control mechanism, while balancing energy consumption and efficiency, and generating flow and pressure optimization parameters; Step 4: According to the flow pressure optimization parameters, the pump speed and valve opening are adjusted through automatic control, the temperature change after adjustment is monitored in real time, and the adjustment effect monitoring result is generated; Step 5: Based on the adjustment effect monitoring results, a fuzzy control algorithm is used to evaluate the temperature control effect of each battery cell. If the preset target is not reached, the cooling intensity is re-enhanced, the cooling strategy is optimized, and a cooling strategy optimization plan is generated; Step 6: Based on the cooling strategy optimization solution, update the control parameters of the liquid cooling, optimize the cooling operation through long-term data monitoring, and make continuous adjustments to generate a continuous operation control framework.

2. The liquid cooling control method of the battery control device according to claim 1, characterized in that: The specific steps of generating the temperature control partition strategy are as follows: The temperature and power output data of each battery cell are collected through the sensor network. During the classification process, the preset temperature threshold is used to screen the high temperature area and the normal working area. The classification results are recorded item by item, and the regional temperature data are collected in sequence to generate the temperature area division results. Based on the temperature zone division result, the deviation between the high temperature zone and the safety standard is compared point by point, the cooling demand range is defined by accumulating and counting the deviation, the cooling demand level of each zone is calibrated item by item, and the cooling demand zoning result is generated; Based on the cooling demand zoning results, the cooling demand levels of each area are matched, temperature control strategy parameters are allocated in sequence, a list of temperature control parameters for each area is established in sequence according to demand, the parameter table after allocation is recorded, and a temperature control zoning strategy is generated.

3. The liquid cooling control method of the battery control device according to claim 1, characterized in that: The specific steps of generating the cooling target setting details are: Based on the temperature control zoning strategy, the difference between the high temperature area and the overall average temperature is measured step by step, the regional target temperature is calculated in combination with the preset target temperature adjustment range, the adjustment target data is recorded in sequence, and the temperature adjustment target parameters are generated; Based on the temperature adjustment target parameters, the temperature change process is monitored in real time, and the cooling parameters are modified item by item to achieve temperature adjustment. During the adjustment process, the parameter settings are optimized according to the monitoring data, and the adjusted parameters are recorded in sequence to generate cooling target setting details; Based on the cooling target setting details, the adjusted regional temperature change data is analyzed point by point, compared with the target value range, and the cooling parameters with large regional deviations are corrected. All regional corrections are gradually completed to generate cooling target setting details.

4. The liquid cooling control method of a battery control device according to claim 1, characterized in that: The specific steps of generating the flow pressure optimization parameters are: Based on the cooling target setting details, a genetic algorithm is used to measure the initial state of the pressure and flow of the liquid cooling pump, and a preliminary setting is performed by gradually adjusting the pressure and flow ratio, and the parameter data after the setting is recorded to generate an initial parameter setting result; Based on the initial parameter setting result, the pressure value and flow value of the pump are gradually adjusted, the ratio relationship between the two is calibrated through synchronous measurement and data recording, and the energy consumption during the adjustment process is recorded to generate dynamic adjustment optimization data; Based on the dynamic adjustment optimization data, the distribution of the flow and pressure of the liquid cooling pump in different areas is recalibrated, and the final configuration values ​​of the flow and pressure in each area are recorded through repeated testing and correction to generate flow and pressure optimization parameters.

5. The liquid cooling control method of the battery control device according to claim 4, characterized in that: The genetic algorithm, according to the formula: in: Indicates the current pressure value of the liquid cooling pump. Indicates the current flow value of the liquid cooling pump. represents the weight parameter of the ratio between pressure and flow, represents the heat load factor of the cooling target, represents the heat load weight parameter, represents the energy efficiency ratio of the liquid cooling pump, represents the energy efficiency weight parameter, Indicates the comprehensive evaluation results.

6. The liquid cooling control method of a battery control device according to claim 1, characterized in that: The specific steps of generating the adjustment effect monitoring result are: Based on the flow pressure optimization parameters, the initial value of the pump speed is gradually adjusted, the valve opening is manually adjusted step by step to match, each state value after the pump speed adjustment is recorded, and the pump speed and valve adjustment data are generated; Based on the pump speed and valve adjustment data, the real-time change value of the temperature of each area is measured, the valve opening is gradually corrected to match the temperature change requirements, and the temperature change of each area is recorded to generate regional temperature monitoring data; Based on the regional temperature monitoring data, the measured value is compared with the target value, and the adjustment parameters of the pump speed and the valve are gradually corrected. The verification is completed through real-time data recording to generate the adjustment effect monitoring results.

7. The liquid cooling control method of a battery control device according to claim 1, characterized in that: The specific steps of generating the cooling strategy optimization solution are: Based on the adjustment effect monitoring results, the temperature of each area is measured item by item, and the deviation between the temperature data and the preset target is analyzed, and the non-compliant areas are gradually classified, and the classified temperature deviation data is recorded to generate the temperature evaluation results; Based on the temperature evaluation results, a fuzzy control algorithm is used to gradually increase the cooling intensity, the cooling effect of each area is remeasured by adjusting the output pressure of the flow control device and adjusting the cooling flow, and the changes in cooling parameters are recorded item by item to generate a cooling parameter adjustment record; Based on the cooling parameter adjustment record, the cooling strategy is optimized one by one, and by gradually adjusting the cooling flow and the regional allocation plan, a regional cooling strategy configuration table is established item by item and the results are recorded to generate a cooling strategy optimization plan.

8. The liquid cooling control method of the battery control device according to claim 7, characterized in that: The fuzzy control algorithm is based on the formula: in: For improved cooling intensity, is the target temperature, is the current measured temperature, is the current cooling flow, is the temperature difference adjustment coefficient, is the flow adjustment coefficient, is the heat distribution unevenness, is the heat distribution weight coefficient, is the flow rate of the cooling medium, is the flow velocity weight coefficient.

9. The liquid cooling control method of a battery control device according to claim 1, characterized in that: The specific steps for generating the continuous operation control framework are: Based on the cooling strategy optimization scheme, the flow and pressure control parameters of each cooling zone are gradually updated, the parameters are imported in batches and the cooling performance of each zone is tested one by one, the updated values ​​of the parameters are recorded, and the parameter update results are generated; Based on the parameter update results, the temperature data of each area is continuously collected, and the change relationship between the temperature and energy consumption data between the areas is gradually analyzed by regularly collecting and recording the operating parameters during the cooling process to generate long-term monitoring data; Based on the long-term monitoring data, the cooling parameters are gradually corrected, and by optimizing the cooling distribution and flow adjustment in each area, the stability of the cooling operation is verified item by item and the final data is recorded to generate a continuous operation control framework.

10. A liquid cooling control system for a battery control device, characterized in that: According to the liquid cooling control method of a battery control device according to any one of claims 1 to 9, the system comprises: Temperature control partition module: collects the temperature and power output data of each battery unit through the sensor network, uses the temperature threshold to screen the high-temperature area and the normal working area, compares the temperature of the high-temperature area with the safety standard, partitions the cooling demand based on the comparison results, and generates a temperature control partition strategy; Cooling target setting module: based on the temperature control zoning strategy, calculate the deviation between the temperature of the high-temperature area and the overall average temperature, compare the temperature deviation of the high-temperature area with the preset temperature reduction target, set the temperature reduction target for each area, and generate cooling target setting details by adjusting the dynamic distribution of cooling flow and pressure through real-time monitoring; Flow and pressure optimization module: Based on the cooling target setting details, a genetic algorithm is used to generate an initial cooling strategy solution, the ratio of the pressure value to the flow value of the liquid cooling pump is compared with the optimization target of the cooling demand, the pressure and flow value of the liquid cooling pump are iteratively adjusted, the adjusted energy consumption and cooling effect are recorded, and the optimized pressure and flow distribution relationship is mapped to the regional cooling strategy table to generate flow and pressure optimization parameters; Cooling adjustment module: Based on the flow pressure optimization parameters, the pump speed and valve opening are adjusted through automatic control, the adjusted pump speed value is compared with the regional cooling demand, and the cooling effect data of each area is recorded by real-time monitoring of the changes in the adjusted flow and regional temperature to generate adjustment effect monitoring results; Cooling strategy evaluation module: Based on the adjustment effect monitoring results, the cooling effect is evaluated using a fuzzy control algorithm, the cooling demand value of the non-compliant area and the adjusted cooling resource allocation value are classified, the resource allocation priority of the non-compliant area is rearranged, and the resource allocation plan is adjusted to generate a cooling strategy optimization plan; Continuous control module: Based on the cooling strategy optimization scheme, analyze the historical operation data and current control parameters, compare the cooling allocation pattern in the historical data with the current cooling parameters, classify the long-term optimization data of regional resource allocation, establish a liquid cooling long-term cooling control model, and generate a continuous operation control framework.

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